Pub Date : 2022-08-01DOI: 10.1177/26339137221114176
G. Mulgan
{"title":"New books on collective intelligence: Growing the field: Interesting new books on collective intelligence","authors":"G. Mulgan","doi":"10.1177/26339137221114176","DOIUrl":"https://doi.org/10.1177/26339137221114176","url":null,"abstract":"","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85702516","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: 10.1177/26339137221114179
J. Flack, Panos Ipeirotis, T. Malone, G. Mulgan, S. Page
Collective behavior is a universal property of biological, social, and many engineered systems. However, the study of collective intelligence—roughly, the production of adaptive, wise, or clever structures and behaviors by groups— remains nascent. Despite that, it is growing in various disciplines, from biology and psychology to computer science and economics, management, and political science to mathematics, complexity science, and neuroscience. With the launch of Collective Intelligence, we aim to create a publication that transcends disciplines, methodologies, and traditional formats. We hope to help discover principles that can be useful to both basic and applied science and encourage the emergence of a unified discipline of study. Collective Intelligence (the Journal) is a global, peerreviewed, open-access journal. It will feature research articles, perspectives, dialogues, and artistic expressions, all geared toward a community of scholars in many disciplines. In this editorial, we highlight issues in collective intelligence research where attention will aid in discovering principles, concepts, and tools needed to unify the discipline. We proceed with a light hand, guided by recognizing that our role is to facilitate deep, provocative analyses and discussions rather than to define and, therefore, delimit the field.
{"title":"Editorial to the Inaugural Issue of Collective Intelligence","authors":"J. Flack, Panos Ipeirotis, T. Malone, G. Mulgan, S. Page","doi":"10.1177/26339137221114179","DOIUrl":"https://doi.org/10.1177/26339137221114179","url":null,"abstract":"Collective behavior is a universal property of biological, social, and many engineered systems. However, the study of collective intelligence—roughly, the production of adaptive, wise, or clever structures and behaviors by groups— remains nascent. Despite that, it is growing in various disciplines, from biology and psychology to computer science and economics, management, and political science to mathematics, complexity science, and neuroscience. With the launch of Collective Intelligence, we aim to create a publication that transcends disciplines, methodologies, and traditional formats. We hope to help discover principles that can be useful to both basic and applied science and encourage the emergence of a unified discipline of study. Collective Intelligence (the Journal) is a global, peerreviewed, open-access journal. It will feature research articles, perspectives, dialogues, and artistic expressions, all geared toward a community of scholars in many disciplines. In this editorial, we highlight issues in collective intelligence research where attention will aid in discovering principles, concepts, and tools needed to unify the discipline. We proceed with a light hand, guided by recognizing that our role is to facilitate deep, provocative analyses and discussions rather than to define and, therefore, delimit the field.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87703266","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: 10.1177/26339137221078005
Ioanna Lykourentzou, F. Vinella, F. Ahmed, Costas Papastathis, Konstantinos Papangelis, Vassilis-Javed Khan, J. Masthoff
As the volume and complexity of distributed online work increases, collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of online collaborations by grouping workers in a top-down fashion and according to a set of predefined decision criteria. This approach often means that workers have little say in the collaboration formation process. Depriving users of control over whom they will work with can stifle creativity and initiative-taking, increase psychological discomfort, and, overall, result in less-than-optimal collaboration results—especially when the task concerned is open-ended, creative, and complex. In this work, we propose an alternative model, called Self-Organizing Pairs (SOPs), which relies on the crowd of online workers themselves to organize into effective work dyads. Supported but not guided by an algorithm, SOPs are a new human-centered computational structure, which enables participants to control, correct, and guide the output of their collaboration as a collective. Experimental results, comparing SOPs to two benchmarks that do not allow user agency, and on an iterative task of fictional story writing, reveal that participants in the SOPs condition produce creative outcomes of higher quality, and report higher satisfaction with their collaboration. Finally, we find that similarly to machine learning-based self-organization, human SOPs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible collaborators.
