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最新文章

• Preventing Price-Mediated Contagion Due to Fire Sales Externalities: Strategic Foundations of Macroprudential Regulation AI翻译
  作者：Braouezec, Yann; Kiani, Keyvan
  作者单位：IESEG School of Management; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Humanities & Social Sciences (INSHS); Universite de Lille; emlyon business school; Centre National de la Recherche Scientifique (CNRS); Ecole Normale Superieure de Lyon (ENS de LYON); Universite Claude Bernard Lyon 1; Universite Jean Monnet; Universite Lyon 2
  摘要：We offer a stress test framework in which interaction between regulated banks occurs through the impact they may have on asset prices when they deleverage. Because banks are constrained to maintain their risk-based capital ratio higher than a threshold, the deleveraging problem yields a generalized game in which the solvency constraint of each bank depends on the decisions of the others. We analyze the game under microprudential but also under macroprudential regulation. Microprudential regula...
• Improving the Security of United States Elections with Robust Optimization AI翻译
  作者：Crimmins, Braden L.; Halderman, J. Alex; Sturt, Bradley
  作者单位：University of Michigan System; University of Michigan; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
  摘要：For more than a century, election officials across the United States have inspected voting machines before elections using a procedure called logic and accuracy testing (LAT). This procedure consists of election officials casting a test deck of ballots into each voting machine and confirming the machine produces the expected vote total for each candidate. We bring a scientific perspective to LAT by introducing the first formal approach to designing test decks with rigorous security guarantees....
• Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting AI翻译
  作者：Dias, Joaquim; Street, Alexandre; Homem-de-Mello, Tito; Munoz, Francisco D.
  作者单位：Universidad Adolfo Ibanez
  摘要：Decision making is generally modeled as sequential forecast-decision steps with no feedback, following an open-loop approach. For instance, in the electricity sector, system operators use the forecast-decision approach followed by ad hoc rules to determine reserve requirements and biased net load forecasts to guard the system against renewable generation and demand uncertainty. Such procedures lack technical formalism to minimize operating and reliability costs. We present a new closed-loop fr...
• Error Propagation in Asymptotic Analysis of the Data-Driven (s, S) Inventory Policy AI翻译
  作者：Zhang, Xun; Ye, Zhi-Sheng; Haskell, William B.
  作者单位：Southern University of Science & Technology; National University of Singapore; Purdue University System; Purdue University
  摘要：We study periodic review stochastic inventory control in the data-driven setting where the retailer makes ordering decisions based only on historical demand observations without any knowledge of the probability distribution of the demand. Because an (s, S)policy is optimal when the demand distribution is known, we investigate the statistical properties of the data-driven (s, S)-policy obtained by recursively computing the empirical cost-to-go functions. This policy is inherently challenging to...
• Optimal No-Regret Learning in Repeated First-Price Auctions AI翻译
  作者：Han, Yanjun; Weissman, Tsachy; Zhou, Zhengyuan
  作者单位：New York University; New York University; Stanford University; New York University
  摘要：We study online learning in repeated first-price auctions where a bidder, only observing the winning bid at the end of each auction, learns to adaptively bid to maximize the cumulative payoff. To achieve this goal, the bidder faces censored feedback: If the bidder wins the bid, then the bidder is not able to observe the highest bid of the other bidders, which we assume is i.i.d. drawn from an unknown distribution. In this paper, we develop the first learning algorithm that achieves a near-opti...
• Learning to Persuade on the Fly: Robustness Against AI翻译
  作者：Zu, You; Iyer, Murthy; Xu, Ifeng
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Chicago
  摘要：Motivated by information sharing in online platforms, we study repeated persuasion between a sender and a stream of receivers, where, at each time, the sender observes a payoff -relevant state drawn independently and identically from an unknown distribution and shares state information with the receivers, who each choose an action. The sender seeks to persuade the receivers into taking actions aligned with the sender's preference by selectively sharing state information. However, in contrast t...
• Pricing Optimal Outcomes in Coupled and Non-convex Markets: Theory and Applications to Electricity Markets AI翻译
  作者：Ahunbay, Mete Seref; Bichler, Martin; Knoerr, Johannes
  作者单位：Technical University of Munich
  摘要：In many real -world markets, participants have non -convex preferences, and the allocation problem needs to consider complex constraints. Electricity markets are a prime example, but similar problems appear in many markets, which has led to a growing literature on market design. Competitive equilibrium does not generally exist in such markets. Today, power markets use heuristic pricing rules based on the dual of a relaxed allocation problem. With increasing levels of renewables, these rules ha...
• Price Interpretability of Prediction Markets: A Convergence Analysis AI翻译
  作者：Gao, Jianjun; Wang, Zizhuo; Wu, Weiping; Yu, Dian
  作者单位：Shanghai University of Finance & Economics; Shanghai University of Finance & Economics; The Chinese University of Hong Kong, Shenzhen; Fuzhou University; Industrial Bank China
  摘要：Prediction markets are long known for prediction accuracy. This study systematically explores the fundamental properties of prediction markets, addressing questions about their information aggregation process and the factors contributing to their remarkable efficacy. We propose a novel multivariate utility-based mechanism that unifies several existing automated market-making schemes. Using this mechanism, we establish the convergence results for markets comprised of risk-averse traders who hav...
• The Role of Lookahead and Approximate Policy Evaluation in Reinforcement Learning with Linear Value Function Approximation AI翻译
  作者：Winnicki, Anna; Lubars, Joseph; Livesay, Michael; Srikant, R.
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; United States Department of Energy (DOE); Sandia National Laboratories; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Function approximation is widely used in reinforcement learning to handle the computational difficulties associated with very large state spaces. However, function approximation introduces errors that may lead to instabilities when using approximate dynamic programming techniques to obtain the optimal policy. Therefore, techniques such as lookahead for policy improvement and m-step rollout for policy evaluation are used in practice to improve the performance of approximate dynamic programming ...
• Drone-Delivery Network for Opioid Overdose: Nonlinear Integer Queueing-Optimization Models and Methods AI翻译
  作者：Lejeune, Miguel A.; Ma, Wenbo
  作者单位：George Washington University
  摘要：We propose a new stochastic emergency network design model that uses a fleet of drones to quickly deliver naloxone in response to opioid overdoses. The network is represented as a collection of M/G/K / G / K queueing systems in which the capacity K of each system is a decision variable, and the service time is modeled as a decision -dependent random variable. The model is a queuing -based optimization problem which locates fixed (drone bases) and mobile (drones) servers and determines the dron...