An active inference model of collective intelligence (2104.01066v1)

Abstract: To date, formal models of collective intelligence have lacked a plausible mathematical description of the relationship between local-scale interactions between highly 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 (operationalized as system-level performance). 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 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.

• The paper introduces a minimalist active inference model that explains how local interactions among autonomous agents lead to emergent collective intelligence.
• It reveals that agents equipped with Theory of Mind and Goal Alignment significantly boost system performance across progressively complex models.
• The study underscores potential pathways for endogenizing cognitive parameters to optimize coordination in complex adaptive systems.

An Active Inference Model of Collective Intelligence

The paper "An Active Inference Model of Collective Intelligence" by Kaufmann et al. introduces a novel approach to modeling collective intelligence through the lens of the Active Inference Formulation (AIF). This investigation into collective intelligence is framed within the context of agent-based models that demonstrate emergent behavior arising from local interactions. Specifically, the research centers on formulating a minimalist model that explicates the relationship between the interactions of highly autonomous agents and the emergent intelligence of the collective they form.

Methodological Approach

Active Inference Formulation (AIF) is leveraged in the paper, based on the Free Energy Principle (FEP), to simulate agents within a composite system that minimize free energy as a means of inferring beliefs and making decisions. The authors construct four models of increasing complexity, varying the agents' cognitive capabilities:

• Model 1 (Baseline): Basic AIF agents operate without social integration.
• Model 2 (Theory of Mind): Agents are equipped with Theory of Mind, allowing them to infer the cognitive states of their peers.
• Model 3 (Goal Alignment): Agents align their objectives with one another, excluding Theory of Mind.
• Model 4 (Theory of Mind with Goal Alignment): Combines elements of Models 2 and 3 to maximize agent cognitive sophistication.

These models offer a comprehensive investigation into how different cognitive capabilities at the individual agent level translate to improved performance at the collective, system-wide level.

Key Findings

The simulations demonstrate a progressive improvement in system-level performance correlating with the sophistication of individual-level cognitive capabilities. Notably, Model 4, which incorporates both Theory of Mind and Goal Alignment, results in the highest collective performance, underscoring the effectiveness of these cognitive capabilities in aligning individual and collective optima within the system. This paper effectively highlights the endogenous emergence of alignment between local agent goals and global system goals absent externally imposed constraints or incentives—a compelling counter to existing computational frameworks that depend heavily on such external structures.

Implications and Future Directions

The integration of AIF in modeling collective intelligence contributes meaningfully to understanding multiscale behavioral processes. The research not only substantiates the relevance of stepwise cognitive complexity but also offers a pathway for further empirical exploration into the active dynamics of emergent collective behavior across scales. For future investigations, the paper suggests the possible endogenization of parameters such as alterity (Theory of Mind) and Goal Alignment through learning mechanisms, which could yield further insights into cognitive evolution within collective systems.

This work aligns computational models with empirical evidence that socially perceptive abilities and goal alignment are critical to the coordination observed in human collectives, providing a robust framework for understanding the emergent intelligence at both individual and collective scales. Moreover, the research opens avenues for examining how the alignment between local interactions and broader system dynamics could inform optimization strategies for multiscale complex systems in AI, impacting not only theory but practical implementations in fields such as organizational psychology and adaptive systems engineering.