An Active Inference Model of Collective Intelligence
Collective intelligence, an emergent phenomenon in which a composite system of multiple interacting agents performs at levels greater than the sum of its parts, has long compelled research efforts in social and behavioral sciences. To date, however, formal models of collective intelligence have lack...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/7/830 |
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| author | Rafael Kaufmann Pranav Gupta Jacob Taylor |
| author_facet | Rafael Kaufmann Pranav Gupta Jacob Taylor |
| author_sort | Rafael Kaufmann |
| collection | DOAJ |
| description | Collective intelligence, an emergent phenomenon in which a composite system of multiple interacting agents performs at levels greater than the sum of its parts, has long compelled research efforts in social and behavioral sciences. To date, however, formal models of collective intelligence have lacked a plausible mathematical description of the relationship between local-scale interactions between autonomous sub-system components (individuals) and global-scale behavior of the composite system (the collective). In this paper we use the Active Inference Formulation (AIF), a framework for explaining the behavior of any non-equilibrium steady state system at any scale, to posit a minimal agent-based model that simulates the relationship between local individual-level interaction and collective intelligence. We explore the effects of providing baseline AIF agents (Model 1) with specific cognitive capabilities: Theory of Mind (Model 2), Goal Alignment (Model 3), and Theory of Mind with Goal Alignment (Model 4). These stepwise transitions in sophistication of cognitive ability are motivated by the types of advancements plausibly required for an AIF agent to persist and flourish in an environment populated by other highly autonomous AIF agents, and have also recently been shown to map naturally to canonical steps in human cognitive ability. Illustrative results show that stepwise cognitive transitions increase system performance by providing complementary mechanisms for alignment between agents’ local and global optima. Alignment emerges endogenously from the dynamics of interacting AIF agents themselves, rather than being imposed exogenously by incentives to agents’ behaviors (contra existing computational models of collective intelligence) or top-down priors for collective behavior (contra existing multiscale simulations of AIF). These results shed light on the types of generic information-theoretic patterns conducive to collective intelligence in human and other complex adaptive systems. |
| first_indexed | 2024-03-09T04:44:47Z |
| format | Article |
| id | doaj.art-47130c351f0c4e68abc17167a2a41420 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T04:44:47Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-47130c351f0c4e68abc17167a2a414202023-12-03T13:17:08ZengMDPI AGEntropy1099-43002021-06-0123783010.3390/e23070830An Active Inference Model of Collective IntelligenceRafael Kaufmann0Pranav Gupta1Jacob Taylor2Independent Researcher, Brooklyn, NY 11215, USATepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213, USAInstitute of Cognitive & Evolutionary Anthropology, University of Oxford, Oxford OX2 6PN, UKCollective intelligence, an emergent phenomenon in which a composite system of multiple interacting agents performs at levels greater than the sum of its parts, has long compelled research efforts in social and behavioral sciences. To date, however, formal models of collective intelligence have lacked a plausible mathematical description of the relationship between local-scale interactions between autonomous sub-system components (individuals) and global-scale behavior of the composite system (the collective). In this paper we use the Active Inference Formulation (AIF), a framework for explaining the behavior of any non-equilibrium steady state system at any scale, to posit a minimal agent-based model that simulates the relationship between local individual-level interaction and collective intelligence. We explore the effects of providing baseline AIF agents (Model 1) with specific cognitive capabilities: Theory of Mind (Model 2), Goal Alignment (Model 3), and Theory of Mind with Goal Alignment (Model 4). These stepwise transitions in sophistication of cognitive ability are motivated by the types of advancements plausibly required for an AIF agent to persist and flourish in an environment populated by other highly autonomous AIF agents, and have also recently been shown to map naturally to canonical steps in human cognitive ability. Illustrative results show that stepwise cognitive transitions increase system performance by providing complementary mechanisms for alignment between agents’ local and global optima. Alignment emerges endogenously from the dynamics of interacting AIF agents themselves, rather than being imposed exogenously by incentives to agents’ behaviors (contra existing computational models of collective intelligence) or top-down priors for collective behavior (contra existing multiscale simulations of AIF). These results shed light on the types of generic information-theoretic patterns conducive to collective intelligence in human and other complex adaptive systems.https://www.mdpi.com/1099-4300/23/7/830collective intelligencefree energy principleactive inferenceagent-based modelcomplex adaptive systemsmultiscale systems |
| spellingShingle | Rafael Kaufmann Pranav Gupta Jacob Taylor An Active Inference Model of Collective Intelligence Entropy collective intelligence free energy principle active inference agent-based model complex adaptive systems multiscale systems |
| title | An Active Inference Model of Collective Intelligence |
| title_full | An Active Inference Model of Collective Intelligence |
| title_fullStr | An Active Inference Model of Collective Intelligence |
| title_full_unstemmed | An Active Inference Model of Collective Intelligence |
| title_short | An Active Inference Model of Collective Intelligence |
| title_sort | active inference model of collective intelligence |
| topic | collective intelligence free energy principle active inference agent-based model complex adaptive systems multiscale systems |
| url | https://www.mdpi.com/1099-4300/23/7/830 |
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