# game: strategic decision making in biological and artificial brains.

Figure 4: Cooperation rates across different learning scenarios in Agent vs. Agent experiments. 

(a) Constrained learning using cooperation (high initial variability) 

(b) Constrained learning using defection (high initial variability) 

(c) Constrained learning using cooperation (low variability) 

(d) Constrained learning using defection (low variability) 

(e) Unconstrained learning.

(...)

<< The aim of (AA) paper is twofold. First, it seeks to uncover the algorithms that humans and other animals employ for learning in decision-making strategies within non-zero-sum games, specifically focusing on fully observable iterated prisoner’s dilemma scenarios. Second, it aims to develop a new model to explain strategic decision-making which reflects previous neurobiological findings showing that different brain circuits are responsible for self-referential processing and understanding others. The model stems from the actor-critic framework and incorporates multiple critics to allow for distinct processing of both self and others’ state. >>

AA << validate the biological plausibility and transferability of (Their) algorithm through comparisons with experimental data from human on the iterated prisoner’s dilemma game. >>️

Anushka Deshpande. Strategic Decision Making in Biological and Artificial Brains. biorxiv. doi: 10.1101/ 2025.02.17.638746. Feb 24, 2025.

https://www.biorxiv.org/content/10.1101/2025.02.17.638746v1

Also: behav, game, tit-for-tat, brain, in https://www.inkgmr.net/kwrds.html 

Keywords: behavior, games, tit-for-tat, brain