This article presents an evolutionary game model where players choose between two sides, with the minority side winning. The game is analyzed to understand how cooperation and organization emerge in a population of players with limited rationality. The model is based on bounded rationality, where players use a finite set of strategies based on past records, and their decisions are influenced by the binary outcome of previous plays. The study shows that even though players are selfish, they can develop cooperation and coordination, leading to more efficient resource usage. The game is analyzed using a binary signal system, where players retain a limited number of past bits (M) to make decisions. The number of strategies increases exponentially with M, making it challenging for players to adapt. The study uses simulations to show how different brain sizes (M) affect the population's performance, with larger brains generally leading to better coordination and lower fluctuations. However, the performance of the population tends to saturate at higher M values. The model also explores the emergence of intelligence and the "red queen" effect, where players must continuously adapt to survive. The study includes a genetic approach where the worst-performing players are replaced by new ones, leading to a learning process and improved population performance. The model is compared to real-world scenarios, such as stock trading, and shows how cooperation and organization can emerge without enforceable authority. The study concludes that the model provides a general approach to understanding the evolutionary nature of games and the emergence of cooperation in society. It highlights the importance of bounded rationality and the role of evolution in shaping collective behavior. The model is a simple yet insightful representation of complex systems, capturing essential aspects of real-world interactions.This article presents an evolutionary game model where players choose between two sides, with the minority side winning. The game is analyzed to understand how cooperation and organization emerge in a population of players with limited rationality. The model is based on bounded rationality, where players use a finite set of strategies based on past records, and their decisions are influenced by the binary outcome of previous plays. The study shows that even though players are selfish, they can develop cooperation and coordination, leading to more efficient resource usage. The game is analyzed using a binary signal system, where players retain a limited number of past bits (M) to make decisions. The number of strategies increases exponentially with M, making it challenging for players to adapt. The study uses simulations to show how different brain sizes (M) affect the population's performance, with larger brains generally leading to better coordination and lower fluctuations. However, the performance of the population tends to saturate at higher M values. The model also explores the emergence of intelligence and the "red queen" effect, where players must continuously adapt to survive. The study includes a genetic approach where the worst-performing players are replaced by new ones, leading to a learning process and improved population performance. The model is compared to real-world scenarios, such as stock trading, and shows how cooperation and organization can emerge without enforceable authority. The study concludes that the model provides a general approach to understanding the evolutionary nature of games and the emergence of cooperation in society. It highlights the importance of bounded rationality and the role of evolution in shaping collective behavior. The model is a simple yet insightful representation of complex systems, capturing essential aspects of real-world interactions.