ALT Starting with Waldman (1984), it has become widely understood that an important role of promotions in labor markets is that promotions serve as signals of high worker ability. In this paper we explore the common and growing practice called job title inflation which serves a signal jamming role that reduces the signaling value of promotions. We first explore a two-period model and show how job title inflation can arise in equilibrium and increase social welfare both because workers directly value impressive sounding job titles and because the practice reduces job assignment inefficiencies due to signaling. We then introduce into the model an investment choice for young workers concerning human capital accumulation and show that job title inflation can decrease rather than increase welfare in that case. That is, when promotions and the associated wage increases serve as an important incentive for investing in human capital, the use of job title inflation can persist even though the pract

ALT When is social learning efficient if agents have heterogeneous preferences over a set of actions? When myopic agents generate information only about the action they choose, efficiency requires balance such that all high-value actions are discovered. We characterize the value of policies that control the flow of past information to future agents. Under full transparency of past information, the probability of efficient outcomes is approximately one if and only if the prior and preferences are balanced and the log-likelihood ratio distribution satisfies a set of intuitive moment conditions. Noisy bad news is a canonical example. By contrast, approximate efficiency is obtainable on the set of arbitrary adaptive information policies if and only if the prior alone is balanced. Moreover, it suffices to use adaptive evidence policies which permanently and fully disclose subsets of past information. When the prior is imbalanced and approximate efficiency is unattainable, we characterize the va

ALT We analyze auctions with interdependent values and horizontally differentiated bidders, where good news for one bidder is bad news for the other bidders. This generates a winner's bliss, reversing the well-known winner's curse: winning means buying an object that is more valuable than a naïve estimate suggests. Our main result is about the English auction. We introduce the “own effect property,” generalizing the single-crossing condition, to prove the existence of an efficient ex-post equilibrium. Regarding revenue, we show that with affiliated signals, the English auction yields no more revenue than the second-price auction, while independent signals yield the standard revenue equivalence.

ALT We study steady-state equilibria of a sequential learning game in which each agent chooses between a safe action and a risky action of unknown profitability. Before choosing, each agent observes how many of the last m agents adopted the risky action, but not whether they were informed or what their adoption costs were. We show that uninformed agents' imitation decisions can be strategic complements: the more likely uninformed agents are to join risky-action herds, the more informative those herds become about the action's profitability. When this complementarity gives rise to multiple equilibria, the one that maximizes the probability of imitation by uninformed agents also maximizes agents' welfare.

ALT We explore the implications of the principle of monotonicity in the problem of sharing the revenues raised from subscriptions to music streaming platforms among content providers. We formalize various forms of this principle as several axioms for popularity indices, which generate sharing rules in this setting. We show that, combined with some other basic axioms (such as additivity, symmetry, or pairwise homogeneity), they provide axiomatic characterizations of focal indices for this problem, as well as more general families comprising them.

ALT I study the coarse revenue guarantee (CRG) of the single-unit first-price auction with common values. The CRG is the minimum expected revenue over all information structures and all Bayes coarse correlated equilibria (BCCE), the equilibrium notion to which no-regret learning leads. Identical play, in which all bidders submit the same bid, attains the worst-case revenue, and this reduction yields a closed-form characterization. The coarse guarantee is positive and increases with the number of bidders N. It lies strictly below the revenue guarantee of Bergemann, Brooks and Morris (2017) at every finite n, and the gap between the two closes at rate log N / N. I extend the analysis to reserve prices, symmetric and asymmetric priors, other standard auctions, and an unknown value distribution.

ALT A principal designs ratings for multiple career-concerned agents with correlated talents, mapping outputs into arbitrary finite-variance statistics. Peer output both benchmarks effort and signals talent, making full disclosure suboptimal under confidential ratings. Relaxing Bayes plausibility to linear orthogonality yields a closed-form rating and computable effort bounds for general priors; under normality, the relaxation is exact. Optimal confidential ratings combine the full-disclosure wage with the Fisher score for effort: they are relative-performance when individual output is informative about talent and team-based when it is noisy. Public ratings must satisfy joint Bayes plausibility, weakening incentives; hence, the principal prefers confidential ratings, whereas agents prefer public ratings. A spectral decomposition characterizes public ratings under normality: small cost heterogeneity implies full disclosure regardless of the noise structure, whereas large cost heterogeneity

ALT We study equilibrium sorting in the canonical search-and-matching model with Nash bargaining and ex-ante and ex-post heterogeneity. Under standard conditions for assortative matching under search, match surplus retains two properties of Becker's frictionless benchmark—single-crossing and single-peakedness—but necessarily loses the third, reciprocity. Instead, search generates consensus: intervals of types share the same surplus-maximizing partner. For such intervals, mismatched pairs are more likely to match, conditional on meeting, than perfectly assortative ones. Two forces drive this: with search, outside options rise less than one-for-one with productivity and fall more for agents with fewer suitable partners. Globally, upper contour sets of the match-probability surface are lattices below an endogenous threshold but almost never above it. This means that MLRP fails for some log-concave survival function.

