Incentive-Compatible Classification

We investigate the possibility of an incentive-compatible (IC, a.k.a. strategy-proof) mechanism for the classification of agents in a network according to their reviews of each other. In the $\alpha$-classification problem we are interested in selecting the top $\alpha$ fraction of users. We give upper bounds (impossibilities) and lower bounds (mechanisms) on the worst-case coincidence between the classification of an IC mechanism and the ideal $\alpha$-classification. We prove bounds which depend on $\alpha$ and on the maximal number of reviews given by a single agent, $\Delta$. Our results show that it is harder to find a good mechanism when $\alpha$ is smaller and $\Delta$ is larger. In particular, if $\Delta$ is unbounded, then the best mechanism is trivial (that is, it does not take into account the reviews). On the other hand, when $\Delta$ is sublinear in the number of agents, we give a simple, natural mechanism, with a coincidence ratio of $\alpha$

What is your level of overconfidence? A strictly incentive compatible measurement of absolute and relative overconfidence.

This study contributes to the ongoing discussion on the appropriate measurement of overconfidence, in particular, its strictly incentive compatible measurement in experiments. Despite a number of significant advances in recent research, several important issues remain to be solved. These relate to the strictness of incentive compatibility, the identification of well-calibrated participants, the trichotomous classification into over- or underconfident and well-calibrated participants, and the generalization to measuring beliefs about the performance relative to other people. This paper develops a measurement of overconfidence that is improved regarding all four of these issues. We theoretically prove that our method is strictly incentive compatible and robust to risk attitudes within the framework of Cumulative Prospect Theory. Furthermore, our method allows the measurement of various levels of overconfidence and the direct comparison of absolute and relative confidence. We tested our method, and the results meet our expectations, replicate recent results, and show that a population can be simultaneously overconfident, well-calibrated, and underconfident. In our specific case, we find that more than ninety-five percent of the population believe to be better than twenty-five percent; about fifty percent believe to be better than fifty percent; and only seven percent believe to be better than seventy-five percent.Belief elicitation, Overconfidence, Better than average, Incentive compatibility

On Footloose Industries, Asymmetric Information, and Wage Bargaining

WP 2003-34 October 2003JEL Classification Codes: F2; J5; D8If capital becomes internationally mobile but labor does not, is the bargaining outcome for workers worsened? In this paper we show that the answer to this question depends critically on the information structure of the bargaining process. In particular, we demonstrate a hitherto under appreciated information role of capital mobility in determining the distribution of output between workers and employers. In doing so we bring together three strands of literature that are not often seen together--incentive compatible contracting, union-employer bargaining, and the consequences of capital mobility

“Planted-shared" Contract Farming, Optimal Production Sharing Rules and Sustainable Development

This article studies the optimality properties of "planted-shared" contract farming compatible with a sustainable development issue. Using a dynamic principal-agent model with bilateral engagement and taking into account the land fertility, we show that in the absence of an agricultural credit market, the optimal remuneration of the agent depends both on his productive performance and a land fertility index. In addition, the optimal long-term contract highlights an intertemporal smoothing of the production sharing index and the land fertility index. This reflects not only the incentive of the agent to redistribute effort and compensation throughout the duration of the contract but also its intertemporal incentive to maintain the land, which is a guarantee of sustainability of agriculture. Public authorities could promote such contracts not only to meet the strong demand for securing "planted-shared" contracts, but also to promote the efficient management of natural resources. Keywords: contract farming, principal-agent model, production sharing, land fertility, sustainable development. JEL Classification: D81, D82, Q01, Q15

Mediation in Situations of Conflict and Limited Commitment

We study the reasons and conditions under which mediation is beneficial when a principal needs information from an agent to implement an action. Assuming a strong form of limited commitment, the principal may employ a mediator who gathers information and makes non-binding proposals. We show that a partial rev-elation of information is more effective through a mediator than through the agent himself. This implies that mediation is strictly helpful if and only if the likelihood of a conflict of interest is positive but not too high. The value of mediation depends non-monotonically on the degree of conflict. Our insights extend to general models of contracting with imperfect commitment

Incentive Compatible Active Learning

We consider active learning under incentive compatibility constraints. The main application of our results is to economic experiments, in which a learner seeks to infer the parameters of a subject's preferences: for example their attitudes towards risk, or their beliefs over uncertain events. By cleverly adapting the experimental design, one can save on the time spent by subjects in the laboratory, or maximize the information obtained from each subject in a given laboratory session; but the resulting adaptive design raises complications due to incentive compatibility. A subject in the lab may answer questions strategically, and not truthfully, so as to steer subsequent questions in a profitable direction. We analyze two standard economic problems: inference of preferences over risk from multiple price lists, and belief elicitation in experiments on choice over uncertainty. In the first setting, we tune a simple and fast learning algorithm to retain certain incentive compatibility properties. In the second setting, we provide an incentive compatible learning algorithm based on scoring rules with query complexity that differs from obvious methods of achieving fast learning rates only by subpolynomial factors. Thus, for these areas of application, incentive compatibility may be achieved without paying a large sample complexity price.Comment: 22 page