Theoretical Economics Papers

ALT Binary choice under risk can depend on how the alternative outcomes are jointly distributed. We show that in a finite outcome space, continuity, completeness, and betweenness impose a local-to-global restriction on this dependence: indifference between two alternatives with one common marginal distribution, however their outcomes are jointly distributed, implies correlation insensitivity for all lotteries supported on the same outcomes. In the correlation-sensitive case, these three axioms alone characterize correlation weighted utility, which combines statewise comparisons with a bilinear function of the marginal lotteries. In the correlation-insensitive case, an additional projective independence axiom yields Fishburn's (1982) skew-symmetric bilinear representation. Strong independence reduces correlation weighted utility to Lanzani's (2022) correlation expected utility representation. We extend the correlation-sensitive characterization to finitely supported lotteries on general met

ALT We study binary choice under uncertainty when the comparison between two acts can depend on the joint distribution of their realized outcomes. This motivates correlation probabilistic sophistication (CPS), a subjective reduction principle that extends both probabilistic sophistication and Fishburn's nontransitive subjective expected utility. We show how simultaneous event exchangeability elicits a unique solvable finitely additive subjective probability and reduces binary comparisons of acts to induced joint laws. Under convex range, the induced domain coincides with the full finite joint-law domain, allowing the transfer of correlation-utility representations from risk and, under completeness and Strong Independence, correlation expected utility with a skew-symmetric comparison kernel. Under completeness and transitivity, CPS collapses to ordinary probabilistic sophistication. We further extend the framework to multiple small worlds, allowing correlation sensitivity to vary with the s

ALT Bankruptcy rules are rules for dividing an insufficient estate among people with different claims. Existing rules assume that the claims are publicly known. When this is not the case, claimants have a strong incentive to claim more than they are due, as bankruptcy rules give larger allocations to claimants with larger claims. A remedy often used by legal systems is to make over-reporting costly by requiring evidence and imposing penalties. But this remedy also increases the legal expenses of proving true claims, which might result in under-reporting. Encouraging true reporting requires fine-tuning of the legal costs. This paper incorporates the costs of supporting both true and false claims into the bankruptcy model. Claimants incur costs for the true and false parts of their claims, with false claims costing at least as much per unit as true claims. The goal is to set cost rates that induce claimants to report their true entitlements, neither more nor less. Whether this is possible de

ALT We study two-stage auctions in which bidders privately know their valuation priors and learning costs, while the seller jointly designs information acquisition, continuation allocation, and payments. Revenue maximization requires screening learning costs while satisfying cross-prior incentives, potentially distorting both experimentation and continuation allocation. We derive a revenue representation involving allocated posterior values, virtual learning costs, and prior-dependent rents, and characterize cross-prior incentives through difference inequalities. We establish existence of optimal mechanisms for finite experiment menus and arbitrary prior distributions. With finite prior support, bounded finite-category linear programs approximate optimal revenue at a linear rate while preserving exact dynamic incentive compatibility and interim individual rationality. Their dual yields joint optimality conditions for experimentation, allocation, and payments. A two-prior example demonstrat

ALT We study Shapley-Scarf housing markets (Shapley and Scarf, 1974) in which houses come in types and agents are indifferent between houses of the same type but rank the types strictly. On this domain of objective indifferences, top trading cycles (TTC) rules with fixed tie-breaking regain Pareto efficiency and group strategy-proofness (Sandholtz and Tai, 2026), properties that need not hold under general weak preferences. We ask whether the characterization of the TTC rule for strict preferences by Ma (1994) and the characterization of TTC rules with fixed tie-breaking for weak preferences by Ehlers (2014) extend to this domain. They do not. With five agents and two types of houses, we construct a rule that satisfies individual rationality, Pareto efficiency, group strategy-proofness, non-bossiness, and consistency, but whose restriction to the five-agent population is not welfare equivalent to any TTC rule with fixed tie-breaking. With two types, five agents are minimal for this fixed-p

ALT People face the problem of classifying objects into categories while aggregating their opinions. We axiomatically study such a classification aggregation problem with multiple memberships, meaning that each individual assigns one category to every object, whereas the resulting classification may assign more than one category. Under the natural settings and axioms (i.e., Independence and Supportiveness), each of our new axioms – Responsibility, Split Stability and Rejection Stability – characterises what we call the junta rules. The rules assign a category to an object exactly when some member of a fixed coalition does so. The coalitions that can secure an assignment form a grill, a topological structure implied by an ultrafilter. Hence, the study parallels the literature on Arrow's theorem characterised by the decisive coalition structures. An infinite population setting is also investigated to clarify the differences among the axioms.