ALT We study two-sided many-to-many matching markets where agents' preferences satisfy strong substitutability and the law of aggregate demand (LAD). In this framework, the set of stable matchings forms a finite distributive lattice, and quantile stable matchings exist and satisfy the polarization of interests. Building on the cycle-reduction machinery of Bonifacio et al. (2022), we provide a purely combinatorial characterization of quantile stable matchings formulated in terms of preference cycles and their induced precedence order. Specifically, we define an order-theoretic counting function on cycles and prove that each quantile stable matching corresponds to the elimination of an endogenously determined closed set of cycles starting from the firm-optimal stable matching. This characterization extends the rotation-based framework of Cheng (2008) from one-to-one markets to many-to-many markets and bypasses the need for exhaustive outcome enumeration or partner sorting across the entire s

ALT This paper studies decision functionals under ambiguity when pure-risk evaluation is determined by a general, possibly non-affine statistic. We represent ambiguity perception by a Bayesian second-order model over first-order models and encode ambiguity attitude with a separate parameter. We formulate four consistency and monotonicity properties for decision functionals combining these objects. We show that second-order averaging does not generally preserve these properties for non-affine statistics, including expected shortfall, the industry-standard risk measure. To address this limitation, we introduce an ambiguity iron, a mapping from a second-order model and an ambiguity attitude to an ambiguity set of first-order models. Worst-case evaluation over this set preserves the chosen statistic as the pure-risk criterion. We construct an ambiguity iron by distorting the second-order model and show that it produces convex and compact sets of predictive models. We derive formulas for severa

ALT In this paper, we study models of restricted heterogeneity. While the random utility model allows for arbitrary heterogeneity among rational types, we consider the case when an analyst ex-ante restricts the set of rational types to some smaller set of allowable types. We study the behavioral content, the model fit, and the identification properties of restrictions of this form. We find that all three of these can be connected through a common value function that is constructed from observable data. Consistency with a model of restricted heterogeneity is characterized by this value function achieving a value of one, while model fit is given by a value that is potentially less than one. The partially identified set for a given model of restricted heterogeneity is characterized by way of the subdifferential of this value function.

ALT We study the route-set allocation problem between end nodes in capacity-constrained flow networks with transfer costs. We introduce an origin–destination (OD) bidding language, in which a participant bids for bounded conic combinations of inlet–outlet–quantity licences and the mechanism performs the routing through a network-flow optimising winner-determination problem. We then study payment rules for the OD mechanism. Under a restricted model with openly and uniformly priced sources and bidders who are indifferent to the source of the gas, the Vickrey–Clarke–Groves (VCG) payment is core-stable; the key structural fact is that in this regime some optimal aggregate flows never use a physical pipe in both directions, so the winner-determination problem reduces to a single-commodity network flow whose value is submodular in the set of active bidders. If source access is allowed to depend on the individual consumer – a realistic feature, e.g. when gas deals are embedded in bilateral politi

ALT Public debt supplies liquidity and collateral services only if institutions coordinate on using the same claim. We study this coordination when use carries a recurring cost and a benefit that rises with other users. Above a quality threshold, two positive-adoption equilibria exist, and under best responses the initial user base decides between collapse and high adoption. Under logit choice, as the population grows and noise vanishes, the long-run distribution selects high adoption only above a strictly higher threshold. Debt issuance improves market depth but eventually dilutes backing per claim. This trade-off creates a range of debt levels supporting the liquidity premium of public debt in macroeconomic models and a narrower range supporting its long-run selection. At the boundary of the wider range, the premium is discontinuous. A matching transfer that corrects the adoption externality makes stationary selection maximize network surplus. Transfers can also help launch a network wit

