TY - JOUR

T1 - Free-energy minimization in joint agent-environment systems

T2 - A niche construction perspective

AU - Bruineberg, Jelle

AU - Rietveld, Erik

AU - Parr, Thomas

AU - van Maanen, Leendert

AU - Friston, Karl J.

N1 - Funding Information: This work was funded by the Netherlands Organisation for Scientific Research (NWO, VIDI Grant) and the ERC (Starting Grant #679190 , EU Horizon 2020), both awarded to ER. TP is funded by the Rosetrees Trust (Award Number 173346 ). KJF is funded by a Wellcome Trust Principal Research Fellowship (Ref: 088130/Z/09/Z ) Publisher Copyright: © 2018 The Author(s)

PY - 2018

Y1 - 2018

N2 - The free-energy principle is an attempt to explain the structure of the agent and its brain, starting from the fact that an agent exists (Friston and Stephan, 2007; Friston et al., 2010). More specifically, it can be regarded as a systematic attempt to understand the ‘fit’ between an embodied agent and its niche, where the quantity of free-energy is a measure for the ‘misfit’ or disattunement (Bruineberg and Rietveld, 2014) between agent and environment. This paper offers a proof-of-principle simulation of niche construction under the free-energy principle. Agent-centered treatments have so far failed to address situations where environments change alongside agents, often due to the action of agents themselves. The key point of this paper is that the minimum of free-energy is not at a point in which the agent is maximally adapted to the statistics of a static environment, but can better be conceptualized an attracting manifold within the joint agent-environment state-space as a whole, which the system tends toward through mutual interaction. We will provide a general introduction to active inference and the free-energy principle. Using Markov Decision Processes (MDPs), we then describe a canonical generative model and the ensuing update equations that minimize free-energy. We then apply these equations to simulations of foraging in an environment; in which an agent learns the most efficient path to a pre-specified location. In some of those simulations, unbeknownst to the agent, the ‘desire paths’ emerge as a function of the activity of the agent (i.e. niche construction occurs). We will show how, depending on the relative inertia of the environment and agent, the joint agent-environment system moves to different attracting sets of jointly minimized free-energy.

AB - The free-energy principle is an attempt to explain the structure of the agent and its brain, starting from the fact that an agent exists (Friston and Stephan, 2007; Friston et al., 2010). More specifically, it can be regarded as a systematic attempt to understand the ‘fit’ between an embodied agent and its niche, where the quantity of free-energy is a measure for the ‘misfit’ or disattunement (Bruineberg and Rietveld, 2014) between agent and environment. This paper offers a proof-of-principle simulation of niche construction under the free-energy principle. Agent-centered treatments have so far failed to address situations where environments change alongside agents, often due to the action of agents themselves. The key point of this paper is that the minimum of free-energy is not at a point in which the agent is maximally adapted to the statistics of a static environment, but can better be conceptualized an attracting manifold within the joint agent-environment state-space as a whole, which the system tends toward through mutual interaction. We will provide a general introduction to active inference and the free-energy principle. Using Markov Decision Processes (MDPs), we then describe a canonical generative model and the ensuing update equations that minimize free-energy. We then apply these equations to simulations of foraging in an environment; in which an agent learns the most efficient path to a pre-specified location. In some of those simulations, unbeknownst to the agent, the ‘desire paths’ emerge as a function of the activity of the agent (i.e. niche construction occurs). We will show how, depending on the relative inertia of the environment and agent, the joint agent-environment system moves to different attracting sets of jointly minimized free-energy.

KW - Active inference

KW - Adaptive environments

KW - Agent-environment complementarity

KW - Desire paths

KW - Free energy principle

KW - Markov decision processes

KW - Niche construction

UR - http://www.scopus.com/inward/record.url?scp=85050505357&partnerID=8YFLogxK

U2 - 10.1016/j.jtbi.2018.07.002

DO - 10.1016/j.jtbi.2018.07.002

M3 - Journal article

C2 - 30012517

AN - SCOPUS:85050505357

VL - 455

SP - 161

EP - 178

JO - Journal of Theoretical Biology

JF - Journal of Theoretical Biology

SN - 0022-5193

ER -