Collective intelligence (CI) is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making. The term appears in sociobiology, political science and in context of mass peer review and crowdsourcing applications. It may involve consensus, social capital and formalisms such as voting systems, social media and other means of quantifying mass activity. Collective IQ is a measure of collective intelligence, although it is often used interchangeably with the term collective intelligence. Collective intelligence has also been attributed to bacteria[1]:63 and animals.[1]:69
(Redirected from Group synergy)
Types of collective intelligence
It can be understood as an emergent property from the synergies among: 1) by Norman Lee Johnson.[4] The concept is used in sociology, business, computer science and mass communications: it also appears in science fiction. Pierre Lévy defines collective intelligence as, 'It is a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills. I'll add the following indispensable characteristic to this definition: The basis and goal of collective intelligence is mutual recognition and enrichment of individuals rather than the cult of fetishized or hypostatized communities.'[5] According to researchers Pierre Lévy and Derrick de Kerckhove, it refers to capacity of networked ICTs (Information communication technologies) to enhance the collective pool of social knowledge by simultaneously expanding the extent of human interactions.[6]
Collective intelligence strongly contributes to the shift of knowledge and power from the individual to the collective. According to Eric S. Raymond (1998) and JC Herz (2005), open source intelligence will eventually generate superior outcomes to knowledge generated by proprietary software developed within corporations (Flew 2008). Media theorist Henry Jenkins sees collective intelligence as an 'alternative source of media power', related to convergence culture. He draws attention to education and the way people are learning to participate in knowledge cultures outside formal learning settings. Henry Jenkins criticizes schools which promote 'autonomous problem solvers and self-contained learners' while remaining hostile to learning through the means of collective intelligence.[7] Both Pierre Lévy (2007) and Henry Jenkins (2008) support the claim that collective intelligence is important for democratization, as it is interlinked with knowledge-based culture and sustained by collective idea sharing, and thus contributes to a better understanding of diverse society.
Similar to the g factor (g) for general individual intelligence, a new scientific understanding of collective intelligence aims to extract a general collective intelligence factor c factor for groups indicating a group's ability to perform a wide range of tasks.[8] Definition, operationalization and statistical methods are derived from g. Similarly as g is highly interrelated with the concept of IQ,[9][10] this measurement of collective intelligence can be interpreted as intelligence quotient for groups (Group-IQ) even though the score is not a quotient per se. Causes for c and predictive validity are investigated as well.
Writers who have influenced the idea of collective intelligence include Francis Galton, Douglas Hofstadter (1979), Peter Russell (1983), Tom Atlee (1993), Pierre Lévy (1994), Howard Bloom (1995), Francis Heylighen (1995), Douglas Engelbart, Louis Rosenberg, Cliff Joslyn, Ron Dembo, Gottfried Mayer-Kress (2003).
History[edit]
H.G. Wells World Brain (1936â1938)
The concept (although not so named) originated in 1785 with the Marquis de Condorcet, whose 'jury theorem' states that if each member of a voting group is more likely than not to make a correct decision, the probability that the highest vote of the group is the correct decision increases with the number of members of the group (see Condorcet's jury theorem).[11] Many theorists have interpreted Aristotle's statement in the Politics that 'a feast to which many contribute is better than a dinner provided out of a single purse' to mean that just as many may bring different dishes to the table, so in a deliberation many may contribute different pieces of information to generate a better decision.[12][13] Recent scholarship,[14] however, suggests that this was probably not what Aristotle meant but is a modern interpretation based on what we now know about team intelligence.[15]
A precursor of the concept is found in entomologist William Morton Wheeler's observation that seemingly independent individuals can cooperate so closely as to become indistinguishable from a single organism (1910).[16] Wheeler saw this collaborative process at work in ants that acted like the cells of a single beast he called a superorganism.
In 1912 Ãmile Durkheim identified society as the sole source of human logical thought. He argued in 'The Elementary Forms of Religious Life' that society constitutes a higher intelligence because it transcends the individual over space and time.[17] Other antecedents are Vladimir Vernadsky's concept of 'noosphere' and H.G. Wells's concept of 'world brain' (see also the term 'global brain'). Peter Russell, Elisabet Sahtouris, and Barbara Marx Hubbard (originator of the term 'conscious evolution')[18] are inspired by the visions of a noosphere â a transcendent, rapidly evolving collective intelligence â an informational cortex of the planet. The notion has more recently been examined by the philosopher Pierre Lévy. In a 1962 research report, Douglas Engelbart linked collective intelligence to organizational effectiveness, and predicted that pro-actively 'augmenting human intellect' would yield a multiplier effect in group problem solving: 'Three people working together in this augmented mode [would] seem to be more than three times as effective in solving a complex problem as is one augmented person working alone'.[19] In 1994, he coined the term 'collective IQ' as a measure of collective intelligence, to focus attention on the opportunity to significantly raise collective IQ in business and society.[20]
The idea of collective intelligence also forms the framework for contemporary democratic theories often referred to as epistemic democracy. Epistemic democratic theories refer to the capacity of the populace, either through deliberation or aggregation of knowledge, to track the truth and relies on mechanisms to synthesize and apply collective intelligence.[21]
Collective intelligence was introduced into the machine learning community in the late 20th century,[22] and matured into a broader consideration of how to design 'collectives' of self-interested adaptive agents to meet a system-wide goal.[23][24] This was related to single-agent work on 'reward shaping'[25] and has been taken forward by numerous researchers inthe game theory and engineering communities.[26]
Dimensions[edit]
Complex adaptive systems model
Howard Bloom has discussed mass behavior â collective behavior from the level of quarks to the level of bacterial, plant, animal, and human societies. He stresses the biological adaptations that have turned most of this earth's living beings into components of what he calls 'a learning machine'. In 1986 Bloom combined the concepts of apoptosis, parallel distributed processing, group selection, and the superorganism to produce a theory of how collective intelligence works.[27] Later he showed how the collective intelligences of competing bacterial colonies and human societies can be explained in terms of computer-generated 'complex adaptive systems' and the 'genetic algorithms', concepts pioneered by John Holland.[28]
Bloom traced the evolution of collective intelligence to our bacterial ancestors 1 billion years ago and demonstrated how a multi-species intelligence has worked since the beginning of life.[28] Ant societies exhibit more intelligence, in terms of technology, than any other animal except for humans and co-operate in keeping livestock, for example aphids for 'milking'.[28] Leaf cutters care for fungi and carry leaves to feed the fungi.[28]
David Skrbina[29] cites the concept of a 'group mind' as being derived from Plato's concept of panpsychism (that mind or consciousness is omnipresent and exists in all matter). He develops the concept of a 'group mind' as articulated by Thomas Hobbes in 'Leviathan' and Fechner's arguments for a collective consciousness of mankind. He cites Durkheim as the most notable advocate of a 'collective consciousness'[30] and Teilhard de Chardin as a thinker who has developed the philosophical implications of the group mind.[31]
Tom Atlee focuses primarily on humans and on work to upgrade what Howard Bloom calls 'the group IQ'. Atlee feels that collective intelligence can be encouraged 'to overcome 'groupthink' and individual cognitive bias in order to allow a collective to cooperate on one process â while achieving enhanced intellectual performance.' George Pór defined the collective intelligence phenomenon as 'the capacity of human communities to evolve towards higher order complexity and harmony, through such innovation mechanisms as differentiation and integration, competition and collaboration.'[32] Atlee and Pór state that 'collective intelligence also involves achieving a single focus of attention and standard of metrics which provide an appropriate threshold of action'.[33] Their approach is rooted in scientific community metaphor.[33]
The term group intelligence is sometimes used interchangeably with the term collective intelligence. Anita Woolley presents Collective intelligence as a measure of group intelligence and group creativity.[8] The idea is that a measure of collective intelligence covers a broad range of features of the group, mainly group composition and group interaction.[34] The features of composition that lead to increased levels of collective intelligence in groups include criteria such as higher numbers of women in the group as well as increased diversity of the group.[34]
Atlee and Pór suggest that the field of collective intelligence should primarily be seen as a human enterprise in which mind-sets, a willingness to share and an openness to the value of distributed intelligence for the common good are paramount, though group theory and artificial intelligence have something to offer.[33] Individuals who respect collective intelligence are confident of their own abilities and recognize that the whole is indeed greater than the sum of any individual parts.[35] Maximizing collective intelligence relies on the ability of an organization to accept and develop 'The Golden Suggestion', which is any potentially useful input from any member.[36]Groupthink often hampers collective intelligence by limiting input to a select few individuals or filtering potential Golden Suggestions without fully developing them to implementation.[33]
Robert David Steele Vivas in The New Craft of Intelligence portrayed all citizens as 'intelligence minutemen,' drawing only on legal and ethical sources of information, able to create a 'public intelligence' that keeps public officials and corporate managers honest, turning the concept of 'national intelligence' (previously concerned about spies and secrecy) on its head.[37]
Stigmergic Collaboration: a theoretical framework for mass collaboration
According to Don Tapscott and Anthony D. Williams, collective intelligence is mass collaboration. In order for this concept to happen, four principles need to exist;[38]
Collective intelligence factor c[edit]
Scree plot showing percent of explained variance for the first factors in Woolley et al.'s (2010) two original studies.
A new scientific understanding of collective intelligence defines it as a group's general ability to perform a wide range of tasks.[8] Definition, operationalization and statistical methods are similar to the psychometric approach of general individual intelligence. Hereby, an individual's performance on a given set of cognitive tasks is used to measure general cognitive ability indicated by the general intelligence factor g extracted via factor analysis.[39] In the same vein as g serves to display between-individual performance differences on cognitive tasks, collective intelligence research aims to find a parallel intelligence factor for groups 'c factor'[8] (also called 'collective intelligence factor' (CI)[40]) displaying between-group differences on task performance. The collective intelligence score then is used to predict how this same group will perform on any other similar task in the future. Yet tasks, hereby, refer to mental or intellectual tasks performed by small groups[8] even though the concept is hoped to be transferrable to other performances and any groups or crowds reaching from families to companies and even whole cities.[41] Since individuals' g factor scores are highly correlated with full-scale IQ scores, which are in turn regarded as good estimates of g,[9][10] this measurement of collective intelligence can also be seen as an intelligence indicator or quotient respectively for a group (Group-IQ) parallel to an individual's intelligence quotient (IQ) even though the score is not a quotient per se.
Mathematically, c and g are both variables summarizing positive correlations among different tasks supposing that performance on one task is comparable with performance on other similar tasks.[42]c thus is a source of variance among groups and can only be considered as a group's standing on the c factor compared to other groups in a given relevant population.[10][43] The concept is in contrast to competing hypotheses including other correlational structures to explain group intelligence,[8] such as a composition out of several equally important but independent factors as found in individual personality research.[44]
Besides, this scientific idea also aims to explore the causes affecting collective intelligence, such as group size, collaboration tools or group members' interpersonal skills.[45] The MIT Center for Collective Intelligence, for instance, announced the detection of The Genome of Collective Intelligence[45] as one of its main goals aiming to develop a taxonomy of organizational building blocks, or genes, that can be combined and recombined to harness the intelligence of crowds.[45]
Causes[edit]
Individual intelligence is shown to be genetically and environmentally influenced.[46][47] Analogously, collective intelligence research aims to explore reasons why certain groups perform more intelligent than other groups given that c is just moderately correlated with the intelligence of individual group members.[8] According to Woolley et al.'s results, neither team cohesion nor motivation or satisfaction is correlated with c. However, they claim that three factors were found as significant correlates: the variance in the number of speaking turns, group members' average social sensitivity and the proportion of females. All three had similar predictive power for c, but only social sensitivity was statistically significant (b=0.33, P=0.05).[8]
The number speaking turns indicates that 'groups where a few people dominated the conversation were less collectively intelligent than those with a more equal distribution of conversational turn-taking'.[40] Hence, providing multiple team members the chance to speak up made a group more intelligent.[8]
Group members' social sensitivity was measured via the Reading the Mind in the Eyes Test[48] (RME) and correlated .26 with c.[8] Hereby, participants are asked to detect thinking or feeling expressed in other peoples' eyes presented on pictures and assessed in a multiple choice format. The test aims to measure peoples' theory of mind (ToM), also called 'mentalizing'[49][50][51][52] or 'mind reading',[53] which refers to the ability to attribute mental states, such as beliefs, desires or intents, to other people and in how far people understand that others have beliefs, desires, intentions or perspectives different from their own ones.[48] RME is a ToM test for adults[48] that shows sufficient test-retest reliability[54] and constantly differentiates control groups from individuals with functional autism or Asperger Syndrome.[48] It is one of the most widely accepted and well-validated tests for ToM within adults.[55] ToM can be regarded as an associated subset of skills and abilities within the broader concept of emotional intelligence.[40][56]
The proportion of females as a predictor of c was largely mediated by social sensitivity (Sobel z = 1.93, P= 0.03)[8] which is in vein with previous research showing that women score higher on social sensitivity tests.[48] While a mediation, statistically speaking, clarifies the mechanism underlying the relationship between a dependent and an independent variable,[57] Wolley agreed in an interview with the Harvard Business Review that these findings are saying that groups of women are smarter than groups of men.[41] However, she relativizes this stating that the actual important thing is the high social sensitivity of group members.[41]
It is theorized that the collective intelligence factor c is an emergent property resulting from bottom-up as well as top-down processes.[34] Hereby, bottom-up processes cover aggregated group-member characteristics. Top-down processes cover group structures and norms that influence a group's way of collaborating and coordinating.[34]
Processes[edit]
Predictors for the collective intelligence factor c. Suggested by Woolley, Aggarwal & Malone[34] (2015)
Top-down processes[edit]
Top-down processes cover group interaction, such as structures, processes, and norms.[58] An example of such top-down processes is conversational turn-taking.[8] Research further suggest that collectively intelligent groups communicate more in general as well as more equally; same applies for participation and is shown for face-to-face as well as online groups communicating only via writing.[40][59]
Bottom-up processes[edit]
Bottom-up processes include group composition,[58] namely the characteristics of group members which are aggregated to the team level[34]. An example of such bottom-up processes is the average social sensitivity or the average and maximum intelligence scores of group members.[8] Furthermore, collective intelligence was found to be related to a group's cognitive diversity[60] including thinking styles and perspectives.[61] Groups that are moderately diverse in cognitive style have higher collective intelligence than those who are very similar in cognitive style or very different. Consequently, groups where members are too similar to each other lack the variety of perspectives and skills needed to perform well. On the other hand, groups whose members are too different seem to have difficulties to communicate and coordinate effectively.[60]
Serial vs Parallel processes[edit]
For most of human history, collective intelligence was confined to small tribal groups in which opinions were aggregated through real-time parallel interactions among members.[62] In modern times, mass communication, mass media, and networking technologies have enabled collective intelligence to span massive groups, distributed across continents and time-zones. To accommodate this shift in scale, collective intelligence in large-scale groups been dominated by serialized polling processes such as aggregating up-votes, likes, and ratings over time. While modern systems benefit from larger group size, the serialized process has been found to introduce substantial noise that distorts the collective output of the group. In one significant study of serialized collective intelligence, it was found that the first vote contributed to a serialized voting system can distort the final result by 34%.[63]
To address the problems of serialized aggregation of input among large-scale groups, recent advancements collective intelligence have worked to replace serialized votes, polls, and markets, with parallel systems such as 'human swarms' modeled after synchronous swarms in nature.[64][65] Based on natural process of Swarm Intelligence, these artificial swarms of networked humans enable participants to work together in parallel to answer questions and make predictions as an emergent collective intelligence. In one high-profile example, a human swarm challenge by CBS Interactive to predict the Kentucky Derby. The swarm correctly predicted the first four horses, in order, defying 542â1 odds and turning a $20 bet into $10,800.[66]
Evidence[edit]
Standardized Regression Coefficients for the collective intelligence factor c as found in Woolley et al.'s[8] (2010) two original studies. c and average (maximum) member intelligence scores are regressed on the criterion tasks.
