This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select the changes that would have improved the system performance. This work is illustrated by experiments on the ad placement system associated with the Bing search engine.

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Counterfactual reasoning and learning systems: the example of computational advertising

Expert Political Judgment: How Good Is It? How Can We Know? By Philip E. Tetlock. Princeton: Princeton University Press, 2005. 352p. $45.00 cloth, $19.95 paper. This is a wonderful and important book. Philip Tetlock is a political psychologist who has a knack for innovative research projects (e.g., his earlier work on how people cope with trade-offs in politics). In this book, he addresses a question that would scare away more timid souls: How well do experts predict political and economic events?

Expert Political Judgment: How Good Is It? How Can We Know?

1. Introduction 2. Screening 3. Bayesian Mechanism Design: Examples 4. Dominant Strategy Mechanisms: Examples 5. Incentive Compatibility 6. Bayesian Mechanism Design 7. Dominant Strategy Mechanisms 8. Non-Transferrable Utility 9. Informational Interdependence 10. Robust Mechanism Design 11. Dynamic Mechanism Design

An introduction to the theory of mechanism design

This paper proposes an estimation method for a repeated auction game under the presence of capacity contraints The estimation strategy is computationally simple as it does not require solving for the equilibrium of the game It uses a two stage approach In the first stage the distribution of bids conditional on state variables is estimated using data on bids, bidder characteristics and contract characteristics In the second stage, an expression of the expected sum of future profits based on the distribution of bids is obtained, and costs are inferred based on the first order condition of optimal bids We apply the estimation method to repeated highway construction procurement auctions in the state of California between May 1996 and May 1999 In this market, previously won uncompleted contracts reduce the probability of winning further contracts We quantify the effect of intertemporal constraints on bidders' costs and on bids Due to the intertemporal effect and also to bidder asymmetry, the auction can be inefficient Based on the estimates of costs, we quantify efficiency losses

Estimation of a Dynamic Auction Game

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Recursive Methods In Economic Dynamics

We survey the recent literature on designing auctions and mechanisms for dynamic settings. Two settings are considered: those with a dynamic population of agents or buyers whose private information remains fixed throughout time; and those with a fixed population of agents or buyers whose private information changes across time. Within each of these settings, we discuss both efficient (welfare-maximizing) and optimal (revenue-maximizing) mechanisms.

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Dynamic Auctions: A Survey

We examine a model of long-term contracting in which the buyer is privately informed about the stochastic process by which her value for a good evolves. In addition, the realized values are also private information. We characterize a class of environments in which the profit-maximizing long-term contract offered by a monopolist takes an especially simple structure: we derive sufficient conditions on primitives under which the optimal contract consists of a menu of deterministic sequences of static contracts. Within each sequence, higher realized values lead to greater quantity provision; however, an increasing proportion of buyer types are excluded over time, eventually leading to inefficiently early termination of the relationship. Moreover, the menu choices differ by future generosity, with more costly (up front) plans guaranteeing greater quantity provision in the future. Thus, the seller screens process information in the initial period and then progressively screens across realized values so as to reduce the information rents paid in future periods. Copyright , Oxford University Press.

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Progressive Screening: Long-Term Contracting with a Privately Known Stochastic Process

https://www.economics.utoronto.ca/debrahul/files/LimitedCommitment.pdf

Dynamic screening with limited commitment

https://mpra.ub.uni-muenchen.de/14925/1/MPRA_paper_14925.pdf

Maher Said

Papers

Dynamic Auctions: A Survey

Progressive Screening: Long-Term Contracting with a Privately Known Stochastic Process

Dynamic Auctions: A Survey

Dynamic screening with limited commitment

Sequential auctions with randomly arriving buyers