Pedro Sequeira, Francisco S. Melo and Ana Paiva
In Proceedings of the 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2014), Genoa, Italy, October 13-16, 2014, pp. 64–69 
Abstract In this paper we analyze the impact of simple social signaling mechanisms in the performance of learning agents within competitive multiagent settings. In our framework, self- interested reinforcement learning agents interact and compete with each other for limited resources. The agents can exchange social signals that influence the total amount of reward received throughout time. In a series of experiments, we vary the amount resources available in the environment, the frequency of interac- tions and the importance each agent in the population gives to the social displays of others. We measure the combined performance of the population according to distinct selection paradigms based on the individual performances of each agent. The results of our study show that by focusing on the social displays of others, agents learn to collectively coordinate their feeding behavior by trading-off immediate benefit for long-term social welfare. Also, given populations where the impact of the social signal on the reward differs, the individuals with the highest fitness appear in the most socially-aware populations. The presence of social signaling gives also origin to more social inequalities where the more fit agents benefit from their higher status being appreciated by other members of the population.
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