Woolley, Chabris, Pentland, Hashmi, & Malone (2010),[8] the originators of this scientific understanding of collective intelligence, found a single statistical factor for collective intelligence in their research across 192 groups with people randomly recruited from the public. In Woolley et al.'s two initial studies, groups worked together on different tasks from the McGrath Task Circumplex,[67] a well-established taxonomy of group tasks. Tasks were chosen from all four quadrants of the circumplex and included visual puzzles, brainstorming, making collective moral judgments, and negotiating over limited resources. The results in these tasks were taken to conduct a factor analysis. Both studies showed support for a general collective intelligence factor c underlying differences in group performance with an initial eigenvalue accounting for 43% (44% in study 2) of the variance, whereas the next factor accounted for only 18% (20%). That fits the range normally found in research regarding a general individual intelligence factor g typically accounting for 40% to 50% percent of between-individual performance differences on cognitive tests.[42]
Afterwards, a more complex criterion task was absolved by each group measuring whether the extracted c factor had predictive power for performance outside the original task batteries. Criterion tasks were playing checkers (draughts) against a standardized computer in the first and a complex architectural design task in the second study. In a regression analysis using both individual intelligence of group members and c to predict performance on the criterion tasks, c had a significant effect, but average and maximum individual intelligence had not. While average (r=0.15, P=0.04) and maximum intelligence (r=0.19, P=0.008) of individual group members were moderately correlated with c, c was still a much better predictor of the criterion tasks. According to Woolley et al., this supports the existence of a collective intelligence factor c, because it demonstrates an effect over and beyond group members' individual intelligence and thus that c is more than just the aggregation of the individual IQs or the influence of the group member with the highest IQ.[8]
Engel et al.[40] (2014) replicated Woolley et al.'s findings applying an accelerated battery of tasks with a first factor in the factor analysis explaining 49% of the between-group variance in performance with the following factors explaining less than half of this amount. Moreover, they found a similar result for groups working together online communicating only via text and confirmed the role of female proportion and social sensitivity in causing collective intelligence in both cases. Similarly to Wolley et al.,[8] they also measured social sensitivity with the RME which is actually meant to measure people's ability to detect mental states in other peoples' eyes. The online collaborating participants, however, did neither know nor see each other at all. The authors conclude that scores on the RME must be related to a broader set of abilities of social reasoning than only drawing inferences from other people's eye expressions.[68]
A collective intelligence factor c in the sense of Woolley et al.[8] was further found in groups of MBA students working together over the course of a semester,[69] in online gaming groups[59] as well as in groups from different cultures[70] and groups in different contexts in terms of short-term versus long-term groups.[70] None of these investigations considered team members' individual intelligence scores as control variables.[59][69][70]
Note as well that the field of collective intelligence research is quite young and published empirical evidence is relatively rare yet. However, various proposals and working papers are in progress or already completed but (supposedly) still in a scholarly peer reviewing publication process.[71][72][73][74]
Predictive validity[edit]
Next to predicting a group's performance on more complex criterion tasks as shown in the original experiments,[8] the collective intelligence factor c was also found to predict group performance in diverse tasks in MBA classes lasting over several months.[69] Thereby, highly collectively intelligent groups earned significantly higher scores on their group assignments although their members did not do any better on other individually performed assignments. Moreover, highly collective intelligent teams improved performance over time suggesting that more collectively intelligent teams learn better.[69] This is another potential parallel to individual intelligence where more intelligent people are found to acquire new material quicker.[10][75]
Individual intelligence can be used to predict plenty of life outcomes from school attainment[76] and career success[77] to health outcomes[78] and even mortality.[78] Whether collective intelligence is able to predict other outcomes besides group performance on mental tasks has still to be investigated.
Potential connections to individual intelligence[edit]
Gladwell[79] (2008) showed that the relationship between individual IQ and success works only to a certain point and that additional IQ points over an estimate of IQ 120 do not translate into real life advantages. If a similar border exists for Group-IQ or if advantages are linear and infinite, has still to be explored. Similarly, demand for further research on possible connections of individual and collective intelligence exists within plenty of other potentially transferable logics of individual intelligence, such as, for instance, the development over time[80] or the question of improving intelligence.[81][82] Whereas it is controversial whether human intelligence can be enhanced via training,[81][82] a group's collective intelligence potentially offers simpler opportunities for improvement by exchanging team members or implementing structures and technologies.[41] Moreover, social sensitivity was found to be, at least temporarily, improvable by reading literary fiction[83] as well as watching drama movies.[84] In how far such training ultimately improves collective intelligence through social sensitivity remains an open question.[85]
There are further more advanced concepts and factor models attempting to explain individual cognitive ability including the categorization of intelligence in fluid and crystallized intelligence[86][87] or the hierarchical model of intelligence differences.[88][89] Further supplementing explanations and conceptualizations for the factor structure of the Genomes' of collective intelligence besides a general c factor', though, are missing yet.[90]
Controversies[edit]
Other scholars explain team performance by aggregating team members' general intelligence to the team level[91][92] instead of building an own overall collective intelligence measure. Devine and Philips[93] (2001) showed in a meta-analysis that mean cognitive ability predicts team performance in laboratory settings (.37) as well as field settings (.14) â note that this is only a small effect. Suggesting a strong dependence on the relevant tasks, other scholars showed that tasks requiring a high degree of communication and cooperation are found to be most influenced by the team member with the lowest cognitive ability.[94] Tasks in which selecting the best team member is the most successful strategy, are shown to be most influenced by the member with the highest cognitive ability.[56]
Since Woolley et al.'s[8] results do not show any influence of group satisfaction, group cohesiveness, or motivation, they, at least implicitly, challenge these concepts regarding the importance for group performance in general and thus contrast meta-analytically proven evidence concerning the positive effects of group cohesion,[95][96][97] motivation[98][99] and satisfaction[100] on group performance.
Noteworthy is also that the involved researchers among the confirming findings widely overlap with each other and with the authors participating in the original first study around Anita Woolley.[8][34][34][40][60][68]
Alternative mathematical techniques[edit]Computational collective intelligence[edit]
Computational Collective Intelligence, by Tadeusz Szuba
In 2001, Tadeusz (Tad) Szuba from the AGH University in Poland proposed a formal model for the phenomenon of collective intelligence. It is assumed to be an unconscious, random, parallel, and distributed computational process, run in mathematical logic by the social structure.[101]
In this model, beings and information are modeled as abstract information molecules carrying expressions of mathematical logic.[101] They are quasi-randomly displacing due to their interaction with their environments with their intended displacements.[101] Their interaction in abstract computational space creates multi-thread inference process which we perceive as collective intelligence.[101] Thus, a non-Turing model of computation is used. This theory allows simple formal definition of collective intelligence as the property of social structure and seems to be working well for a wide spectrum of beings, from bacterial colonies up to human social structures. Collective intelligence considered as a specific computational process is providing a straightforward explanation of several social phenomena. For this model of collective intelligence, the formal definition of IQS (IQ Social) was proposed and was defined as 'the probability function over the time and domain of N-element inferences which are reflecting inference activity of the social structure'.[101] While IQS seems to be computationally hard, modeling of social structure in terms of a computational process as described above gives a chance for approximation.[101] Prospective applications are optimization of companies through the maximization of their IQS, and the analysis of drug resistance against collective intelligence of bacterial colonies.[101]
Collective intelligence quotient[edit]
One measure sometimes applied, especially by more artificial intelligence focused theorists, is a 'collective intelligence quotient'[102] (or 'cooperation quotient') â which can be normalized from the 'individual' intelligence quotient (IQ)[102] â thus making it possible to determine the marginal intelligence added by each new individual participating in the collective action, thus using metrics to avoid the hazards of group think and stupidity.[103]
Applications[edit]Elicitation of point estimates[edit]
Here, the goal is to get an estimate (in a single value) of something. For example, estimating the weight of an object, or the release date of a product or probability of success of a project etc. as seen in prediction markets like Intrade, HSX or InklingMarkets and also in several implementations of crowdsourced estimation of a numeric outcome. Essentially, we try to get the average value of the estimates provided by the members in the crowd.
Opinion aggregation[edit]
In this situation, opinions are gathered from the crowd regarding an idea, issue or product. For example, trying to get a rating (on some scale) of a product sold online (such as Amazonâs star rating system). Here, the emphasis is to collect and simply aggregate the ratings provided by customers/users.
Idea Collection[edit]
In these problems, someone solicits ideas for projects, designs or solutions from the crowd. For example, ideas on solving a data science problem (as in Kaggle) or getting a good design for a T-shirt (as in Threadless) or in getting answers to simple problems that only humans can do well (as in Amazonâs Mechanical Turk). The objective is to gather the ideas and devise some selection criteria to choose the best ideas.
James Surowiecki divides the advantages of disorganized decision-making into three main categories, which are cognition, cooperation and coordination.[104][full citation needed]
Cognition[edit]Market judgment[edit]
Because of the Internet's ability to rapidly convey large amounts of information throughout the world, the use of collective intelligence to predict stock prices and stock price direction has become increasingly viable.[105] Websites aggregate stock market information that is as current as possible so professional or amateur stock analysts can publish their viewpoints, enabling amateur investors to submit their financial opinions and create an aggregate opinion.[105] The opinion of all investor can be weighed equally so that a pivotal premise of the effective application of collective intelligence can be applied: the masses, including a broad spectrum of stock market expertise, can be utilized to more accurately predict the behavior of financial markets.[106][107]
Collective intelligence underpins the efficient-market hypothesis of Eugene Fama[108] â although the term collective intelligence is not used explicitly in his paper. Fama cites research conducted by Michael Jensen[109] in which 89 out of 115 selected funds underperformed relative to the index during the period from 1955 to 1964. But after removing the loading charge (up-front fee) only 72 underperformed while after removing brokerage costs only 58 underperformed. On the basis of such evidence index funds became popular investment vehicles using the collective intelligence of the market, rather than the judgement of professional fund managers, as an investment strategy.[109]
Predictions in politics and technology[edit]
Voting methods used in the United States 2016
Political parties mobilize large numbers of people to form policy, select candidates and finance and run election campaigns.[110] Knowledge focusing through various voting methods allows perspectives to converge through the assumption that uninformed voting is to some degree random and can be filtered from the decision process leaving only a residue of informed consensus.[110] Critics point out that often bad ideas, misunderstandings, and misconceptions are widely held, and that structuring of the decision process must favor experts who are presumably less prone to random or misinformed voting in a given context.[111]
Companies such as Affinnova (acquired by Nielsen), Google, InnoCentive, Marketocracy, and Threadless[112] have successfully employed the concept of collective intelligence in bringing about the next generation of technological changes through their research and development (R&D), customer service, and knowledge management.[112][113] An example of such application is Google's Project Aristotle in 2012, where the effect of collective intelligence on team makeup was examined in hundreds of the company's R&D teams.[114]
Cooperation[edit]Networks of trust[edit]
Application of collective intelligence in the Millennium Project
In 2012, the Global Futures Collective Intelligence System (GFIS) was created by The Millennium Project,[115] which epitomizes collective intelligence as the synergistic intersection among data/information/knowledge, software/hardware, and expertise/insights that has a recursive learning process for better decision-making than the individual players alone.[116]
New media are often associated with the promotion and enhancement of collective intelligence. The ability of new media to easily store and retrieve information, predominantly through databases and the Internet, allows for it to be shared without difficulty. Thus, through interaction with new media, knowledge easily passes between sources (Flew 2008) resulting in a form of collective intelligence. The use of interactive new media, particularly the internet, promotes online interaction and this distribution of knowledge between users.
Francis Heylighen, Valentin Turchin, and Gottfried Mayer-Kress are among those who view collective intelligence through the lens of computer science and cybernetics. In their view, the Internet enables collective intelligence at the widest, planetary scale, thus facilitating the emergence of a global brain.
The makeup of a global brain
The developer of the World Wide Web, Tim Berners-Lee, aimed to promote sharing and publishing of information globally. Later his employer opened up the technology for free use. In the early '90s, the Internet's potential was still untapped, until the mid-1990s when 'critical mass', as termed by the head of the Advanced Research Project Agency (ARPA), Dr. J.C.R. Licklider, demanded more accessibility and utility.[117] The driving force of this Internet-based collective intelligence is the digitization of information and communication. Henry Jenkins, a key theorist of new media and media convergence draws on the theory that collective intelligence can be attributed to media convergence and participatory culture (Flew 2008). He criticizes contemporary education for failing to incorporate online trends of collective problem solving into the classroom, stating 'whereas a collective intelligence community encourages ownership of work as a group, schools grade individuals'. Jenkins argues that interaction within a knowledge community builds vital skills for young people, and teamwork through collective intelligence communities contribute to the development of such skills.[118] Collective intelligence is not merely a quantitative contribution of information from all cultures, it is also qualitative.[118]
Lévy and de Kerckhove consider CI from a mass communications perspective, focusing on the ability of networked information and communication technologies to enhance the community knowledge pool. They suggest that these communications tools enable humans to interact and to share and collaborate with both ease and speed (Flew 2008). With the development of the Internet and its widespread use, the opportunity to contribute to knowledge-building communities, such as Wikipedia, is greater than ever before. These computer networks give participating users the opportunity to store and to retrieve knowledge through the collective access to these databases and allow them to 'harness the hive'[119] Researchers at the MIT Center for Collective Intelligence research and explore collective intelligence of groups of people and computers.[120]
In this context collective intelligence is often confused with shared knowledge. The former is the sum total of information held individually by members of a community while the latter is information that is believed to be true and known by all members of the community.[121] Collective intelligence as represented by Web 2.0 has less user engagement than collaborative intelligence. An art project using Web 2.0 platforms is 'Shared Galaxy', an experiment developed by an anonymous artist to create a collective identity that shows up as one person on several platforms like MySpace, Facebook, YouTube and Second Life. The password is written in the profiles and the accounts named 'Shared Galaxy' are open to be used by anyone. In this way many take part in being one.[122] Another art project using collective intelligence to produce artistic work is Curatron, where a large group of artists together decides on a smaller group that they think would make a good collaborative group. The process is used based on an algorithm computing the collective preferences[123] In creating what he calls 'CI-Art', Nova Scotia based artist Mathew Aldred follows Pierry Lévy's definition of collective intelligence.[124] Aldred's CI-Art event in March 2016 involved over four hundred people from the community of Oxford, Nova Scotia, and internationally.[125][126] Later work developed by Aldred used the UNU swarm intelligence system to create digital drawings and paintings.[127] The Oxford Riverside Gallery (Nova Scotia) held a public CI-Art event in May 2016, which connected with online participants internationally.[128]
Parenting social network and collaborative tagging as pillars for automatic IPTV content blocking system
In social bookmarking (also called collaborative tagging),[129] users assign tags to resources shared with other users, which gives rise to a type of information organisation that emerges from this crowdsourcing process. The resulting information structure can be seen as reflecting the collective knowledge (or collective intelligence) of a community of users and is commonly called a 'Folksonomy', and the process can be captured by models of collaborative tagging.[129]
Recent research using data from the social bookmarking website Delicious, has shown that collaborative tagging systems exhibit a form of complex systems (or self-organizing) dynamics.[130][131][132] Although there is no central controlled vocabulary to constrain the actions of individual users, the distributions of tags that describe different resources has been shown to converge over time to a stable power law distributions.[130] Once such stable distributions form, examining the correlations between different tags can be used to construct simple folksonomy graphs, which can be efficiently partitioned to obtained a form of community or shared vocabularies.[133] Such vocabularies can be seen as a form of collective intelligence, emerging from the decentralised actions of a community of users. The Wall-it Project is also an example of social bookmarking.[134]
P2P business[edit]
Research performed by Tapscott and Williams has provided a few examples of the benefits of collective intelligence to business:[38]
Open source software[edit]
Cultural theorist and online community developer, John Banks considered the contribution of online fan communities in the creation of the Trainz product. He argued that its commercial success was fundamentally dependent upon 'the formation and growth of an active and vibrant online fan community that would both actively promote the product and create content- extensions and additions to the game software'.[135]
The increase in user created content and interactivity gives rise to issues of control over the game itself and ownership of the player-created content. This gives rise to fundamental legal issues, highlighted by Lessig[136] and Bray and Konsynski,[137] such as intellectual property and property ownership rights.
