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Goldstone RL, Andrade-Lotero EJ, Hawkins RD, Roberts ME. The Emergence of Specialized Roles Within Groups. Top Cogn Sci 2024; 16:257-281. [PMID: 36843212 DOI: 10.1111/tops.12644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 02/28/2023]
Abstract
Humans routinely form groups to achieve goals that no individual can accomplish alone. Group coordination often brings to mind synchrony and alignment, where all individuals do the same thing (e.g., driving on the right side of the road, marching in lockstep, or playing musical instruments on a regular beat). Yet, effective coordination also typically involves differentiation, where specialized roles emerge for different members (e.g., prep stations in a kitchen or positions on an athletic team). Role specialization poses a challenge for computational models of group coordination, which have largely focused on achieving synchrony. Here, we present the CARMI framework, which characterizes role specialization processes in terms of five core features that we hope will help guide future model development: Communication, Adaptation to feedback, Repulsion, Multi-level planning, and Intention modeling. Although there are many paths to role formation, we suggest that roles emerge when each agent in a group dynamically allocates their behavior toward a shared goal to complement what they expect others to do. In other words, coordination concerns beliefs (who will do what) rather than simple actions. We describe three related experimental paradigms-"Group Binary Search," "Battles of the Exes," and "Find the Unicorn"-that we have used to study differentiation processes in the lab, each emphasizing different aspects of the CARMI framework.
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2
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Goldstone RL, Dubova M, Aiyappa R, Edinger A. The Spread of Beliefs in Partially Modularized Communities. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2024; 19:404-417. [PMID: 38019565 DOI: 10.1177/17456916231198238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Many life-influencing social networks are characterized by considerable informational isolation. People within a community are far more likely to share beliefs than people who are part of different communities. The spread of useful information across communities is impeded by echo chambers (far greater connectivity within than between communities) and filter bubbles (more influence of beliefs by connected neighbors within than between communities). We apply the tools of network analysis to organize our understanding of the spread of beliefs across modularized communities and to predict the effect of individual and group parameters on the dynamics and distribution of beliefs. In our Spread of Beliefs in Modularized Communities (SBMC) framework, a stochastic block model generates social networks with variable degrees of modularity, beliefs have different observable utilities, individuals change their beliefs on the basis of summed or average evidence (or intermediate decision rules), and parameterized stochasticity introduces randomness into decisions. SBMC simulations show surprising patterns; for example, increasing out-group connectivity does not always improve group performance, adding randomness to decisions can promote performance, and decision rules that sum rather than average evidence can improve group performance, as measured by the average utility of beliefs that the agents adopt. Overall, the results suggest that intermediate degrees of belief exploration are beneficial for the spread of useful beliefs in a community, and so parameters that pull in opposite directions on an explore-exploit continuum are usefully paired.
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Affiliation(s)
- Robert L Goldstone
- Department of Psychological and Brain Sciences, Indiana University
- Program in Cognitive Science, Indiana University
| | | | - Rachith Aiyappa
- Center for Complex Networks and Systems, Luddy School of Informatics, Computing, and Engineering, Indiana University
| | - Andy Edinger
- Program in Cognitive Science, Indiana University
- Center for Complex Networks and Systems, Luddy School of Informatics, Computing, and Engineering, Indiana University
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3
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Galesic M, Barkoczi D, Berdahl AM, Biro D, Carbone G, Giannoccaro I, Goldstone RL, Gonzalez C, Kandler A, Kao AB, Kendal R, Kline M, Lee E, Massari GF, Mesoudi A, Olsson H, Pescetelli N, Sloman SJ, Smaldino PE, Stein DL. Beyond collective intelligence: Collective adaptation. J R Soc Interface 2023; 20:20220736. [PMID: 36946092 PMCID: PMC10031425 DOI: 10.1098/rsif.2022.0736] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 02/27/2023] [Indexed: 03/23/2023] Open
Abstract
We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.