{"title":"Self-organization in online collaborative work settings","authors":"Ioanna Lykourentzou, F. Vinella, F. Ahmed, Costas Papastathis, Konstantinos Papangelis, Vassilis-Javed Khan, J. Masthoff","doi":"10.1177/26339137221078005","DOIUrl":"https://doi.org/10.1177/26339137221078005","url":null,"abstract":"As the volume and complexity of distributed online work increases, collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of online collaborations by grouping workers in a top-down fashion and according to a set of predefined decision criteria. This approach often means that workers have little say in the collaboration formation process. Depriving users of control over whom they will work with can stifle creativity and initiative-taking, increase psychological discomfort, and, overall, result in less-than-optimal collaboration results—especially when the task concerned is open-ended, creative, and complex. In this work, we propose an alternative model, called Self-Organizing Pairs (SOPs), which relies on the crowd of online workers themselves to organize into effective work dyads. Supported but not guided by an algorithm, SOPs are a new human-centered computational structure, which enables participants to control, correct, and guide the output of their collaboration as a collective. Experimental results, comparing SOPs to two benchmarks that do not allow user agency, and on an iterative task of fictional story writing, reveal that participants in the SOPs condition produce creative outcomes of higher quality, and report higher satisfaction with their collaboration. Finally, we find that similarly to machine learning-based self-organization, human SOPs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible collaborators.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"54 5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84815269","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: 10.1177/26339137221083293
N. Leonard, S. Levin
We discuss measures of collective intelligence in evolved and designed self-organizing ensembles, defining collective intelligence in terms of the benefits to be gained through the exchange of information and other resources, as well as through coordination or cooperation, in the interests of a public good. These benefits can be numerous, from estimating a hard-to-observe cue to efficiently searching for resource. The measures should also account for costs to individuals, such as in attention or energy, and trade-offs for the ensemble, such as the flexibility to respond to an important change in the environment versus stability that is robust to unimportant variability. When there is a tension between the interests of the individual and those of the group, game-theoretic considerations may affect the level of collective intelligence that can be achieved. Models of individual rules that yield collective dynamics with multi-stable solutions provide a means to examine and shape collective intelligence in evolved and designed systems.
{"title":"Collective intelligence as a public good","authors":"N. Leonard, S. Levin","doi":"10.1177/26339137221083293","DOIUrl":"https://doi.org/10.1177/26339137221083293","url":null,"abstract":"We discuss measures of collective intelligence in evolved and designed self-organizing ensembles, defining collective intelligence in terms of the benefits to be gained through the exchange of information and other resources, as well as through coordination or cooperation, in the interests of a public good. These benefits can be numerous, from estimating a hard-to-observe cue to efficiently searching for resource. The measures should also account for costs to individuals, such as in attention or energy, and trade-offs for the ensemble, such as the flexibility to respond to an important change in the environment versus stability that is robust to unimportant variability. When there is a tension between the interests of the individual and those of the group, game-theoretic considerations may affect the level of collective intelligence that can be achieved. Models of individual rules that yield collective dynamics with multi-stable solutions provide a means to examine and shape collective intelligence in evolved and designed systems.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"160 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84959777","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: 10.1177/26339137221133400
S. Garnier, M. Moussaïd
Collective decision-making constitutes a core function of social systems and is, therefore, a central tenet of collective intelligence research. From fish schools to human crowds, we start by interrogating ourselves about the very definition of collective decision-making and the scope of the scientific research that falls under it. We then summarize its history through the lenses of social choice theory and swarm intelligence and their accelerating collaboration over the past 20 or so years. Finally, we offer our perspective on the future of collective decision-making research in 3 mutually inclusive directions. We argue (1) that the possibility to collect data of a new nature, including fine-grain tracking information, virtual reality, and brain imaging inputs, will enable a direct link between plastic individual cognitive processes and the ontogeny of collective behaviors; (2) that current theoretical frameworks are not well suited to describe the long-term consequences of individual plasticity on collective decision-making processes and that, therefore, new formalisms are necessary; and finally (3) that applying the results of collective decision-making research to real-world situations will require the development of practical tools, the implementation of monitoring processes that respect civil liberties, and, possibly, government regulations of social interventions by public and private actors.
