Analyzing the Impact of Strategic Behavior in an Evolutionary Learning Model Using a Genetic Algorithm

This study presents an experimental approach to strategic behavior and economic learning by integrating game theory and Genetic Algorithms in a novel heuristic-based simulation model. Inspired by strategic scenarios that change over time, we propose a model where games can change based on agents’ behavior. The goal is to document the model design and examine how strategic behavior impacts the evolution of optimal outcomes in various choice scenarios. For diversity, 144 unique $$ 2\times 2 $$ 2 × 2 games and three different strategy selection criteria were used: Nash equilibrium, Hurwicz rule, and a random selection technique. The originality of this study is that the introduced evolutionary algorithm changes the games based on their overall outcome rather than changing the strategies or player-specific traits. The results indicated optimal player scenarios for both The Nash equilibrium and Hurwicz rules, the first being the best-performing strategy. The random selection method failed to converge to optimal values in most of the selected populations, acting as a control feature and reinforcing the need for strategic behavior in evolutionary learning. Two further observations were recorded. First, games were frequently transformed so agents could coordinate their strategies to create stable optimal equilibria. Second, we observed the evolution of game populations into groups of fewer (repeating) isomorphic games with strong preceding game characteristics.