On the Generic (Im)Possibility of Full Surplus Extraction in Mechanism Design

A number of studies, most notably Crémer and McLean (1985, 1988), have shown that in generic type spaces that admit a common prior and are of a fixed finite size, an uninformed seller can design mechanisms that extract all the surplus from privately informed bidders. We show that this result hinges on the nonconvexity of such a family of priors. When the ambient family of priors is convex, generic priors do not allow for full surplus extraction provided that for at least one prior in this family, players' beliefs about other players' types do not pin down the players' own preferences. In particular, full surplus extraction is generically impossible in finite type spaces with a common prior. Similarly, generic priors on the universal type space do not allow for full surplus extraction.     

Genericity and Robustness of Full Surplus Extraction

We study whether priors that admit full surplus extraction (FSE) are generic, an issue that becomes a gauge to evaluate the validity of the current mechanism design paradigm. We consider the space of priors on the universal type space, and thereby relax the assumption of a fixed finite number of types made by Crémer and McLean (1988). We show that FSE priors are topologically generic, contrary to the result of Heifetz and Neeman (2006) that FSE is generically impossible, both geometrically and measure‐theoretically. Instead of using the BDP approach or convex combinations of priors adopted in Heifetz and Neeman (2006), we prove our genericity results by showing a robustness property of Crémer–McLean mechanisms.     

Ordinally Bayesian Incentive Compatible Voting Rules

We study strategic voting after weakening the notion of strategy‐proofness to Ordinal Bayesian Incentive Compatibility (OBIC). Under OBIC, truth‐telling is required to maximize the expected utility of every voter, expected utility being computed with respect to the voter's prior beliefs and under the assumption that everybody else is also telling the truth. We show that for a special type of priors, i.e., the uniform priors, there exists a large class of social choice functions that are OBIC. However, for priors that are generic in the set of independent beliefs, a social choice function is OBIC only if it is dictatorial. This result underlines the robustness of the Gibbard–Satterthwaite Theorem.     

Cooperation in Repeated Games When the Number of Stages is not Commonly Known

It is shown that an exponentially small departure from the common knowledge assumption on the number T of repetitions of the prisoners' dilemma already enables cooperation. More generally, with such a departure, any feasible individually rational outcome of any one-shot game can be approximated by a subgame perfect equilibrium of a finitely repeated version of that game. The sense in which the departure from common knowledge is small is as follows: (I) With probability one, the players know T with precision ±K. (ii) With probability 1 −ε, the players know T precisely; moreover, this knowledge is mutual of order εT. (iii) The deviation of T from its finite expectation is exponentially small.     

Common Learning

Consider two agents who learn the value of an unknown parameter by observing a sequence of private signals. The signals are independent and identically distributed across time but not necessarily across agents. We show that when each agent's signal space is finite, the agents will commonly learn the value of the parameter, that is, that the true value of the parameter will become approximate common knowledge. The essential step in this argument is to express the expectation of one agent's signals, conditional on those of the other agent, in terms of a Markov chain. This allows us to invoke a contraction mapping principle ensuring that if one agent's signals are close to those expected under a particular value of the parameter, then that agent expects the other agent's signals to be even closer to those expected under the parameter value. In contrast, if the agents' observations come from a countably infinite signal space, then this contraction mapping property fails. We show by example that common learning can fail in this case.     

RNA multiple structural alignment with longest common subsequences

In this paper, we present a new model for RNA multiple sequence structural alignment based on the longest common subsequence. We consider both the off-line and on-line cases. For the off-line case, i.e., when the longest common subsequence is given as a linear graph with n vertices, we first present a polynomial O(n ²) time algorithm to compute its maximum nested loop. We then consider a slightly different problem—the Maximum Loop Chain problem and present an algorithm which runs in O(n ⁵) time. For the on-line case, i.e., given m RNA sequences of lengths n, compute the longest common subsequence of them such that this subsequence either induces a maximum nested loop or the maximum number of matches, we present efficient algorithms using dynamic programming when m is small. This research is partially supported by EPSCOR Visiting Scholar's Program and MSU Short-term Professional Development Program.     

