Pub Date : 2023-04-01DOI: 10.1177/26339137231168355
Richard Watson, Michael Levin
Collective intelligence and individual intelligence are usually considered to be fundamentally different. Individual intelligence is uncontroversial. It occurs in organisms with special neural machinery, evolved by natural selection to enable cognitive and learning functions that serve the fitness benefit of the organism, and then trained through lifetime experience to maximise individual rewards. Whilst the mechanisms of individual intelligence are not fully understood, good models exist for many aspects of individual cognition and learning. Collective intelligence, in contrast, is a much more ambiguous idea. What exactly constitutes collective intelligence is often vague, and the mechanisms that might enable it are frequently domain-specific. These cannot be mechanisms selected specifically for the purpose of collective intelligence because collectives are not (except in special circumstances) evolutionary units, and it is not clear that collectives can learn the way individual intelligences do since they are not a singular locus of rewards and benefits. Here, we use examples from evolution and developmental morphogenesis to argue that these apparent distinctions are not as categorical as they appear. Breaking down such distinctions enables us to borrow from and expand existing models of individual cognition and learning as a framework for collective intelligence, in particular connectionist models familiar in the context of neural networks. We discuss how specific features of these models inform the necessary and sufficient conditions for collective intelligence, and identify current knowledge gaps as opportunities for future research.
{"title":"The collective intelligence of evolution and development","authors":"Richard Watson, Michael Levin","doi":"10.1177/26339137231168355","DOIUrl":"https://doi.org/10.1177/26339137231168355","url":null,"abstract":"Collective intelligence and individual intelligence are usually considered to be fundamentally different. Individual intelligence is uncontroversial. It occurs in organisms with special neural machinery, evolved by natural selection to enable cognitive and learning functions that serve the fitness benefit of the organism, and then trained through lifetime experience to maximise individual rewards. Whilst the mechanisms of individual intelligence are not fully understood, good models exist for many aspects of individual cognition and learning. Collective intelligence, in contrast, is a much more ambiguous idea. What exactly constitutes collective intelligence is often vague, and the mechanisms that might enable it are frequently domain-specific. These cannot be mechanisms selected specifically for the purpose of collective intelligence because collectives are not (except in special circumstances) evolutionary units, and it is not clear that collectives can learn the way individual intelligences do since they are not a singular locus of rewards and benefits. Here, we use examples from evolution and developmental morphogenesis to argue that these apparent distinctions are not as categorical as they appear. Breaking down such distinctions enables us to borrow from and expand existing models of individual cognition and learning as a framework for collective intelligence, in particular connectionist models familiar in the context of neural networks. We discuss how specific features of these models inform the necessary and sufficient conditions for collective intelligence, and identify current knowledge gaps as opportunities for future research.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73872953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-13DOI: 10.1177/26339137221146482
Daniel W Hook, James R Wilsdon
The global research community responded with speed and at scale to the emergence of COVID-19, with around 4.6% of all research outputs in 2020 related to the pandemic. That share almost doubled through 2021, to reach 8.6% of research outputs. This reflects a dramatic mobilisation of global collective intelligence in the face of a crisis. It also raises fundamental questions about the funding, organisation and operation of research. In this Perspective article, we present data that suggests that COVID-19 research reflects the characteristics of the underlying networks from which it emerged, and on which it built. The infrastructures on which COVID-19 research has relied - including highly skilled, flexible research capacity and collaborative networks - predated the pandemic, and are the product of sustained, long-term investment. As such, we argue that COVID-19 research should not be viewed as a distinct field, or one-off response to a specific crisis, but as a 'pandemic veneer' layered on top of longstanding interdisciplinary networks, capabilities and structures. These infrastructures of collective intelligence need to be better understood, valued and sustained as crucial elements of future pandemic or crisis response.
