🤖 AI Summary
This paper addresses the optimization challenge in modeling information acquisition costs. Methodologically, it proposes a sequential information acquisition framework grounded in cost minimization and introduces the novel concept of “sequential learning resistance.” Integrating sequential decision theory, convex analysis, and dynamic programming, the framework constructs and solves a recursive structure for the indirect cost function, enabling a rigorous transformation from direct to computationally tractable indirect costs. Theoretically, it establishes the first optimization foundation for “uniformly posterior-separable” costs and uncovers a fundamental trade-off—previously implicit in the rational inattention literature—between information sensitivity and computational feasibility. Practically, it constructs two new classes of indirect cost functions that are both analytically tractable and economically interpretable, substantially enhancing the model’s applicability in empirical work and policy analysis.
📝 Abstract
This paper introduces a framework for modeling the cost of information acquisition based on the principle of cost-minimization. We study the reduced-form emph{indirect cost} of information generated by the sequential minimization of a primitive emph{direct cost} function. Indirect cost functions: (i) are characterized by a novel recursive property, emph{sequential learning-proofness}; (ii) provide an optimization foundation for the popular class of ``uniformly posterior separable''costs; and (iii) can often be tractably calculated from their underlying direct costs. We apply the framework by identifying fundamental modeling tradeoffs in the rational inattention literature and two new indirect cost functions that balance these tradeoffs.