{"title":"We the swarm—Methodological, theoretical, and societal (r)evolutions in collective decision-making research","authors":"S. Garnier, M. Moussaïd","doi":"10.1177/26339137221133400","DOIUrl":"https://doi.org/10.1177/26339137221133400","url":null,"abstract":"Collective decision-making constitutes a core function of social systems and is, therefore, a central tenet of collective intelligence research. From fish schools to human crowds, we start by interrogating ourselves about the very definition of collective decision-making and the scope of the scientific research that falls under it. We then summarize its history through the lenses of social choice theory and swarm intelligence and their accelerating collaboration over the past 20 or so years. Finally, we offer our perspective on the future of collective decision-making research in 3 mutually inclusive directions. We argue (1) that the possibility to collect data of a new nature, including fine-grain tracking information, virtual reality, and brain imaging inputs, will enable a direct link between plastic individual cognitive processes and the ontogeny of collective behaviors; (2) that current theoretical frameworks are not well suited to describe the long-term consequences of individual plasticity on collective decision-making processes and that, therefore, new formalisms are necessary; and finally (3) that applying the results of collective decision-making research to real-world situations will require the development of practical tools, the implementation of monitoring processes that respect civil liberties, and, possibly, government regulations of social interventions by public and private actors.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74306342","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: 10.1177/26339137221107924
A. Berditchevskaia, Eirini Maliaraki, Konstantinos Stathoulopoulos
Collective intelligence (CI) is an interdisciplinary field that draws on a wide range of academic disciplines but has struggled to capitalise on cross-pollination between fields, particularly ones which do not self-identify with the collective intelligence label. Past studies have largely undertaken a qualitative and manual approach to classifying different trends in the CI literature. This method risks missing a significant proportion of publications in the field. To this end, we present the first attempt to reflect the field to itself through an automated and quantitative descriptive approach using Microsoft Academic Graph (MAG) to collect and analyse 39,334 CI papers. We further focus our investigation on a subset of the CI literature, at the intersection of artificial intelligence (AI) and CI to understand how these two fields are interacting. We show that while the annual number of CI-only publications has remained steady since 2015, AI+CI research has continued to increase. Publications in the crossover of AI+CI are growing at a faster rate than CI-only papers but show less topical and disciplinary breadth. This may be having a spillover effect on the topical focus of non-AI collective intelligence research. We hope this analysis sheds more light on the dynamics of the CI ecosystem.
{"title":"A descriptive analysis of collective intelligence publications since 2000, and the emerging influence of artificial intelligence","authors":"A. Berditchevskaia, Eirini Maliaraki, Konstantinos Stathoulopoulos","doi":"10.1177/26339137221107924","DOIUrl":"https://doi.org/10.1177/26339137221107924","url":null,"abstract":"Collective intelligence (CI) is an interdisciplinary field that draws on a wide range of academic disciplines but has struggled to capitalise on cross-pollination between fields, particularly ones which do not self-identify with the collective intelligence label. Past studies have largely undertaken a qualitative and manual approach to classifying different trends in the CI literature. This method risks missing a significant proportion of publications in the field. To this end, we present the first attempt to reflect the field to itself through an automated and quantitative descriptive approach using Microsoft Academic Graph (MAG) to collect and analyse 39,334 CI papers. We further focus our investigation on a subset of the CI literature, at the intersection of artificial intelligence (AI) and CI to understand how these two fields are interacting. We show that while the annual number of CI-only publications has remained steady since 2015, AI+CI research has continued to increase. Publications in the crossover of AI+CI are growing at a faster rate than CI-only papers but show less topical and disciplinary breadth. This may be having a spillover effect on the topical focus of non-AI collective intelligence research. We hope this analysis sheds more light on the dynamics of the CI ecosystem.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86204647","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: 10.1177/26339137221128542
Marcin Waniek, César A. Hidalgo
