1
|
Olsson H, Galesic M. Analogies for modeling belief dynamics. Trends Cogn Sci 2024:S1364-6613(24)00172-4. [PMID: 39069399 DOI: 10.1016/j.tics.2024.07.001] [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: 08/01/2023] [Revised: 07/03/2024] [Accepted: 07/05/2024] [Indexed: 07/30/2024]
Abstract
Belief dynamics has an important role in shaping our responses to natural and societal phenomena, ranging from climate change and pandemics to immigration and conflicts. Researchers often base their models of belief dynamics on analogies to other systems and processes, such as epidemics or ferromagnetism. Similar to other analogies, analogies for belief dynamics can help scientists notice and study properties of belief systems that they would not have noticed otherwise (conceptual mileage). However, forgetting the origins of an analogy may lead to some less appropriate inferences about belief dynamics (conceptual baggage). Here, we review various analogies for modeling belief dynamics, discuss their mileage and baggage, and offer recommendations for using analogies in model development.
Collapse
Affiliation(s)
- Henrik Olsson
- Santa Fe Institute, Santa Fe, NM 87501, USA; Complexity Science Hub, 1080 Vienna, Austria.
| | - Mirta Galesic
- Santa Fe Institute, Santa Fe, NM 87501, USA; Complexity Science Hub, 1080 Vienna, Austria; Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA.
| |
Collapse
|
2
|
Tump AN, Deffner D, Pleskac TJ, Romanczuk P, M. Kurvers RHJ. A Cognitive Computational Approach to Social and Collective Decision-Making. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2024; 19:538-551. [PMID: 37671891 PMCID: PMC10913326 DOI: 10.1177/17456916231186964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
Collective dynamics play a key role in everyday decision-making. Whether social influence promotes the spread of accurate information and ultimately results in adaptive behavior or leads to false information cascades and maladaptive social contagion strongly depends on the cognitive mechanisms underlying social interactions. Here we argue that cognitive modeling, in tandem with experiments that allow collective dynamics to emerge, can mechanistically link cognitive processes at the individual and collective levels. We illustrate the strength of this cognitive computational approach with two highly successful cognitive models that have been applied to interactive group experiments: evidence-accumulation and reinforcement-learning models. We show how these approaches make it possible to simultaneously study (a) how individual cognition drives social systems, (b) how social systems drive individual cognition, and (c) the dynamic feedback processes between the two layers.
Collapse
Affiliation(s)
- Alan N. Tump
- Center for Adaptive Rationality, Max Planck Institute for Human Development
- Science of Intelligence, Technische Universität Berlin
| | - Dominik Deffner
- Center for Adaptive Rationality, Max Planck Institute for Human Development
- Science of Intelligence, Technische Universität Berlin
| | | | - Pawel Romanczuk
- Science of Intelligence, Technische Universität Berlin
- Institute for Theoretical Biology, Department of Biology, Humboldt Universität zu Berlin
- Bernstein Center for Computational Neuroscience Berlin
| | - Ralf H. J. M. Kurvers
- Center for Adaptive Rationality, Max Planck Institute for Human Development
- Science of Intelligence, Technische Universität Berlin
| |
Collapse
|
3
|
Linn S, Lawley SD, Karamched BR, Kilpatrick ZP, Josić K. Fast decisions reflect biases, slow decisions do not. ARXIV 2024:arXiv:2401.00306v2. [PMID: 38259347 PMCID: PMC10802676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Decisions are often made by heterogeneous groups of individuals, each with distinct initial biases and access to information of different quality. We show that in large groups of independent agents who accumulate evidence the first to decide are those with the strongest initial biases. Their decisions align with their initial bias, regardless of the underlying truth. In contrast, agents who decide last make decisions as if they were initially unbiased, and hence make better choices. We obtain asymptotic expressions in the large population limit that quantify how agents' initial inclinations shape early decisions. Our analysis shows how bias, information quality, and decision order interact in non-trivial ways to determine the reliability of decisions in a group.
Collapse
Affiliation(s)
- Samantha Linn
- Department of Mathematics, University of Utah, Salt Lake City, Utah, USA
| | - Sean D. Lawley
- Department of Mathematics, University of Utah, Salt Lake City, Utah, USA
| | - Bhargav R. Karamched
- Department of Mathematics, Florida State University, Tallahassee, Florida 32306, USA
- Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, USA
- Program in Neuroscience, Florida State University, Tallahassee, Florida 32306, USA
| | - Zachary P. Kilpatrick
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, Colorado 80309, USA
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, Texas 77004, USA
- Department of Biology and Biochemistry, University of Houston, Houston, Texas 77004, USA
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|