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on Economic Design |
| By: | Masaki Miyashita; Takashi Ui |
| Abstract: | A linear-quadratic-Gaussian (LQG) game is an incomplete information game with quadratic payoff functions and Gaussian payoff states. This study addresses an information design problem to identify an information structure that maximizes a quadratic objective function. Gaussian information structures are found to be optimal among all information structures. Furthermore, the optimal Gaussian information structure can be determined by semidefinite programming, which is a natural extension of linear programming. This paper provides sufficient conditions for the optimality and suboptimality of both no and full information disclosure. In addition, we characterize optimal information structures in symmetric LQG games and optimal public information structures in asymmetric LQG games, with each structure presented in a closed-form expression. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.09479&r=des |
| By: | Billy A. Ferguson; Paul Milgrom |
| Abstract: | Many proposed surface water transfers undergo a series of regulatory reviews designed to mitigate hydrological and economic externalities. While these reviews help limit externalities, they impose substantial transaction costs that also limit trade. To promote a well-functioning market for surface water in California, we describe how a new kind of water right and related regulatory practices can balance the trade-off between externalities and transaction costs, and how a Water Incentive Auction can incentivize a sufficient number of current rights holders to swap their old rights for the new ones. The Water Incentive Auction adapts lessons learned from the US government’s successful Broadcast Incentive Auction. |
| JEL: | D23 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32010&r=des |
| By: | John M. Abowd |
| Abstract: | McCartan et al. (2023) call for "making differential privacy work for census data users." This commentary explains why the 2020 Census Noisy Measurement Files (NMFs) are not the best focus for that plea. The August 2021 letter from 62 prominent researchers asking for production of the direct output of the differential privacy system deployed for the 2020 Census signaled the engagement of the scholarly community in the design of decennial census data products. NMFs, the raw statistics produced by the 2020 Census Disclosure Avoidance System before any post-processing, are one component of that design--the query strategy output. The more important component is the query workload output--the statistics released to the public. Optimizing the query workload--the Redistricting Data (P.L. 94-171) Summary File, specifically--could allow the privacy-loss budget to be more effectively managed. There could be fewer noisy measurements, no post-processing bias, and direct estimates of the uncertainty from disclosure avoidance for each published statistic. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.14191&r=des |
| By: | Juan Carlos Perdomo |
| Abstract: | Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into likelihood of future events as a means to improve decision making quality, and enhance social welfare. However, if maximizing welfare is the ultimate goal, prediction is only a small piece of the puzzle. There are various other policy levers a social planner might pursue in order to improve bottom-line outcomes, such as expanding access to available goods, or increasing the effect sizes of interventions. Given this broad range of design decisions, a basic question to ask is: What is the relative value of prediction in algorithmic decision making? How do the improvements in welfare arising from better predictions compare to those of other policy levers? The goal of our work is to initiate the formal study of these questions. Our main results are theoretical in nature. We identify simple, sharp conditions determining the relative value of prediction vis-\`a-vis expanding access, within several statistical models that are popular amongst quantitative social scientists. Furthermore, we illustrate how these theoretical insights may be used to guide the design of algorithmic decision making systems in practice. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.08511&r=des |
| By: | Pesce, Elena; Rapallo, Fabio; Riccomagno, Eva; Wynn, Henry P. |
| Abstract: | After a rich history in medicine, randomized control trials (RCTs), both simple and complex, are in increasing use in other areas, such as web-based A/B testing and planning and design of decisions. A main objective of RCTs is to be able to measure parameters, and contrasts in particular, while guarding against biases from hidden confounders. After careful definitions of classical entities such as contrasts, an algebraic method based on circuits is introduced which gives a wide choice of randomization schemes. |
| Keywords: | A/B testing; algebraic statistics and combinatorics; bias and confounders; big data; design of experiments; AAM requested |
| JEL: | C1 |
| Date: | 2022–12–23 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:118011&r=des |