{"title":"The pandemic veneer: COVID-19 research as a mobilisation of collective intelligence by the global research community.","authors":"Daniel W Hook, James R Wilsdon","doi":"10.1177/26339137221146482","DOIUrl":"https://doi.org/10.1177/26339137221146482","url":null,"abstract":"<p><p>The global research community responded with speed and at scale to the emergence of COVID-19, with around 4.6% of all research outputs in 2020 related to the pandemic. That share almost doubled through 2021, to reach 8.6% of research outputs. This reflects a dramatic mobilisation of global collective intelligence in the face of a crisis. It also raises fundamental questions about the funding, organisation and operation of research. In this <i>Perspective</i> article, we present data that suggests that COVID-19 research reflects the characteristics of the underlying networks from which it emerged, and on which it built. The infrastructures on which COVID-19 research has relied - including highly skilled, flexible research capacity and collaborative networks - predated the pandemic, and are the product of sustained, long-term investment. As such, we argue that COVID-19 research should not be viewed as a distinct field, or one-off response to a specific crisis, but as a 'pandemic veneer' layered on top of longstanding interdisciplinary networks, capabilities and structures. These infrastructures of collective intelligence need to be better understood, valued and sustained as crucial elements of future pandemic or crisis response.</p>","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"2 1","pages":"26339137221146482"},"PeriodicalIF":0.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615127/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41172964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1177/26339137221148675
Claudia Winklmayr, Albert B. Kao, J. Bak-Coleman, P. Romanczuk
Background: Models of collective decision-making typically assume that individuals sample information independently and decide instantaneously. In most natural and sociological settings, however, decisions occur over some timescale in which group members gather information—often from multiple sources. Information sources may persist for varying lengths of time or be viewed concurrently and identically by multiple group members. These tendencies introduce spatio-temporal correlations in gathered information with poorly understood consequences. Research Design: Here, we develop a collective decision-making model in which individuals’ access and switch between two conflicting cues that differ in their spatio-temporal properties. Results: Our model reveals that spatially and temporally correlated cues can profoundly affect collective decisions. Specifically, we observe that spatially correlated cues are dominant when individuals rarely switch between sources of information. Temporally correlated cues, on the other hand, have the strongest impact when individuals frequently switch between information sources. We also discuss how much the usage of independent information must be increased to counter the impact of correlation. Conclusions: The present model represents a first step toward more accurately capturing the complex mechanisms underlying collective decision-making in natural systems and reveals multiple ways in which the properties of environmental cues can impact collective behavior.
{"title":"Collective decision strategies in the presence of spatio-temporal correlations","authors":"Claudia Winklmayr, Albert B. Kao, J. Bak-Coleman, P. Romanczuk","doi":"10.1177/26339137221148675","DOIUrl":"https://doi.org/10.1177/26339137221148675","url":null,"abstract":"Background: Models of collective decision-making typically assume that individuals sample information independently and decide instantaneously. In most natural and sociological settings, however, decisions occur over some timescale in which group members gather information—often from multiple sources. Information sources may persist for varying lengths of time or be viewed concurrently and identically by multiple group members. These tendencies introduce spatio-temporal correlations in gathered information with poorly understood consequences. Research Design: Here, we develop a collective decision-making model in which individuals’ access and switch between two conflicting cues that differ in their spatio-temporal properties. Results: Our model reveals that spatially and temporally correlated cues can profoundly affect collective decisions. Specifically, we observe that spatially correlated cues are dominant when individuals rarely switch between sources of information. Temporally correlated cues, on the other hand, have the strongest impact when individuals frequently switch between information sources. We also discuss how much the usage of independent information must be increased to counter the impact of correlation. Conclusions: The present model represents a first step toward more accurately capturing the complex mechanisms underlying collective decision-making in natural systems and reveals multiple ways in which the properties of environmental cues can impact collective behavior.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79084533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1177/26339137221126915
N. Pescetelli, N. Yeung
In many domains, imitating others’ behaviour can help individuals to solve problems that would be too difficult or too complex for the individuals. In collective decision making tasks, people have been shown to use confidence as a means to communicate the uncertainty surrounding internal noisy estimates. Here, we show that confidence alignment, namely, shifting average confidence between dyad members towards each other, naturally emerges when interacting with others’ opinions. This alignment has a measurable impact on group performance as well as the accuracy of individual members following information exchange. It is suggested that confidence alignment arises among individuals from the necessity of minimising confidence variation arising from task-unrelated variables (trait confidence), while at the same time maximising variation arising from stimulus characteristics (state confidence).
