Search arXiv⌕ Search

arXiv · 2511.20303

Recursive contracts in non-convex environments

Abstract

In this paper we examine non-convex dynamic optimization problems with forward looking constraints. We prove that the recursive multiplier formulation in \cite{marcet2019recursive} gives the optimal value if one assumes that the planner has access to a public randomization device and forward looking constraints only have to hold in expectations. Whether one formulates the functional equation as a sup-inf problem or as an inf-sup problem is essential for the timing of the optimal lottery and for determining which constraints have to hold in expectations. We discuss for which economic problems the use of lotteries can be considered a reasonable assumption. We provide a general method to recover the optimal policy from a solution of the functional equation. As an application of our results, we consider the Ramsey problem of optimal government policy and give examples where lotteries are essential for the optimal solution.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chengfeng Shen, Felix Kübler, Zhennan Zhou. 2025-11-25. Recursive contracts in non-convex environments. https://arxiv.org/abs/2511.20303

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Constraint Preferences: Inattention and Aggregation

We study robust decision problems when individuals have maxmin preferences whose belief sets are neighborhoods around reference models, commonly known as "constraint preferences." We first show that a more disciplined form of rationally inattentive behavior is equivalent to the behavior implied by a subclass of constraint preferences. We then introduce an aggregation principle that requires collective beliefs to satisfy every individual's constraint. This requirement links collective beliefs to the information-processing technologies that generate individual constraints. Applications reveal how these technologies determine asset prices, when prediction-market prices become self-confirming, and how much dynamic mechanisms can reduce information rents.

econ.TH↗

Insuring the Fallback: Capital, Monitoring, and the Certification of Preserved Human Capability under Improving AI

When generative AI makes the deliverable uninformative, a professional-services provider can still certify the preserved human capability to catch the machine's errors, through a liability pledge whose expected cost falls in that capability. The pledge is credible only up to what can be collected, and that ceiling is set by an underwriter, which bears part of the pledge and audits the insured. The range of client stakes over which one certificate separates has a width bounded by the provider's own capital plus the audited share of the underwriter's capacity, so unmonitored capacity adds nothing to it. Blind capital instead relocates that range upward, through a cross-subsidy that exists only under class rating. Monitoring converts capital into width, removes the cross-subsidy, raises the return to preserving skill, and, because audit information leaks, makes the certificate redundant beyond an interior precision.

econ.TH↗

Complementary Information Sources

A decision maker may have several information sources available and choose which one to consult only after learning the decision problem she faces. When is one such set of sources uniformly more valuable than another? For unrestricted Bayesian decision problems, we show that the answer can be stated entirely in terms of Blackwell comparisons. Form a tagged mixture by drawing a source independently of the state and revealing both its identity and its signal. One source set is more valuable in every decision problem if and only if each tagged mixture of the second source set is Blackwell dominated by some tagged mixture of the first. The result applies to compact, possibly infinite source sets and general signal spaces. It also has an exact quantitative counterpart: the largest normalized value shortfall is the directed Le Cam deficiency between the source sets' tagged hulls. The analogous program for monotone decision problems reveals a boundary. We call the passage from problem-by-problem source-set superiority to a problem-independent pairwise dominance a lifting. The Blackwell lifting does not extend directly to the Lehmann order: mixing sources that individually satisfy the monotone likelihood ratio property (MLRP) need not preserve MLRP, and even when it does, no fixed Lehmann-dominating mixture need exist. Requiring one source to serve a finite bundle of monotone decision problems restores the equivalence.

econ.TH↗