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Register a new userMechanism Design meets Priority Design: Redesigning the US Armys Branching Process
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Army cadets obtain occupations through a centralized process. Three objectives -- increasing retention, aligning talent, and enhancing trust -- have guided reforms to this process since 2006. West Points mechanism for the Class of 2020 exacerbated challenges implementing Army policy aims. We formulate these desiderata as axioms and study their implications theoretically and with administrative data. We show that the Armys objectives not only determine an allocation mechanism, but also a specific priority policy, a uniqueness result that integrates mechanism and priority design. These results led to a re-design of the mechanism, now adopted at both West Point and ROTC.
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Optimal mechanism design enjoys a beautiful and well-developed theory, and also a number of killer applications. Rules of thumb produced by the field influence everything from how governments sell wireless spectrum licenses to how the major search engines auction off online advertising. There are, however, some basic problems for which the traditional optimal mechanism design approach is ill-suited---either because it makes overly strong assumptions, or because it advocates overly complex designs. This survey reviews several common issues with optimal mechanisms, including exorbitant communication, computation, and informational requirements; and it presents several examples demonstrating that passing to the relaxed goal of an approximately optimal mechanism allows us to reason about fundamental questions that seem out of reach of the traditional theory.
We show the role that an important equation first studied by Fritz John plays in mechanism design.
How to guarantee that firms perform due diligence before launching potentially dangerous products? We study the design of liability rules when (i) limited liability prevents firms from internalizing the full damage they may cause, (ii) penalties are paid only if damage occurs, regardless of the products inherent riskiness, (iii) firms have private information about their products riskiness before performing due diligence. We show that (i) any liability mechanism can be implemented by a tariff that depends only on the evidence acquired by the firm if a damage occurs, not on any initial report by the firm about its private information, (ii) firms that assign a higher prior to product riskiness always perform more due diligence but less than is socially optimal, and (iii) under a simple and intuitive condition, any type-specific launch thresholds can be implemented by a monotonic tariff.
The electricity sector has tended to be one of the first industries to face technology change motivated by sustainability concerns. Whilst efficient market designs for electricity have tended to focus upon market power concerns, environmental externalities pose extra challenges for efficient solutions. Thus, we show that ad hoc remedies for market power alongside administered carbon prices are inefficient unless they are integrated. Accordingly, we develop an incentive-based market clearing design that can include externalities as well as market power mitigation. A feature of the solution is that it copes with incomplete information of the system operator regarding generation costs. It is uses a network representation of the power system and the proposed incentive mechanism holds even with energy limited technologies having temporal constraints, e.g., storage. The shortcomings of price caps to mitigate market power, in the context of sustainability externalities, are overcome under the proposed incentive mechanism.
Game theory is often used as a tool to analyze decentralized systems and their properties, in particular, blockchains. In this note, we take the opposite view. We argue that blockchains can and should be used to implement economic mechanisms because they can help to overcome problems that occur if trust in the mechanism designer cannot be assumed. Mechanism design deals with the allocation of resources to agents, often by extracting private information from them. Some mechanisms are immune to early information disclosure, while others may heavily depend on it. Some mechanisms have to randomize to achieve fairness and efficiency. Both issues, information disclosure, and randomness require trust in the mechanism designer. If there is no trust, mechanisms can be manipulated. We claim that mechanisms that use randomness or sequential information disclosure are much harder, if not impossible, to audit. Therefore, centralized implementation is often not a good solution. We consider some of the most frequently used mechanisms in practice and identify circumstances under which manipulation is possible. We propose a decentralized implementation of such mechanisms, that can be, in practical terms, realized by blockchain technology. Moreover, we argue in which environments a decentralized implementation of a mechanism brings a significant advantage.
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