When is multidimensional screening a convex program?
Alessio Figalli, Young-Heon Kim, Robert J. McCann

TL;DR
This paper identifies a structural condition called non-negative cross-curvature that makes multidimensional screening problems convex, enabling better theoretical analysis and computational solutions for principal-agent models with aggregate preferences.
Contribution
It introduces the non-negative cross-curvature condition as necessary and sufficient for convexity in multidimensional screening, extending the theoretical framework and computational tractability.
Findings
Convexity of the principal's problem under non-negative cross-curvature.
Uniqueness and stability of optimal strategies are guaranteed.
Economic implications like high pricing strategies are analyzed.
Abstract
A principal wishes to transact business with a multidimensional distribution of agents whose preferences are known only in the aggregate. Assuming a twist (= generalized Spence-Mirrlees single-crossing) hypothesis and that agents can choose only pure strategies, we identify a structural condition on the preference b(x,y) of agent type x for product type y -- and on the principal's costs c(y) -- which is necessary and sufficient for reducing the profit maximization problem faced by the principal to a convex program. This is a key step toward making the principal's problem theoretically and computationally tractable; in particular, it allows us to derive uniqueness and stability of the principal's optimum strategy -- and similarly of the strategy maximizing the expected welfare of the agents when the principal's profitability is constrained. We call this condition non-negative…
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Taxonomy
TopicsEconomic theories and models · Consumer Market Behavior and Pricing · Game Theory and Voting Systems
