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作者：Salem, Tad; Gupta, Swati; Kamble, Vijay
作者单位：United States Department of Defense; United States Navy; United States Naval Academy; Massachusetts Institute of Technology (MIT); University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：Algorithmic decision making in societal contexts, such as retail pricing, loan administration, recommendations on online platforms, etc., can be framed as stochastic optimization under bandit feedback, which typically requires experimentation with different decisions for the sake of learning. Such experimentation often results in perceptions of unfairness among people impacted by these decisions; for instance, there have been several recent lawsuits accusing companies that deploy algorithmic p...
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作者：Najy, Waleed; Diabat, Ali; Elbassioni, Khaled
作者单位：New York University; New York University Abu Dhabi; New York University; New York University Tandon School of Engineering
摘要：The difficulty of analyzing and optimizing the stochastic one-warehouse multiretailer problem under the (S, T) policy motivates the need to consider approximate but high-fidelity systems that are easier to scrutinize. We consider one such model in the setting in which retailers face independent normally distributed demand with given (nonidentical) means and variances. Safety stock is computed via a type-I service-level formula that ignores allocation issues, and the cost function is computed b...
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作者：Bayrak, Halil Ibrahim; Kocyigit, Cagil; Kuhn, Daniel; Pinar, Mustafa Celebi
作者单位：Ihsan Dogramaci Bilkent University; University of Luxembourg; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：We consider the mechanism design problem of a principal allocating a single good to one of several agents without monetary transfers. Each agent desires the good and uses it to create value for the principal. We designate this value as the agent's private type. Even though the principal does not know the agents' types, she can verify them at a cost. The allocation of the good thus depends on the agents' self-declared types and the results of any verification performed, and the principal's payo...
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作者：Bai, Yicheng; El Housni, Omar; Rusmevichientong, Paat; Topaloglu, Huseyin
作者单位：University of Southern California
摘要：We study a joint inventory stocking and assortment customization problem. We have access to a set of products that can be used to stock a storage facility with limited capacity. At the beginning of the selling horizon, we decide how many units of each product to stock. Customers of different types with type-dependent preferences for the products arrive over the selling horizon. Depending on the remaining product inventories and the type of the customer, we offer a product assortment to the arr...
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作者：Podinovski, Victor V.; Papaioannou, Grammatoula
作者单位：Loughborough University
摘要：In data envelopment analysis, value judgements expressed as weight restrictions in multiplier models correspond to production tradeoffs in the dual envelopment models. Such tradeoffs are interpretable as simultaneous changes to the inputs and outputs that are assumed to be technologically possible for all decision-making units (DMUs) in the technology. The specification of production tradeoffs leads to the creation of additional DMUs, expansion of technology, and improved discriminating power ...
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作者：Liu, Huikang; Wiesemann, Wolfram; Yue, Man-Chung
作者单位：Shanghai Jiao Tong University; Imperial College London; Hong Kong Polytechnic University
摘要：Factored Markov decision processes (MDPs) are a prominent paradigm within the artificial intelligence community for modeling and solving large-scale MDPs whose rewards and dynamics decompose into smaller, loosely interacting components. Through the use of value function approximations, dynamic Bayesian networks, and context-specific independence, factored MDPs can achieve an exponential reduction in the state space of an MDP and, thus, scale to problem sizes that are beyond the reach of classi...
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作者：Shamsi, Davood; Luenberger, Robert; Ye, Yinyu
作者单位：Shanghai Jiao Tong University; Shanghai Institute for Mathematics & Interdisciplinary Sciences
摘要：This research note revisits the framework proposed in our earlier work and explores its conceptual and algorithmic connection to recent advances in dual-based online resource allocation-particularly the dual mirror descent method introduced previously. Both approaches address the challenge of making real-time sequential allocation decisions under dynamically revealed constraints. Although the dual mirror descent method relies on Bregman divergence to guide dual updates, our framework derives n...
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作者：Chen, Du; Chua, Geoffrey A.
作者单位：Nanyang Technological University
摘要：Data are now widely considered a key firm asset for enabling better operational decisions. However, data-driven decisions can inadvertently expose private data, leaving firms vulnerable to unforeseen danger. How to manage data security risks by protecting data from being inferred from observable decisions thus becomes an important question. In this paper, we focus on data security in supply chains because of their data-intensive nature. Specifically, we examine a data-driven contextual newsven...
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作者：Bichuch, Maxim; Feinstein, Zachary
作者单位：State University of New York (SUNY) System; University at Albany, SUNY; University at Buffalo, SUNY; Stevens Institute of Technology
摘要：Within this work we consider an axiomatic framework for Automated Market Makers (AMMs). AMMs are smart contracts that set prices for swaps on a pool of assets. By imposing reasonable axioms on the underlying utility function, we are able to characterize the properties of the swap size of the assets and of the resulting pricing oracle. In providing these general axioms, we define a novel measure of price impacts that can be used to quantify those costs between different AMM constructions. We ha...
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作者：Shapiro, Alexander; Zhou, Enlu; Lin, Yifan; Wang, Yuhao
作者单位：University System of Georgia; Georgia Institute of Technology
摘要：Stochastic optimal control with unknown randomness distributions has been studied for a long time, encompassing robust control, distributionally robust control, and adaptive control. We propose a new episodic Bayesian approach that incorporates Bayesian learning with optimal control. In each episode, the approach learns the randomness distribution with a Bayesian posterior and subsequently solves the corresponding Bayesian average estimate of the true problem. The resulting policy is exercised...