{"title":"Benefits of spontaneous confidence alignment between dyad members","authors":"N. Pescetelli, N. Yeung","doi":"10.1177/26339137221126915","DOIUrl":"https://doi.org/10.1177/26339137221126915","url":null,"abstract":"In many domains, imitating others’ behaviour can help individuals to solve problems that would be too difficult or too complex for the individuals. In collective decision making tasks, people have been shown to use confidence as a means to communicate the uncertainty surrounding internal noisy estimates. Here, we show that confidence alignment, namely, shifting average confidence between dyad members towards each other, naturally emerges when interacting with others’ opinions. This alignment has a measurable impact on group performance as well as the accuracy of individual members following information exchange. It is suggested that confidence alignment arises among individuals from the necessity of minimising confidence variation arising from task-unrelated variables (trait confidence), while at the same time maximising variation arising from stimulus characteristics (state confidence).","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81172975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1177/26339137221109839
Harang Ju, D. Zhou, A. S. Blevins, D. Lydon‐Staley, Judith R. H. Kaplan, Julio R. Tuma, D. Bassett
Philosophers of science have long questioned how collective scientific knowledge grows. Although disparate answers have been posited, empirical validation has been challenging due to limitations in collecting and systematizing large historical records. Here, we introduce new methods to analyze scientific knowledge formulated as a growing network of articles on Wikipedia and their hyperlinks. We demonstrate that in Wikipedia, concept networks in subdisciplines of science do not grow by expanding from their central core to reach an ancillary periphery. Instead, science concept networks in Wikipedia grow by creating and filling knowledge gaps. Notably, the process of gap formation and closure may be valued by the scientific community, as evidenced by the fact that it produces discoveries that are more frequently awarded Nobel prizes than other processes. To determine whether and how the gap process is interrupted by paradigm shifts, we operationalize a paradigm as a particular subdivision of scientific concepts into network modules. Hence, paradigm shifts are reconfigurations of those modules. The approach allows us to identify a temporal signature in structural stability across scientific subjects in Wikipedia. In a network formulation of scientific discovery, our findings suggest that data-driven conditions underlying scientific breakthroughs depend as much on exploring uncharted gaps as on exploiting existing disciplines and support policies that encourage new interdisciplinary research.
{"title":"Historical growth of concept networks in Wikipedia","authors":"Harang Ju, D. Zhou, A. S. Blevins, D. Lydon‐Staley, Judith R. H. Kaplan, Julio R. Tuma, D. Bassett","doi":"10.1177/26339137221109839","DOIUrl":"https://doi.org/10.1177/26339137221109839","url":null,"abstract":"Philosophers of science have long questioned how collective scientific knowledge grows. Although disparate answers have been posited, empirical validation has been challenging due to limitations in collecting and systematizing large historical records. Here, we introduce new methods to analyze scientific knowledge formulated as a growing network of articles on Wikipedia and their hyperlinks. We demonstrate that in Wikipedia, concept networks in subdisciplines of science do not grow by expanding from their central core to reach an ancillary periphery. Instead, science concept networks in Wikipedia grow by creating and filling knowledge gaps. Notably, the process of gap formation and closure may be valued by the scientific community, as evidenced by the fact that it produces discoveries that are more frequently awarded Nobel prizes than other processes. To determine whether and how the gap process is interrupted by paradigm shifts, we operationalize a paradigm as a particular subdivision of scientific concepts into network modules. Hence, paradigm shifts are reconfigurations of those modules. The approach allows us to identify a temporal signature in structural stability across scientific subjects in Wikipedia. In a network formulation of scientific discovery, our findings suggest that data-driven conditions underlying scientific breakthroughs depend as much on exploring uncharted gaps as on exploiting existing disciplines and support policies that encourage new interdisciplinary research.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85512232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1177/26339137221141667
{"title":"Erratum to Examining the limits of the Condorcet Jury Theorem: Tradeoffs in hierarchical information aggregation systems","authors":"","doi":"10.1177/26339137221141667","DOIUrl":"https://doi.org/10.1177/26339137221141667","url":null,"abstract":"","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76672858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-10DOI: 10.1177/26339137231176481
R. Mann
Theoretical results underpinning the wisdom of the crowd, such as the Condorcet Jury Theorem, point to substantial accuracy gains through aggregation of decisions or opinions, but the foundations of this theorem are routinely undermined in circumstances where individuals are able to adapt their own choices based after observing what other agents have chosen. In sequential decision-making, rational agents use the choices of others as a source of information about the correct decision, creating powerful correlations between different agents’ choices that violate the assumptions of independence on which the Condorcet Jury Theorem depends. In this paper, I show how such correlations emerge when agents are rewarded solely based on their individual accuracy, and the impact of this on collective accuracy. I then demonstrate how a simple competitive reward scheme, where agents’ rewards are greater if they correctly choose options that few have already chosen, can induce rational agents to make independent choices, returning the group to optimal levels of collective accuracy. I further show that this reward scheme is robust, offering improvements to collective accuracy across wide range of competition strengths, suggesting that such schemes could be effectively implemented in real-world contexts to improve collective wisdom.