Gosney extends this issue of Collective Intelligence in videogames one step further in his discussion of alternate reality gaming. This genre, he describes as an 'across-media game that deliberately blurs the line between the in-game and out-of-game experiences'[138] as events that happen outside the game reality 'reach out' into the player's lives in order to bring them together. Solving the game requires 'the collective and collaborative efforts of multiple players'; thus the issue of collective and collaborative team play is essential to ARG. Gosney argues that the Alternate Reality genre of gaming dictates an unprecedented level of collaboration and 'collective intelligence' in order to solve the mystery of the game.[138]
Benefits of co-operation[edit]
Co-operation helps to solve most important and most interesting multi-science problems. In his book, James Surowiecki mentioned that most scientists think that benefits of co-operation have much more value when compared to potential costs. Co-operation works also because at best it guarantees number of different viewpoints. Because of the possibilities of technology global co-operation is nowadays much easier and productive than before. It is clear that, when co-operation goes from university level to global it has significant benefits.
For example, why do scientists co-operate? Science has become more and more isolated and each science field has spread even more and it is impossible for one person to be aware of all developments. This is true especially in experimental research where highly advanced equipment requires special skills. With co-operation scientists can use information from different fields and use it effectively instead of gathering all the information just by reading by themselves.'[104][full citation needed]
Coordination[edit]Ad-hoc communities[edit]
Military, trade unions, and corporations satisfy some definitions of CI â the most rigorous definition would require a capacity to respond to very arbitrary conditions without orders or guidance from 'law' or 'customers' to constrain actions. Online advertising companies are using collective intelligence to bypass traditional marketing and creative agencies.[139]
The UNU open platform for 'human swarming' (or 'social swarming') establishes real-time closed-loop systems around groups of networked users molded after biological swarms, enabling human participants to behave as a unified collective intelligence.[140][141] When connected to UNU, groups of distributed users collectively answer questions and make predictions in real-time.[142] Early testing shows that human swarms can out-predict individuals.[140] In 2016, an UNU swarm was challenged by a reporter to predict the winners of the Kentucky Derby, and successfully picked the first four horses, in order, beating 540 to 1 odds.[143][144]
Specialized information sites such as Digital Photography Review[145] or Camera Labs[146] is an example of collective intelligence. Anyone who has an access to the internet can contribute to distributing their knowledge over the world through the specialized information sites.
In learner-generated context a group of users marshal resources to create an ecology that meets their needs often (but not only) in relation to the co-configuration, co-creation and co-design of a particular learning space that allows learners to create their own context.[147][148][149] Learner-generated contexts represent an ad hoc community that facilitates coordination of collective action in a network of trust. An example of learner-generated context is found on the Internet when collaborative users pool knowledge in a 'shared intelligence space'. As the Internet has developed so has the concept of CI as a shared public forum. The global accessibility and availability of the Internet has allowed more people than ever to contribute and access ideas. (Flew 2008)
Games such as The Sims Series, and Second Life are designed to be non-linear and to depend on collective intelligence for expansion. This way of sharing is gradually evolving and influencing the mindset of the current and future generations.[117] For them, collective intelligence has become a norm. In Terry Flew's discussion of 'interactivity' in the online games environment, the ongoing interactive dialogue between users and game developers,[150] he refers to Pierre Lévy's concept of Collective Intelligence (Lévy 1998) and argues this is active in videogames as clans or guilds in MMORPG constantly work to achieve goals. Henry Jenkins proposes that the participatory cultures emerging between games producers, media companies, and the end-users mark a fundamental shift in the nature of media production and consumption. Jenkins argues that this new participatory culture arises at the intersection of three broad new media trends.[151] Firstly, the development of new media tools/technologies enabling the creation of content. Secondly, the rise of subcultures promoting such creations, and lastly, the growth of value adding media conglomerates, which foster image, idea and narrative flow.
Coordinating collective actions[edit]
The cast of After School Improv learns an important lesson about improvisation and life
Improvisational actors also experience a type of collective intelligence which they term 'group mind', as theatrical improvisation relies on mutual cooperation and agreement,[152] leading to the unity of 'group mind'.[152][153]
Growth of the Internet and mobile telecom has also produced 'swarming' or 'rendezvous' events that enable meetings or even dates on demand.[22] The full impact has yet to be felt but the anti-globalization movement, for example, relies heavily on e-mail, cell phones, pagers, SMS and other means of organizing.[154] The Indymedia organization does this in a more journalistic way.[155] Such resources could combine into a form of collective intelligence accountable only to the current participants yet with some strong moral or linguistic guidance from generations of contributors â or even take on a more obviously democratic form to advance shared goal.[155]
A further application of collective intelligence is found in the 'Community Engineering for Innovations'.[156] In such an integrated framework proposed by Ebner et al., idea competitions and virtual communities are combined to better realize the potential of the collective intelligence of the participants, particularly in open-source R&D.[157]
Coordination in different types of tasks[edit]
Collective actions or tasks require different amounts of coordination depending on the complexity of the task. Tasks vary from being highly independent simple tasks that require very little coordination to complex interdependent tasks that are built by many individuals and require a lot of coordination. In the article written by Kittur, Lee and Kraut the writers introduce a problem in cooperation: 'When tasks require high coordination because the work is highly interdependent, having more contributors can increase process losses, reducing the effectiveness of the group below what individual members could optimally accomplish'. Having a team too large the overall effectiveness may suffer even when the extra contributors increase the resources. In the end the overall costs from coordination might overwhelm other costs.[158]
Group collective intelligence is a property that emerges through coordination from both bottom-up and top-down processes. In a bottom-up process the different characteristics of each member are involved in contributing and enhancing coordination. Top-down processes are more strict and fixed with norms, group structures and routines that in their own way enhance the group's collective work.[159]
Alternative views[edit]A tool for combating self-preservation[edit]
Tom Atlee reflects that, although humans have an innate ability to gather and analyze data, they are affected by culture, education and social institutions.[160] A single person tends to make decisions motivated by self-preservation. Therefore, without collective intelligence, humans may drive themselves into extinction based on their selfish needs.[36]
Separation from IQism[edit]
Phillip Brown and Hugh Lauder quotes Bowles and Gintis (1976) that in order to truly define collective intelligence, it is crucial to separate 'intelligence' from IQism.[161] They go on to argue that intelligence is an achievement and can only be developed if allowed to.[161] For example, earlier on, groups from the lower levels of society are severely restricted from aggregating and pooling their intelligence. This is because the elites fear that the collective intelligence would convince the people to rebel. If there is no such capacity and relations, there would be no infrastructure on which collective intelligence is built (Brown & Lauder 2000, p. 230). This reflects how powerful collective intelligence can be if left to develop.[161]
Artificial intelligence views[edit]
Skeptics, especially those critical of artificial intelligence and more inclined to believe that risk of bodily harm and bodily action are the basis of all unity between people, are more likely to emphasize the capacity of a group to take action and withstand harm as one fluid mass mobilization, shrugging off harms the way a body shrugs off the loss of a few cells.[162][163] This strain of thought is most obvious in the anti-globalization movement and characterized by the works of John Zerzan, Carol Moore, and Starhawk, who typically shun academics.[162][163] These theorists are more likely to refer to ecological and collective wisdom and to the role of consensus process in making ontological distinctions than to any form of 'intelligence' as such, which they often argue does not exist, or is mere 'cleverness'.[162][163]
Harsh critics of artificial intelligence on ethical grounds are likely to promote collective wisdom-building methods, such as the new tribalists and the Gaians.[164] Whether these can be said to be collective intelligence systems is an open question. Some, e.g. Bill Joy, simply wish to avoid any form of autonomous artificial intelligence and seem willing to work on rigorous collective intelligence in order to remove any possible niche for AI.[165]
In contrast to these views, Artificial Intelligence companies such as Amazon Mechanical Turk and CrowdFlower are using collective intelligence and crowdsourcing or consensus-based assessment to collect the enormous amounts of data for machine learning algorithms such as Keras and IBM Watson.
Solving climate change[edit]
Global collective intelligence is seen as the key in solving the challenges humankind faces now and in the future. Climate change is an example of a global issue which collective intelligence is currently trying to tackle. With the help of collective intelligence applications such as online crowdsourcing, people across the globe are collaborating in developing solutions to climate change.[166]
See also[edit]Similar concepts and applications[edit]
Computation and computer science[edit]Others[edit]Notes and references[edit]
Bibliography[edit]
External links[edit]
Retrieved from 'https://en.wikipedia.org/w/index.php?title=Collective_intelligence&oldid=905054041'
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(Redirected from Group synergy)
Types of collective intelligence
It can be understood as an emergent property from the synergies among: 1) by Norman Lee Johnson.[4] The concept is used in sociology, business, computer science and mass communications: it also appears in science fiction. Pierre Lévy defines collective intelligence as, 'It is a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills. I'll add the following indispensable characteristic to this definition: The basis and goal of collective intelligence is mutual recognition and enrichment of individuals rather than the cult of fetishized or hypostatized communities.'[5] According to researchers Pierre Lévy and Derrick de Kerckhove, it refers to capacity of networked ICTs (Information communication technologies) to enhance the collective pool of social knowledge by simultaneously expanding the extent of human interactions.[6]
Collective intelligence strongly contributes to the shift of knowledge and power from the individual to the collective. According to Eric S. Raymond (1998) and JC Herz (2005), open source intelligence will eventually generate superior outcomes to knowledge generated by proprietary software developed within corporations (Flew 2008). Media theorist Henry Jenkins sees collective intelligence as an 'alternative source of media power', related to convergence culture. He draws attention to education and the way people are learning to participate in knowledge cultures outside formal learning settings. Henry Jenkins criticizes schools which promote 'autonomous problem solvers and self-contained learners' while remaining hostile to learning through the means of collective intelligence.[7] Both Pierre Lévy (2007) and Henry Jenkins (2008) support the claim that collective intelligence is important for democratization, as it is interlinked with knowledge-based culture and sustained by collective idea sharing, and thus contributes to a better understanding of diverse society.
Similar to the g factor (g) for general individual intelligence, a new scientific understanding of collective intelligence aims to extract a general collective intelligence factor c factor for groups indicating a group's ability to perform a wide range of tasks.[8] Definition, operationalization and statistical methods are derived from g. Similarly as g is highly interrelated with the concept of IQ,[9][10] this measurement of collective intelligence can be interpreted as intelligence quotient for groups (Group-IQ) even though the score is not a quotient per se. Causes for c and predictive validity are investigated as well.
Writers who have influenced the idea of collective intelligence include Francis Galton, Douglas Hofstadter (1979), Peter Russell (1983), Tom Atlee (1993), Pierre Lévy (1994), Howard Bloom (1995), Francis Heylighen (1995), Douglas Engelbart, Louis Rosenberg, Cliff Joslyn, Ron Dembo, Gottfried Mayer-Kress (2003).
History[edit]
H.G. Wells World Brain (1936â1938)
The concept (although not so named) originated in 1785 with the Marquis de Condorcet, whose 'jury theorem' states that if each member of a voting group is more likely than not to make a correct decision, the probability that the highest vote of the group is the correct decision increases with the number of members of the group (see Condorcet's jury theorem).[11] Many theorists have interpreted Aristotle's statement in the Politics that 'a feast to which many contribute is better than a dinner provided out of a single purse' to mean that just as many may bring different dishes to the table, so in a deliberation many may contribute different pieces of information to generate a better decision.[12][13] Recent scholarship,[14] however, suggests that this was probably not what Aristotle meant but is a modern interpretation based on what we now know about team intelligence.[15]
A precursor of the concept is found in entomologist William Morton Wheeler's observation that seemingly independent individuals can cooperate so closely as to become indistinguishable from a single organism (1910).[16] Wheeler saw this collaborative process at work in ants that acted like the cells of a single beast he called a superorganism.
In 1912 Ãmile Durkheim identified society as the sole source of human logical thought. He argued in 'The Elementary Forms of Religious Life' that society constitutes a higher intelligence because it transcends the individual over space and time.[17] Other antecedents are Vladimir Vernadsky's concept of 'noosphere' and H.G. Wells's concept of 'world brain' (see also the term 'global brain'). Peter Russell, Elisabet Sahtouris, and Barbara Marx Hubbard (originator of the term 'conscious evolution')[18] are inspired by the visions of a noosphere â a transcendent, rapidly evolving collective intelligence â an informational cortex of the planet. The notion has more recently been examined by the philosopher Pierre Lévy. In a 1962 research report, Douglas Engelbart linked collective intelligence to organizational effectiveness, and predicted that pro-actively 'augmenting human intellect' would yield a multiplier effect in group problem solving: 'Three people working together in this augmented mode [would] seem to be more than three times as effective in solving a complex problem as is one augmented person working alone'.[19] In 1994, he coined the term 'collective IQ' as a measure of collective intelligence, to focus attention on the opportunity to significantly raise collective IQ in business and society.[20]
The idea of collective intelligence also forms the framework for contemporary democratic theories often referred to as epistemic democracy. Epistemic democratic theories refer to the capacity of the populace, either through deliberation or aggregation of knowledge, to track the truth and relies on mechanisms to synthesize and apply collective intelligence.[21]
Collective intelligence was introduced into the machine learning community in the late 20th century,[22] and matured into a broader consideration of how to design 'collectives' of self-interested adaptive agents to meet a system-wide goal.[23][24] This was related to single-agent work on 'reward shaping'[25] and has been taken forward by numerous researchers inthe game theory and engineering communities.[26]
Dimensions[edit]
Complex adaptive systems model
Howard Bloom has discussed mass behavior â collective behavior from the level of quarks to the level of bacterial, plant, animal, and human societies. He stresses the biological adaptations that have turned most of this earth's living beings into components of what he calls 'a learning machine'. In 1986 Bloom combined the concepts of apoptosis, parallel distributed processing, group selection, and the superorganism to produce a theory of how collective intelligence works.[27] Later he showed how the collective intelligences of competing bacterial colonies and human societies can be explained in terms of computer-generated 'complex adaptive systems' and the 'genetic algorithms', concepts pioneered by John Holland.[28]
Bloom traced the evolution of collective intelligence to our bacterial ancestors 1 billion years ago and demonstrated how a multi-species intelligence has worked since the beginning of life.[28] Ant societies exhibit more intelligence, in terms of technology, than any other animal except for humans and co-operate in keeping livestock, for example aphids for 'milking'.[28] Leaf cutters care for fungi and carry leaves to feed the fungi.[28]
David Skrbina[29] cites the concept of a 'group mind' as being derived from Plato's concept of panpsychism (that mind or consciousness is omnipresent and exists in all matter). He develops the concept of a 'group mind' as articulated by Thomas Hobbes in 'Leviathan' and Fechner's arguments for a collective consciousness of mankind. He cites Durkheim as the most notable advocate of a 'collective consciousness'[30] and Teilhard de Chardin as a thinker who has developed the philosophical implications of the group mind.[31]
Tom Atlee focuses primarily on humans and on work to upgrade what Howard Bloom calls 'the group IQ'. Atlee feels that collective intelligence can be encouraged 'to overcome 'groupthink' and individual cognitive bias in order to allow a collective to cooperate on one process â while achieving enhanced intellectual performance.' George Pór defined the collective intelligence phenomenon as 'the capacity of human communities to evolve towards higher order complexity and harmony, through such innovation mechanisms as differentiation and integration, competition and collaboration.'[32] Atlee and Pór state that 'collective intelligence also involves achieving a single focus of attention and standard of metrics which provide an appropriate threshold of action'.[33] Their approach is rooted in scientific community metaphor.[33]
The term group intelligence is sometimes used interchangeably with the term collective intelligence. Anita Woolley presents Collective intelligence as a measure of group intelligence and group creativity.[8] The idea is that a measure of collective intelligence covers a broad range of features of the group, mainly group composition and group interaction.[34] The features of composition that lead to increased levels of collective intelligence in groups include criteria such as higher numbers of women in the group as well as increased diversity of the group.[34]
Atlee and Pór suggest that the field of collective intelligence should primarily be seen as a human enterprise in which mind-sets, a willingness to share and an openness to the value of distributed intelligence for the common good are paramount, though group theory and artificial intelligence have something to offer.[33] Individuals who respect collective intelligence are confident of their own abilities and recognize that the whole is indeed greater than the sum of any individual parts.[35] Maximizing collective intelligence relies on the ability of an organization to accept and develop 'The Golden Suggestion', which is any potentially useful input from any member.[36]Groupthink often hampers collective intelligence by limiting input to a select few individuals or filtering potential Golden Suggestions without fully developing them to implementation.[33]
Robert David Steele Vivas in The New Craft of Intelligence portrayed all citizens as 'intelligence minutemen,' drawing only on legal and ethical sources of information, able to create a 'public intelligence' that keeps public officials and corporate managers honest, turning the concept of 'national intelligence' (previously concerned about spies and secrecy) on its head.[37]
Stigmergic Collaboration: a theoretical framework for mass collaboration
According to Don Tapscott and Anthony D. Williams, collective intelligence is mass collaboration. In order for this concept to happen, four principles need to exist;[38]
Collective intelligence factor c[edit]
Scree plot showing percent of explained variance for the first factors in Woolley et al.'s (2010) two original studies.
