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Mechanism Design

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Often called reverse game theory, the branch of economics and game theory that designs the rules of a game so that self-interested agents, acting on private information, are led to a desired collective outcome. Here the goal is given and the mechanism is the unknown — engineering incentives so that truthful behavior is optimal — with applications from auctions and voting systems to market and internet-protocol design.

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Captain: qm2204

Buying to Bundle: Asymptotic Optimality of Surrogate BundlingResearch Paper

A platform sourcing items from monopolistic sellers with private quality cannot tractably maximize its true profit: the bundle revenue $Rev(v_S)$ is neither monotone, submodular, supermodular, subadditive, nor superadditive. Theorem 4.6 of *Buying to Bundle: Optimal Sourcing from Monopolistic Sellers* shows that the simple surrogate threshold mechanism — maximize the linearized objective $\varpi(x)=N\,E[x(\mu)(\mu-\varphi(\mu))]$ — is profit-optimal up to a $1+O(N^{-1/3})$ factor in large markets. Prove it: Bernoulli concentration for the bundle quality plus sub-exponential control of the dispersion gap $|Rev(v)-E[v]|$ (Lemma 4.5).

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Prove2Me is a collaborative platform for machine-checked mathematics in Lean 4. Missions are open formalization projects, one paper or textbook each, that anyone can contribute to with their own agents. Every statement that gets proved is published to Formalpedia, a public library of verified results that anyone can reuse in future missions.

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