6期

最新文章

• How Should We Score Athletes and Candidates: Geometric Scoring Rules AI翻译
  作者：Kondratev, Aleksei Y.; Ianovski, Egor; Nesterov, Alexander S.
  作者单位：HSE University (National Research University Higher School of Economics)
  摘要：Scoring rules are widely used to rank athletes in sports and candidates in elections. Each position in each individual ranking is worth a certain number of points; the total sum of points determines the aggregate ranking. The question is how to choose a scoring rule for a specific application. First, we derive a one-parameter family with geometric scores that satisfies two principles of independence: once an extremely strong or weak candidate is removed, the aggregate ranking ought to remain i...
• Optimal Auction Design with Deferred Inspection and Reward AI翻译
  作者：Alaei, Saeed; Belloni, Alexandre; Makhdoumi, Ali; Malekian, Azarakhsh
  作者单位：Alphabet Inc.; Google Incorporated; Duke University; Amazon.com; University of Toronto
  摘要：Consider a mechanism run by an auctioneer who can use both payment and inspection instruments to incentivize agents. The timeline of the events is as follows. Based on a prespecified allocation rule and the reported values of agents, the auctioneer allocates the item and secures the reported values as deposits. The auctioneer then inspects the values of agents and, using a prespecified reward rule, rewards the ones who have reported truthfully. Using techniques from convex analysis and calculu...
• Monte Carlo Estimation of CoVaR AI翻译
  作者：Huang, Weihuan; Lin, Nifei; Hong, L. Jeff
  作者单位：Nanjing University; Fudan University; Fudan University; Fudan University
  摘要：CoVaR is one of the most important measures of financial systemic risks. It is defined as the risk of a financial portfolio conditional on another financial portfolio being at risk. In this paper we first develop a Monte Carlo simulation-based batching estimator of CoVaR and study its consistency and asymptotic normality. We show that the best rate of convergence that the batching estimator can achieve is n(-1/3), where n is the sample size. We then develop an importance sampling-inspired esti...
• Information Relaxation and a Duality-Driven Algorithm for Stochastic Dynamic Programs AI翻译
  作者：Chen, Nan; Ma, Xiang; Liu, Yanchu; Yu, Wei
  作者单位：Chinese University of Hong Kong; Sun Yat Sen University
  摘要：We use the technique of information relaxation to develop a duality-driven iterative approach (DDP) to obtain and improve confidence interval estimates for the true value of finite-horizon stochastic dynamic programming problems. Each iteration of the algorithm solves an optimization-expectation procedure. We show that the sequence of dual value estimates yielded from the proposed approach monotonically converges to the true value function in a finite number of dual iterations. Aiming to overc...
• Stochastic Approximation of Symmetric Nash Equilibria in Queueing Games AI翻译
  作者：Ravner, Liron; Snitkovsky, Ran I.
  作者单位：University of Haifa; Tel Aviv University
  摘要：We suggest a novel stochastic-approximation algorithm to compute a symmetric Nash-equilibrium strategy in a general queueing game with a finite action space. The algorithm involves a single simulation of the queueing process with dynamic updating of the strategy at regeneration times. Under mild assumptions on the utility function and on the regenerative structure of the queueing process, the algorithm converges to a symmetric equilibrium strategy almost surely. This yields a powerful tool tha...
• Sequential Learning with a Similarity Selection Index AI翻译
  作者：Zhou, Yi; Fu, Michael C.; Ryzhov, Ilya O.
  作者单位：University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
  摘要：We consider the problem of selecting the best alternative in a setting where prior similarity information between the performance output of different alternatives can be learned from data. Incorporating similarity information enables efficient budget allocation for faster identification of the best alternative in sequential selection. Using a new selection criterion, the similarity selection index, we develop two new allocation methods: one based on a mathematical programming characterization ...
• Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing AI翻译
  作者：Bansak, Kirk; Paulson, Elisabeth
  作者单位：University of California System; University of California Berkeley; Harvard University
  摘要：This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multiyear randomized control trial in Switzerland, seeks to maximize the average predicted employment level (or any measured outcome of interest) of refugees through a minimum-discord online assignment algorithm. The performance of this algorithm is tested on real refugee resettlement data from both the United State...
• 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...
• Side-Constrained Dynamic Traffic Equilibria AI翻译
  作者：Graf, Lukas; Harks, Tobias
  作者单位：University of Passau
  摘要：We study dynamic traffic assignment with side constraints. We first give a counter-example to a previous result from the literature regarding the existence of dynamic equilibria for volume-constrained traffic models in the classical linear edge-delay model. Our counter-example shows that the feasible flow space need not be convex, and it further reveals that classical infinite dimensional variational inequalities are not suited for the definition of general side-constrained dynamic equilibria....
• Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games AI翻译
  作者：Yan, Yuling; Li, Gen; Chen, Yuxin; Fan, Jianqing
  作者单位：Massachusetts Institute of Technology (MIT); Chinese University of Hong Kong; University of Pennsylvania; Princeton University
  摘要：This paper makes progress toward learning Nash equilibria in two-player, zero-sum Markov games from offline data. Specifically, consider a gamma-discounted, infinite-horizon Markov game with S states, in which the max-player has A actions and the min-player has B actions. We propose a pessimistic model-based algorithm with Bernstein-style lower confidence bounds-called the value iteration with lower confidence bounds for zero-sum Markov games-that provably finds an epsilon-approximate Nash equ...