Center for Algorithms, Data, and Market Design at Yale (CADMY)

We study efficient dynamic mechanism design with independent private values when agents do not share a common prior over the stochastic environment. Each agent privately observes the stochastic kernel governing the evolution of her own type and may hold arbitrary beliefs about the kernels of others. We extend the agents’ type space to include the kernel itself and show that the dynamic team mechanism of Athey and Segal (2013) and the dynamic pivot mechanism of Bergemann and Välimäki (2010) implement the socially efficient allocation in periodic ex-post equilibrium. We further show that kernels can be elicited only once, at the outset, and that the same mechanisms induce the efficient private acquisition of the stochastic kernels.

We analyze a multidimensional screening model in which a principal offers a menu of quality-price pairs to a consumer with multiple dimensions of private information and a quasilinear utility function. We derive necessary conditions for optimality, and use them to provide insight into optimal exclusion, positive trade, and screening. We then recast the problem in terms of incremental quality levels and prices, the so-called demand-profile approach (DPA). Under DPA, the problem decouples across increments and can be solved one at a time. We provide novel conditions under which DPA recovers the solution to the full problem exactly or approximately, and which make the necessary conditions sufficient for optimality: essentially, valuations must be sufficiently correlated across quality increments. Applied to empirical estimates of demand for health insurance, we show that DPA is approximately valid, and we apply it to understand equilibrium outcomes in a monopoly insurance market.

We analyze consumer surplus when a monopolist can adjust both prices and prod-uct qualities across segments, engaging in second- and third-degree price discrimination simultaneously. We characterize the consumer-optimal segmentation and show that it has a striking structure: consumers with the same value receive the same quality in every segment, though prices differ. Under mild conditions, any segmentation harms consumers if and only if demand is sufficiently more elastic than supply. Hence, po-tential benefits for consumers depend critically on demand and supply elasticities. These findings have implications for regulatory policy regarding price discrimination and market segmentation.

To meet voluntary climate targets, firms often complement internal decarbonization efforts by purchasing carbon credits in the voluntary carbon market (VCM), which finance projects that reduce emissions elsewhere. However, these emissions reductions are difficult to verify, and growing evidence of overcrediting has cast doubt on the VCM's potential to genuinely offset emissions. We investigate how the VCM's defining features shape its climate effectiveness. Our model captures three central elements: adverse selection, as high-quality projects that truly reduce emissions are costlier yet difficult to distinguish from low-quality ones; imperfect third-party certification, as projects are screened based on a noisy signal of quality; and buyer preferences for non-carbon attributes, as some firms value credits that generate observable social or economic co-benefits beyond reducing emissions. We show that the market fails to sustain trade if certification is sufficiently noisy, as quality uncertainty erodes buyer confidence and triggers a market-for-lemons collapse. However, demand for co-benefits can sustain markets that would otherwise collapse. Yet in such cases, the market remains active but yields limited carbon abatement, as most traded credits are low-quality. We then examine policy and market design interventions reflecting recent developments in practice, such as penalizing buyers for greenwashing and offering credit portfolios. We show that these measures can be counterproductive for carbon mitigation if certification remains inaccurate. Accordingly, we demonstrate that the certifier’s incentives for accuracy can be strengthened by modifying its fee structure so that its revenue is tied to the market value rather than the volume of credits.

How does wartime rebel governance shape post-conflict institutions? We study this in Nepal, where the Maoist People's War (1996–2006) dismantled a 240-year caste-based monarchy and ended with Maoists entering democratic politics. During the conflict, Maoists established sub-national “People’s Governments” that administered justice, collected taxes, and delivered local services. Using a spatial regression-discontinuity design, we show that exposure to People's Governments increased political knowledge and participation especially among historically marginalized indigenous groups (Janajatis). Exposure also reshaped party institutions and inter-party competition: candidate-selection committees in more exposed areas have 26 percent more Janajati members who, drawing on novel implicit-attitude data, exhibit less pro-upper caste bias. Non-Maoist parties' Janajati nomination rates nearly double in fully exposed areas, consistent with competition for newly mobilized voters. Nearly two decades on, local governments in exposed areas score 0.2–0.3 standard deviations higher on state capacity indices and receive 13% more in conditional federal grants. These findings show that when rebel groups enter competitive democratic politics, wartime governance institutions can — through citizen mobilization, party gatekeeping, and cross-party competition — enable a more inclusive and capable post-war state.

We compare how well agents aggregate information in two repeated social learning environments. In the first setting agents have access to a public data set. In the second they have access to the same data, and also to the past actions of others. Despite the fact that actions contain no additional payoff-relevant information, and despite potential herd behavior, free riding and information overload issues, observing and imitating the actions of others leads agents to take the optimal action more often in the second setting. We also investigate the effect of group size, as well as a setting in which agents observe private data and others’ actions.