Deterministic versus Stochastic Mechanisms in Principal–Agent Models

This paper shows that, contrary to what is generally believed, decreasing concavity of the agent’s utility function with respect to the screening variable is not sufficient to ensure that stochastic mechanisms are suboptimal. The paper demonstrates, however, that they are suboptimal whenever the optimal deterministic mechanism exhibits no bunching. This is the case for most applications of the theory and therefore validates the literature’s usual focus on deterministic mechanisms

Deterministic versus Stochastic Mechanisms in Principal--Agent Models

This paper shows that, contrary to what is generally believed, decreasing concavity of the agent's utility function with respect to the screening variable is not sufficient to ensure that stochastic mechanisms are suboptimal. The paper demonstrates, however, that they are suboptimal whenever the optimal deterministic mechanism exhibits no bunching. This is the case for most applications of the theory and therefore validates the literature's usual focus on deterministic mechanisms.principal-agent theory, mechanism design, deterministic mechanisms, randomization.

Deterministic versus Stochastic Mechanisms in Principal–Agent Models

This paper shows that, contrary to what is generally believed, decreasing concavity of the agent’s utility function with respect to the screening variable is not sufficient to ensure that stochastic mechanisms are suboptimal. The paper demonstrates, however, that they are suboptimal whenever the optimal deterministic mechanism exhibits no bunching. This is the case for most applications of the theory and therefore validates the literature’s usual focus on deterministic mechanisms.principal-agent theory; mechanism design; deterministic mechanisms; randomization; bunching.

Randomization in contracts with endogenous information

I consider a situation, where the agent can acquire payoff-relevant information either before or after the contract is signed. To raise efficiency, the principal might solicit information; to retain all surplus, however, she must prevent precontractual information gathering. The following class of stochastic contracts may solve this trade-off optimally: before signing, information acquisition is not solicited, and afterwards randomly. The key insight is that randomization makes precontractual information costlier for the agent.Information acquisition, Principal-agent, Mechanism design, Randomization

Dynamic Mechanism Design: Incentive Compatibility, Profit Maximization and Information Disclosure

This paper examines the problem of how to design incentive-compatible mechanisms in environments in which the agents' private information evolves stochastically over time and in which decisions have to be made in each period. The environments we consider are fairly general in that the agents' types are allowed to evolve in a non-Markov way, decisions are allowed to affect the type distributions and payoffs are not restricted to be separable over time. Our first result is the characterization of a dynamic payoff formula that describes the evolution of the agents' equilibrium payoffs in an incentive-compatible mechanism. The formula summarizes all local first-order conditions taking into account how current information affects the dynamics of expected payoffs. The formula generalizes the familiar envelope condition from static mechanism design: the key difference is that a variation in the current types now impacts payoffs in all subsequent periods both directly and through the effect on the distributions of future types. First, we identify assumptions on the primitive environment that guarantee that our dynamic payoff formula is a necessary condition for incentive compatibility. Next, we specialize this formula to quasi-linear environments and show how it permits one to establish a dynamic "revenue-equivalence" result and to construct a formula for dynamic virtual surplus which is instrumental for the design of optimal mechanisms. We then turn to the characterization of sufficient conditions for incentive compatibility. Lastly, we show how our results can be put to work in a variety of applications that include the design of profit-maximizing dynamic auctions with AR(k) values and the provision of experience goods.dynamic mechanisms, asymmetric information, stochastic processes, incentives

If services aren't delivered, people won't pay: the role of measurement problems and monitoring in Payments for Environmental Services

The idea of Payments for environmental services (PES) has an appealing simplicity, which may explain the success of the concept. However, successful projects are far limited though and two constraints have been identified in literature. The first is limited demand: too few service users are so confident about the mechanism that they are willing to pay. The second obstacle is poor knowledge on the institutional requirements entailing incentive and livelihood mechanisms which so far have received comparatively less attention. This paper focuses on both constraints by arguing that monitoring effectiveness and conditionality of PES schemes are crucial and that institutional arrangements for monitoring should be in place. By analysing in a systematic way what types of measurement problems there are, the paper shows that the type of monitoring that is required within a PES has consequences for the institutional arrangement needed for a successful PES. We find that the institutional arrangements for monitoring vary according to (i) the type of environmental service and its underlying production process, (ii) the extent to which the environmental service can be freely observed or measured, (iii) the extent to which activities of the resource managers who provide the environmental service can be freely observed, and finally (iv) the deterministic or stochastic nature of production processes.PES, monitoring, measurement, institutional arrangement, Environmental Economics and Policy,

A guided tour of asynchronous cellular automata

Research on asynchronous cellular automata has received a great amount of attention these last years and has turned to a thriving field. We survey the recent research that has been carried out on this topic and present a wide state of the art where computing and modelling issues are both represented.Comment: To appear in the Journal of Cellular Automat

Deterministic versus Stochastic Contracts in a Dynamic Principal-Agent Model

I show that deterministic dynamic contracts between a principal and an agent are always at least as profitable to the principal as stochastic ones, if the so-called first-order approach in dynamic mechanism design is satisfied. The principal commits, while the agent's type evolution follows a Markov process. My results demonstrate, even when allowing for potential correlation of stochastic contracts across periods that the usual restriction in the literature to deterministic contracts is admissible, as long as the first-order approach is valid

Delegated Stochastic Probing

Delegation covers a broad class of problems in which a principal doesn\u27t have the resources or expertise necessary to complete a task by themselves, so they delegate the task to an agent whose interests may not be aligned with their own. Stochastic probing describes problems in which we are tasked with maximizing expected utility by "probing" known distributions for acceptable solutions subject to certain constraints. In this work, we combine the concepts of delegation and stochastic probing into a single mechanism design framework which we term delegated stochastic probing. We study how much a principal loses by delegating a stochastic probing problem, compared to their utility in the non-delegated solution. Our model and results are heavily inspired by the work of Kleinberg and Kleinberg in "Delegated Search Approximates Efficient Search." Building on their work, we show that there exists a connection between delegated stochastic probing and generalized prophet inequalities, which provides us with constant-factor deterministic mechanisms for a large class of delegated stochastic probing problems. We also explore randomized mechanisms in a simple delegated probing setting, and show that they outperform deterministic mechanisms in some instances but not in the worst case