The End of Average: Deploying Agent-Based Modeling to Antitrust


Antitrust law and policy rely on a hypothetical average consumer. But no one is average. With this basic observation in mind, we show how agent-based modeling (“ABM”) allows enforcers and policymakers to bypass imaginary averages by observing interactions between unique agents. We argue that agent-based regulatory and enforcement policies have a greater potential than average-based public policies because they are more realistic. As we show, the realism brought by ABM enables antitrust agencies and policymakers to better anticipate the effects of their actions and, perhaps more importantly, to time their interventions better.

Read at SSRN.