Promoting the Emergence of Behavior Norms in a Principal–Agent Problem—An Agent-Based Modeling Approach Using Reinforcement Learning

One of the complexities of social systems is the emergence of behavior norms that are costly for individuals. Study of such complexities is of interest in diverse fields ranging from marketing to sustainability. In this study we built a conceptual Agent-Based Model to simulate interactions between a group of agents and a governing agent, where the governing agent encourages other agents to perform, in exchange for recognition, an action that is beneficial for the governing agent but costly for the individual agents. We equipped the governing agent with six Temporal Difference Reinforcement Learning algorithms to find sequences of decisions that successfully encourage the group of agents to perform the desired action. Our results show that if the individual agents’ perceived cost of the action is low, then the desired action can become a trend in the society without the use of learning algorithms by the governing agent. If the perceived cost to individual agents is high, then the desired output may become rare in the space of all possible outcomes but can be found by appropriate algorithms. We found that Double Learning algorithms perform better than other algorithms we used. Through comparison with a baseline, we showed that our algorithms made a substantial difference in the rewards that can be obtained in the simulations