Multi-Agent replication Evaluation for Computer Agriculture Market-Power Detection Under Collusive Bidding: An Artificial Intelligence Equilibrium-Robustness Bound

Collusive bidding in agricultural input and output markets is difficult to detect because the counterfactual competitive benchmark is unobserved and because detection rules calibrated on one market structure may fail under another. The bound characterises the largest collusive deviation that a detection rule can be guaranteed to flag when the simulator's equilibrium selection is misspecified within a declared neighbourhood of the true bidding game. The argument proceeds in three steps. First, a multi-agent bidding game is defined for agricultural markets with capacity-constrained suppliers, and a detection rule is formalised as a test on the empirical bid distribution. Second, an equilibrium-robustness radius is derived as a function of the detection rule's power against the competitive null and the simulator's equilibrium-selection error. Third, the bound is shown to be tight for a class of two-bidder capacity games and to degrade gracefully as the number of agents grows. The contribution is a computable certificate that a replication-based detector's guarantees transfer to the field only within an explicitly stated equilibrium-misspecification radius, which reframes replication evaluation from a point estimate of detector performance to a robustness statement.