ALT This paper studies a market in which AI providers choose technologies and prices for assistance used to signal ability. Individual adoption is unobserved. Profitable assistance can lower welfare relative to signaling without AI, even when ability remains fully revealed. Easier imitation increases the output required to demonstrate ability, and the extra signaling can outweigh production savings. Providers can profit from improving assistance for lower-ability senders because this increases higher-ability senders' purchases. Such improvements can lower welfare relative to retaining existing tools. Competition among providers offering the same technology supports full information, but development costs can sustain monopoly provision that conceals ability. Greater verification capacity can also reduce information by changing which customers providers serve.

ALT We study how different institutions governing property rights and economic transactions shape occupational choices and welfare. Individuals in our society choose occupations that determine their production and consumption opportunities, anticipating either anonymous, voluntary market exchange supported by public enforcement (the market), or allocations through a random coercive hierarchy (the jungle) without monetary compensation. We establish conditions for occupational equilibria to exist in the market and in the jungle, and compare their performance. At a common occupational profile, the market maximizes aggregate surplus, whereas allocations in the jungle are Pareto efficient with monetary holdings fixed but need not maximize surplus. Endogenous occupational choices introduce an additional source of institutional differences. We find that a market occupational equilibrium outperforms a jungle occupational equilibrium, in aggregate terms, under two conditions: (i) every occupation i

ALT A fitted theory that predicts well may do so because of the structure it commits to or because it was fitted to the data it is evaluated on. We propose measures that separate the two by operationalising a theory as a functional form, a support restriction on behavior and a distribution over the parameters, with no parameter fitted to the evaluation sample. Probabilistic completeness is the share of the predictable variation that a rule drawn from the theory captures out of sample. Probabilistic restrictiveness is how much conceivable behavior such a rule excludes; it separates into what the functional form can reach and how the parameter distribution concentrates on it. The parameter distribution can be interpreted as uncertainty about a representative agent or as the distribution of parameters across heterogeneous agents, and the measures adapt to both interpretations through the choice of scoring rule, baseline and ceiling. A scope layer prices the domains a theory claims and compare

ALT We study a two-sided assortative matching market in which agents attract partners through costly investment and strategic risk taking. Each agent chooses an investment level and an arbitrary distribution of nonnegative quality that is a mean-preserving spread of investment. We allow for unequal populations and different investment costs across populations. Finite markets have a unique nontrivial semi-symmetric equilibrium, with investment below the Pareto frontier. We show that this inefficiency disappears as the economy grows with the population ratio held fixed and characterise its rate of convergence. The limit provides a foundation for the analysis of large matching markets, by selecting a unique efficient equilibrium from the continuum economy's infinitely many equilibria, which feature efficient investments but different risk-taking profiles.

ALT Learning a partner's preferences through joint activity can make both partners worse off than receiving no feedback from the activity. We study a responder who values the partner's payoff but bears a cost when joining is attributed to personal interest. Both partners observe noisy evidence of whether the responder wanted the activity, available only after participation. Evidence of interest raises this cost, discouraging joining on unwanted days; joining only on wanted days would then be too revealing to sustain. With choices based on current payoffs, we derive a condition under which learning can end participation without ever making stopping beneficial to either partner. Feedback accuracy and preference persistence described by couples' diaries satisfy this condition at baseline payoffs. Once activity stops, no further evidence arrives and the belief that the responder generally wants the activity stays fixed, so waiting may never restore it; preference information available without

ALT This paper studies how generative AI, by lowering the cost of research production, affects scientific verification in a general-equilibrium model of peer review. Privately informed researchers choose observable output, an editor optimally assigns review depth, and reviewers supply time according to its expected scientific contribution. Costly output helps screen research quality, and cheaper production lets weaker researchers imitate stronger ones more readily. Beyond a technological threshold, reviewer capacity and verified knowledge weakly decline even under optimal editorial decisions, and strictly when a screening constraint has positive shadow value. Reviewer withdrawal can amplify the loss. With observable quality, the same progress weakly increases both.

ALT A recommender faces consumers who can verify recommendations, but differ in their attention costs or in the salience of their choice of adoption. We characterize the conditions under which a technology that increases transparency by lowering the cost of verifying a product's quality will increase or decrease false recommendations. Without commitment, reducing verification costs never increases false recommendations, but when the recommender can commit to a policy, transparency can worsen both the quality of recommendations and consumer welfare. The necessary and sufficient condition is that the log-elasticity of the distribution of consumer cost types is decreasing. It can hold when the distribution is sufficiently fat-tailed, or when it mixes sophisticated and naive consumers. Our results can be applied to platform-recommended listings, security ratings, and audits.

