6期

最新文章

• How Endogenization of the Reference Point Affects Loss Aversion: A Study of Portfolio Selection AI翻译
  作者：He, Xue Dong; Strub, Moris S.
  作者单位：Chinese University of Hong Kong; Southern University of Science & Technology; Southern University of Science & Technology
  摘要：We study the implications of various models of partially endogenous reference point formation on optimal decision making in the context of portfolio optimization under loss aversion. Specifically, we first consider the partially endogenous model of De Giorgi and Post [Management Science (2011) 57(6):1094-1110], where the reference point is determined in equilibrium but contains an exogenous component. We find that optimal trading behavior is as if the reference point were completely exogenous ...
• Building a Location-Based Set of Social Media Users AI翻译
  作者：Marks, Christopher Edward; Zaman, Tauhid
  作者单位：Massachusetts Institute of Technology (MIT); The Charles Stark Draper Laboratory, Inc.; Yale University
  摘要：In many instances, one may want to gain situational awareness in an environment by monitoring the content of local social media users. Often the challenge is how to build a set of users from a target location. Here, we introduce a method for building such a set of users by using an expand-classify approach, which begins with a small set of seed users from the target location and then iteratively collects their neighbors and classifies their locations. We perform this classification using maxim...
• Advertising Cycling to Manage Exclusivity Loss in Fashion Styles AI翻译
  作者：Bruce, Norris I.; Krishnamoorthy, Anand; Prasad, Ashutosh
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; State University System of Florida; University of Central Florida; University of California System; University of California Riverside
  摘要：This paper uses dynamic optimization to study the optimal advertising of fash-ion products over time. For fashion products, brand advertising and exclusivity are impor-tant sales drivers. Therefore, we propose a dynamic model of the sales of multiple styles of a fashion brand based on these variables. The model is estimated using a particle filter method on data from two fashion categories (handbags and sunglasses) and has good fit and prediction. We also derive explicit analytical solutions o...
• Procurement Mechanisms with Post-Auction Pre-Award Cost-Reduction Investigations AI翻译
  作者：Chen, Qi (George); Beil, Damian R.; Duenyas, Izak
  作者单位：University of London; London Business School; University of Michigan System; University of Michigan
  摘要：A buyer seeking to outsource production may be able to find ways to reduce a potential supplier's cost, for example, by suggesting improvements to the supplier's proposed production methods. We study how a buyer could use such cost-reduction investigations by proposing a three-step supplier selection mechanism: First, each of several potential suppliers submits a price bid for a contract. Second, for each potential supplier, the buyer can exert an effort to see if the buyer can identify how th...
• Dark Matter in (Volatility and) Equity Option Risk Premiums AI翻译
  作者：Bakshi, Gurdip; Crosby, John; Gao, Xiaohui
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Old Dominion University
  摘要：Emphasizing the statistics of jumps crossing the strike and local time, we develop a decomposition of equity option risk premiums. Operationalizing this theoretical treatment, we equip the pricing kernel process with unspanned risks, embed (unspanned) jump risks, and allow equity return volatility to contain unspanned risks. Unspanned risks are consistent with negative risk premiums for jumps crossing the strike and local time and imply negative risk premiums for out-of-the-money call options ...
• Continuous Patrolling Games AI翻译
  作者：Alpern, Steve; Bui, Thuy; Lidbetter, Thomas; Papadaki, Katerina
  作者单位：University of Warwick; Rutgers University System; Rutgers University Newark; Rutgers University New Brunswick; University of Virginia; University of London; London School Economics & Political Science
  摘要：We study a patrolling game played on a network Q, considered as a metric space. The Attacker chooses a point of Q (not necessarily a node) to attack during a chosen time interval of fixed duration. The Patroller chooses a unit speed path on Q and intercepts the attack (and wins) if she visits the attacked point during the attack-time interval. This zero-sum game models the problem of protecting roads or pipelines from an adversarial attack. The payoff to the maximizing Patroller is the probabi...
• A Classifier to Decide on the Linearization of Mixed-Integer Quadratic Problems in CPLEX AI翻译
  作者：Bonami, Pierre; Lodi, Andrea; Zarpellon, Giulia
  作者单位：Universite de Montreal; Polytechnique Montreal; Vector Institute for Artificial Intelligence
  摘要：With the aim of fully embedding learned predictions in the algorithmic design of a mixed-integer quadratic programming (MIQP) solver, we translate the algorithmic question of whether to linearize convex MIQPs into a classification task and use machine learning (ML) techniques to tackle it. We represent MIQPs and the linearization decision by careful target and feature engineering. Computational experiments and evaluation metrics are designed to further incorporate the optimization knowledge in...
• High-Dimensional Learning Under Approximate Sparsity with Applications to Nonsmooth Estimation and Regularized Neural Networks AI翻译
  作者：Liu, Hongcheng; Ye, Yinyu; Lee, Hung Yi
  作者单位：State University System of Florida; University of Florida; Stanford University
  摘要：High-dimensional statistical learning (HDSL) has wide applications in data analysis, operations research, and decision making. Despite the availability of multiple theoretical frameworks, most existing HDSL schemes stipulate the following two conditions: (a) the sparsity and (b) restricted strong convexity (RSC). This paper generalizes both conditions via the use of the folded concave penalty (FCP). More specifically, we consider an M-estimation problem where (i) (conventional) sparsity is rel...
• Bayesian Optimization Allowing for Common Random Numbers AI翻译
  作者：Pearce, Michael Arthur Leopold; Poloczek, Matthias; Branke, Juergen
  作者单位：University of Warwick; Amazon.com; University of Warwick
  摘要：Bayesian optimization is a powerful tool for expensive stochastic black-box optimization problems, such as simulation-based optimization or machine learning hyperparameter tuning. Many stochastic objective functions implicitly require a random number seed as input. By explicitly reusing a seed, a user can exploit common random numbers, comparing two or more inputs under the same randomly generated scenario, such as a common customer stream in a job shop problem or the same random partition of ...
• Optimistic Gittins Indices AI翻译
  作者：Farias, Vivek F.; Gutin, Eli
  作者单位：Massachusetts Institute of Technology (MIT); Uber Technologies, Inc.
  摘要：Recent years have seen a resurgence of interest in Bayesian algorithms for the multiarmed bandit (MAB) problem, such as Thompson sampling. These algorithms seek to exploit prior information on arm biases. The empirically observed performance of these algorithms makes them a compelling alternative to their frequentist counterparts. Nonetheless, there appears to be a wide range in empirical performance among such Bayesian algorithms. These algorithms also vary substantially in their design (as o...