Gender-sensitive automated negotiators

This paper introduces an innovative approach for automated negotiating using the gender of human opponents. Our ap-proach segments the information acquired from previous op-ponents, stores it in two databases, and models the typical behavior of males and of females. The two models are used in order to match an optimal strategy to each of the two sub-populations. In addition to the basic separation, we propose a learning algorithm which supplies an online indicator for the gender separability-level of the population, which tunes the level of separation the algorithm activates. The algorithm we present can be generally applied in different environments with no need for configuration of parameters. Experiments in 4 different one-shot domains, comparing the performance of the gender based separation approach with a basic approach which is not gender sensitive, revealed higher payoffs of the former in almost all the domains. Moreover, using the pro-posed learning algorithm further improved the results