Adaptive Proposer for Ultimatum Game

Ultimate Game serves for extensive studies of various aspects of human decision making. The current paper contribute to them by designing proposer optimising its policy using Markov-decision-process (MDP) framework combined with recursive Bayesian learning of responder’s model. Its foreseen use: i) standardises experimental conditions for studying rationality and emotion-influenced decision making of human responders; ii) replaces the classical game-theoretical design of the players’ policies by an adaptive MDP, which is more realistic with respect to the knowledge available to individual players and decreases player’s deliberation effort; iii) reveals the need for approximate learning and dynamic programming inevitable for coping with the curse of dimensionality; iv) demonstrates the influence of the fairness attitude of the proposer on the game course; v) prepares the test case for inspecting exploration-exploitation dichotomy.