Bayesian Learning,Smooth Approximate Optimal Behavior,and Convergence to ε‐Nash Equilibrium

In this paper, I construct players' prior beliefs and show that these prior beliefs lead the players to learn to play an approximate Nash equilibrium uniformly in any infinitely repeated slightly perturbed game with discounting and perfect monitoring. That is, given any ε > 0, there exists a (single) profile of players' prior beliefs that leads play to almost surely converge to an ε‐Nash equilibrium uniformly for any (finite normal form) stage game with slight payoff perturbation and any discount factor less than 1.     

Sign Restrictions,Structural Vector Autoregressions,and Useful Prior Information

This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just‐identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n‐variable VAR is confined to the set of values that orthogonalize the population variance–covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse‐response functions that are implicit in the traditional sign‐restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just‐identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.     

Robust Predictions for Bilateral Contracting with Externalities

The paper studies bilateral contracting between one principal and N agents when each agent's utility depends on the principal's unobservable contracts with other agents. We show that allowing deviations to menu contracts from which the principal chooses bounds equilibrium outcomes in a wide class of bilateral contracting games without imposing ad hoc restrictions on the agents' beliefs. This bound yields, for example, competitive convergence as N →∞ in environments in which an appropriately‐defined notion of competitive equilibrium exists. We also examine the additional restrictions arising in two common bilateral contracting games: the “offer game” in which the principal makes simultaneous offers to the agents, and the “bidding game” in which the agents make simultaneous offers to the principal.     

MARKET POWER AND INFORMATION REVELATION IN DYNAMIC TRADING

We study a strategic model of dynamic trading where agents are asymmetrically informed over common value sources of uncertainty. There is a continuum of buyers and a finite number n of sellers. All buyers are uninformed, while at least one seller is privately informed about the true state of the world. When n = 1, full information revelation never occurs in equilibrium and the only information transmission happens in the first period. With n > 1 the outcome depends both on the structure of the sellers' information and, even more importantly, on the intensity of competition allowed by the existing trading rules. When there is intense competition (absence of clienteles), information is fully and immediately revealed to the buyers in every equilibrium for n large enough, regardless of the number of informed sellers. On the other hand, for trading arrangements characterized by less intense forms of competition (presence of clienteles), for any n we always have equilibria where information is never fully revealed. Moreover, in that case, when only one seller is informed, for many parameter configurations there are no equilibria with full information revelation, even for large n. (JEL: C72, C78, D82, D83)     

Identifying Promising Messages to Increase Hurricane Mitigation Among Coastal Homeowners in the United States

Hurricanes threaten the physical and financial well-being of coastal residents throughout the United States. Though hurricane-related losses are largely avoidable through property mitigation (e.g., structural modifications to existing homes), few homeowners invest in mitigation. Communication campaigns, which have influenced risk-related behaviors in other domains, hold promise for persuading coastal residents to engage in hurricane mitigation. The development of successful campaign messages relies, in part, on formative research to assess the potential influence of candidate message strategies. We present results from mixed-methods, theory-driven research to identify promising beliefs for persuading homeowners in coastal/coastal-adjacent regions of Alabama and Florida to install a high wind–resistant (HWR) roof. In Study 1, we elicited homeowners’ (n = 74) salient behavioral, normative, and control beliefs about installing an HWR roof. Using established procedures, we content analyzed open-ended responses and categorized them by thematic content. In Study 2, we surveyed another sample of homeowners (n = 533) to examine the extent to which salient beliefs/themes about installing an HWR roof (elicited in Study 1) are promising targets for a communication campaign, given their associations with homeowners’ intentions to retrofit. Results demonstrate that across elicited beliefs, common themes include the protection and property resilience reroofing affords, and anticipated expenses and financial barriers associated with reroofing. The most promising beliefs include behavioral beliefs that installing an HWR roof will protect oneself and one's family, and normative beliefs about the likelihood that one's family and community will install an HWR roof. We discuss the implications of findings for the development of hurricane mitigation messaging.     

