Search

This paper studies a distributionally robust multi-item newsvendor problem, where the demand distribution is unknown but specified with a general event-wise ambiguity set. Using the event-wise affine decision rules, we can obtain a conservative approximation formulation of the problem, which can typically be further reformulated as a linear program. In order to efficiently solve the resulting large-scale linear program, we develop a column generation-based decomposition scheme and speed up the computational efficiency by exploiting a special column selection strategy and stopping early based on a Karush-Kuhn-Tucker condition test. Focusing on the Wasserstein ambiguity set and the event-wise mean absolute deviation set, a computational study demonstrates both the computational efficiency of the proposed algorithm, which significantly outperforms a commercial solver and a Benders decomposition method, and the out-of-sample superiority of distributionally robust solutions relative to their sample average approximation counterparts. History: Accepted by Nicola Secomandi, Area Editor for Stochastic Models & Reinforcement Learning. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [492997-2016, RGPIN-2016-05208], the National Natural Science Foundation of China [71972012], Alliance de recherche numérique du Canada, and Canada Research Chairs [CRC-2018-00105]. It was also supported by Groupe d'études et de recherche en analyse des décisions (GERAD). Finally, this research was enabled in part by support provided by Digital Research Alliance of Canada (https://alliancecan.ca/en). Supplemental Material: The software that supports the findings of this study is available within the paper and its supplemental information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0010) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0010). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

This paper presents the first comprehensive study of a data-driven formulation of the distributionally robust second order stochastic dominance constrained problem (DRSSDCP) that hinges on using a type-1 Wasserstein ambiguity set. It is, furthermore, for the first time shown to be axiomatically motivated in an environment with distribution ambiguity. We formulate the DRSSDCP as a multistage robust optimization problem and further propose a tractable conservative approximation that exploits finite adaptability and a scenario-based lower bounding problem. We then propose the first exact optimization algorithm for this DRSSDCP. We illustrate how the data-driven DRSSDCP can be applied in practice on resource-allocation problems with both synthetic and real data. Our empirical results show that, with a proper adjustment of the size of the Wasserstein ball, DRSSDCP can reach acceptable out-of-sample feasibility yet still generating strictly better performance than what is achieved by the reference strategy. Optimization with stochastic dominance constraints has recently received an increasing amount of attention in the quantitative risk management literature. Instead of requiring that the probabilistic description of the uncertain parameters be exactly known, this paper presents a comprehensive study of a data-driven formulation of the distributionally robust second order stochastic dominance constrained problem (DRSSDCP) that hinges on using a type-1 Wasserstein ambiguity set. This formulation allows us to identify solutions with finite sample guarantees and solutions that are asymptotically consistent when observations are independent and identically distributed. It is, furthermore, shown to be axiomatically motivated in an environment with distribution ambiguity. Leveraging recent results in the field of robust optimization, we further formulate the DRSSDCP as a multistage robust optimization problem and further propose a tractable conservative approximation that exploits finite adaptability and a scenario-based lower bounding problem, both of which can reduce to linear programs under mild conditions. We then propose, to the best of our knowledge, the first exact optimization algorithm for this DRSSDCP, the efficiency of which is confirmed by our numerical results. Finally, we illustrate how the data-driven DRSSDCP can be applied in practice on resource-allocation problems with both synthetic and real data. Our empirical results show that, with a proper adjustment of the size of the Wasserstein ball, DRSSDCP can reach "acceptable" out-of-sample feasibility yet still generating strictly better performance than what is achieved by the reference strategy. Funding: This research was partially supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2016-05208], the Canada Research Chair program [Grant 950-230057], and Groupe d'études et de recherche en analyse des décisions, and it was enabled in part by support provided by Calcul Quebec (https://www.calculquebec.ca/en/) and the Digital Research Alliance of Canada (https://www.alliancecan.ca/). Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2387. [ABSTRACT FROM AUTHOR]

As firms accumulate more data, users' data-sharing decisions may polarize. Some users may share all data, whereas others may share no data, becoming "digital hermits." When users share multidimensional data about themselves with a firm, the firm learns about the correlations between different dimensions of user data. We incorporate this type of learning into a model of a data market in which a firm acquires data from users with privacy concerns. Each user can share no data, only nonsensitive data, or their full data with the firm. As the firm collects more data and becomes better at drawing inferences about a user's privacy-sensitive data from their nonsensitive data, the share of new users who share no data ("digital hermits") grows. This growth of digital hermits occurs even though the firm offers higher compensation for a user's nonsensitive data and a user's full data as its ability to draw inferences improves. At the same time, the share of new users who share their full data also grows. The model thus predicts a polarization of users' data-sharing choices away from nonsensitive data sharing to no sharing and full sharing. Our model suggests that recent privacy policies, which are focused on control of data rather than inferences, may be misplaced. History: Anthony Dukes served as the senior editor. This paper was accepted through the Marketing Science: Frontiers review process. Funding: Partial financial support was received from the Social Sciences and Humanities Research Council of Canada [Grant 435-2023-0492]. [ABSTRACT FROM AUTHOR]