A new scientific understanding of collective intelligence defines it as a group's general ability to perform a wide range of tasks.[8] Definition, operationalization and statistical methods are similar to the psychometric approach of general individual intelligence. Hereby, an individual's performance on a given set of cognitive tasks is used to measure general cognitive ability indicated by the general intelligence factor g extracted via factor analysis.[39] In the same vein as g serves to display between-individual performance differences on cognitive tasks, collective intelligence research aims to find a parallel intelligence factor for groups 'c factor'[8] (also called 'collective intelligence factor' (CI)[40]) displaying between-group differences on task performance. The collective intelligence score then is used to predict how this same group will perform on any other similar task in the future. Yet tasks, hereby, refer to mental or intellectual tasks performed by small groups[8] even though the concept is hoped to be transferrable to other performances and any groups or crowds reaching from families to companies and even whole cities.[41] Since individuals' g factor scores are highly correlated with full-scale IQ scores, which are in turn regarded as good estimates of g,[9][10] this measurement of collective intelligence can also be seen as an intelligence indicator or quotient respectively for a group (Group-IQ) parallel to an individual's intelligence quotient (IQ) even though the score is not a quotient per se.
Mathematically, c and g are both variables summarizing positive correlations among different tasks supposing that performance on one task is comparable with performance on other similar tasks.[42]c thus is a source of variance among groups and can only be considered as a group's standing on the c factor compared to other groups in a given relevant population.[10][43] The concept is in contrast to competing hypotheses including other correlational structures to explain group intelligence,[8] such as a composition out of several equally important but independent factors as found in individual personality research.[44]
Besides, this scientific idea also aims to explore the causes affecting collective intelligence, such as group size, collaboration tools or group members' interpersonal skills.[45] The MIT Center for Collective Intelligence, for instance, announced the detection of The Genome of Collective Intelligence[45] as one of its main goals aiming to develop a taxonomy of organizational building blocks, or genes, that can be combined and recombined to harness the intelligence of crowds.[45]
Causes[edit]
Individual intelligence is shown to be genetically and environmentally influenced.[46][47] Analogously, collective intelligence research aims to explore reasons why certain groups perform more intelligent than other groups given that c is just moderately correlated with the intelligence of individual group members.[8] According to Woolley et al.'s results, neither team cohesion nor motivation or satisfaction is correlated with c. However, they claim that three factors were found as significant correlates: the variance in the number of speaking turns, group members' average social sensitivity and the proportion of females. All three had similar predictive power for c, but only social sensitivity was statistically significant (b=0.33, P=0.05).[8]
The number speaking turns indicates that 'groups where a few people dominated the conversation were less collectively intelligent than those with a more equal distribution of conversational turn-taking'.[40] Hence, providing multiple team members the chance to speak up made a group more intelligent.[8]
Group members' social sensitivity was measured via the Reading the Mind in the Eyes Test[48] (RME) and correlated .26 with c.[8] Hereby, participants are asked to detect thinking or feeling expressed in other peoples' eyes presented on pictures and assessed in a multiple choice format. The test aims to measure peoples' theory of mind (ToM), also called 'mentalizing'[49][50][51][52] or 'mind reading',[53] which refers to the ability to attribute mental states, such as beliefs, desires or intents, to other people and in how far people understand that others have beliefs, desires, intentions or perspectives different from their own ones.[48] RME is a ToM test for adults[48] that shows sufficient test-retest reliability[54] and constantly differentiates control groups from individuals with functional autism or Asperger Syndrome.[48] It is one of the most widely accepted and well-validated tests for ToM within adults.[55] ToM can be regarded as an associated subset of skills and abilities within the broader concept of emotional intelligence.[40][56]
The proportion of females as a predictor of c was largely mediated by social sensitivity (Sobel z = 1.93, P= 0.03)[8] which is in vein with previous research showing that women score higher on social sensitivity tests.[48] While a mediation, statistically speaking, clarifies the mechanism underlying the relationship between a dependent and an independent variable,[57] Wolley agreed in an interview with the Harvard Business Review that these findings are saying that groups of women are smarter than groups of men.[41] However, she relativizes this stating that the actual important thing is the high social sensitivity of group members.[41]
It is theorized that the collective intelligence factor c is an emergent property resulting from bottom-up as well as top-down processes.[34] Hereby, bottom-up processes cover aggregated group-member characteristics. Top-down processes cover group structures and norms that influence a group's way of collaborating and coordinating.[34]
Processes[edit]
Predictors for the collective intelligence factor c. Suggested by Woolley, Aggarwal & Malone[34] (2015)
Top-down processes[edit]
Top-down processes cover group interaction, such as structures, processes, and norms.[58] An example of such top-down processes is conversational turn-taking.[8] Research further suggest that collectively intelligent groups communicate more in general as well as more equally; same applies for participation and is shown for face-to-face as well as online groups communicating only via writing.[40][59]
Bottom-up processes[edit]
Bottom-up processes include group composition,[58] namely the characteristics of group members which are aggregated to the team level[34]. An example of such bottom-up processes is the average social sensitivity or the average and maximum intelligence scores of group members.[8] Furthermore, collective intelligence was found to be related to a group's cognitive diversity[60] including thinking styles and perspectives.[61] Groups that are moderately diverse in cognitive style have higher collective intelligence than those who are very similar in cognitive style or very different. Consequently, groups where members are too similar to each other lack the variety of perspectives and skills needed to perform well. On the other hand, groups whose members are too different seem to have difficulties to communicate and coordinate effectively.[60]
Serial vs Parallel processes[edit]
For most of human history, collective intelligence was confined to small tribal groups in which opinions were aggregated through real-time parallel interactions among members.[62] In modern times, mass communication, mass media, and networking technologies have enabled collective intelligence to span massive groups, distributed across continents and time-zones. To accommodate this shift in scale, collective intelligence in large-scale groups been dominated by serialized polling processes such as aggregating up-votes, likes, and ratings over time. While modern systems benefit from larger group size, the serialized process has been found to introduce substantial noise that distorts the collective output of the group. In one significant study of serialized collective intelligence, it was found that the first vote contributed to a serialized voting system can distort the final result by 34%.[63]
To address the problems of serialized aggregation of input among large-scale groups, recent advancements collective intelligence have worked to replace serialized votes, polls, and markets, with parallel systems such as 'human swarms' modeled after synchronous swarms in nature.[64][65] Based on natural process of Swarm Intelligence, these artificial swarms of networked humans enable participants to work together in parallel to answer questions and make predictions as an emergent collective intelligence. In one high-profile example, a human swarm challenge by CBS Interactive to predict the Kentucky Derby. The swarm correctly predicted the first four horses, in order, defying 542â1 odds and turning a $20 bet into $10,800.[66]
Evidence[edit]
Standardized Regression Coefficients for the collective intelligence factor c as found in Woolley et al.'s[8] (2010) two original studies. c and average (maximum) member intelligence scores are regressed on the criterion tasks.
Woolley, Chabris, Pentland, Hashmi, & Malone (2010),[8] the originators of this scientific understanding of collective intelligence, found a single statistical factor for collective intelligence in their research across 192 groups with people randomly recruited from the public. In Woolley et al.'s two initial studies, groups worked together on different tasks from the McGrath Task Circumplex,[67] a well-established taxonomy of group tasks. Tasks were chosen from all four quadrants of the circumplex and included visual puzzles, brainstorming, making collective moral judgments, and negotiating over limited resources. The results in these tasks were taken to conduct a factor analysis. Both studies showed support for a general collective intelligence factor c underlying differences in group performance with an initial eigenvalue accounting for 43% (44% in study 2) of the variance, whereas the next factor accounted for only 18% (20%). That fits the range normally found in research regarding a general individual intelligence factor g typically accounting for 40% to 50% percent of between-individual performance differences on cognitive tests.[42]
Afterwards, a more complex criterion task was absolved by each group measuring whether the extracted c factor had predictive power for performance outside the original task batteries. Criterion tasks were playing checkers (draughts) against a standardized computer in the first and a complex architectural design task in the second study. In a regression analysis using both individual intelligence of group members and c to predict performance on the criterion tasks, c had a significant effect, but average and maximum individual intelligence had not. While average (r=0.15, P=0.04) and maximum intelligence (r=0.19, P=0.008) of individual group members were moderately correlated with c, c was still a much better predictor of the criterion tasks. According to Woolley et al., this supports the existence of a collective intelligence factor c, because it demonstrates an effect over and beyond group members' individual intelligence and thus that c is more than just the aggregation of the individual IQs or the influence of the group member with the highest IQ.[8]
Synergy Player Models Not Showing On Mac
Engel et al.[40] (2014) replicated Woolley et al.'s findings applying an accelerated battery of tasks with a first factor in the factor analysis explaining 49% of the between-group variance in performance with the following factors explaining less than half of this amount. Moreover, they found a similar result for groups working together online communicating only via text and confirmed the role of female proportion and social sensitivity in causing collective intelligence in both cases. Similarly to Wolley et al.,[8] they also measured social sensitivity with the RME which is actually meant to measure people's ability to detect mental states in other peoples' eyes. The online collaborating participants, however, did neither know nor see each other at all. The authors conclude that scores on the RME must be related to a broader set of abilities of social reasoning than only drawing inferences from other people's eye expressions.[68]
A collective intelligence factor c in the sense of Woolley et al.[8] was further found in groups of MBA students working together over the course of a semester,[69] in online gaming groups[59] as well as in groups from different cultures[70] and groups in different contexts in terms of short-term versus long-term groups.[70] None of these investigations considered team members' individual intelligence scores as control variables.[59][69][70]
Note as well that the field of collective intelligence research is quite young and published empirical evidence is relatively rare yet. However, various proposals and working papers are in progress or already completed but (supposedly) still in a scholarly peer reviewing publication process.[71][72][73][74]
Predictive validity[edit]
Next to predicting a group's performance on more complex criterion tasks as shown in the original experiments,[8] the collective intelligence factor c was also found to predict group performance in diverse tasks in MBA classes lasting over several months.[69] Thereby, highly collectively intelligent groups earned significantly higher scores on their group assignments although their members did not do any better on other individually performed assignments. Moreover, highly collective intelligent teams improved performance over time suggesting that more collectively intelligent teams learn better.[69] This is another potential parallel to individual intelligence where more intelligent people are found to acquire new material quicker.[10][75]
Individual intelligence can be used to predict plenty of life outcomes from school attainment[76] and career success[77] to health outcomes[78] and even mortality.[78] Whether collective intelligence is able to predict other outcomes besides group performance on mental tasks has still to be investigated.
Potential connections to individual intelligence[edit]
Gladwell[79] (2008) showed that the relationship between individual IQ and success works only to a certain point and that additional IQ points over an estimate of IQ 120 do not translate into real life advantages. If a similar border exists for Group-IQ or if advantages are linear and infinite, has still to be explored. Similarly, demand for further research on possible connections of individual and collective intelligence exists within plenty of other potentially transferable logics of individual intelligence, such as, for instance, the development over time[80] or the question of improving intelligence.[81][82] Whereas it is controversial whether human intelligence can be enhanced via training,[81][82] a group's collective intelligence potentially offers simpler opportunities for improvement by exchanging team members or implementing structures and technologies.[41] Moreover, social sensitivity was found to be, at least temporarily, improvable by reading literary fiction[83] as well as watching drama movies.[84] In how far such training ultimately improves collective intelligence through social sensitivity remains an open question.[85]
There are further more advanced concepts and factor models attempting to explain individual cognitive ability including the categorization of intelligence in fluid and crystallized intelligence[86][87] or the hierarchical model of intelligence differences.[88][89] Further supplementing explanations and conceptualizations for the factor structure of the Genomes' of collective intelligence besides a general c factor', though, are missing yet.[90]
Controversies[edit]
Other scholars explain team performance by aggregating team members' general intelligence to the team level[91][92] instead of building an own overall collective intelligence measure. Devine and Philips[93] (2001) showed in a meta-analysis that mean cognitive ability predicts team performance in laboratory settings (.37) as well as field settings (.14) â note that this is only a small effect. Suggesting a strong dependence on the relevant tasks, other scholars showed that tasks requiring a high degree of communication and cooperation are found to be most influenced by the team member with the lowest cognitive ability.[94] Tasks in which selecting the best team member is the most successful strategy, are shown to be most influenced by the member with the highest cognitive ability.[56]
Since Woolley et al.'s[8] results do not show any influence of group satisfaction, group cohesiveness, or motivation, they, at least implicitly, challenge these concepts regarding the importance for group performance in general and thus contrast meta-analytically proven evidence concerning the positive effects of group cohesion,[95][96][97] motivation[98][99] and satisfaction[100] on group performance.
Noteworthy is also that the involved researchers among the confirming findings widely overlap with each other and with the authors participating in the original first study around Anita Woolley.[8][34][34][40][60][68]
Alternative mathematical techniques[edit]Computational collective intelligence[edit]
Computational Collective Intelligence, by Tadeusz Szuba
In 2001, Tadeusz (Tad) Szuba from the AGH University in Poland proposed a formal model for the phenomenon of collective intelligence. It is assumed to be an unconscious, random, parallel, and distributed computational process, run in mathematical logic by the social structure.[101]
In this model, beings and information are modeled as abstract information molecules carrying expressions of mathematical logic.[101] They are quasi-randomly displacing due to their interaction with their environments with their intended displacements.[101] Their interaction in abstract computational space creates multi-thread inference process which we perceive as collective intelligence.[101] Thus, a non-Turing model of computation is used. This theory allows simple formal definition of collective intelligence as the property of social structure and seems to be working well for a wide spectrum of beings, from bacterial colonies up to human social structures. Collective intelligence considered as a specific computational process is providing a straightforward explanation of several social phenomena. For this model of collective intelligence, the formal definition of IQS (IQ Social) was proposed and was defined as 'the probability function over the time and domain of N-element inferences which are reflecting inference activity of the social structure'.[101] While IQS seems to be computationally hard, modeling of social structure in terms of a computational process as described above gives a chance for approximation.[101] Prospective applications are optimization of companies through the maximization of their IQS, and the analysis of drug resistance against collective intelligence of bacterial colonies.[101]
Collective intelligence quotient[edit]
One measure sometimes applied, especially by more artificial intelligence focused theorists, is a 'collective intelligence quotient'[102] (or 'cooperation quotient') â which can be normalized from the 'individual' intelligence quotient (IQ)[102] â thus making it possible to determine the marginal intelligence added by each new individual participating in the collective action, thus using metrics to avoid the hazards of group think and stupidity.[103]
Applications[edit]Elicitation of point estimates[edit]
Here, the goal is to get an estimate (in a single value) of something. For example, estimating the weight of an object, or the release date of a product or probability of success of a project etc. as seen in prediction markets like Intrade, HSX or InklingMarkets and also in several implementations of crowdsourced estimation of a numeric outcome. Essentially, we try to get the average value of the estimates provided by the members in the crowd.
