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We prove—in the standard independent private-values model—that the outcome, in terms of interim expected probabilities of trade and interim expected transfers, of any Bayesian mechanism can also be obtained with a dominant-strategy mechanism.

We consider a standard social choice environment with linear utilities and independent, one-dimensional, private types. We prove that for any Bayesian incentive compatible mechanism there exists an equivalent dominant strategy incentive compatible mechanism that delivers the same interim expected utilities for all agents and the same ex ante expected social surplus. The short proof is based on an extension of an elegant result due to Gutmann, Kemperman, Reeds, and Shepp (1991). We also show that the equivalence between Bayesian and dominant strategy implementation generally breaks down when the main assumptions underlying the social choice model are relaxed or when the equivalence concept is strengthened to apply to interim expected allocations.

We explore mechanism design with outcome-based social preferences. Agents' social preferences and private payoffs are all subject to asymmetric information. We assume quasi-linear utility and independent types. We show how the asymmetry of information about agents' social preferences can be operationalized to satisfy agents' participation constraints. Our main result is a possibility result for groups of \\textit{at least three} agents: Any such group can resolve any given allocation problem with an ex-post budget-balanced mechanism that is Bayesian incentive-compatible, interim individually rational, and ex-post Pareto-efficient.

We extend the equivalence between Bayesian and dominant strategy implementation (Manelli and Vincent in Econometrica 78:1905-1938, 2010; Gershkov et al. in Econometrica 81: 197-220, 2013) to environments with nonlinear utilities satisfying a property of increasing differences over distributions and a convex-valued assumption. The new equivalence result produces novel implications to the literature on the principal-agent problem with allocative externalities, environmental mechanism design, and public good provision.

Consider a mechanism design setting in which agents acquire costly information about an unknown, payoff-relevant state of nature before participating in the mechanism. Information gathering is covert. We investigate conditions under which (i) efficient implementation and (ii) full surplus extraction are Bayesian incentive compatible and interim individually rational.

The incomplete contracts literature often cites indescribable contingencies as a major obstacle to the creation of complete contracts. Using agents’ minimum foresight concerning possible future payoffs , Maskin and Tirole (Rev Econ Stud 66:83–114, 1999) show that indescribability does not matter for contractual incompleteness as long as there is symmetric information at both the contracting stage and the trading stage. This is called the irrelevance theorem . The following generalization of the irrelevance theorem is shown here: indescribability does not matter even in the presence of asymmetric information at the trading stage, as long as there is symmetric information at the contracting stage. This is an important clarification because Kunimoto (Econ Lett 99:367–370, 2008) shows that indescribability can matter if there is asymmetric information at both stages. It is thus argued that asymmetric information at the contracting stage is necessary for indescribability to be important in the rational agents contracting model.

Nowadays many employers offer their employees the possibility of an insurance against too large losses in income when retiring or becoming disabled. This paper models the optimization problem of the employer when setting up such a so-called pension fund. Not surprisingly, it turns out that the optimal solution depends on the premium the employees are willing to pay at most for such an insurance. Since this is private information for an employee and hence not known to the employer, he needs to collect information regarding these maximum premiums. It is shown that if employees' characteristics only differ in the maximum premium they are willing to pay, the employer is unable to perfectly inform himself on these maximum premiums, i.e. he cannot create the right incentives for his employees to reveal their maximum premiums truthfully.

Technology implementation at the individual level within an organization, after the organization has adopted the technology, has been an ongoing challenge in every field. In this study, we develop a hierarchical Bayesian learning model to examine the impact of social learning, through both targeted early adopter effects and general peer effects, and experiential learning on the information technology implementation process by physicians in a community health system. Our unique data allow us to disentangle the most common and challenging endogeneity issues associated with most social influence studies. We find that the experiential learning signal is more accurate than the social learning signals in the technology implementation process; and, between the two types of social learning signals studied here, targeted early adopter effects are much more informative than general peer effects. Furthermore, we experiment with several policy simulations to illustrate and quantify the two different types of social influence on this implementation process. The simulation results suggest that maintaining consistency in technology usage by targeted early adopters is more effective than increasing the frequency of their technology usage in reducing their colleagues’ perceptions of uncertainty about the new technology. More specifically, we find that technology implementation probability would increase: (a) by 15%, on average, by adding a targeted early adopter to a group without early adopters; (b) by 25% by adding peer effects to solo users; and (c) by 47% by adding early adopter effects to solo users. The model can be adapted and generalized to other similar settings that examine social influence on the technology implementation process and also provide quantifiable measures of the improvements that the interventions may produce.

Aldrich-McKelvey scaling is a powerful method that corrects for differential-item functioning (DIF) in estimating the positions of political stimuli (e.g., parties and candidates) and survey respondents along a latent policy dimension from issue scale data. DIF arises when respondents interpret issue scales (e.g., the standard liberal-conservative scale) differently and distort their placements of the stimuli and themselves. We develop a Bayesian implementation of the classical maximum likelihood Aldrich-McKelvey scaling method that overcomes some important shortcomings in the classical procedure. We then apply this method to study citizens' ideological preferences and perceptions using data from the 2004–2012 American National Election Studies and the 2010 Cooperative Congressional Election Study. Our findings indicate that DIF biases self-placements on the liberal-conservative scale in a way that understates the extent of polarization in the contemporary American electorate and that citizens have remarkably accurate perceptions of the ideological positions of senators and Senate candidates.

A Bayesian non-parametric methodology has been recently proposed to deal with the issue of prediction within species sampling problems. Such problems concern the evaluation, conditional on a sample of size n, of the species variety featured by an additional sample of size m. Genomic applications pose the additional challenge of having to deal with large values of both n and m. In such a case the computation of the Bayesian non-parametric estimators is cumbersome and prevents their implementation. We focus on the two-parameter Poisson-Dirichlet model and provide completely explicit expressions for the corresponding estimators, which can be easily evaluated for any sizes of n and m. We also study the asymptotic behaviour of the number of new species conditionally on the observed sample: such an asymptotic result, combined with a suitable simulation scheme, allows us to derive asymptotic highest posterior density intervals for the estimates of interest. Finally, we illustrate the implementation of the proposed methodology by the analysis of five expressed sequence tags data sets.