The paper explores the potential of Large Language Models to substitute for or to augment human participants in market research. This paper explores the potential of large language models (LLMs) to substitute for human participants in market research. Such LLMs can be used to generate text given a prompt. We argue that perceptual analysis is a particularly promising use case for such automated market research for certain product categories. The proposed new method generates outputs that closely match those generated from human surveys: agreement rates between human- and LLM- generated data sets reach over 75%. Moreover, this applies for perceptual analysis based on both brand similarity measures and product attribute ratings. The paper demonstrates that, for some categories, this new method of fully or partially automated market research will increase the efficiency of market research by meaningfully speeding up the process and potentially reducing the cost. Further results also suggest that with an ever larger training corpus applied to large language models, LLM-based market research will be applicable to answer more nuanced questions based on demographic variables or contextual variation that would be prohibitively expensive or infeasible with human respondents. History: Catherine Tucker served as the senior editor. This paper was accepted through the Marketing Science: Frontiers review process. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant 430-2021-00057]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2023.0454. [ABSTRACT FROM AUTHOR]

Managing on-time delivery systems is challenging because of the underlying uncertainties and combinatorial nature of the routing decision. In practice, the efficiency of such systems also hinges on the driver's familiarity with the local neighborhood. In "Provably Good Region Partitioning for On-Time Last-Mile Delivery," Carlsson et al. study a region partitioning policy to minimize the expected delivery time of customer orders in a stochastic and dynamic setting. This policy assigns every driver to a subregion, ensuring that drivers are only dispatched to their territories. The authors characterize the structure of the optimal partitioning policy and show its expected on-time performance converges to that of the flexible dispatching policy in heavy traffic. The optimal characterization features two insightful conditions that are critical to the on-time performance of last-mile delivery systems. Furthermore, the paper develops partitioning algorithms with performance guarantees, leveraging ham sandwich cuts and three-partitions from discrete geometry. On-time last-mile delivery is expanding rapidly as people expect faster delivery of goods ranging from grocery to medicines. Managing on-time delivery systems is challenging because of the underlying uncertainties and combinatorial nature of the routing decision. In practice, the efficiency of such systems also hinges on the driver's familiarity with the local neighborhood. This paper studies the optimal region partitioning policy to minimize the expected delivery time of customer orders in a stochastic and dynamic setting. We allow both the order locations and on-site service times to be random and generally distributed. This policy assigns every driver to a subregion, hence making sure drivers will only be dispatched to their own territories. We characterize the structure of the optimal partitioning policy and show its expected on-time performance converges to that of the flexible dispatching policy in heavy traffic. The optimal characterization features two insightful conditions that are critical to the on-time performance of last-mile delivery systems. We then develop partitioning algorithms with performance guarantees, leveraging ham sandwich cuts and three-partitions from discrete geometry. This algorithmic development can be of independent interest for other logistics problems. We demonstrate the efficiency of the proposed region partitioning policy via numerical experiments using synthetic and real-world data sets. Funding: The first author gratefully acknowledges the support of the Office of Naval Research [Grant N00014-21-1-2208] and METRANS [Grant PSR-21-22]. The second author gratefully acknowledges the support of the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2022-04950] and the National Natural Science Foundation of China [Grant 72242106]. The third author gratefully acknowledges the support of the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2023-04453]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2021.0588. [ABSTRACT FROM AUTHOR]