Opinion aggregation[edit]
In this situation, opinions are gathered from the crowd regarding an idea, issue or product. For example, trying to get a rating (on some scale) of a product sold online (such as Amazonâs star rating system). Here, the emphasis is to collect and simply aggregate the ratings provided by customers/users.
Idea Collection[edit]
In these problems, someone solicits ideas for projects, designs or solutions from the crowd. For example, ideas on solving a data science problem (as in Kaggle) or getting a good design for a T-shirt (as in Threadless) or in getting answers to simple problems that only humans can do well (as in Amazonâs Mechanical Turk). The objective is to gather the ideas and devise some selection criteria to choose the best ideas.
James Surowiecki divides the advantages of disorganized decision-making into three main categories, which are cognition, cooperation and coordination.[104][full citation needed]
Cognition[edit]Market judgment[edit]
Because of the Internet's ability to rapidly convey large amounts of information throughout the world, the use of collective intelligence to predict stock prices and stock price direction has become increasingly viable.[105] Websites aggregate stock market information that is as current as possible so professional or amateur stock analysts can publish their viewpoints, enabling amateur investors to submit their financial opinions and create an aggregate opinion.[105] The opinion of all investor can be weighed equally so that a pivotal premise of the effective application of collective intelligence can be applied: the masses, including a broad spectrum of stock market expertise, can be utilized to more accurately predict the behavior of financial markets.[106][107]
Collective intelligence underpins the efficient-market hypothesis of Eugene Fama[108] â although the term collective intelligence is not used explicitly in his paper. Fama cites research conducted by Michael Jensen[109] in which 89 out of 115 selected funds underperformed relative to the index during the period from 1955 to 1964. But after removing the loading charge (up-front fee) only 72 underperformed while after removing brokerage costs only 58 underperformed. On the basis of such evidence index funds became popular investment vehicles using the collective intelligence of the market, rather than the judgement of professional fund managers, as an investment strategy.[109]
Predictions in politics and technology[edit]
Voting methods used in the United States 2016
Sims 4 mods nexus. Political parties mobilize large numbers of people to form policy, select candidates and finance and run election campaigns.[110] Knowledge focusing through various voting methods allows perspectives to converge through the assumption that uninformed voting is to some degree random and can be filtered from the decision process leaving only a residue of informed consensus.[110] Critics point out that often bad ideas, misunderstandings, and misconceptions are widely held, and that structuring of the decision process must favor experts who are presumably less prone to random or misinformed voting in a given context.[111]
Companies such as Affinnova (acquired by Nielsen), Google, InnoCentive, Marketocracy, and Threadless[112] have successfully employed the concept of collective intelligence in bringing about the next generation of technological changes through their research and development (R&D), customer service, and knowledge management.[112][113] An example of such application is Google's Project Aristotle in 2012, where the effect of collective intelligence on team makeup was examined in hundreds of the company's R&D teams.[114]
Cooperation[edit]Networks of trust[edit]
Application of collective intelligence in the Millennium Project
In 2012, the Global Futures Collective Intelligence System (GFIS) was created by The Millennium Project,[115] which epitomizes collective intelligence as the synergistic intersection among data/information/knowledge, software/hardware, and expertise/insights that has a recursive learning process for better decision-making than the individual players alone.[116]
New media are often associated with the promotion and enhancement of collective intelligence. The ability of new media to easily store and retrieve information, predominantly through databases and the Internet, allows for it to be shared without difficulty. Thus, through interaction with new media, knowledge easily passes between sources (Flew 2008) resulting in a form of collective intelligence. The use of interactive new media, particularly the internet, promotes online interaction and this distribution of knowledge between users.
Francis Heylighen, Valentin Turchin, and Gottfried Mayer-Kress are among those who view collective intelligence through the lens of computer science and cybernetics. In their view, the Internet enables collective intelligence at the widest, planetary scale, thus facilitating the emergence of a global brain.
The makeup of a global brain
The developer of the World Wide Web, Tim Berners-Lee, aimed to promote sharing and publishing of information globally. Later his employer opened up the technology for free use. In the early '90s, the Internet's potential was still untapped, until the mid-1990s when 'critical mass', as termed by the head of the Advanced Research Project Agency (ARPA), Dr. J.C.R. Licklider, demanded more accessibility and utility.[117] The driving force of this Internet-based collective intelligence is the digitization of information and communication. Henry Jenkins, a key theorist of new media and media convergence draws on the theory that collective intelligence can be attributed to media convergence and participatory culture (Flew 2008). He criticizes contemporary education for failing to incorporate online trends of collective problem solving into the classroom, stating 'whereas a collective intelligence community encourages ownership of work as a group, schools grade individuals'. Jenkins argues that interaction within a knowledge community builds vital skills for young people, and teamwork through collective intelligence communities contribute to the development of such skills.[118] Collective intelligence is not merely a quantitative contribution of information from all cultures, it is also qualitative.[118]
Lévy and de Kerckhove consider CI from a mass communications perspective, focusing on the ability of networked information and communication technologies to enhance the community knowledge pool. They suggest that these communications tools enable humans to interact and to share and collaborate with both ease and speed (Flew 2008). With the development of the Internet and its widespread use, the opportunity to contribute to knowledge-building communities, such as Wikipedia, is greater than ever before. These computer networks give participating users the opportunity to store and to retrieve knowledge through the collective access to these databases and allow them to 'harness the hive'[119] Researchers at the MIT Center for Collective Intelligence research and explore collective intelligence of groups of people and computers.[120]
In this context collective intelligence is often confused with shared knowledge. The former is the sum total of information held individually by members of a community while the latter is information that is believed to be true and known by all members of the community.[121] Collective intelligence as represented by Web 2.0 has less user engagement than collaborative intelligence. An art project using Web 2.0 platforms is 'Shared Galaxy', an experiment developed by an anonymous artist to create a collective identity that shows up as one person on several platforms like MySpace, Facebook, YouTube and Second Life. The password is written in the profiles and the accounts named 'Shared Galaxy' are open to be used by anyone. In this way many take part in being one.[122] Another art project using collective intelligence to produce artistic work is Curatron, where a large group of artists together decides on a smaller group that they think would make a good collaborative group. The process is used based on an algorithm computing the collective preferences[123] In creating what he calls 'CI-Art', Nova Scotia based artist Mathew Aldred follows Pierry Lévy's definition of collective intelligence.[124] Aldred's CI-Art event in March 2016 involved over four hundred people from the community of Oxford, Nova Scotia, and internationally.[125][126] Later work developed by Aldred used the UNU swarm intelligence system to create digital drawings and paintings.[127] The Oxford Riverside Gallery (Nova Scotia) held a public CI-Art event in May 2016, which connected with online participants internationally.[128]
Parenting social network and collaborative tagging as pillars for automatic IPTV content blocking system
In social bookmarking (also called collaborative tagging),[129] users assign tags to resources shared with other users, which gives rise to a type of information organisation that emerges from this crowdsourcing process. The resulting information structure can be seen as reflecting the collective knowledge (or collective intelligence) of a community of users and is commonly called a 'Folksonomy', and the process can be captured by models of collaborative tagging.[129]
Recent research using data from the social bookmarking website Delicious, has shown that collaborative tagging systems exhibit a form of complex systems (or self-organizing) dynamics.[130][131][132] Although there is no central controlled vocabulary to constrain the actions of individual users, the distributions of tags that describe different resources has been shown to converge over time to a stable power law distributions.[130] Once such stable distributions form, examining the correlations between different tags can be used to construct simple folksonomy graphs, which can be efficiently partitioned to obtained a form of community or shared vocabularies.[133] Such vocabularies can be seen as a form of collective intelligence, emerging from the decentralised actions of a community of users. The Wall-it Project is also an example of social bookmarking.[134]
P2P business[edit]
Research performed by Tapscott and Williams has provided a few examples of the benefits of collective intelligence to business:[38]
Open source software[edit]
Cultural theorist and online community developer, John Banks considered the contribution of online fan communities in the creation of the Trainz product. He argued that its commercial success was fundamentally dependent upon 'the formation and growth of an active and vibrant online fan community that would both actively promote the product and create content- extensions and additions to the game software'.[135]
The increase in user created content and interactivity gives rise to issues of control over the game itself and ownership of the player-created content. This gives rise to fundamental legal issues, highlighted by Lessig[136] and Bray and Konsynski,[137] such as intellectual property and property ownership rights.
Gosney extends this issue of Collective Intelligence in videogames one step further in his discussion of alternate reality gaming. This genre, he describes as an 'across-media game that deliberately blurs the line between the in-game and out-of-game experiences'[138] as events that happen outside the game reality 'reach out' into the player's lives in order to bring them together. Solving the game requires 'the collective and collaborative efforts of multiple players'; thus the issue of collective and collaborative team play is essential to ARG. Gosney argues that the Alternate Reality genre of gaming dictates an unprecedented level of collaboration and 'collective intelligence' in order to solve the mystery of the game.[138]
Benefits of co-operation[edit]
Co-operation helps to solve most important and most interesting multi-science problems. In his book, James Surowiecki mentioned that most scientists think that benefits of co-operation have much more value when compared to potential costs. Co-operation works also because at best it guarantees number of different viewpoints. Because of the possibilities of technology global co-operation is nowadays much easier and productive than before. It is clear that, when co-operation goes from university level to global it has significant benefits.
For example, why do scientists co-operate? Science has become more and more isolated and each science field has spread even more and it is impossible for one person to be aware of all developments. This is true especially in experimental research where highly advanced equipment requires special skills. With co-operation scientists can use information from different fields and use it effectively instead of gathering all the information just by reading by themselves.'[104][full citation needed]
Coordination[edit]Ad-hoc communities[edit]
Military, trade unions, and corporations satisfy some definitions of CI â the most rigorous definition would require a capacity to respond to very arbitrary conditions without orders or guidance from 'law' or 'customers' to constrain actions. Online advertising companies are using collective intelligence to bypass traditional marketing and creative agencies.[139]
The UNU open platform for 'human swarming' (or 'social swarming') establishes real-time closed-loop systems around groups of networked users molded after biological swarms, enabling human participants to behave as a unified collective intelligence.[140][141] When connected to UNU, groups of distributed users collectively answer questions and make predictions in real-time.[142] Early testing shows that human swarms can out-predict individuals.[140] In 2016, an UNU swarm was challenged by a reporter to predict the winners of the Kentucky Derby, and successfully picked the first four horses, in order, beating 540 to 1 odds.[143][144]
Specialized information sites such as Digital Photography Review[145] or Camera Labs[146] is an example of collective intelligence. Anyone who has an access to the internet can contribute to distributing their knowledge over the world through the specialized information sites.
In learner-generated context a group of users marshal resources to create an ecology that meets their needs often (but not only) in relation to the co-configuration, co-creation and co-design of a particular learning space that allows learners to create their own context.[147][148][149] Learner-generated contexts represent an ad hoc community that facilitates coordination of collective action in a network of trust. An example of learner-generated context is found on the Internet when collaborative users pool knowledge in a 'shared intelligence space'. As the Internet has developed so has the concept of CI as a shared public forum. The global accessibility and availability of the Internet has allowed more people than ever to contribute and access ideas. (Flew 2008)
Games such as The Sims Series, and Second Life are designed to be non-linear and to depend on collective intelligence for expansion. This way of sharing is gradually evolving and influencing the mindset of the current and future generations.[117] For them, collective intelligence has become a norm. In Terry Flew's discussion of 'interactivity' in the online games environment, the ongoing interactive dialogue between users and game developers,[150] he refers to Pierre Lévy's concept of Collective Intelligence (Lévy 1998) and argues this is active in videogames as clans or guilds in MMORPG constantly work to achieve goals. Henry Jenkins proposes that the participatory cultures emerging between games producers, media companies, and the end-users mark a fundamental shift in the nature of media production and consumption. Jenkins argues that this new participatory culture arises at the intersection of three broad new media trends.[151] Firstly, the development of new media tools/technologies enabling the creation of content. Secondly, the rise of subcultures promoting such creations, and lastly, the growth of value adding media conglomerates, which foster image, idea and narrative flow.
Coordinating collective actions[edit]
The cast of After School Improv learns an important lesson about improvisation and life
Improvisational actors also experience a type of collective intelligence which they term 'group mind', as theatrical improvisation relies on mutual cooperation and agreement,[152] leading to the unity of 'group mind'.[152][153]
Growth of the Internet and mobile telecom has also produced 'swarming' or 'rendezvous' events that enable meetings or even dates on demand.[22] The full impact has yet to be felt but the anti-globalization movement, for example, relies heavily on e-mail, cell phones, pagers, SMS and other means of organizing.[154] The Indymedia organization does this in a more journalistic way.[155] Such resources could combine into a form of collective intelligence accountable only to the current participants yet with some strong moral or linguistic guidance from generations of contributors â or even take on a more obviously democratic form to advance shared goal.[155]
A further application of collective intelligence is found in the 'Community Engineering for Innovations'.[156] In such an integrated framework proposed by Ebner et al., idea competitions and virtual communities are combined to better realize the potential of the collective intelligence of the participants, particularly in open-source R&D.[157]
Coordination in different types of tasks[edit]
Collective actions or tasks require different amounts of coordination depending on the complexity of the task. Tasks vary from being highly independent simple tasks that require very little coordination to complex interdependent tasks that are built by many individuals and require a lot of coordination. In the article written by Kittur, Lee and Kraut the writers introduce a problem in cooperation: 'When tasks require high coordination because the work is highly interdependent, having more contributors can increase process losses, reducing the effectiveness of the group below what individual members could optimally accomplish'. Having a team too large the overall effectiveness may suffer even when the extra contributors increase the resources. In the end the overall costs from coordination might overwhelm other costs.[158]
Group collective intelligence is a property that emerges through coordination from both bottom-up and top-down processes. In a bottom-up process the different characteristics of each member are involved in contributing and enhancing coordination. Top-down processes are more strict and fixed with norms, group structures and routines that in their own way enhance the group's collective work.[159]
Alternative views[edit]A tool for combating self-preservation[edit]
Tom Atlee reflects that, although humans have an innate ability to gather and analyze data, they are affected by culture, education and social institutions.[160] A single person tends to make decisions motivated by self-preservation. Therefore, without collective intelligence, humans may drive themselves into extinction based on their selfish needs.[36]
Separation from IQism[edit]
Phillip Brown and Hugh Lauder quotes Bowles and Gintis (1976) that in order to truly define collective intelligence, it is crucial to separate 'intelligence' from IQism.[161] They go on to argue that intelligence is an achievement and can only be developed if allowed to.[161] For example, earlier on, groups from the lower levels of society are severely restricted from aggregating and pooling their intelligence. This is because the elites fear that the collective intelligence would convince the people to rebel. If there is no such capacity and relations, there would be no infrastructure on which collective intelligence is built (Brown & Lauder 2000, p. 230). This reflects how powerful collective intelligence can be if left to develop.[161]
Artificial intelligence views[edit]
Skeptics, especially those critical of artificial intelligence and more inclined to believe that risk of bodily harm and bodily action are the basis of all unity between people, are more likely to emphasize the capacity of a group to take action and withstand harm as one fluid mass mobilization, shrugging off harms the way a body shrugs off the loss of a few cells.[162][163] This strain of thought is most obvious in the anti-globalization movement and characterized by the works of John Zerzan, Carol Moore, and Starhawk, who typically shun academics.[162][163] These theorists are more likely to refer to ecological and collective wisdom and to the role of consensus process in making ontological distinctions than to any form of 'intelligence' as such, which they often argue does not exist, or is mere 'cleverness'.[162][163]
Harsh critics of artificial intelligence on ethical grounds are likely to promote collective wisdom-building methods, such as the new tribalists and the Gaians.[164] Whether these can be said to be collective intelligence systems is an open question. Some, e.g. Bill Joy, simply wish to avoid any form of autonomous artificial intelligence and seem willing to work on rigorous collective intelligence in order to remove any possible niche for AI.[165]
In contrast to these views, Artificial Intelligence companies such as Amazon Mechanical Turk and CrowdFlower are using collective intelligence and crowdsourcing or consensus-based assessment to collect the enormous amounts of data for machine learning algorithms such as Keras and IBM Watson.
