Personality for buyer and seller agents in electronic marketplace based on reputation and reinforcement learning

Abstract

In this paper, we propose a marketplace model which is based on personality, reputation and reinforcement learning algorithms for buying and selling agents. We use two personality traits for seller agents: stingy and conscientiousness. In this marketplace, sellers with low score of stingy earn more benefits in comparison with high stingy sellers. Also, conscientious seller agents gain more reputation relative to conscienceless seller agents. In addition, we use three personality traits for buyer agents: stingy, openness and agreeableness. Buyer agents with high score of openness and low score of stingy purchase more new goods and more expensive goods relative to buyers with low score of openness and high score of stingy. Buyer agent’s seller with high score of agreeableness would be less cheated in the marketplace. Also, buyer agents apply reinforcement learning to evaluate the reputation of seller agents and then focus their trading on reputable sellers. On the other hand, the personality of seller agents affects them to consider discount for buyer agents. In addition, seller agents apply reinforcement learning to establish a model of reputation of buyer agents. The results show that selling/buying agents that model the reputation of buying/selling agents obtain more satisfaction rather than selling/buying agents who only use the reinforcement learning. Key words: Agent, marketplace, personality, reputation, reinforcement learning.