Unit training to increase support for military personnel with mental health problems

The present study examined the effectiveness of training designed to increase support toward military personnel with mental health problems. Soldiers from two Battalions (N?=?349) were randomly assigned by squad to receive the training (n?=?179) or to a survey-only control group (n?=?170). Soldiers completed survey assessments at baseline and three months later. Soldiers also completed an implicit association test assessing attitudes toward mental health treatment at the three-month follow-up. Results revealed that soldiers in the training condition reported an increase in supportive behaviours toward soldiers with mental health problems three months following the training, whereas there was no increase in soldiers assigned to the control group. Soldiers in the training condition were also marginally more likely to increase their own treatment seeking in the three months following the training. Discussion focuses on the importance of unit member support for military personnel with mental health problems and the implications for employees in other occupations characterised by the expectation for resilience.     

On the number of separable partitions

Consider partitions of a given set A of n distinct points in general position in ℝ ^d into parts where each pair of parts can be separated by a hyperplane that contains a given set of points E. We consider the problem of counting and generating all such partitions (correcting a classic 1967 result of Harding about the number of such partitions into two parts). Applications of the result to partition problems are presented.     

Climate Change Beliefs and Perceptions of Weather‐Related Changes in the United Kingdom

Public perception research in different countries has suggested that real and perceived periods of high temperature strengthen people's climate change beliefs. Such findings raise questions about the climate change beliefs of people in regions with moderate climates. Relatively little is known about whether public concerns about climate change may also be associated with perceived changes in other weather‐related events, such as precipitation or flooding. We examine the relationship between perceived changes in weather‐related events and climate change beliefs among U.K. residents at a time of below‐average winter temperatures and recent flooding. National survey data (n = 1,848) revealed that heat waves and hot summers were perceived to have become less common during respondents’ lifetimes, while flooding, periods of heavy rainfall, coastal erosions, and mild winters were perceived to have increased in frequency and cold winters were perceived to be unchanged. Although perceived changes in hot‐weather‐related events were positively associated with climate change beliefs, perceived changes in wet‐weather‐related events were found to be an even stronger predictor. Self‐reported experience of “flooding in own area” and “heat‐wave discomfort” also significantly contributed to climate change beliefs. These findings highlight the importance of salient weather‐related events and experiences in the formation of beliefs about climate change. We link our findings to research in judgment and decision making, and propose that those wishing to engage with the public on the issue of climate change should not limit their focus to heat.     

A MODEL OF NONBELIEF IN THE LAW OF LARGE NUMBERS

People believe that, even in very large samples, proportions of binary signals might depart significantly from the population mean. We model this “nonbelief in the Law of Large Numbers” by assuming that a person believes that proportions in any given sample might be determined by a rate different than the true rate. In prediction, a nonbeliever expects the distribution of signals will have fat tails. In inference, a nonbeliever remains uncertain and influenced by priors even after observing an arbitrarily large sample. We explore implications for beliefs and behavior in a variety of economic settings.     

Testing a Parametric Model Against a Nonparametric Alternative with Identification Through Instrumental Variables

This paper is concerned with inference about a function g that is identified by a conditional moment restriction involving instrumental variables. The paper presents a test of the hypothesis that g belongs to a finite‐dimensional parametric family against a nonparametric alternative. The test does not require nonparametric estimation of g and is not subject to the ill‐posed inverse problem of nonparametric instrumental variables estimation. Under mild conditions, the test is consistent against any alternative model. In large samples, its power is arbitrarily close to 1 uniformly over a class of alternatives whose distance from the null hypothesis is O(n^−1/2), where n is the sample size. In Monte Carlo simulations, the finite‐sample power of the new test exceeds that of existing tests.     

