Incentive Compatibility Mechanism Design for the Auto-bidding Meta-Game

In auto-bidding, advertisers no longer bid per impression but set a high-level target (e.g. tCPA), competing in an Auto-bidding Meta-Game (AMG) over reported targets. Running a truthful per-impression auction underneath does not make this meta-game truthful—an advertiser can still gain by misreporting its target. Within the platform-supported uniform-bidding interface, we formalize the AMG as a Myerson single-parameter environment and design a deployable Dominant-Strategy Incentive Compatible (DSIC) mechanism for it: with exact allocation information under first-price, truthfulness is restored by an end-of-day rebate that returns the advertiser's information rent and leaves the auction untouched. The truthful profile is also welfare-optimal under first-price. For other pricing rules the same payment construction is DSIC whenever the induced allocation is monotone; second-price numerical audits expose allocation nonmonotonicity and integration-sensitive deviation gains, so they do not validate that condition, though welfare optimality does not carry over. Deployment estimates the allocation curve $G$ offline; its signed settlement implements an approximately DSIC payment, not the exact rebate in general. We prove that uniform allocation error yields approximate DSIC. Under an additional uniform quadratic-growth condition, damped best responses track a neighborhood of truth; convergence to truth requires vanishing estimation error, not merely repeated estimation. We evaluate these theoretical predictions on a synthetic simulator across market sizes and value/conversion-rate distributions. An online A/B test on a deployed tCPA product gives an encouraging behavioral signal: a significant market-wide gain in cost, and significant gains in reported targets for the cohort it was designed to move.