ALT A principal hires an agent to work on a project of unknown quality. Success is contractible, but before it occurs, the agent may privately obtain a favorable finding. For example, a scientist developing a drug may observe a promising microscope reading. An informed agent may conceal the finding; an uninformed agent may fake one. The agent's private information could guide both the project's duration and his pay. Yet I show that an optimal contract pays only success bonuses tied to time, not reported findings, consistent with time-based payment schedules often used in research funding. Dynamically arriving private information drives this simplicity: the agent can conceal good news to retain higher bonuses, so deterring concealment limits pay differences across report histories. Bonuses are high and rising in a research stage that induces learning, then fall to a low, constant level in a development stage where only an informed agent continues working. Rising bonuses reflect a novel forc

ALT We show that a von Neumann-Morgenstern (vNM) stable set generically exists in the many-to-one job-matching model. Unlike the core, which can be empty in this model, a vNM stable set exists whenever there is a unique efficient matching: the set of all individually rational (IR) and efficient outcomes form a vNM stable set. If an economy has multiple efficient matchings, a vNM stable set may not exist. If the vNM stable sets are required to be rich in a certain sense, then such a rich vNM stable set exists if and only if the set of efficient matchings is essentially unique, and the set of all efficient and IR outcomes forms the unique rich vNM stable set.

ALT I study uniqueness of stationary expected payoffs in random-proposer coalitional bargaining with transferable utility. Proposer independence of coalition surplus does not guarantee uniqueness, even when the surplus is strictly monotone and every equilibrium features immediate agreement. For M♮-concave characteristic functions, including non-monotone ones, I show that stationary expected payoffs are unique and continuous in the primitives, provided that every equilibrium features immediate agreement. To prove uniqueness, I identify an equitable grouping property of maximizing coalitions and show that M♮-concavity implies it at every no-delay candidate payoff vector. Using equitable grouping, I show that every no-delay equilibrium payoff vector is isolated and has positive local index. For parameter changes that preserve immediate agreement in every equilibrium, a uniform increase in a player's contribution to every coalition containing her strictly increases her expected payoff, while a

ALT We study whether randomization is needed to generate a prescribed distribution of posterior means for a one-dimensional state. Drawing on backward Monge martingale transport, we show that every mean-preserving contraction of an atomless prior with finite first moment admits a deterministic implementation. Consequently, restricting attention to deterministic signals leaves the designer's value unchanged whenever objectives and constraints depend only on the posterior-mean distribution. We decompose implementation into independent problems on non-separating intervals and describe a constructive procedure based on barcode transport. We also characterize when lower or upper contour sets can take a nested-interval form, and provide applications to monopolistic pricing with buyer-optimal learning and robust persuasion with ambiguous receiver preferences.

ALT We propose a simple model of training an agent with an endogenous bias. A principal chooses in which states to train the agent and what actions to prescribe in these states. In response, the agent updates their bias and then acts optimally given their new bias in untrained states, while following their training in trained states. The agent chooses their new bias to minimize a weighted average of their loss under their initial and final biases. Training can thus generalize to untrained states via the agent's bias updating. We characterize optimal training, which involves over-correction in trained states and generalization-aware (non-greedy) selection of training states. If only a small share of states can be trained, greedy state selection is approximately optimal, but over-correction remains essential. If any bias function is possible, or if the principal evaluates training robustly over bias functions that admit the possibility of full alignment, the value of training generalization

ALT Strategy-proofness is the traditional standard for predictable behavior, yet participants often fail to play dominant strategies. Most existing simplicity concepts attribute these failures to the cost of contingent reasoning, which they lower by comparing the worst case of the dominant strategy with the best case of an alternative. I argue that there is another approach, one that makes more use of the structure of the strategy set, which I formalize as a neighbor relation. Comparing neighboring strategies can reduce the need for contingent reasoning, and, even with a single contingency, it can simplify the search through the strategy set. I introduce a new simplicity criterion: a mechanism is local improvement solvable under a neighbor relation if repeatedly moving to an improving neighbor always reaches a dominant strategy. Strategy-proofness is the criterion under the coarsest relation, and many classical mechanisms satisfy it under finer ones. In extensive-form mechanisms without du

ALT We study a multiplayer communication coordination game in which privately informed agents communicate through a social network and choose their optimal actions while balancing them against their own bliss points and those of their neighbours. We show that equilibrium actions are convex combinations of the expected bliss points of all players, with weights determined by a degree-discounted Bonacich centrality measure. We also characterize when players report truthfully, over-report, or under-report their private information, and show that the incentive to misreport depends critically on the receiver's position in the network. A striking implication is that even under identical preferences, fully revealing communication may not be realized, and information transmission may remain coarse in a star network while becoming almost fully informative in a complete network. Finally, when biases are endogenously chosen, strategic responses to neighbours' biases are governed by their centralities,