2024

最新文章

• Online Learning for Constrained Assortment Optimization Under Markov Chain Choice Model AI翻译
  作者：Li, Shukai; Luo, Qi; Huang, Zhiyuan; Shi, Cong
  作者单位：Northwestern University; University of Iowa; Tongji University; University of Miami
  摘要：We study a dynamic assortment selection problem where arriving customers make purchase decisions among offered products from a universe of products under a Markov chain choice (MCC) model. The retailer only observes the assortment and the customer's single choice per period. Given limited display capacity, resource constraints, and no a priori knowledge of problem parameters, the retailer's objective is to sequentially learn the choice model and optimize cumulative revenues over a finite selli...
• Reshaping National Organ Allocation Policy AI翻译
  作者：Papalexopoulos, Theodore; Alcorn, James; Bertsimas, Dimitris; Goff, Rebecca; Stewart, Darren; Trichakis, Nikolaos
  作者单位：Massachusetts Institute of Technology (MIT); United Network for Organ Sharing; New York University
  摘要：The Organ Procurement & Transplantation Network (OPTN) initiated in 2018 a major overhaul of all U.S. deceased-donor organ allocation policies, aiming to gradually migrate them to a so-called continuous distribution model, with the goal of creating an allocation system that is more efficient, more equitable, and more inclusive. Development of policies within this model, however, represents a major challenge because multiple efficiency and fairness objectives need to be delicately balanced. We ...
• Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes AI翻译
  作者：Bennett, Andrew; Kallus, Nathan
  作者单位：Cornell University
  摘要：In applications of offline reinforcement learning to observational data, such as in healthcare or education, a general concern is that observed actions might be affected by unobserved factors, inducing confounding and biasing estimates derived under the assumption of a perfect Markov decision process (MDP) model. Here we tackle this by considering off-policy evaluation in a partially observed MDP (POMDP). Specifically, we consider estimating the value of a given target policy in an unknown POM...
• Demand Estimation Under Uncertain Consideration Sets AI翻译
  作者：Jagabathula, Srikanth; Mitrofanov, Dmitry; Vulcano, Gustavo
  作者单位：New York University; Boston College; Universidad Torcuato Di Tella
  摘要：To estimate customer demand, choice models rely both on what the individuals do and do not purchase. A customer may not purchase a product because it was not offered but also because it was not considered. To account for this behavior, existing literature has proposed the so-called consider-then-choose (CTC) models, which posit that customers sample a consideration set and then choose the most preferred product from the intersection of the offer set and the consideration set. CTC models have b...
• Unified Moment-Based Modeling of Integrated Stochastic Processes AI翻译
  作者：Kyriakou, Ioannis; Brignone, Riccardo; Fusai, Gianluca
  作者单位：City St Georges, University of London; University of Freiburg; University of Eastern Piedmont Amedeo Avogadro; City St Georges, University of London
  摘要：In this paper, we present a new method for simulating integrals of stochastic processes. We focus on the nontrivial case of time integrals, conditional on the state variable levels at the endpoints of a time interval through a moment-based probability distribution construction. We present different classes of models with important uses in finance, medicine, epidemiology, climatology, bioeconomics, and physics. The method is generally applicable in well-posed moment problem settings. We study i...
• The Fragility of Optimized Bandit Algorithms AI翻译
  作者：Fan, Lin; Glynn, Peter W.
  作者单位：Northwestern University; Stanford University
  摘要：Much of the literature on optimal design of bandit algorithms is based on minimization of expected regret. It is well known that algorithms that are optimal over certain exponential families can achieve expected regret that grows logarithmically in the number of trials at a rate specified by the Lai-Robbins lower bound. In this paper, we show that when one uses such optimized algorithms, the resulting regret distribution necessarily has a very heavy tail, specifically that of a truncated Cauch...
• Incentive-Aware Models of Financial Networks AI翻译
  作者：Jalan, Akhil; Chakrabarti, Deepayan; Sarkar, Purnamrita
  作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
  摘要：Financial networks help firms manage risk but also enable financial shocks to spread. Despite their importance, existing models of financial networks have several limitations. Prior works often consider a static network with a simple structure (e.g., a ring) or a model that assumes conditional independence between edges. We propose a new model where the network emerges from interactions between heterogeneous utility-maximizing firms. Edges correspond to contract agreements between pairs of fir...
• Selection and Ordering Policies for Hiring Pipelines via Linear Programming AI翻译
  作者：Epstein, Boris; Ma, Will
  作者单位：Columbia University
  摘要：Motivated by hiring pipelines, we study three selection and ordering problems in which applicants for a finite set of positions are interviewed or sent offers. There is a finite time budget for interviewing/sending offers, and every interview/offer is followed by a stochastic realization of discovering the applicant's quality or acceptance decision, leading to computationally challenging problems. In the first problem, we study sequential interviewing and show that a computationally tractable,...
• Risk Sensitivity and Firm Power: Price Competition with Mean2-Variance Profit Objective Under Multinomial Logit Demand AI翻译
  作者：Li, Hongmin; Webster, Scott
  作者单位：Arizona State University; Arizona State University-Tempe
  摘要：This paper is the first in the literature to address a risk-sensitive price competition under the multinomial logit choice model, with each participating firm maximizing a riskadjusted profit objective. We find that, at equilibrium, a subset of firms earns a positive profit, whereas others are driven to zero profit, contrasting with the risk-neutral equilibrium in which all firms earn a positive profit regardless of quality and cost. We identify a power index-the ratio of effective product att...
• Optimal Dynamic Control of an Epidemic AI翻译
  作者：Kruse, Thomas; Strack, Philipp
  作者单位：University of Wuppertal; Yale University
  摘要：We analyze how to optimally engage in social distancing in order to minimize the spread of an infectious disease. We identify conditions under which any optimal policy is single peaked (i.e., first engages in increasingly more social distancing and subsequently decreases its intensity). We show that an optimal policy might substantially delay measures that decrease the transmission rate to create herd immunity and that engaging in social distancing suboptimally early can increase the number of...