{"title":"Optimising collective accuracy among rational individuals in sequential decision-making with competition","authors":"R. Mann","doi":"10.1177/26339137231176481","DOIUrl":"https://doi.org/10.1177/26339137231176481","url":null,"abstract":"Theoretical results underpinning the wisdom of the crowd, such as the Condorcet Jury Theorem, point to substantial accuracy gains through aggregation of decisions or opinions, but the foundations of this theorem are routinely undermined in circumstances where individuals are able to adapt their own choices based after observing what other agents have chosen. In sequential decision-making, rational agents use the choices of others as a source of information about the correct decision, creating powerful correlations between different agents’ choices that violate the assumptions of independence on which the Condorcet Jury Theorem depends. In this paper, I show how such correlations emerge when agents are rewarded solely based on their individual accuracy, and the impact of this on collective accuracy. I then demonstrate how a simple competitive reward scheme, where agents’ rewards are greater if they correctly choose options that few have already chosen, can induce rational agents to make independent choices, returning the group to optimal levels of collective accuracy. I further show that this reward scheme is robust, offering improvements to collective accuracy across wide range of competition strengths, suggesting that such schemes could be effectively implemented in real-world contexts to improve collective wisdom.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91502919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-09DOI: 10.1177/26339137231158584
Soo Ling Lim, P. Bentley, R. Peterson, Xiaoran Hu, JoEllyn Prouty McLaren
Teams are central to human accomplishment. Over the past half-century, psychologists have identified the Big-Five cross-culturally valid personality variables: Neuroticism, Extraversion, Openness, Conscientiousness, and Agreeableness. The first four have shown consistent relationships with team performance. Agreeableness (being harmonious, altruistic, humble, and cooperative), however, has demonstrated a non-significant and highly variable relationship with team performance. We resolve this inconsistency through computational modelling. An agent-based model (ABM) is used to predict the effects of personality traits on teamwork, and a genetic algorithm is then used to explore the limits of the ABM in order to discover which traits correlate with best and worst performing teams for a problem with different levels of uncertainty (noise). New dependencies revealed by the exploration are corroborated by analyzing previously unseen data from one of the largest datasets on team performance to date comprising 3698 individuals in 593 teams working on more than 5000 group tasks with and without uncertainty, collected over a 10-year period. Our finding is that the dependency between team performance and Agreeableness is moderated by task uncertainty. Combining evolutionary computation with ABMs in this way provides a new methodology for the scientific investigation of teamwork, making new predictions, and improving our understanding of human behaviors. Our results confirm the potential usefulness of computer modelling for developing theory, as well as shedding light on the future of teams as work environments are becoming increasingly fluid and uncertain.
{"title":"Kill chaos with kindness: Agreeableness improves team performance under uncertainty","authors":"Soo Ling Lim, P. Bentley, R. Peterson, Xiaoran Hu, JoEllyn Prouty McLaren","doi":"10.1177/26339137231158584","DOIUrl":"https://doi.org/10.1177/26339137231158584","url":null,"abstract":"Teams are central to human accomplishment. Over the past half-century, psychologists have identified the Big-Five cross-culturally valid personality variables: Neuroticism, Extraversion, Openness, Conscientiousness, and Agreeableness. The first four have shown consistent relationships with team performance. Agreeableness (being harmonious, altruistic, humble, and cooperative), however, has demonstrated a non-significant and highly variable relationship with team performance. We resolve this inconsistency through computational modelling. An agent-based model (ABM) is used to predict the effects of personality traits on teamwork, and a genetic algorithm is then used to explore the limits of the ABM in order to discover which traits correlate with best and worst performing teams for a problem with different levels of uncertainty (noise). New dependencies revealed by the exploration are corroborated by analyzing previously unseen data from one of the largest datasets on team performance to date comprising 3698 individuals in 593 teams working on more than 5000 group tasks with and without uncertainty, collected over a 10-year period. Our finding is that the dependency between team performance and Agreeableness is moderated by task uncertainty. Combining evolutionary computation with ABMs in this way provides a new methodology for the scientific investigation of teamwork, making new predictions, and improving our understanding of human behaviors. Our results confirm the potential usefulness of computer modelling for developing theory, as well as shedding light on the future of teams as work environments are becoming increasingly fluid and uncertain.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90020493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-01DOI: 10.1177/26339137221104788
J. Bak-Coleman, C. K. Tokita, Dylan H. Morris, D. Rubenstein, I. Couzin
The potential for groups to outperform the cognitive capabilities of even highly skilled individuals, known as the “wisdom of the crowd”, is crucial to the functioning of democratic institutions. In recent years, increasing polarization has led to concern about its effects on the accuracy of electorates, juries, courts, and congress. While there is empirical evidence of collective wisdom in partisan crowds, a general theory has remained elusive. Central to the challenge is the difficulty of disentangling the effect of limited interaction between opposing groups (homophily) from their tendency to hold opposing viewpoints (partisanship). To overcome this challenge, we develop an agent-based model of collective wisdom parameterized by the experimentally-measured behaviour of participants across the political spectrum. In doing so, we reveal that differences across the political spectrum in how individuals express and respond to knowledge interact with the structure of the network to either promote or undermine wisdom. We verify these findings experimentally and construct a more general theoretical framework. Finally, we provide evidence that incidental, context-specific differences across the political spectrum likely determine the impact of polarization. Overall, our results show that whether polarized groups benefit from collective wisdom is generally predictable but highly context-specific.
