2020

最新文章

• Nonstationary Bandits with Habituation and Recovery Dynamics AI翻译
  作者：Mintz, Yonatan; Aswani, Anil; Kaminsky, Philip; Flowers, Elena; Fukuoka, Yoshimi
  作者单位：University System of Georgia; Georgia Institute of Technology; University of California System; University of California Berkeley; University of California System; University of California San Francisco; University of California System; University of California San Francisco
  摘要：Many settings involve sequential decision making where a set of actions can be chosen at each time step, each action provides a stochastic reward, and the distribution for the reward provided by each action is initially unknown. However, frequent selection of a specific action may reduce the expected reward for that action, whereas abstaining from choosing an action may cause its expected reward to increase. Such nonstationary phenomena are observed in many real-world settings such as personal...
• Optimal Online Learning for Nonlinear Belief Models Using Discrete Priors AI翻译
  作者：Han, Weidong; Powell, Warren B.
  作者单位：Princeton University
  摘要：We consider an optimal learning problem where we are trying to learn a function that is nonlinear in unknown parameters in an online setting. We formulate the problem as a dynamic program, provide the optimality condition using Bellman's equation, and propose a multiperiod lookahead policy to overcome the nonconcavity in the value of information. We adopt a sampled belief model, which we refer to as a discrete prior. For an infinite-horizon problem with discounted cumulative rewards, we prove ...
• Technical Note-Central Limit Theorems for Estimated Functions at Estimated Points AI翻译
  作者：Glynn, Peter W.; Fan, Lin; Fu, Michael C.; Hu, Jian-Qiang; Peng, Yijie
  作者单位：Stanford University; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park; Fudan University; Peking University
  摘要：We provide a simple proof of the central limit theorem (CLT) for estimated functions at estimated points. Such estimators arise in a number of different simulationbased computational settings. We illustrate the methodology via applications to quantile estimation and related sensitivity analysis, as well as to computation of conditional valueat-risk.
• Decision Making When Things Are Only a Matter of Time AI翻译
  作者：Ebert, Sebastian
  作者单位：Frankfurt School Finance & Management; Tilburg University
  摘要：This article gives a comprehensive treatment of preferences regarding time risk-the risk of something happening sooner or later-within the expected discounted utility model. We characterize the signs of the discount function's derivatives of all orders and show how these signs are decisive for time risk preferences. We introduce the notions of prudent and temperate discounting and illustrate their importance for economic behavior. Several applications in which an important event is only a matt...
• Time Inconsistency of Optimal Policies of Distributionally Robust Inventory Models AI翻译
  作者：Shapiro, Alexander; Xin, Linwei
  作者单位：University System of Georgia; Georgia Institute of Technology; University of Chicago
  摘要：In this paper, we investigate optimal policies of distributionally robust (risk averse) inventory models. We demonstrate that if the respective risk measures are not strictly monotone, then there may exist infinitely many optimal policies that are not basestock and not time consistent. This is in a sharp contrast with the risk neutral formulation of the inventory model where all optimal policies are time consistent. This also extends previous studies of time inconsistency in the robust setting.
• Learning in Combinatorial Optimization: What and How to Explore AI翻译
  作者：Modaresi, Sajad; Saure, Denis; Vielma, Juan Pablo
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Universidad de Chile; Massachusetts Institute of Technology (MIT)
  摘要：We study dynamic decision making under uncertainty when, at each period, a decision maker implements a solution to a combinatorial optimization problem. The objective coefficient vectors of said problem, which are unobserved before implementation, vary from period to period. These vectors, however, are known to be random draws from an initially unknown distribution with known range. By implementing different solutions, the decision maker extracts information about the underlying distribution b...
• Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms AI翻译
  作者：Hazimeh, Hussein; Mazumder, Rahul
  作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
  摘要：The L-0-regularized least squares problem (a.k.a. best subsets) is central to sparse statistical learning and has attracted significant attention across the wider statistics, machine learning, and optimization communities. Recent work has shown that modern mixed integer optimization (MIO) solvers can be used to address small to moderate instances of this problem. In spite of the usefulness of L-0-based estimators and generic MIO solvers, there is a steep computational price to pay when compare...
• Optimization of Tree Ensembles AI翻译
  作者：Misic, Velibor V.
  作者单位：University of California System; University of California Los Angeles
  摘要：Tree ensemble models such as random forests and boosted trees are among the most widely used and practically successful predictive models in applied machine learning and business analytics. Although such models have been used to make predictions based on exogenous, uncontrollable independent variables, they are increasingly being used to make predictions where the independent variables are controllable and are also decision variables. In this paper, we study the problem of tree ensemble optimi...
• When Risk Perception Gets in the Way: Probability Weighting and Underprevention AI翻译
  作者：Baillon, Aurelien; Bleichrodt, Han; Emirmahmutoglu, Aysil; Jaspersen, Johannes; Peter, Richard
  作者单位：Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC; Australian National University; Norwegian School of Economics (NHH); University of Munich; University of Iowa
  摘要：Personal decisions about health hazards are the main cause of impaired health and premature death. People smoke and eat too much, and they exercise too little. The lack of preventive efforts is surprising given their proven effectiveness. In the early 1960s, Arrow suggested that moral hazard might be a reason for underprevention, but this explanation was later challenged. In this paper, we show that underprevention might be caused by misperceived probabilities. We derive when and how probabili...
• Regime Classification and Stock Loan Valuation AI翻译
  作者：Cai, Ning; Zhang, Wei
  作者单位：Hong Kong University of Science & Technology
  摘要：For traditional perpetual American put options under regime-switching models, the risk-free interest rates are typically assumed to be positive, and optimal stopping usually can occur in any regime. However, this may not hold true when the risk-free interest rates are allowed to be equal to zero (the interest rate may drop to zero in reality, e.g., in certain periods in Japan); there may exist continuation regimes within which optimal stopping can never occur, that is, within which stopping is...