Active Inferants: An Active Inference Framework for Ant Colony Behavior

Abstract

In this paper, we introduce an active inference model of ant colony foraging behavior, and implement the model in a series of in silico experiments. Active inference is a multiscale approach to behavioral modeling that is being applied across settings in theoretical biology and ethology. The ant colony is a classic case system in the function of distributed systems in terms of stigmergic decision-making and information sharing. Here we specify and simulate a Markov decision process (MDP) model for ant colony foraging. We investigate a well-known paradigm from laboratory ant colony behavioral experiments, the alternating T-maze paradigm, to illustrate the ability of the model to recover basic colony phenomena such as trail formation after food location discovery. We conclude by outlining how the active inference ant colony foraging behavioral model can be extended and situated within a nested multiscale framework and systems approaches to biology more generally.

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“…In this paper, we employed a Bayesian framework—known as active inference—to formally account for the dynamics underlying (local) communication and (global) cumulative culture dynamics, thus contributing to the ever-growing body of research on multi-agent Bayesian models (e.g., Gunji et al, 2018 ) and collective active inference (e.g., Friedman et al, 2021 ; Heins et al, 2022 ) Under our account, the social “transmission” of cultural information has been cast as a fundamentally bidirectional process of communication, which has been shown in the previous active inference literature to induce a generalized synchrony between the internal (belief) states of agents holding sufficiently similar generative models. Building on this work, we operationalized generalized synchrony as a particular convergence between the internal states of interlocutors, and show that it depends sensitively on the precision of observation or likelihood mappings in a generative model of communicative exchange.…”

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“…This is a very interesting translation, because it suggests the use of a predictive coding model (a predictive gradient) to understand progressive morphogenesis. 88,89 This is testable, as biophysical or biochemical representations of cell expectations (homeostatic setpoints, in the sense of priors [90][91][92][93][94][95][96] ) can be looked for experimentally.…”

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