Free-energy minimization in joint agent-environment systems: A niche construction perspective
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Free-energy minimization in joint agent-environment systems : A niche construction perspective. / Bruineberg, Jelle; Rietveld, Erik; Parr, Thomas; van Maanen, Leendert; Friston, Karl J.
In: Journal of Theoretical Biology, Vol. 455, 2018, p. 161-178.Research output: Contribution to journal › Journal article › Research › peer-review
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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 -
ID: 367754525