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An Active Inference Model of Collective Intelligence

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Kaufmann, Rafael, Gupta, Pranav, Taylor, Jacob·2021·Entropy
DOI 10.3390/e23070830PMC PMC8306784
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Abstract

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.

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  • Norm acquisition has been formalized as free energy minimization with respect to a model of others' expectations.

    “Norm acquisition has also been formalized as free energy minimization with respect to a model of others' expectations (Veissi` ere et al., 2019; Kaufmann et al., 2021).”

    From Social Reality Construction via Active Inference: Modeling the Dialectic of Conformity and Creativity (2026) · cites this paper as Kaufmann et al., 2021

    Section 2.8 formalizes free-energy minimization using an agent’s model of its partner, and Section 2.9 describes the agent modelling what its partner thinks about it. But Section 1.3 discusses shared norms as a potential capacity; it does not formalize norm acquisition as minimization with respect to others’ expectations. Page numbers are unavailable in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Kaufmann, R., Gupta, P., & Taylor, J.
Affiliations
  • Independent Researcher, Brooklyn, NY 11215, USA
  • Carnegie Mellon University (US)
  • Australian National University (AU)
Corresponding author
Taylor, Jacob
ORCID
Venue
Entropy
Publisher
MDPI AG
Published
2021-06-29
Volume
23
Issue
7
Pages
830
ISSN
1099-4300 (ISSN-L)
DOI
10.3390/e23070830
PMC
PMC8306784
OpenAlex
W3152150563
Cited by
35
References
14
Citations by year
2026: 4 · 2025: 9 · 2024: 6 · 2023: 8 · 2022: 6 · 2021: 2
FWCI
2.89
Type
journal article
Language
en
Open access
Licence
Locations
  • Entropy (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • ANU Open Research (Australian National University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University) (cc-by)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Systems and Dynamics
  • Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
Keywords
collective intelligence, active inference, agent-based modeling, theory of mind, goal alignment, emergent behavior, multiscale dynamics, cognitive evolution, complex adaptive systems, information theory

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