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Affiliation(s)
- Mirta Galesic
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Complexity Science Hub Vienna, 1080 Vienna, Austria
- Vermont Complex Systems Center, University of Vermont, Burlington, VM 05405, USA
| | | | - Andrew M. Berdahl
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, USA
| | - Dora Biro
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - Giuseppe Carbone
- Department of Mechanics, Mathematics and Management, Politecnico di Bari, Bari 70125, Italy
| | - Ilaria Giannoccaro
- Department of Mechanics, Mathematics and Management, Politecnico di Bari, Bari 70125, Italy
| | - Robert L. Goldstone
- Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Cleotilde Gonzalez
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Anne Kandler
- Department of Mathematics, Max-Planck-Institute for Evolutionary Anthropology, Leipzig 04103, Germany
| | - Albert B. Kao
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Biology Department, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Rachel Kendal
- Centre for Coevolution of Biology and Culture, Durham University, Anthropology Department, Durham, DH1 3LE, UK
| | - Michelle Kline
- Centre for Culture and Evolution, Division of Psychology, Brunel University London, Uxbridge, UB8 3PH, UK
| | - Eun Lee
- Department of Scientific Computing, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan, 48513, Republic of Korea
| | | | - Alex Mesoudi
- Department of Ecology and Conservation, University of Exeter, Penryn TR10 9FE, UK
| | | | | | - Sabina J. Sloman
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Computer Science, University of Manchester, Manchester, M13 9PL, UK
| | - Paul E. Smaldino
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Department of Cognitive and Information Sciences, University of California, Merced, CA 95343, USA
| | - Daniel L. Stein
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Department of Physics and Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
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4
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Dubova M, Galesic M, Goldstone RL. Cognitive Science of Augmented Intelligence. Cogn Sci 2022; 46:e13229. [PMID: 36515371 DOI: 10.1111/cogs.13229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/22/2022] [Accepted: 11/27/2022] [Indexed: 12/15/2022]
Abstract
Cognitive science has been traditionally organized around the individual as the basic unit of cognition. Despite developments in areas such as communication, human-machine interaction, group behavior, and community organization, the individual-centric approach heavily dominates both cognitive research and its application. A promising direction for cognitive science is the study of augmented intelligence, or the way social and technological systems interact with and extend individual cognition. The cognitive science of augmented intelligence holds promise in helping society tackle major real-world challenges that can only be discovered and solved by teams made of individuals and machines with complementary skills who can productively collaborate with each other.
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Affiliation(s)
| | - Mirta Galesic
- Santa Fe Institute.,Complexity Science Hub Vienna.,Vermont Complex Systems Center, University of Vermont
| | - Robert L Goldstone
- Cognitive Science Program, Indiana University.,Department of Psychological and Brain Sciences, Indiana University
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5
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Chater N. The computational society. Trends Cogn Sci 2022; 26:1015-1017. [PMID: 36207263 DOI: 10.1016/j.tics.2022.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 07/18/2022] [Indexed: 11/11/2022]
Abstract
How do individual human minds create languages, legal systems, scientific theories, and technologies? From a cognitive science viewpoint, such collective phenomena may be considered a type of distributed computation in which human minds together solve computational problems beyond any individual. This viewpoint may also shift our perspective on individual minds.
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Affiliation(s)
- Nick Chater
- Behavioral Science Group, Warwick Business School, University of Warwick, Coventry, CV4 7AL, UK.
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8
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Graesser AC, Fiore SM, Greiff S, Andrews-Todd J, Foltz PW, Hesse FW. Advancing the Science of Collaborative Problem Solving. Psychol Sci Public Interest 2018; 19:59-92. [PMID: 30497346 DOI: 10.1177/1529100618808244] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Collaborative problem solving (CPS) has been receiving increasing international attention because much of the complex work in the modern world is performed by teams. However, systematic education and training on CPS is lacking for those entering and participating in the workforce. In 2015, the Programme for International Student Assessment (PISA), a global test of educational progress, documented the low levels of proficiency in CPS. This result not only underscores a significant societal need but also presents an important opportunity for psychological scientists to develop, adopt, and implement theory and empirical research on CPS and to work with educators and policy experts to improve training in CPS. This article offers some directions for psychological science to participate in the growing attention to CPS throughout the world. First, it identifies the existing theoretical frameworks and empirical research that focus on CPS. Second, it provides examples of how recent technologies can automate analyses of CPS processes and assessments so that substantially larger data sets can be analyzed and so students can receive immediate feedback on their CPS performance. Third, it identifies some challenges, debates, and uncertainties in creating an infrastructure for research, education, and training in CPS. CPS education and assessment are expected to improve when supported by larger data sets and theoretical frameworks that are informed by psychological science. This will require interdisciplinary efforts that include expertise in psychological science, education, assessment, intelligent digital technologies, and policy.
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Affiliation(s)
- Arthur C Graesser
- 1 Department of Psychology and Institute for Intelligent Systems, University of Memphis
| | - Stephen M Fiore
- 2 Department of Philosophy and Institute for Training & Simulation, University of Central Florida
| | - Samuel Greiff
- 3 Cognitive Science and Assessment, University of Luxembourg
| | | | - Peter W Foltz
- 5 Pearson, Boulder, Colorado.,6 Institute of Cognitive Science, University of Colorado
| | - Friedrich W Hesse
- 7 Leibniz-Institut fur Wissensmedien, Tübingen, Germany.,8 Department for Applied Cognitive and Media Psychology, University of Tübingen
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