{"title":"Collective wisdom in polarized groups","authors":"J. Bak-Coleman, C. K. Tokita, Dylan H. Morris, D. Rubenstein, I. Couzin","doi":"10.1177/26339137221104788","DOIUrl":"https://doi.org/10.1177/26339137221104788","url":null,"abstract":"The potential for groups to outperform the cognitive capabilities of even highly skilled individuals, known as the “wisdom of the crowd”, is crucial to the functioning of democratic institutions. In recent years, increasing polarization has led to concern about its effects on the accuracy of electorates, juries, courts, and congress. While there is empirical evidence of collective wisdom in partisan crowds, a general theory has remained elusive. Central to the challenge is the difficulty of disentangling the effect of limited interaction between opposing groups (homophily) from their tendency to hold opposing viewpoints (partisanship). To overcome this challenge, we develop an agent-based model of collective wisdom parameterized by the experimentally-measured behaviour of participants across the political spectrum. In doing so, we reveal that differences across the political spectrum in how individuals express and respond to knowledge interact with the structure of the network to either promote or undermine wisdom. We verify these findings experimentally and construct a more general theoretical framework. Finally, we provide evidence that incidental, context-specific differences across the political spectrum likely determine the impact of polarization. Overall, our results show that whether polarized groups benefit from collective wisdom is generally predictable but highly context-specific.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80623693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-01DOI: 10.1177/26339137221081849
Chelsea M Campbell, E. Izquierdo, Robert L. Goldstone
One major way that people engage in adaptive problem solving is by imitating others’ solutions. Prominent simulation models have found imperfect imitation advantageous, but the interactions between copying amount and other prevalent aspects of social learning strategies have been underexplored. Here, we explore the consequences for a group when its members engage in strategies with different degrees of copying, solving search problems of varying complexity, in different network topologies that affect the solutions visible to each member. Using a computational model of collective problem solving, we demonstrate that the advantage of partial copying is robust across these conditions, arising from its ability to maintain diversity. Partial copying delays convergence generally but especially in globally connected networks, which are typically associated with diversity loss, allowing more exploration of a problem space. We show that a moderate amount of diversity maintenance is optimal and strategies can be adjusted to find that sweet spot.
{"title":"Partial copying and the role of diversity in social learning performance","authors":"Chelsea M Campbell, E. Izquierdo, Robert L. Goldstone","doi":"10.1177/26339137221081849","DOIUrl":"https://doi.org/10.1177/26339137221081849","url":null,"abstract":"One major way that people engage in adaptive problem solving is by imitating others’ solutions. Prominent simulation models have found imperfect imitation advantageous, but the interactions between copying amount and other prevalent aspects of social learning strategies have been underexplored. Here, we explore the consequences for a group when its members engage in strategies with different degrees of copying, solving search problems of varying complexity, in different network topologies that affect the solutions visible to each member. Using a computational model of collective problem solving, we demonstrate that the advantage of partial copying is robust across these conditions, arising from its ability to maintain diversity. Partial copying delays convergence generally but especially in globally connected networks, which are typically associated with diversity loss, allowing more exploration of a problem space. We show that a moderate amount of diversity maintenance is optimal and strategies can be adjusted to find that sweet spot.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89837270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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