We study a courier dispatching problem in an on-demand delivery system in which customers are sensitive to delay. Specifically, we evaluate the effect of temporal pooling by comparing systems using the dedicated strategy, with which only one order is delivered per trip, versus the pooling strategy, with which a batch of consecutive orders is delivered on each trip. We capture the courier delivery system's spatial dimension by assuming that, following a Poisson process, demand arises at a uniformly generated point within a service region. With the same objective of revenue maximization, we find that the dispatching strategy depends critically on customers' patience level, the size of the service region, and whether the firm can endogenize the demand. We obtain concise but informative results with a single courier and assuming that customers' underlying arrival rate is large enough, meaning a crowded market, such as rush hour delivery. In particular, when the firm has a growth target and needs to achieve an exogenously given demand rate, using the pooling strategy is optimal if the service area is large enough to fully exploit the pooling efficiency in delivery. Otherwise, using the dedicated strategy is optimal. In contrast, if the firm can endogenize the demand rate by varying the delivery fee, using the dedicated strategy is optimal for a large service area. The reason is that it is optimal for the firm to sustain a relatively low demand rate by charging a high fee for a large service radius: within this large area, the pooling strategy leads to a long wait because it takes a long time for multiple orders to accumulate. Moreover, with an exogenous demand rate to meet, customers' patience level has no impact on the dispatch strategy. However, when the demand rate can be endogenized, the dedicated strategy is preferable if customers are impatient. Furthermore, we extend our model to account for social welfare maximization, a hybrid contingent delivery policy, a general arrival rate that does not have to be large, a nonuniform distribution of orders in the service region, and multiple couriers. We also conduct numerical analysis and simulations to complement our main results and find that most insights in our base model still hold in these extensions and numerical studies. This paper was accepted by Jeannette Song, operations management. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4858. [ABSTRACT FROM AUTHOR]

We study how social connectedness affected active mutual fund manager trading behavior in the first half of 2020. In the first quarter during which the coronavirus disease 2019 (COVID-19) outbreak occurred, fund managers located in or socially connected to COVID-19 hotspots sold more stock holdings compared with a control group of unconnected managers. The economic impact of social connectedness on stock holdings was comparable with that of COVID-19 hotspots and was elevated among "epicenter" stocks most susceptible to the pandemic shock. In the second quarter, social interaction had an overall negative effect on fund performance, but this effect depended on manager skill; unskilled managers who were connected to the hotspots underperformed, whereas skillful managers suffered no deleterious effect. Our evidence suggests that social connections can intensify salience bias for all but the most skilled institutional investors, and policy makers should be wary of the destabilizing role of social networks during market downturns. This paper was accepted by Gustavo Manso, finance. Funding: This work was supported by the Social Science and Humanities Research Council of Canada. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4814. [ABSTRACT FROM AUTHOR]

This study investigates whether investors can reap economic benefits from analyzing differences in analyst quality. Although high-quality analysts' average forecast is more accurate than the consensus forecast for firms with a large analyst following, the benefits of using high-quality analysts' average forecasts are not economically significant. In contrast, the value of analyst quality differentiation exists in the second moment of forecasts. High-quality analysts' forecast dispersion gives investors an advantage in dealing with uncertainty by predicting return volatility and providing opportunities for economically significant returns using option straddle and post-earnings announcement drift investment strategies. This paper was accepted by Suraj Srinivassan, accounting. Funding: A. Rubin and A. Vedrashko thank the financial support of the Social Sciences and Humanities Research Council of Canada (SSHRC). Supplemental Material: The data are available at https://doi.org/10.1287/mnsc.2023.4699. [ABSTRACT FROM AUTHOR]

More than 40 years ago, Lions and Mercier introduced in a seminal paper the Douglas–Rachford algorithm. Today, this method is well-recognized as a classic and highly successful splitting method to find minimizers of the sum of two (not necessarily smooth) convex functions. Whereas the underlying theory has matured, one case remains a mystery: the behavior of the shadow sequence when the given functions have disjoint domains. Building on previous work, we establish for the first time weak and value convergence of the shadow sequence generated by the Douglas–Rachford algorithm in a setting of unprecedented generality. The weak limit point is shown to solve the associated normal problem, which is a minimal perturbation of the original optimization problem. We also present new results on the geometry of the minimal displacement vector. Funding: The research of H. H. Bauschke and W. M. Moursi was partially supported by Discovery Grants of the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2018-03703 and RGPIN-2019-04803], respectively. [ABSTRACT FROM AUTHOR]

Patient scheduling is a difficult task involving stochastic factors, such as the unknown arrival times of patients. Similarly, the scheduling of radiotherapy for cancer treatments needs to handle patients with different urgency levels when allocating resources. High-priority patients may arrive at any time, and there must be resources available to accommodate them. A common solution is to reserve a flat percentage of treatment capacity for emergency patients. However, this solution can result in overdue treatments for urgent patients, a failure to fully exploit treatment capacity, and delayed treatments for low-priority patients. This problem is especially severe in large and crowded hospitals. In this paper, we propose a prediction-based approach for online dynamic radiotherapy scheduling that dynamically adapts the present scheduling decision based on each incoming patient and the current allocation of resources. Our approach is based on a regression model trained to recognize the links between patients' arrival patterns and their ideal waiting time in optimal off-line solutions when all future arrivals are known in advance. When our prediction-based approach is compared with flat-reservation policies, it does a better job of preventing overdue treatments for emergency patients and also maintains comparable waiting times for the other patients. We also demonstrate how our proposed approach supports explainability and interpretability in scheduling decisions using Shapley additive explanation values. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: Mitacs Accélération IT26995 and Canada Research Chair in Analytics and Logistics in Healthcare (HANALOG). Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1289) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0342) at (http://dx.doi.org/10.5281/zenodo.7579533). [ABSTRACT FROM AUTHOR]

