A Cultural Decision-Making Model for Negotiation based on Inverse Reinforcement Learning

Abstract

We learn culture-specific weights for a multi-attribute model of decision-making in negotiation, using Inverse Reinforcement Learning (IRL). The model considers multiple individual and social factors for evaluating the available choices in a decision set, and attempts to account for observed behavior differences across cultures by the different weights that members of those cultures place on each factor. We apply this model to the Ultimatum Game and show that weights learned from IRL surpass both a simple baseline with random weights, and a high baseline that only seeks to maximize gain in own wealth in accounting for the behavior of human players from four different cultures. We also show that the weights learned with our model for one culture outperform weights learned for other cultures when playing against opponents of the first culture. We conclude that decision-making in negotiation is a complex, culture-specific process that can be learned using IRL techniques.


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