Solving climate change[edit]
Global collective intelligence is seen as the key in solving the challenges humankind faces now and in the future. Climate change is an example of a global issue which collective intelligence is currently trying to tackle. With the help of collective intelligence applications such as online crowdsourcing, people across the globe are collaborating in developing solutions to climate change.[166]
See also[edit]Similar concepts and applications[edit]
Computation and computer science[edit]Others[edit]Notes and references[edit]
Bibliography[edit]
External links[edit]
Retrieved from 'https://en.wikipedia.org/w/index.php?title=Collective_intelligence&oldid=905054041'
Published online 2016 Sep 21. doi: 10.3389/fpsyg.2016.01449
Synergy Player Models Not Showing Back
PMID: 27708609
This article has been cited by other articles in PMC.
Abstract
Individual players act as a coherent unit during team sports performance, forming a team synergy. A synergy is a collective property of a task-specific organization of individuals, such that the degrees of freedom of each individual in the system are coupled, enabling the degrees of freedom of different individuals to co-regulate each other. Here, we present an explanation for the emergence of such collective behaviors, indicating how these can be assessed and understood through the measurement of key system properties that exist, considering the contribution of each individual and beyond These include: to (i) dimensional compression, a process resulting in independent degree of freedom being coupled so that the synergy has fewer degrees of freedom than the set of components from which it arises; (ii) reciprocal compensation, if one element do not produce its function, other elements should display changes in their contributions so that task goals are still attained; (iii) interpersonal linkages, the specific contribution of each element to a group task; and (iv), degeneracy, structurally different components performing a similar, but not necessarily identical, function with respect to context. A primary goal of our analysis is to highlight the principles and tools required to understand coherent and dynamic team behaviors, as well as the performance conditions that make such team synergies possible, through perceptual attunement to shared affordances in individual performers. A key conclusion is that teams can be trained to perceive how to use and share specific affordances, explaining how individualâs behaviors self-organize into a group synergy. Ecological dynamics explanations of team behaviors can transit beyond mere ratification of sport performance, providing a comprehensive conceptual framework to guide the implementation of diagnostic measures by sport scientists, sport psychologists and performance analysts. Complex adaptive systems, synergies, group behaviors, team sport performance, ecological dynamics, performance analysis.
The sorting of the 'All' apps list is actually pretty good, in that such apps tend to be at the very bottom; but if I go on a tangent trying a dozen different similar apps until I decide on one, I'll have those in my list for a bit.And, sure. Possible Duplicate:I'm looking to see if there is a way to clear the actual history of installed apps in Google Play, as opposed to the history of searches (which one can do right from the app).It'd be nice to be able to wipe out the old apps that I tried months ago and uninstalled. How to delete google play order history.
Keywords: complex adaptive systems, synergies, team sport performance, ecological dynamics, group behavior, performance analysis
Introduction
Sport is a human activity characterized by particular organization and functioning in given performance contexts. The ecology of sport is not only distinguished by physical characteristics of locations at which player activity takes place, but also by its social significance and cultural aspects. A factor of interest for audiences as well as sport scientists and performance analysts, is to observe which players or teams succeed in competition, characterized by a complex interaction of physical (e.g., surfaces, areas, weather conditions) and social (e.g., rules) constraints specific to each sport. In team sports, the overarching aim of a team is to score more points/goals than the other team, implying an initiative to score (attacking), and to prevent the other team from scoring (defending). This distinction between attacking and defending is somewhat of an oversimplification, because there are sports where a team can be defending a lead, when in possession of the ball, and a losing team without the ball may be pressurizing space or the opposition ball carrier to re-gain possession. It may be more appropriate to talk about each teamâs offensive and defensive capacity (Mateus, 2005). In each case, a specific feature of team ball sports is the flow of interactions between cooperating and competing players, with or without the ball, to achieve the competitive performance aims of each team (Mateus, 2005). The aims of the teams are thus mutually exclusive during competitive performance. Essentially, each team tries to prevent the implementation of the performance strategy of the other team, as well as dissipate their tactical actions (Davids et al., 2005). The result is the emergence of a complex web of interactive behaviors of players and teams during competition.
Team sport competitions have been conceptualized as complex dynamical systems composed of many interacting parts (e.g., Davids et al., 2005). A dynamical systems approach to sport performance describes how patterns of coordinated movement come about (âemergeâ), persist, and change. It builds on the insight that teams, as social systems, consist of a large number of interacting parts, endowing them with a capacity for spontaneous pattern formation or self-organization. The spontaneous creation of coherent macroscopic patterns (e.g., team coordination) is important scientifically, and the resulting macroscopic patterns of the dynamics of one or a few collective variables or order parameters can be studied carefully (e.g., the cluster phase; ), without needing to record all the microscopic states of the individual parts (e.g., the movement of each player; see Kelso, 1995). Conversely, when the dynamics of macroscopic phenomena have been identified, the contributions of relevant dynamical components (e.g., the movement of certain players) to the overall dynamics may be investigated in a topâdown fashion (Araújo et al., 2015).
Performers can generate behavioral patterns that are tightly coordinated with environmental events (e.g., the match configuration), in order to achieve a specific performance goal. In team sports, athletes are surrounded by physical (e.g., gravity, altitude, ambient temperature) and social (e.g., audience, rules of the game, local rivalries) constraints. Successful performance in sport is predicated on the constraints of an individualâs perceptual and action capabilities, and is grounded on information used for action selection and goal achievement (Araújo et al., 2006).
Assuming the mutuality and reciprocity between the performer and the environment, i.e., the performer-environment system as the key level of analysis, implies, not only an active agent, but also that the environment is an active part of a system, facilitating specific behavioral outcomes (). One consequence of this account is that behavior can be understood as self-organized, in contrast to organization being imposed from inside (e.g., by instructions of the team captain in sports teams) or outside (e.g., directions of the coach). Team performance does not always need to be prescribed by internal or external structures, due to inherent self-organization tendencies that exist for exploitation in sports teams (Araújo et al., 2016) yet traditional sports psychology and pedagogical practice has decreed that internal/external prescription is the default mode to face this challenge (e.g., Lidor and Henschen, 2003). Ecological dynamics suggests that, from a playerâs point of view, the task is to exploit physical (e.g., pitch characteristics determined by federation rules) and informational (e.g., perception of the movements of other players) constraints to stabilize emergence of intended behaviors. Constraints have the effect of reducing the number of configurations available to a dynamical sports team (conceptualized as a social adaptive system) at any instance. In team sports competitions, coordination patterns (individual or collective) emerge under constraints as less functional states of organization are dissipated. Every team sport presents its own set of interacting constraints that helps define competitive functioning. This view contrasts with the traditional use of team statistics and notations that are often used to mechanistically operationalize team sports performance in a data-driven way (e.g., frequency counts and averages in sports: see ; Travassos et al., 2013; ).
In fact, the ever-increasing successful implementation of new technologies in sports inevitably leads to sequential stages of: data capture, pre-processing, transfer, post-processing, visualization, and analysis. This relentless emphasis in current performance analysis (PA) methodologies involves a high volume, variety and complexity of data, to meet the demands of practice, evaluation and training in different sports.
As fundamental theories have failed to make any reliable analysis or predictions around football, most contemporary research has focused on probabilistic models (e.g., Owramipur et al., 2013). Nevertheless, and despite the success of some of these methods, they rely only on the overall information retrieved from previous matches, without considering on-the-fly information retrieved during a current match, such as each athleteâs positional and physiological data, nor producing any macroscopic tactical metric inherent to such data (see ) for a critical analysis of these methods. A critical point here is that, typically, notational analysis tends to describe performance, but omits reference to the whys and hows that underlie the structure of many recorded behaviors, which would clearly define their functional utility (McGarry, 2009). Recently, Gudmundsson and Horton (2016), in an exhaustive review of measures of team behavior in âinvasion sportsâ (i.e., association football, hockey, rugby union), proposed how information obtained from spatio-temporal data (Araújo et al., 2004; McGarry et al., 2014) could be categorized in what they called a âcoherent framework.â The categories of this framework were: (i) description of variables that can be obtained from spatio-temporal data (i.e., player trajectories, sequence of events); (ii) divisions of the playing area; (iii) playersâ interactions and temporal sequences of events (captured by networks and related metrics); (iv) types of information obtained by data mining techniques (labeling events, predicting future events, identifying formations, identifying plays and group movements, segmenting a match); (v) metrics to measure player and team performance; and finally (vi) visualization techniques used to present metrics calculated from the visuo-spatial data. This framework, as well as others (Bartlett et al., 2012), is useful, but its rationale is essentially methodological, focusing on the production of an operational account of performance. This means that it has limited use for understanding team behaviors, only a capacity to measure them and operationally define them. The questions that arise are: what is the meaning of these metrics? What theoretical concepts of a theory of team behaviors can be used to explain how these behaviors emerge?
To address these, and related, questions PA should not focus on production of operational measures and performance descriptors only. Rather, PA could really benefit from the development of a detailed account providing theoretical support for interpretation of data that arise from the descriptive measures. Ecological dynamics is such a theoretical framework for studying behaviors in team games (Davids et al., 2005; Araújo et al., 2006). It has the advantage of recognizing the âflexibilityâ of social systems (i.e., teams), and its principles can explain how the same performance outcomes can emerge from different behavioral patterns. More than a decade ago (e.g., Araújo et al., 2004; Davids et al., 2006), we began to develop a theory of interpersonal behaviors in team sports, applying concepts such as that of âsuperorganismâ to characterizing a team (Araújo, 2006; ; Araújo et al., 2016). This path led us to the âteam synergyâ hypothesis and its operationalization, presented here (see Passos et al., in press for an extensive synthesis of findings and methods applied in different team sports). The hypothesis of team behavior as a synergy is possible only if we conceive the origin of a team synergy as an emergent phenomenon, which is a consequence of playersâ perceptual attunement to shared affordances, as framed by the ecological dynamics approach that we briefly overview next. But before that, we concisely present an overview of other competing hypotheses exiting in the sport sciences literature.
Some Alternative Hypotheses to Explain Team Behaviors
There are a limited number of hypotheses in the literature to explain team behaviors (see Araújo and Bourbousson, 2016, for a review). Here we outline the two more prominent proposals for the team synergy hypothesis: the hypothesis of shared knowledge grounded in the socio-cognitive approach and the participatory sense-making hypothesis grounded in the enactive approach.
The assumption of shared knowledge is predicated on the possession by team members of mental models that provide a basic shared understanding of how to achieve desired performance outcomes (Ward and Eccles, 2006). It is argued that team efficacy could increase when a sophisticated, global and comprehensive mental representation of a performance context of a collective action is somehow shared by all players and put into practice. Lack of coordination between the intentions of individual performers and those of the team imply that a shared cognitive state has yet to be achieved, with resulting difficulties in team performance (Eccles, 2010). In this view, it is the construction and updating of the individualâs mental model that explains how multiple performers may simultaneously act together. As many team members are simultaneously coordinating, the amount of similarity within individualsâ representation acting together becomes a key feature, thus indicating a state of shared understanding within the team. However, knowing âwho knows whatâ at each moment of a match would involve an immense computational load for a representational system. Particularly, the mechanism to explain re-formulations of a team memberâs schema, when changes occur in the content of another memberâs schema, has proved difficult to verify (Mohammed et al., 2000). A key difficulty is to justify how mental representations exist beyond the mind of an individual organism and can be somehow shared in a collective representation (; ).
Another alternative is the participatory sense-making hypothesis which suggests that team coordination processes should be investigated by reconstructing how individual âcognitionsâ articulate during performance environments. With the claim that a performer possesses a unique âinteriority,â this hypothesis has given a special attention to the implicit ways how each performer experiences his/her ongoing activity. Participatory sense-making processes refer to how the meanings that each performer internally builds from his/her activity corroborate with the meanings simultaneously built by co-performers, and how this participation in sense-making is experienced (De Jaegher and Di Paolo, 2007; Froese and Di Paolo, 2011). From this perspective, it is assumed that putative âparticipatory sense-makingâ emerges from a cooperative effort (De Jaegher and Di Paolo, 2007). Performers working together achieve the experience of mutual engagement in real-time. Any divergence in how each member experiences a performance situation leads to varied degrees of participation in sense-making, variations in feelings of connectedness with the other, and variations in expectations for actions from others (e.g., Bourbousson et al., 2010a, 2015). This enactive approach tries to avoid representationalism, but by being grounded in the âinteriorityâ of each individual it needs to operationally define what the internal sense-making process is and contrast it with representations of lived situations which are heavily reliant on memory. In fact, it should be clarified why there is a need to add the label âsense-makingâ to the process of team coordination, unless we are operating in an approach that overstates the asymmetry of organism and environment (Fultot et al., 2016). The idea proposed by enactive theorists that meaning (about the world) is internally built is not operationally defined in terms that exclude representations (prompting the question: where is meaning constructed, and how, and in what form?) and therefore, may be subject to the same criticisms as the shared knowledge hypothesis about the overreliance on mental representations, individual or collective.
In general, both the participatory sense-making and the shared knowledge hypotheses rely on data from a posteriori verbalizations of team sports performers. Therefore, the organization of behavior, from these perspectives, is mainly understood and derived from the verbalized conceptions and perceptions about behavior, not from the actual behaviors themselves. From these perspectives, overt behavior and its organization in contexts like team sports is a surrogate of verbalized shared sense-making or knowledge, without a self-organization of its own. In contrast to these perspectives, it is argued here that behavior has an organization that goes beyond what a performer possesses (verbal information expressing knowledge or its interiority). Rather it is deeply rooted in the specific circumstances of behavior, in which continuous performer-environment interactions are not determined by one component of such a system.
Ecological Dynamics and Team Sports Performance: The Shared Affordances Hypothesis and its Relationship with Team Synergies
Understanding group coordination from the perspective of ecological dynamics requires investigating the dynamical principles that underpin the self-organizing patterns of group dynamics in performance contexts (Araújo et al., 2016; Passos et al., in press). Interpersonal patterns at the behavioral scale can be understood and modeled in terms of their own dynamics, without the need to investigate each performerâs particular movement patterns (nor their verbalizations of what these might be; ). Analyses of group dynamics capture the continuous interactions of system components to form stable behavioral patterns, characterized as attractor states of system dynamics (). Important for the study of group dynamics is the fact that synchronization processes (i.e., temporal coordination of unfolding events in a system) have been found to occur between organisms that are connected, not only mechanically, but also informationally. In this approach, information is conceptualized in the specific patterns in surrounding energy distributions (e.g., light, sound, neural) that can specify properties of the world, meaning that the mapping from patterned energy distributions to properties of the world is direct (not reflected upon or inferred from representations of these relations). Much research has demonstrated how this dynamical synchronization process can operate predicated on information to produce coordinated timing of interpersonal interactions (e.g., ). In these studies, the only possible way that the rhythmic units could have interacted was via the information available to the visual systems of the human participants. Importantly, distributions of energy surrounding an organism, when properly described, are rich with information that specify action-relevant properties of the world. More to the point, specificational information is the invariant structure of (low) energy distributions lawfully structured by emergent individualâenvironment interactions that, because of that lawfulness, specify relationships of each individual with the environment. This is precisely the type of invariant structure that supports inter-player coordination in sports teams.