Efficacy,Action, and Support for Reducing Climate Change Risks

A growing body of research demonstrates that believing action to reduce the risks of climate change is both possible (self‐efficacy) and effective (response efficacy) is essential to motivate and sustain risk mitigation efforts. Despite this potentially critical role of efficacy beliefs, measures and their use vary wildly in climate change risk perception and communication research, making it hard to compare and learn from efficacy studies. To address this problem and advance our understanding of efficacy beliefs, this article makes three contributions. First, we present a theoretically motivated approach to measuring climate change mitigation efficacy, in light of diverse proposed, perceived, and previously researched strategies. Second, we test this in two national survey samples (Amazon's Mechanical Turk N = 405, GfK Knowledge Panel N = 1,820), demonstrating largely coherent beliefs by level of action and discrimination between types of efficacy. Four additive efficacy scales emerge: personal self‐efficacy, personal response efficacy, government and collective self‐efficacy, and government and collective response efficacy. Third, we employ the resulting efficacy scales in mediation models to test how well efficacy beliefs predict climate change policy support, controlling for specific knowledge, risk perceptions, and ideology, and allowing for mediation by concern. Concern fully mediates the relatively strong effects of perceived risk on policy support, but only partly mediates efficacy beliefs. Stronger government and collective response efficacy beliefs and personal self‐efficacy beliefs are both directly and indirectly associated with greater support for reducing the risks of climate change, even after controlling for ideology and causal beliefs about climate change.     

Inferential Theory for Factor Models of Large Dimensions

This paper develops an inferential theory for factor models of large dimensions. The principal components estimator is considered because it is easy to compute and is asymptotically equivalent to the maximum likelihood estimator (if normality is assumed). We derive the rate of convergence and the limiting distributions of the estimated factors, factor loadings, and common components. The theory is developed within the framework of large cross sections (N) and a large time dimension (T), to which classical factor analysis does not apply. We show that the estimated common components are asymptotically normal with a convergence rate equal to the minimum of the square roots of N and T. The estimated factors and their loadings are generally normal, although not always so. The convergence rate of the estimated factors and factor loadings can be faster than that of the estimated common components. These results are obtained under general conditions that allow for correlations and heteroskedasticities in both dimensions. Stronger results are obtained when the idiosyncratic errors are serially uncorrelated and homoskedastic. A necessary and sufficient condition for consistency is derived for large N but fixed T.     

Utilizing job resources: Qualitative evidence of the roles of job control and social support in problem solving

Abstract

In resource-based models of job design, job resources, such as control and social support, are thought to help workers to solve problems. Few studies have examined this assumption. We analyzed 80 qualitative diary entries (N=29) and interviews (N=37) concerned with the in-role requirements of medical technology designers in the UK for problem solving. Four themes linked to using the resources of job control and social support for problem solving emerged. These were: (1) eliciting social support to solve problems; (2) exercising job control to solve problems; (3) co-dependence between eliciting social support and exercising job control to solve problems; and (4) using job resources to regulate affect. The results were largely supportive of the assumptions underpinning resource-based models of job design. They also indicated that the explanatory power of resource-based models of job design may be enhanced by considering interdependencies between various factors: how different job resources are used, workers' motivation to use resources, workers' knowledge of how to use resources and the use of resources from across organizational boundaries. The study provides qualitative support for the assumption that social support and job control are used to cope with demands.     

Confidence Intervals for Diffusion Index Forecasts and Inference for Factor‐Augmented Regressions

We consider the situation when there is a large number of series, N, each with T observations, and each series has some predictive ability for some variable of interest. A methodology of growing interest is first to estimate common factors from the panel of data by the method of principal components and then to augment an otherwise standard regression with the estimated factors. In this paper, we show that the least squares estimates obtained from these factor‐augmented regressions are consistent and asymptotically normal if . The conditional mean predicted by the estimated factors is consistent and asymptotically normal. Except when T/N goes to zero, inference should take into account the effect of “estimated regressors” on the estimated conditional mean. We present analytical formulas for prediction intervals that are valid regardless of the magnitude of N/T and that can also be used when the factors are nonstationary.