Decentralized exchanges (DEXs) are an essential component of the nascent decentralized finance (DeFi) ecosystem. The most common DEXs are so-called automated market makers (AMMs): smart contracts that pool liquidity and process trades as atomic swaps of tokens. AMMs price transactions with a deterministic liquidity invariance rule that only uses the AMM's token deposits as inputs and that has no precedent in traditional finance. Yet, in the context of transparent and open blockchain operations, any liquidity invariance pricing function allows so-called sandwich attacks (akin to front running) that increase the cost of trading and threaten the long-term viability of the DeFi ecosystem. Invariance pricing is also not regret free. Linear pricing rules have similar problems except for uniform pricing, which has regret-free prices and limits sandwich attack profits but which invites excessive order splitting. Comparing trading costs using a model of liquidity provision, constant product pricing is often cheaper except when the variance of the underlying asset is small or when the order is large. This paper was accepted by Will Cong, Special Section of Management Science: Blockchains and Crypto Economics. Funding: A. Park received financial support from the Global Risk Institute and the Social Sciences and Humanities Research Council of Canada [Grant 435-2017-0647]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.02802. [ABSTRACT FROM AUTHOR]

In this paper, for partially observed Markov decision problems (POMDPs), we provide the convergence of a Q learning algorithm for control policies using a finite history of past observations and control actions, and consequentially, we establish near optimality of such limit Q functions under explicit filter stability conditions. We present explicit error bounds relating the approximation error to the length of the finite history window. We establish the convergence of such Q learning iterations under mild ergodicity assumptions on the state process during the exploration phase. We further show that the limit fixed point equation gives an optimal solution for an approximate belief Markov decision problem (MDP). We then provide bounds on the performance of the policy obtained using the limit Q values compared with the performance of the optimal policy for the POMDP, in which we also present explicit conditions using recent results on filter stability in controlled POMDPs. Whereas there exist many experimental results, (i) the rigorous asymptotic convergence (to an approximate MDP value function) for such finite memory Q learning algorithms and (ii) the near optimality with an explicit rate of convergence (in the memory size) under filter stability are results that are new to the literature to our knowledge. Funding: This research was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada. [ABSTRACT FROM AUTHOR]

We study the problem when a firm sets prices for products based on the transaction data, that is, which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers' valuations and only assumes, instead, that purchase choices satisfy incentive-compatible constraints. The uncertainty set of potential valuations of each past customer can then be encoded as a polyhedral set, and our approach maximizes the worst case revenue, assuming that new customers' valuations are drawn from the empirical distribution implied by the collection of such polyhedra. We study the single-product case analytically and relate it to the traditional model-based approach. Then, we show that the optimal prices in the general case can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. We further design three approximation strategies that are of low computational complexity and interpretable. In particular, the cutoff pricing heuristic has a competent provable performance guarantee. Comprehensive numerical studies based on synthetic and real data suggest that our pricing approach is uniquely beneficial when the historical data has a limited size or is susceptible to model misspecification. This paper was accepted by Omar Besbes, revenue management and market analytics. Funding: This work was supported by the Rotman School of Management (TD-MDAL Research Grant) and the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757, RGPIN-2020-04038, RGPIN-2020-06054, RGPIN-2021-04295]. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022.4651. [ABSTRACT FROM AUTHOR]

We examine the impact of product market competition on innovation in markets for technology. An innovator makes an investment in quality-improving innovation that can be licensed to one (targeted licensing) or all (market-wide licensing) product market competitors. Our model points to a U-shaped relationship between competition in licensee product markets and innovation in the market for technology: at low levels of competition, market-wide licensing is optimal, and competition reduces innovation, whereas at high levels of competition, targeted licensing is optimal and competition increases innovation. Our empirical analysis using a large panel of U.S. data provides clear support for these predictions linking competition, innovation, and licensing. This paper was accepted by Joshua Gans, business strategy. Funding: J.-E. (de) Bettignies gratefully acknowledges financial support by the Social Sciences and Humanities Research Council of Canada [Grant 435-2013-1863]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4574. [ABSTRACT FROM AUTHOR]