Understanding of the informational basis for the emergence of group coordination, and the dynamics that permit the self-organization of coordinated group action to occur is, therefore, a priority for research from this viewpoint. This is not because it implies athletes do not have intentions, thoughts and mental states, but rather because research demonstrates how integrally intertwined are body (e.g., nervous, physiological, psychological, emotional) and contextual (e.g., social, cultural, climate, altitude) sub-systems, during interpersonal coordination (). Adopting an environment-individual system perspective (Richardson et al., 2008; Järvilehto, 2009; ), an ecological approach proposes that knowledge of the world is based upon recurrent processes of perception and action through which humans perceive affordances (i.e., opportunities for action, see Gibson, 1979). The concept of affordances presupposes that the environment is directly perceived in terms of what actions a performer can achieve within a performance environment (i.e., it is not dependent on a perceiverâs expectations, Richardson et al., 2008). Moreover, to perceive an affordance is to perceive how one could act with respect to an environmental layout. This way, affordances are neither external properties of an environment nor are they representational properties of mind; they are relational properties of a performer-environment system, which capture the link between individual and environment and are perception-action system specific (Fajen et al., 2009). Affordances capture the action specific relations that exist between the skills/capacities of an individual performer and the action relevant properties of a task, including social tasks (Heft, 1989; Withagen et al., 2012). The theory of affordances is based on the dual interdependence of perception and action, where affordances are the primary objects of perception, and action is the realization of affordances (Araújo et al., 2013). Consequently, it is the role of scientists to discover information in ambient energy arrays that specifies action-relevant properties (such as team synergies) and to show how team synergies may be constrained by such information (Araújo et al., 2006).
Recently, challenged the hypotheses for team coordination described in the previous section. We argued that these hypotheses were predicated on âknowledge aboutâ the environment, which can be used to share knowledge and participatory sense-making and consequently organize action. Rather, during competitive performance, the organization of action by perceiving surrounding informational constraints is expressed in âknowledge ofâ the environment. This crucial distinction emphasizes that the perception of shared affordances (for others and of others) underpins the main communication channel between team members during team coordination tasks. We grounded these explanations on Reedâs (1996) conception of affordances where affordances are resources in the environment. In this view, the relative persistence of some affordances in the animalâs environment has given rise to the evolution of several distinct action systems, enabling the animal to take advantage of these affordances (, 1996). This view suggests that evolution gives rise to animals that are capable of taking advantage of the relatively persistent affordances in a performance environment. These resources in a performance environment have imposed selection pressures on some group of individuals, causing them to evolve perceptual systems to perceive these relations. Predicated on the key ideas of Reed, we went as far as arguing that affordances are collective environmental resources that have existed prior to the upskilling of the individuals that came to perceive and use them. At the timescale of team sports performance, these ideas can be taken to imply that more successful teams are composed of athletes who have learned to perceive shared affordances for and of other players.
, 1996) also asserted that the exertion of selection pressures by affordances is a dominant force in evolution by natural selection. However, an important criticism of this view is that the environment not only shapes individuals, but individuals also modify their environment (see Withagen and Van Wermeskerken, 2010). More precisely, individuals and their environments evolve together. In fact, Heft (2007) brought the process of niche construction to attention when arguing that the human environment is largely a product of social processes, aligned with an important discussion about the process of âniche constructionâ in evolutionary psychology (Odling-Smee et al., 2003). The phrase âniche constructionâ refers to how the modifications that the organism brings about in its environment can create new evolutionary equilibria and trajectories (Withagen and Van Wermeskerken, 2010).
Aligned with the ideas of Reed, we concur that affordances constitute the context of selection, but updating our hypothesis of shared affordances we take the dynamic view presented by Withagen and Van Wermeskerken (2010) that individualsâ dissolution and construction of affordances change this context, demonstrating the key roles affordances play in learning, development, and evolution. This clearly signifies that individuals do not passively evolve so as to fit in a preexisting environment. Rather, individuals and their environments actively co-evolve, and each individualâs modification of the environment has a constitutive role in this co-evolution process. Individuals modify their (social, physical) environment, which can change the selection pressures to which they are exposed. Lewontin (1983) emphasized that there are constraints on the co-evolution of animal and environment. Hence, contrasting with the ideas of Reed, Lewontin would not conceive the physical properties of the world as a resource that exerts selection pressures, but would conceive of them as constraints on the evolution of the individualâenvironment system. This ideation indicates, that affordances arise along with the evolution of action systems and provide the context of selection and adaptation after they have evolved (Withagen and Van Wermeskerken, 2010). In short, niche construction not only requires the utilization of affordances, it also consists of a change in the affordance layout. Hence, individuals often create and dissolve affordances, with other individuals (members of a species or, in humans, sports teams) being exposed to these modified environments as new members of a group (Withagen and Van Wermeskerken, 2010). Thus, the ecological inheritance from one group to the next encompasses an inheritance of affordances. It is important to emphasize, however, that changes in the affordance layout are not exclusively the result of individual activity. Indeed, as mentioned above, geo-physical (including built environments) and social processes can also alter the affordances in an individualsâ eco-niche, such as a training setting. Furthermore, it is important to reiterate that creating a niche is of course not always evolutionarily consequential â it does not have to change the context of selection. However, as we have discussed, niche construction can alter the developmental trajectory of a collective system in small or extensive ways, which could be significant or not for group performance.
Extending this idea to the level of interpersonal interactions, ecological dynamics predicts that the presence of others extends action possibilities that are realizable by individuals to action possibilities realizable by groups. Indeed, Gibson (1979) argued that âbehavior affords behaviorâ (p. 135) and that it was important to note that the ârichest and most elaborate affordancesâ (p. 135) of all are affordances of other people in social contexts. What do these rich insights imply for understanding of coordination in team sports? The suggestion is that affordances can be perceived by a group of individuals trained to become perceptually attuned to them (). Given the mutually exclusive performance aims of both teams in a sports contest, affordances are shared and sustained by common task goals of team members who cooperate and compete to achieve group success. From this perspective, team coordination depends on the collective attunement of individual athletes in a team to shared affordances (). Through practice, players can become perceptually attuned to affordances of others and affordances for others during competitive performance, and can refine their actions (Fajen et al., 2009) by adjusting behaviors to functionally adapt to those of other teammates and opponents. Moreover, individuals in a team can act in a way to create competitive circumstances (the affordances) that are favorable to them. For example, by pressuring opponents to play more in one zone of the playing area, this strategy can create affordances for attacking play by freeing other areas of the field/court/pitch. This active construction of affordances can be trained too. These processes enable a group of players to act synergistically with respect to specific match circumstances (Araújo et al., 2015). Importantly, shared affordances are predicated on perception, whereas synergies regard action, meaning that a synergy is a physical process that realizes a task goal under the guidance of affordance-specific information.
Synergies and EcologiesSynergy Player Models Not Showing One
A synergy is a functional concept, not a structural, component-based concept. In analyses of human movement it relates directly to explanations of coordination processes in multi-articular systems such as the body of an athlete or teams that compete in sport. defined a synergy as a group âof relatively independent degrees of freedom that behave as a single functional unit â meaning that the internal degrees of freedom take care of themselves, adjusting to their mutual fluctuations and to the fluctuations of the external force field, and do so in a way that preserves the functional integrity of theâ group (p. 659). More broadly a synergy is â[T]he adaptive fit of parts of a system to each other and to the system as a wholeâ (, p. 152). Considering this definition, in a collective system, a synergy is a task-specific organization of individuals, with the degrees of freedom of each individual having the potential for coupling, enabling the degrees of freedom of different individuals to regulate each other (Bernstein, 1967). Synergies require the modulation of fewer parameters than the separate control of each degree of freedom, in order to bring about coordinated movement. This system capacity reduces the need for control of each degree of freedom, and allows for compensatory variability in one element of the synergy by another. Importantly, coordination processes that characterize a synergy are not predicated on a cooperativity of individual structural components, but rather on the cooperativity of their functional roles. In other words, synergies being task specific, they are not conceived by design; they are not hard-wired to behave in a pre-arranged way, and therefore the context-dependent functionality of synergy components should always be recognized (). Synergies are, thus, context-dependent, time-evolving dynamical systems (Thelen and Smith, 1994) that, according to circumstances, self-organize several, individual system components in an appropriate and timely fashion.
An important feature of a team synergy is the capacity of one individual (e.g., a player in a team) to influence behaviors of others (; Araújo et al., 2016). Decisions and actions of players forming a synergy should not be viewed as independent, explaining how multiple players synchronize activities in accordance with dynamic performance environments in fractions of a second (). The coupling of players, as independent degrees of freedom, into synergies is based upon perception-action systems in a social context supported by the collective perception of shared affordances (). As we elucidate in the next section, research has demonstrated that inherent degeneracy (i.e., flexibility) in perception-action systems provides the neurobiological basis for the diversity of actions required to negotiate information-rich, dynamic social environments toward a task goal, as well as providing a huge evolutionary fitness advantage (see ). Therefore, the relationship between the characteristics of the environment and each individualâs skills and capacities can be captured in the perception of an affordance (Withagen et al., 2012). System degeneracy between individuals would reflect each individualâs actualization of an affordance through various coordination patterns. That is, the same affordance can be actualized with different coordination patterns as individuals interact with task and environmental constraints.
In line with this notion, an ecological dynamics perspective seeks to predict conditions under which individuals are better able to coordinate movements with others, and which features of a situation facilitate/perturb interpersonal coordination in completing some task. This view has major implications for designing experiments for studying team performance behaviors, as well as for practice and training design. Brunswik (1956) was likely the first psychologist advocating theoretical principles for sampling the features of a task, using the concept of ârepresentative experimental design.â He argued that perceptual variables incorporated into experimental designs should be sampled from the performerâs typical performance environment, to which behavior is intended to be generalized. This powerful idea implies that experimental designs, aligned with an ecological dynamics perspective, need to focus on player-player-environment interactions that can be elucidated in compound variables specifying functional collective behaviors of teams (e.g., a given attack configuration), underpinned by interpersonal synergies created between performers (see Araújo et al., 2015, for a review). An important pedagogical principle in sport is the need to ensure that there is adequate âsamplingâ of informational variables from the performance environment in a practice task, ensuring that modified training tasks will correspond to an actual competition context so that important sources of information are present (Araújo and Davids, 2015).
Ecological dynamics analyses of team sports have attempted to explain how interactions between players and information from the performance environment constrain the emergence of patterns of stability, variability and transitions in organizational states of such team synergies. With motion sensors, it is possible to examine interpersonal rhythmic coordination of movement (Kelso, 1995). The emergent coordination patterns in team sports are channeled by surrounding constraints that structure the state space of all possible configurations available to the team game as a complex system (Davids et al., 2006). For example, the surrounding patterned energy distributions (i.e., information) that performers can perceive act as important sources of information to support their decisions and actions (e.g., reflected light from the ball; Araújo and Davids, 2009).
By using tracked positional data, recent studies have started to reveal how players and teams continuously interact during competition. For example, it has been observed that teams tend to be tightly synchronized in their lateral and longitudinal movements (Vilar et al., 2013), with a counterphase relation in their expansion and contraction movement patterns (Yue et al., 2008), commonly instigated by changes in ball possession (). The coordination patterns observed showed compensatory behaviors within the team, an essential characteristic of a synergy (). Under this theoretical rationale, several existing variables obtained from spatio-temporal data can be organized according to the team synergy hypothesis, and synergy properties can also guide the discovery of new variables.
Properties of Team Synergies and their Measurement
There are slightly different perspectives on the relevant properties of a synergy (; Latash, 2008; Araújo et al., 2016). Latash (2008) identified characteristics that should be met for a group of components to be considered a synergy, including: (i) sharing patterns, where the components should all contribute to performance of a particular task; (ii) error compensation, where some components show changes in their contributions to a task, compensating for a component that may not be making a relevant contribution; and (iii), task-dependence, the capacity of a synergy to change its functioning in a task-specific way, or to form a different synergy for a different purpose based on the same set of components. Task dependence is an expression of redundancy (Bernstein, 1967), or more generally degeneracy (Davids et al., 2006; ), defined as the capacity of structurally different components to perform a similar, but not necessarily identical, function with respect to context (). Alternatively, identified two properties of a synergy: (i) dimensional compression, where the degrees of freedom that potentially are independent become coupled so that the synergy has fewer degrees of freedom (a lower dimensionality) than the set of components from which it arises (Bingham, 1988). Interestingly, the behavior that emerges from the interactions among degrees of freedom, not among the structural components, constitutes a second level of dimensional compression. Dimensional compression at both stages results from imposing environmental, task and individual constraints, which couple components so they change together, rather than independently. The second property of a synergy for is reciprocal compensation and it is a less biased description of what Latash (2008) described as âerror compensation.â To label a behavior as âerrorâ is to assume normativity in observers. Another possible label for an error is creativity, even though creativity goes much beyond reciprocal compensation ().
Therefore, we address four properties of a synergy which are: (1) dimensional compression (Bingham, 1988; ), (2) reciprocal compensation (Latash, 2008; ); (3) Interpersonal linkages. According to Latash (2008), who termed this property âsharing patterns,â a way to quantify the amount of sharing is the matching of the sum of the individual contributions to the task, where the overall measurement of task performance may be related to the dimensional compression property. In contrast, and inspired by Ingold (2015), we address this property in relation to the individuality of each element of a synergy, and therefore, the different ways they can link together; and (4) degeneracy (Davids et al., 2006; Latash, 2008; ). However, degeneracy may be seen as a more general property that can be expressed in different ways, compared to the previous properties which are more specific. This broader view is needed because it shows adaptability across tasks (e.g., competitive matches) and across changes in system components (e.g., players) as occurs in a sport team. Next we describe how these properties can be measured in team sport performance.
Dimensional Compression: âOne for All, All for Oneâ
A synergy, conceptualized as a controllable organization of many individual degrees of freedom, is an organization of afference (perception). From an ecological dynamics point of view, shared affordances guide team behaviors. This implies that the afferent and efferent flows comprising a synergy unfold in a collective variable (or order parameter) â a measurable quantity that expresses a coherent relation among the synergyâs parts and processes (). The time-evolution of a synergy, subject to continuous afferent and efferent influences, can then be captured, in principle, by a single first-order equation in the order parameter.
Order Parameters
In a synergy, all the composing individual components become arbitrarily quick so that they can adapt instantaneously to changes in the control parameters (i.e., the variables that change the state of a system, Kelso, 1995). The synergy dynamics thus amount to those of the order parameters (collective variables), implying that the ordered states can always be described by very few variables, if in the neighborhood of behavioral transitions. In other words, the state (i.e., the synergy) of the originally high-dimensional system (i.e., the team) can be summarized by a few variables or even a single collective variable, the order parameter (Beek and Daffertshofer, 2014).
Self-organization in dynamical systems is predicated on dimensional compression. The work of Kelso (1995) exemplified a system order parameter in the form of ârelative phase,â in the study of human rhythmic movement coordination. Phase is an angular measure of an oscillatorâs position within its cycle of movement, and relative phase is simply the difference in phase relations between two oscillators, This example of an order parameter captures the low-dimensional behaviors (the synergies) that arise from a high-dimensional system (e.g., sports teams). Relative phase describes the spatiotemporal pattern of rhythmic coordination and the changes in coordination that occur as sudden adaptations to manipulations of a system control parameter (e.g., movement velocity). In this case, the dynamics of relative phase is understood to reflect the behavior of a synergy (Kelso, 1995; Turvey and Carello, 1996).
