Optimal Dynamic Nonlinear Income Taxes with No Commitment

We wish to study optimal dynamic nonlinear income taxes. Do real world taxes share some of their features? What policy prescriptions can be made? We study a two period model, where the consumers and government each have separate budget constraints in the two periods, so income cannot be transferred between periods. Labor supply in both periods is chosen by the consumers. The government has memory, so taxes in the first period are a function of first period labor income, whereas taxes in the second period are a function of both first and second period labor income. The government cannot commit to future taxes. Time consistency is thus imposed as a requirement. The main results of the paper show that time consistent incentive compatible two period taxes involve separation of types in the first period and a differentiated lump sum tax in the second period, provided that the discount rate is high or utility is separable between labor and consumption. In the natural extension of the Diamond (1998) model with quasi-linear utility functions to two periods, an equivalence of dynamic and static optimal taxes is demonstrated, and a necessary condition for the top marginal tax rate on first period income is found.Optimal Income Taxation; Time Consistency; Incentive Compatibility; Sequential Information Revelation; Optimal Dynamic Taxation

Optimizing the computation of overriding

We introduce optimization techniques for reasoning in DLN---a recently introduced family of nonmonotonic description logics whose characterizing features appear well-suited to model the applicative examples naturally arising in biomedical domains and semantic web access control policies. Such optimizations are validated experimentally on large KBs with more than 30K axioms. Speedups exceed 1 order of magnitude. For the first time, response times compatible with real-time reasoning are obtained with nonmonotonic KBs of this size

Bankruptcy: is it enough to forgive or must we also forget?

In many countries, lenders are restricted in their access to information about borrowers' past defaults. The authors study this provision in a model of repeated borrowing and lending with moral hazard and adverse selection. They analyze its effects on borrowers' incentives and access to credit, and identify conditions under which it is optimal. The authors argue that “forgetting” must be the outcome of a regulatory intervention by the government. Their model's predictions are consistent with the cross-country relationship between credit bureau regulations and the provision of credit, as well as the evidence on the impact of these regulations on borrowers' and lenders' behavior.Bankruptcy

A Random Attention Model

This paper illustrates how one can deduce preference from observed choices when attention is not only limited but also random. In contrast to earlier approaches, we introduce a Random Attention Model (RAM) where we abstain from any particular attention formation, and instead consider a large class of nonparametric random attention rules. Our model imposes one intuitive condition, termed Monotonic Attention, which captures the idea that each consideration set competes for the decision-maker's attention. We then develop revealed preference theory within RAM and obtain precise testable implications for observable choice probabilities. Based on these theoretical findings, we propose econometric methods for identification, estimation, and inference of the decision maker's preferences. To illustrate the applicability of our results and their concrete empirical content in specific settings, we also develop revealed preference theory and accompanying econometric methods under additional nonparametric assumptions on the consideration set for binary choice problems. Finally, we provide general purpose software implementation of our estimation and inference results, and showcase their performance using simulations

Policy-Aware Unbiased Learning to Rank for Top-k Rankings

Counterfactual Learning to Rank (LTR) methods optimize ranking systems using logged user interactions that contain interaction biases. Existing methods are only unbiased if users are presented with all relevant items in every ranking. There is currently no existing counterfactual unbiased LTR method for top-k rankings. We introduce a novel policy-aware counterfactual estimator for LTR metrics that can account for the effect of a stochastic logging policy. We prove that the policy-aware estimator is unbiased if every relevant item has a non-zero probability to appear in the top-k ranking. Our experimental results show that the performance of our estimator is not affected by the size of k: for any k, the policy-aware estimator reaches the same retrieval performance while learning from top-k feedback as when learning from feedback on the full ranking. Lastly, we introduce novel extensions of traditional LTR methods to perform counterfactual LTR and to optimize top-k metrics. Together, our contributions introduce the first policy-aware unbiased LTR approach that learns from top-k feedback and optimizes top-k metrics. As a result, counterfactual LTR is now applicable to the very prevalent top-k ranking setting in search and recommendation.Comment: SIGIR 2020 full conference pape

Stock Market Development and Economic Growth

financial markets development, economic growth, economic development, stock markets development