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https://doi.org/10.1103/physre...
Article . 2025 . Peer-reviewed
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Article . 2025
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Bayesian ecoevolutionary game dynamics

Authors: Patra, Arunava; Bairagya, Joy Das; Chakraborty, Sagar;

Bayesian ecoevolutionary game dynamics

Abstract

The symbiotic relationship between the frameworks of classical game theory and evolutionary game theory is well-established. However, evolutionary game theorists have mostly tapped into the classical game of complete information where players are completely informed of all other players' payoffs. Of late, there is a surge of interest in eco-evolutionary interactions where the environment's state is changed by the players' actions which, in turn, are influenced by the changing environment. However, in real life, the information about the true environmental state must pass through some noisy channel (like usually imperfect sensory apparatus of the players) before it is perceived by the players: The players naturally are prone to sometimes perceive the true state erroneously. Given the uncertain perceived environment, the players may adopt bet-hedging kind of strategies in which they play different actions in different perceptions. In a population of such ill-informed players, a player would be confused about the information state of her opponent, and an incomplete information situation akin to a Bayesian game surfaces. In short, we contemplate possibility of natural emergence of symbiotic relationship between the frameworks of Bayesian games and eco-evolutionary games when the players are equipped with inefficient sensory apparatus. Herein, we illustrate this connection using a setup of infinitely large, well-mixed population of players equipped with two actions for exploiting a resource (the environment) at two different rates so that the resource state evolves accordingly. The state of the resource impacts every player's decision of playing particular action. We investigate continuous state environment in the presence of a Gaussian noisy channel. Employing the formalism of replicator dynamics, we find that noisy information can be effective in preventing resource from going extinct.

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FOS: Economics and business, Biological Physics (physics.bio-ph), FOS: Biological sciences, Economics - Theoretical Economics, Populations and Evolution (q-bio.PE), Theoretical Economics (econ.TH), FOS: Physical sciences, Physics - Biological Physics, Chaotic Dynamics (nlin.CD), Quantitative Biology - Populations and Evolution, Nonlinear Sciences - Chaotic Dynamics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green