In an attempt to capture group synchrony tendencies in a single variable (i.e., to capture dimensional compression, one of the properties of a synergy), shed light on how the players composing a team influenced each other to create a collective movement at the team level. For this, instead of a relative phase value that only linked two players, applied the cluster phase method (Frank and Richardson, 2010), based on the Kuramoto order parameter, to analyze the movements of 11 football players from two teams during a competitive football match to assess whole team and player-team synchrony. Synergistic relations within a whole team showed superior mean values and high levels of stability in a longitudinal direction compared to a lateral direction on field. Player-team synchrony revealed a tendency for a near in-phase mode of coordination. Also, the coupling of the two competing teamâs measures showed that synchronization increased between both teams over time. This was likely the first time a formal measure of dimensional compression was used to characterize team synergies in sport and changes to them throughout the course of a competitive match between two professional teams. In sport sciences, there are team-based variables that may be precursors of measures for dimensional compression. It is important to clarify that these spatial team-based variables are not measures of compression along any dimensional axis, nor measures of coupled degrees of freedom of elements that potentially could be independent. This is why they may be seen as preliminary attempts to measure dimensional compression of a team synergy.
Team-Based Variables
Coaches often discuss the importance of the âcenter of gravityâ of a team (Gréhaigne et al., 2011). An operational approach to this tactical concept is a teamâs center (also denominated centroid, or geometrical center). It has been used in various ways to evaluate intra- and inter-team coordination in team sports (e.g., ; Frencken et al., 2011; ). Team centers can represent, in a single variable, the relative positioning of both teams in forward-backward and side-to-side movement displacements.
According to coaching knowledge, the team in possession of the ball possession should seek to create space by stretching and expanding on field (increasing distances between players), while a defending team should close down space by contracting and reducing distances between players. Such collective movements may be captured by specific measures of team coordination that quantify the overall spatial dispersion of players on field. The stretch index (or radius), the team spread and the effective playing space (or surface area) are quantities that have been used to assess such spatial distributions (e.g., ; ; ). Also, it is important for a successful team to outnumber the opposition (creation of numerical overloads) during different performance phases (attack and defense) in spatial regions adjacent to the ball, expressed through inter-team coordination. Inter-team coordination was recently examined through analysis of the distances separating the horizontal and vertical opposing line-forces in competing football teams (). This measure captures the existence of possible differences in playersâ interactive behaviors at specific locations on field (e.g., wings and midfield sectors).
Individual playing areas of each player in a team can be delimited by Voronoi cells, offering a time-evolving analysis of the trajectories of these areas (Fonseca et al., 2013). A Voronoi cell contains all the spatial points nearer to the player to whom a cell is allocated, than to other players. By measuring the total area of all Voronoi cells in each team, one can obtain a graphic capturing the dominant ratio of one team over another ().
In sum, although dimensional compression implies the formalization of a low-dimensional variable that captures the behavior of a synergy, several team-based variables exist that offer relevant heuristics to better explore new order parameters in future research.
Reciprocal Compensation: âWe will Cover Your Backâ
Reciprocal compensation indicates that, if one player contributes more or less in his/her expected role, other team elements should adjust their contributions, so that task performance goals are still attained (Latash, 2008).
In studies of team synergies, the property of reciprocal compensation has received less attention from researchers, although have suggested the significance of the uncontrolled manifold approach (; ), which assumes that coordinated movement is achieved by stabilizing the value of a coordination variable (e.g., cluster phase). In doing so, a subspace (i.e., manifold) is created within a state space of task-relevant elements (the degrees of freedom that participate in the task), such that within the subspace, the uncontrolled manifold, the value of the coordination variable remains constant ().
Recently, we created the variable readjustment delay (), that measured the delay in co-positioning by footballers in adapting to teammate movements. It is a measure of the coherence and fluency in teamwork, capturing team readiness and synchronization speed during attacking and defending team actions. Lower delay values indicate rapid readjustment movements and faster spatial temporal synchrony between players, whereas a larger readjustment delay might impede spatial-temporal synchrony of player movements. We sought to understand whether practice could influence changes in these measures of teamwork. found that playersâ readjustment delay values decreased over the 15-week program in the study, evidencing faster readjustments of coupled players, showing how this synergistic property evolved in a team as a function of practice.
More research is needed on this property, both by exploring the relevance of existing variables, and by developing new variables more specific to the constraints of different sports.
Interpersonal Linkages: âIt Makes a Difference if the Pope is on Your Sideâ
Interpersonal linkages, also known as sharing patterns (Latash, 2008), or division of labor (; Araújo et al., 2015), refers to the specific contribution of each element to a group task (Latash, 2008). The behavior of each individual in a team is constrained by several factors like his/her position on the playing area (in relation to other teammates and opponents), strategic and tactical missions, playing phases (i.e., attacking and defending), game rules, etc. According to Latash (2008), sharing is equated to the sum of the individual contributions to the task. However, we term this property âinterpersonal linkagesâ within teams, because when players work together, they do not lose their individuality to a momentary sum, they create properties at the team level and at the same time they establish links that persist. Based on the conceptualization of Ingold (2015), we briefly explain this idea.
This property of interpersonal linkages highlights the need to consider that each element is unique, and this implies an understanding about team behavior that is different from considering a team as a superorganism. For example, in social biology, a âcolonyâ of conspecifics may be regarded either as an aggregate of discrete organisms or as a single superorganism. In a recent essay, Ingold (2015) clarified that an aggregate of individuals joined by mutual self-interest, is different from a superorganism situated above the individuals, where individuals are fused together in a new entity. Forming a group by aggregation or by fusion imply different links among elements (Ingold, 2015). In aggregation, individuals meet along their surfaces, turning every such surface into an interface separating the contents on either side. In fusion, these surfaces partially dissolve, so as to yield a whole that is more than the sum of its parts. However, the portion that an individual might share with others is instantly ceded to this higher-level, emergent entity, and what is left to the individual remains exclusive to its owner. The whole may encompass and transcend its parts, but the parts have nothing of the whole (Ingold, 2015).
However, a sport team can go beyond assemblage and fusion. What happens in expert teams is simultaneously a new entity with properties beyond the individuals, and at the same time there are individuals who can contribute with their unique skills. Ingold (2015) calls this way of linking individuals âcorrespondence.â Correspondence, implies regarding individuals, not as closed-in entities that can be enumerated and added up, but as open-ended processes that carry on. For Ingold (2015), âcarrying onâ means that individuals wrap around one another. A sport team is not simply an articulation of independent components, nor a totality that ignores the unique skills of individuals; but ever-extending lives, and its synergies âreside in the way each strand, as it issues forth, coils around the others as is coiled in its turn, in a counter-valence of equal and opposite twists which hold it together and prevent it from unravelingâ (Ingold, 2015, p. 11). A team is neither additive nor exponential, it is contrapuntal or embodied. Players in a team move together (i.e., are connected) in a movement of correspondence with each other, not in a mechanistic repetition. In a team, individuals offer themselves to one another, yet without losing their identities in the composite whole. Like lines in music, whose harmony lies in the alternating tension and resolution, the individuals possess a feel for one another, an âinterstitial differentiation,â and are not simply linked by external accretion (Ingold, 2015). This whole is a correspondence, not an assemblage or a fusion. The behavior of a team does not stand over it or lie behind it but emerges from playersâ mutual shaping, within a gathering of forces, established through the engagement of individuals that have their own unique skills. Importantly, this understanding of what a sports team is implies a new understanding of how they can be separated, where it is not a matter of cutting an external connection. Something from the history of connection, from the memory together, is lost. Ingold (2015), argues that if you untie a knotted rope, the rope will retain kinks and bends and will want to curl up into similar conformations as before. The memory is suffused into the very material of the rope, in the torsions and flexions of its constituent fibers. If a new knot is tied the rope will retain a memory of its former association.
Other typologies exist for interpersonal linkages (Thompson, 1967; Bell and Kozlowski, 2002), used in organizational management, with recent applications to sport (Reynolds and Salas, 2016). These four types of interdependence are: (i) pooled interdependence where each person makes a discrete contribution to the whole; (ii) sequential interdependence where a player X must act before player Y can act. We argue that these two types are included in the aggregation type of linkage; (iii) reciprocal interdependence requiring player X to act so that player Y can act and player Yâs actions then impact player Xâs next action. This type of linkage is, in our view, a type of correspondence linkage. And finally, (iv) intensive interdependence in which team members interact simultaneously during task performance. This type of interaction may be related to fusion and aggregation, unless we can trace the contribution of each member to the team and understand how the team influences an elementâs behavior. In this case we have correspondence.
In operational terms, there are assembly methods such as measures of heat maps, major ranges. Heat maps provide a clear picture of the distribution of each player on the field. Heat maps highlight with warmer colors the zones where each player has lingered for larger periods of time during the match (Araújo et al., 2015). Another approach to assess the division of labor in team sports is by measuring the area covered by each player. Major ranges imply the calculation of an ellipse centered at each playerâs locus and with semi-axes being the standard deviations in the x- and y-directions, respectively (Yue et al., 2008). Through the simple visualization of major ranges it is possible to identify preferred spatial positions, major roles for each player and playing styles (Araújo et al., 2015). A more dynamic view of aggregation can be captured by player-to-locus distance, and Voronoi cells, which contrary to the former two measurements, the distance of each player to a private locus on field, over time, capture the time-evolving nature of their movementsâ trajectories. The locus represents the playerâs spatial positional reference around which he/she oscillates (McGarry et al., 2014). Individual playing areas attributed to each player on a team, delimited the Voronoi cells of players in team ball sports, and offer a time-evolving analysis of the trajectories of these areas (Fonseca et al., 2013). A Voronoi cell contains all spatial points that are nearer to the player to whom that cell is allocated than to the other players. By measuring the total area of all Voronoi cells from each team, it is possible to obtain a dominant ratio of one team over the other (). The view of Latash (2008) of sharing patterns seems to be more related to fusion and captured by the methods described in our discussion of dimensional compression. However one of such methods, cluster phase, can capture simultaneously interpersonal linkages, in a way more related to âcorrespondence.â Cluster phase measures assess not only synchronies between whole teams, but also between individual players with their team as a function of time, ball possession and field direction (). In fact showed that in player-team synchronization, players tended to be coordinated to different extents under near in-phase modes (near total synchronization) with the team.
Degeneracy: âTo a Good Rider, Right or Left Makes No Differenceâ
Understanding synergies is far more than identifying and understanding the functional structure of each individual synergy. In addition, we need to understand how one synergy can transform into another at specific moments and/or in specific spatial orientations, how different synergic functions can be incorporated, how distinct synergies can co-exist in the same system elements, and how individual components, constituting a synergy, can be added or withdrawn specific to changing performance circumstances ().
Bernstein (1967) emphasized that degrees of freedom are temporarily coordinated together according to circumstances of a performance environment and task requirements. The varying role of synergy degrees of freedom in assembling actions is essential, and is exemplified by the degenerate networks existing at different levels of human movement systems (). Degeneracy refers to structurally different components (e.g., players in a team) performing a similar, but not necessarily identical, function with respect to context (). In this sense, behavioral adaptability reflects the modification of one individual component in a synergy and/or a whole modification of coordination realized by âredundantâ elements (i.e., the presence of isomorphic â same components â and isofunctional components â similar function), or more generally by âdegenerateâ elements (i.e., the presence of heteromorphic â different elements â variants that are isofunctional; see Mason, 2010). Degeneracy signifies that an individual can vary his/her motor behavior (structurally) without compromising function (Mason, 2010), providing evidence for the adaptive and functional role of coordination pattern variability in order to satisfy changing task constraints (; ). Degeneracy may emerge in a synergy of components that performs a function. It signifies that, regardless of whether some components are able to perform an initial function independently, other components are available for modification (), supporting interchangeability of different components. Importantly, adaptive team behaviors, where degeneracy is well-exploited, signify that the perception of shared affordances (opportunities for action) is stable when needed, and flexible when needed. Notably, flexibility is not a loss of stability but, conversely, is a sign of adaptability (i.e., a perceptual and motor adaptions to interacting constraints), in order to facilitate (structural or not) changes in coordination patterns and at the same time maintaining functional performance ().
The functional role of movement variability in sport performance exemplifies how degeneracy emerges at a team level in sport (Davids et al., 2006; ). Performance in a team ball game is sustained by continuous adaptive interactions among players (Araújo et al., 2015). The behavior of such complex systems emerges from the orchestrated activity of many system components (players) that adaptively interact through pairwise local interactions. A common feature of such complex, social networks is that any two nodes or system individuals can become interconnected for action through a path of a few links only (Newman, 2003). Studies of complex networks have revealed that certain forms of network growth produce scale-free networks, that is, the distribution of connections per node in the networks is scale invariant (), as happens with phase transitions and critical points in the dynamics of order parameters. This observation indicates that, degeneracy, as a major synergetic property, might be quantified by different metrics of social networks.
showed that social networks could be used to analyze the local structure of organization among players, during sub-phases of play in team sports. In these networks, nodes represent players, and links are weighted according to the number of passes or positional changes completed between players. Players with major competitive roles (centrality) may be easily identified through social network analyses, since they display stronger connections with other players. Additionally, different match networks can be compared to extract the tactic behavioral patterns of a team under changing competitive conditions, such as the: (i) in-degree that measures the number of players who pass the ball to a focal player; (ii) out-degree that measures the number of players to which the focal player passes the ball; and (iii), preferential attachments between some team members in certain matches (; ; Grund, 2012). However, it is possible to advance the understanding of degeneracy in team sports performance, by using other existing metrics that consider more than the links between a focal node and its neighbors. For example, for understanding the playing style of a sports team, Gyarmati et al. (2014) identified and quantified connection patterns. , on the other hand, included other metrics such as flow centrality which provides a quantification of individual and team performance regarding a specific task goal such as a shooting attempt at a target (at a basket or at a goal).
Emergent patterns of interaction have been studied using different representations of the interactions between the different individuals. These include hypernetworks, where hyperlinks may connect more than a pair of nodes. This latter approach has been applied to analysis of robotic soccer (Johnson and Iravani, 2007) and has proven particularly powerful.
In summary, networks are a valuable tool to analyze the functional variability during sub-phases of play in team sports, since they facilitate identification of players engaged in more and less frequent interactions within a team, interacting with the ball and the goal/basket/tryline according to competitive events.
Conclusion
In this overview paper we have discussed the relevance of key concepts from ecological dynamics, a theoretical framework that has provided a rationale for explaining how specific constraints might impact on team synergies formed by players during competitive performance. It has been found that these ecological constraints shape the perception of shared affordances available for players, which underpin the assembly of interpersonal synergies expressed in collective actions within a team. These important group processes support the formation of synergies. Their key properties have begun to be identified including, dimensional compression, reciprocal compensation and degeneracy, guiding the current meaning of operational variables of relevance for PA, such as team center, team dispersion, team synchrony, and team communication. Theoretical and empirical developments in methods of analysis of team coordination and performance can benefit from a powerful theoretical approach that situates and traces relevant team properties as defined by synergies.
For example, actual networks are static: after defining a time interval and collecting all the observable edges during a set period, a network refers to a discrete aggregated view of the whole system. However, the dynamic behaviors of the network are what strongly affect its functionality and efficiency (Moody et al., 2005). Well-connected nodes can quickly become weakly connected (or even disconnected) over time (), which is important to consider in future studies because each dynamical system is constrained by universal principles, but at the same time requires its unique suite of analytical and numerical tools to understand its behaviors (). Moreover, in sport teams, spatial constraints captured in playing areas, rigorously limit, mark, and focus performance behaviors, e.g., a target area, such as a goal, hoop or tryline, has a strong effect on network connectivity patterns. This observation indicates where it is important to develop metrics for analysis and modeling, clarifying where performers attain different impacts, according to their relative positioning, considering key constraints as well as previous performance contributions in a competitive match.
In this paper, we have suggested how concepts like shared affordances, and synergies, framed in an ecological dynamics perspective, present key principles to substantiate the meaning of existing and future operational metrics in PA, and inform about team performance and training development.
Author Contributions
DA, KD: discussed the structure of the paper; DA: lead the writing of the ms; KD: refined the writing of the paper.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
DA acknowledges financial support from the Fundação paara a Ciência e Tecnologia to CIPER â Centro Interdisciplinar para o Estudo da Performance Humana (unit 447), with the Grant UID/DTP/UI447/2013.
References
Articles from Frontiers in Psychology are provided here courtesy of Frontiers Media SA
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