In lowest unique integer games (LUIGs), continually choosing the same number has been experimentally and computationally shown to be effective. However, this result holds only when all players behave differently, and it is unclear whether such behavior performs well under population dynamics. This study analyzed the types of agents that survive and how successfully they behave within an evolutionary environment of small-size LUIGs. Here, the author identified a learning model to create agents using behavioral data obtained from a laboratory experiment by Yamada and Hanaki. Then, evolutionary competition was pursued. The main findings are three fold. First, more agents are ruled out in three-person LUIGs than in four-person LUIGs. Second, the most successful agents do not win as much as the generations increase. Instead, they manage to win by adaptively changing their strategies. Third, as the scale of the LUIG increases, the number of wins for each agent is not correlated with that in a round-robin contest.

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