Polarized networks, composed of weakly connected and self-reinforcing groups, can limit the diffusion of ideas, behaviors, and innovations. Here, we use a complex contagion model, in which diffusion depends on both the connectivity and the similarity of individuals, to ask how to optimally build bridges and enhance diffusion in networks characterized by fragmentation and homophily. First, we show that the problem is NP-hard. Then, we explore the space of solutions using heuristics, finding that connecting high degree nodes, or hubs, is an ineffective strategy to accelerate diffusion in fragmented and homophilous networks. We show that in these networks, diffusion is more effectively accelerated by connecting similar but low degree nodes. These results tell us that, in the presence of homophily and polarization, connecting communities through their most central actors may impede rather than facilitate diffusion. Instead, strategies to accelerate the diffusion of innovation, behaviors, and ideas should focus on creating links among the most similar members of different communities. These findings shed light on the diffusion of ideas and innovations in polarized networks. CCS Concepts: • Mathematics of computing → Network optimization; • Information systems → Social networks
{"title":"Bridging the polarization gap: Maximizing diffusion among dissimilar communities","authors":"Marcin Waniek, César A. Hidalgo","doi":"10.1177/26339137221128542","DOIUrl":"https://doi.org/10.1177/26339137221128542","url":null,"abstract":"Polarized networks, composed of weakly connected and self-reinforcing groups, can limit the diffusion of ideas, behaviors, and innovations. Here, we use a complex contagion model, in which diffusion depends on both the connectivity and the similarity of individuals, to ask how to optimally build bridges and enhance diffusion in networks characterized by fragmentation and homophily. First, we show that the problem is NP-hard. Then, we explore the space of solutions using heuristics, finding that connecting high degree nodes, or hubs, is an ineffective strategy to accelerate diffusion in fragmented and homophilous networks. We show that in these networks, diffusion is more effectively accelerated by connecting similar but low degree nodes. These results tell us that, in the presence of homophily and polarization, connecting communities through their most central actors may impede rather than facilitate diffusion. Instead, strategies to accelerate the diffusion of innovation, behaviors, and ideas should focus on creating links among the most similar members of different communities. These findings shed light on the diffusion of ideas and innovations in polarized networks. CCS Concepts: • Mathematics of computing → Network optimization; • Information systems → Social networks","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88182355","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: 10.1177/26339137221133401
L. Böttcher, G. Kernell
Condorcet’s Jury Theorem states that the correct outcome is reached in direct majority voting systems with sufficiently large electorates as long as each voter’s independent probability of voting for that outcome is greater than 1/2. Previous research has found that switching to a hierarchical system always leads to an inferior result. Yet, in many situations direct voting is infeasible (e.g., due to high implementation or infrastructure costs), and hierarchical voting may provide a reasonable alternative. This paper examines differences in accuracy rates of hierarchical and direct voting systems for varying group sizes, abstention rates, and voter competences. We derive three main results. First, we prove that indirect two-tier systems differ most from their direct counterparts when group size and number are equal (i.e., when each equals N d , where Nd is the total number of voters in the direct system). In multitier systems, we prove that this difference is maximized when group size equals N d n , where n is the number of hierarchical levels. Second, we show that while direct majority rule always outperforms indirect voting for homogeneous electorates, hierarchical voting gains in accuracy when either the number of groups or the number of individuals within each group increases. Third, we prove that when voter abstention and competency are correlated within groups, hierarchical systems can outperform direct voting. The results have implications beyond voting, including information processing in the brain, collective cognition in animal groups, and information aggregation in machine learning.
孔多塞的陪审团定理指出，只要每个选民对该结果的独立投票概率大于1/2，在有足够多选民的直接多数投票系统中就会得出正确的结果。先前的研究发现，转换到等级制度总是导致较差的结果。然而，在许多情况下，直接投票是不可行的(例如，由于高实施或基础设施成本)，分层投票可能提供一个合理的替代方案。本文研究了不同群体规模、弃权率和选民能力的分层和直接投票系统的准确率差异。我们得出了三个主要结果。首先，我们证明了当群体规模和数量相等时(即当每个群体都等于Nd时，其中Nd是直接系统中选民的总数)，间接双层系统与直接双层系统的差异最大。在多层系统中，我们证明了当群体大小等于N d N时，这种差异是最大的，其中N是分层层的数量。其次，我们表明，虽然直接多数决原则在同质选民中总是优于间接投票，但当群体数量或每个群体中的个人数量增加时，等级投票的准确性就会提高。第三，我们证明了当选民弃权和能力在群体内相关时，等级制度可以优于直接投票。研究结果的影响不仅限于投票，还包括大脑中的信息处理、动物群体的集体认知以及机器学习中的信息聚合。
{"title":"Examining the limits of the Condorcet Jury Theorem: Tradeoffs in hierarchical information aggregation systems","authors":"L. Böttcher, G. Kernell","doi":"10.1177/26339137221133401","DOIUrl":"https://doi.org/10.1177/26339137221133401","url":null,"abstract":"Condorcet’s Jury Theorem states that the correct outcome is reached in direct majority voting systems with sufficiently large electorates as long as each voter’s independent probability of voting for that outcome is greater than 1/2. Previous research has found that switching to a hierarchical system always leads to an inferior result. Yet, in many situations direct voting is infeasible (e.g., due to high implementation or infrastructure costs), and hierarchical voting may provide a reasonable alternative. This paper examines differences in accuracy rates of hierarchical and direct voting systems for varying group sizes, abstention