Andrei Iakovlev | Searcharxiv

arXiv2026

Adversarial Elicitation

When multiple informative equilibria are possible in a general cheap talk game, how much information can a principal guarantee herself? To answer this question, I define the notion of worst-case implementation-implementation via the worst non-trivial equilibrium of a mechanism. Under this objective, standard full-commitment mechanisms fail, yielding the principal no more than her no-communication payoff. Partial commitment, however, can provide a strict improvement. The possibility of facing a strategic, uncommitted principal disciplines the agent's reporting incentives across all equilibria. I characterize the worst-case optimal mechanism and payoff under weak assumptions on the players' preferences. The optimal mechanism has a simple two-message structure. The agent's messages are polarizing, designed to maximize their strategic impact on the uncommitted principal's actions. If full commitment is interpreted as decision automation, these results highlight a fundamental complementarity between automated and human decision-makers: the presence of a human aligns the agent's incentives to reveal information, while the automated system leverages these informative reports to take accurate actions. This strategic interaction is often overlooked by literature that compares the two based on standalone decision accuracy. Applications of the model include bail-setting automation, fintech lending, delegation, lobbying, and audit design.

econ.TH↗

arXiv2024

The Value of Context: Human versus Black Box Evaluators

Machine learning algorithms are now capable of performing evaluations previously conducted by human experts (e.g., medical diagnoses). How should we conceptualize the difference between evaluation by humans and by algorithms, and when should an individual prefer one over the other? We propose a framework to examine one key distinction between the two forms of evaluation: Machine learning algorithms are standardized, fixing a common set of covariates by which to assess all individuals, while human evaluators customize which covariates are acquired to each individual. Our framework defines and analyzes the advantage of this customization -- the value of context -- in environments with high-dimensional data. We show that unless the agent has precise knowledge about the joint distribution of covariates, the benefit of additional covariates generally outweighs the value of context.

econ.TH↗