ALT Many firms that run A/B tests struggle with low statistical power, increasing the risk that launch decisions are driven by noise rather than true impact. We argue that in decentralized organizations, low power can reflect misaligned incentives: product teams choose what and how to test on a customer-facing experience to maximize their private reward, while the experience owner sets evaluation rules and launch criteria to optimize the experience. We formalize this interaction via a principal-agent model and show that evaluating tests using raw impact estimates creates a reward to sampling noise. Correctly calibrated posterior-mean evaluation removes this reward and creates a local incentive to reallocate toward fewer, more precise tests of more ambitious ideas. Using data on A/B tests run at Amazon, we document patterns consistent with these model predictions. After an Amazon experience shifted to posterior-mean-based evaluation in 2024, test volume declined while both precision and the

ALT We study how retained ownership supports the value of an asset that pays no dividends and is held only for resale. A sponsor chooses hidden maintenance that determines market survival, bears its full cost, and captures only the benefit accruing to its retained tokens. A positive stationary equilibrium exists if and only if retention meets an endogenous minimum. Under additional curvature restrictions, retention above this threshold supports two positive stationary equilibria. Conditional on survival, prices below the upper equilibrium converge to the lower one while the upper price is the largest feasible initial valuation. Greater retention expands the feasible price interval. Credible news that future retention will be below the minimum causes an immediate collapse through backward unraveling. The model identifies a governance channel through which insider unlocks can affect valuation without changing total token supply or fundamental cash flows.

ALT Relying on manipulable characteristics to target the allocation of resources is fraught with difficulties. Naively optimal rules lead to falsification and allocative inefficiency. Optimizing the rule for allocative efficiency while accounting for falsification typically induces falsification, at a heavy cost to the agents. Moreover, falsification generates negative externalities. We characterize optimal falsification-proof rules under a broad range of falsification technologies. We also provide an honesty condition under which they solve the unconstrained problem of a planner who maximizes a weighted sum of allocative surplus and agents' welfare.

ALT A seller without commitment to future offers interacts with a buyer over time. There are many goods for sale, with the buyer's value for a bundle the sum of each bundled good's value. Each date, the seller jointly announces a menu of subsets of the remaining inventory and current prices, and the buyer may purchase one of the offered subsets or wait. In the next period, the seller may again offer any menu of the remaining goods. Neither buyer nor seller can commit to future behavior. I show that as players become maximally patient, if scope increases sufficiently fast compared to patience, revenue per good converges to expected value per good and buyer utility per good converges to zero, in any equilibrium. But if patience instead grows sufficiently fast compared to scope, seller revenue per good converges to the lowest possible value and the buyer captures all remaining surplus, in any equilibrium.

ALT Artificial intelligence (AI) can synthesize dispersed information into balanced and informative content, raising the possibility that AI may help reduce polarization. We show that this promise can be undermined by the strategic response of content producers. We develop a game-theoretic model in which consumers acquire information to make downstream decisions, content producers allocate limited informational capacity between broadly valuable vertical information and horizontally differentiated information, and AI provides an alternative source of synthesized information. The effects of AI unfold in three phases as its reliability increases. When reliability is low, AI has no effect on the market. At intermediate levels, AI disciplines prices without attracting consumers or changing content. When AI becomes sufficiently reliable, consumers begin to substitute toward it, and content producers respond by shifting informational capacity away from the vertical dimension and toward horizontal

ALT We study a continuum version of the stable marriage problem, in which each agent has a variable mass to be paired to the opposite side. An agent's mass is interpretable as the divisible time of a single atomistic agent, or as a mass of infinitesimal agents of a particular type. Our object of interest is the mapping between the vector of masses to the set of stable matches. As in the canonical stable marriage model, the set of stable matches can be multi-valued, so this mapping is a correspondence. This correspondence is continuous if and only if the stable match is unique, implying that small changes in the masses may cause discontinuous jumps in the set of stable matches.

ALT A principal funds an agent's project over two periods, with a midterm review providing a public signal about project quality. The agent privately knows the project's quality and always prefers more funding, while the principal wants to match funding to quality. A natural benchmark uses the review only after it arrives, to update beliefs and improve the second-period allocation. The optimal mechanism instead frontloads the review's information value: it also uses the review as an incentive device to elicit truthful reporting up front, thereby strictly improving the principal's first-period expected payoff. In the optimal mechanism, higher-quality projects receive more funding initially but face larger cuts after a bad review, leaving them with less funding than lower-quality projects. A good review increases funding, but the resulting allocation need not preserve the initial ranking of projects and eventually declines with quality.