rates, and voter competences. We derive three main results. First, we prove that indirect two-tier systems differ most from their direct counterparts when group size and number are equal (i.e., when each equals N d , where Nd is the total number of voters in the direct system). In multitier systems, we prove that this difference is maximized when group size equals N d n , where n is the number of hierarchical levels. Second, we show that while direct majority rule always outperforms indirect voting for homogeneous electorates, hierarchical voting gains in accuracy when either the number of groups or the number of individuals within each group increases. Third, we prove that when voter abstention and competency are correlated within groups, hierarchical systems can outperform direct voting. The results have implications beyond voting, including information processing in the brain, collective cognition in animal groups, and information aggregation in machine learning.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78098059","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: 10.1177/26339137221078593
D. Kahneman, D. Krakauer, O. Sibony, C. Sunstein, David Wolpert
A key but neglected issue in the search for collective intelligence principles is the role of noise. Does noise inhibit collective intelligence or can it amplify the discovery of intelligent solutions? In this exchange of letters, the authors explore the pros and cons of noise.
{"title":"An exchange of letters on the role of noise in collective intelligence","authors":"D. Kahneman, D. Krakauer, O. Sibony, C. Sunstein, David Wolpert","doi":"10.1177/26339137221078593","DOIUrl":"https://doi.org/10.1177/26339137221078593","url":null,"abstract":"A key but neglected issue in the search for collective intelligence principles is the role of noise. Does noise inhibit collective intelligence or can it amplify the discovery of intelligent solutions? In this exchange of letters, the authors explore the pros and cons of noise.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76111317","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: 10.1177/26339137221104785
A. Lo, Ruixun Zhang
Despite its success in financial markets and other domains, collective intelligence seems to fall short in many critical contexts, including infrequent but repeated financial crises, political polarization and deadlock, and various forms of bias and discrimination. We propose an evolutionary framework that provides fundamental insights into the role of heterogeneity and feedback loops in contributing to failures of collective intelligence. The framework is based on a binary choice model of behavior that affects fitness; hence, behavior is shaped by evolutionary dynamics and stochastic changes in environmental conditions. We derive collective intelligence as an emergent property of evolution in this framework, and also specify conditions under which it fails. We find that political polarization emerges in stochastic environments with reproductive risks that are correlated across individuals. Bias and discrimination emerge when individuals incorrectly attribute random adverse events to observable features that may have nothing to do with those events. In addition, path dependence and negative feedback in evolution may lead to even stronger biases and levels of discrimination, which are locally evolutionarily stable strategies. These results suggest potential policy interventions to prevent such failures by nudging the “madness of mobs” towards the “wisdom of crowds” through targeted shifts in the environment.
{"title":"The wisdom of crowds versus the madness of mobs: An evolutionary model of bias, polarization, and other challenges to collective intelligence","authors":"A. Lo, Ruixun Zhang","doi":"10.1177/26339137221104785","DOIUrl":"https://doi.org/10.1177/26339137221104785","url":null,"abstract":"Despite its success in financial markets and other domains, collective intelligence seems to fall short in many critical contexts, including infrequent but repeated financial crises, political polarization and deadlock, and various forms of bias and discrimination. We propose an evolutionary framework that provides fundamental insights into the role of heterogeneity and feedback loops in contributing to failures of collective intelligence. The framework is based on a binary choice model of behavior that affects fitness; hence, behavior is shaped by evolutionary dynamics and stochastic changes in environmental conditions. We derive collective intelligence as an emergent property of evolution in this framework, and also specify conditions under which it fails. We find that political polarization emerges in stochastic environments with reproductive risks that are correlated across individuals. Bias and discrimination emerge when individuals incorrectly attribute random adverse events to observable features that may have nothing to do with those events. In addition, path dependence and negative feedback in evolution may lead to even stronger biases and levels of discrimination, which are locally evolutionarily stable strategies. These results suggest potential policy interventions to prevent such failures by nudging the “madness of mobs” towards the “wisdom of crowds” through targeted shifts in the environment.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81544457","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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