Interacting traders

Contact person: Alex Lenkoski

Status: Project

The digital revolution has facilitated markets for a broad range of goods and services. As the volume and complexity of these markets grows, algorithmic pricing naturally takes over, with agent-based reinforcement learning (RL) algorithms becoming increasingly prevalent. When a small number of RL algorithms control the pricing for a large share of a market, there is increasing concern that they may begin to exhibit the same sort of anticompetitive behavior that competition law prohibits when perpetrated by human actors. This project will develop a class of RL algorithms designed to trade in digital markets with a particular focus on understanding, anticipating and mitigating the negative consequences of interaction between independent agents. Our intent is to (1) extend the theory of RL trading algorithms to markets driven by complex heterogeneous processes; (2) create a general and extendable testbed for new RL research, development and deployment; (3) develop a flexible class of "whistle blower" agents that are capable of monitoring market actors and assessing the chances of tacit or explicit algorithmic; and (4) clarify and update important legal concepts in competition law related to collusion, use of insider information and systemic risk when algorithms act and interact autonomously in digital markets.