AI Models Optimize Due Diligence in Complex Takeover Auctions

When companies compete to acquire a target, they often invest in due diligence to refine their valuation estimates. This paper investigates the optimal level of such costly, imperfect information gathering. The study uses a computational model of bidding contests, allowing an AI to learn effective strategies through self-play, similar to how game engines master chess. The core finding suggests that the ideal amount of due diligence is moderate and limited. This amount decreases as diligence costs rise and further diminishes when multiple bidders are performing their own research, as competition reduces the advantage of having more information. The research also validates the effectiveness of simple, general self-play AI methods, showing they can develop robust bidding strategies in scenarios too complex for traditional exact algorithms, particularly in the large-scale, real-world deal environments.