Comparison of competing market mechanisms with reinforcement learning in a carpooling scenario

Abstract

In this paper a multi-agent simulation was implemented to analyze the dynamics of different market mechanisms with a Reinforcement Learning algorithm in the context of a carpooling market. The agents in the simulation, car owners (COs) and non car owners (NCOs), had to sell or buy a car seat for multiple rounds by picking one of two possible mechanisms: Dutch Auction or Fixed Price. In the beginning of the simulation the agents have no information about the efficiency of these mechanisms and they are chosen with the same probability. In the course of the simulation a Reinforcement Learning algorithm alters the agents' preferences for the two mechanisms depending on their cumulative payoffs. The key finding is that sellers have a clear preference for the Dutch auction mechanism with differing degrees dependent on the seller/buyer ratio. Buyers on the other hand have no significant preference for any mechanism. If these results are replicable, they suggest that an increased utilization of the Dutch auction could lead to an expansion of the carpooling market, increasing its impact as an alternative means of transportation.

Keywords

• Multi-agent simulation
• Reinforcement learning
• Traffic
• Carpooling
• Auction
• Market