We study the maintenance of a machine that deteriorates according to a Markov process until it fails. When failure occurs (which is observable), corrective replacement is made. Otherwise, the machine can be in one of two unobservable working states, and the decision maker can choose production, inspection, or preventive replacement. The state is revealed upon inspection and is reset by corrective or preventive replacement. The objective is to minimize the expected total discounted cost over an infinite horizon. We derive an exact, analytical solution to this problem via a dual framework for partially observable Markov decision processes. The solution can be easily computed without value iteration. We identify six possible structures of the optimal solution, which are represented as graphs. Each graph contains an absorbing, cyclic subgraph that governs the steady-state behavior of the machine. The exact analytical solution facilitates comparative statics analysis, comprehensive numerical studies, and the generation of insights. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2014-04979]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4547. [ABSTRACT FROM AUTHOR]

A quantum computer exhibits quantum advantage when it can perform a calculation that a classical computer is unable to complete. It follows that a company with a quantum computer would be a monopolist in the market for such a calculation if its only competitor was a company with a classical computer. Conversely, economic outcomes are unclear if quantum computers do not exhibit a quantum advantage, but classical and quantum computers have different cost structures. We model a Cournot duopoly where a quantum computing company competes against a classical computing company. The model features an asymmetric variable cost structure between the two companies and the potential for an asymmetric fixed cost structure, where each firm can invest in scaling its hardware to expand its respective market. We find that even if (1) the companies can complete identical calculations, and thus there is no quantum advantage, and (2) it is more expensive to scale the quantum computer, the quantum computing company may be more profitable and also invest more in market creation due to efficiency gains from using quantum algorithms. Finally, we provide examples of settings where the classical computer can also perform a calculation, but not in a cost-effective enough manner to be commercially viable. In such a setting, the quantum computing company becomes a monopolist despite exhibiting no quantum advantage. Taken together, quantum computers may not need to display a quantum advantage to be able to generate a quantum economic advantage for the companies that deploy them. This paper was accepted by D. J. Wu, information systems. Funding: R. G. Melko is supported by the Natural Sciences and Engineering Research Council of Canada, Canada Research Chair program, and the Perimeter Institute for Theoretical Physics. Research at the Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development Canada and by the Province of Ontario through the Ministry of Colleges and Universities. A. Goldfarb is supported by the Sloan Foundation and the Social Sciences and Humanities Council of Canada. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4578. [ABSTRACT FROM AUTHOR]

In "Operations Research: Topics, Impact and Trends from 1952–2019," A. Calma, W. Ho, L. Shao, and H. Li retrospectively look at 68 years of publication of the Operations Research. Using 5,440 journal articles, they highlight the top contributing countries and authors and top research methods and problems investigated. Mathematical programming is the most common research method, whereas inventory is the most investigated problem. Investigations related to pricing are growing significantly. The United States, Canada, and the United Kingdom publish the most papers, with the United States and Canada having similar publication profiles per capita. Inventory is the most popular research problem studied by North American, Asian, and Middle Eastern countries, whereas European countries focus on scheduling problems. Network visualizations of the journal's last 10 years show dynamic programming as the most used method and pricing as the most studied problem. Coauthor networks on collaborations on both dynamic programming and pricing are also shown. This paper is a retrospective look at 68 years of publication output of Operations Research, revealing changes in its publications, its authors, and their impact over time and how these changes might affect researchers and practitioners in the present. A total of 5,440 journal articles from its inception in 1952 to 2019 are used. The analysis initially focuses on the most studied topics and then continues with the top research methods and research problems investigated. The top contributing countries and authors to the most investigated research problems are also studied. The results indicate that mathematical programming is the most common research method, whereas inventory is the most investigated problem. However, investigations related to pricing are growing significantly. The United States, Canada, and the United Kingdom publish the most papers, with the United States and Canada having similar publication profiles per capita. Inventory is the most popular research problem studied by North American, Asian, and Middle Eastern countries, whereas European countries focus on scheduling problems. In order to understand the latest research trend, we visualize the networks of the last 10 years of Operations Research that show dynamic programming as the most used method and pricing as the most studied problem. We further visualize the coauthor networks on both dynamic programming and pricing to identify the most significant clusters of researchers and the topics these research clusters collaborate on. Finally, we provide researchers information about where Operations Research is and where it is likely heading. [ABSTRACT FROM AUTHOR]

Since 2016, Canada's federal government has pledged to factor in upstream emissions during the environmental impact assessment of fossil fuel energy projects. The upstream emissions attributable to a proposed project could be compared against a rejection threshold—a maximum permissible level of emissions—or the firm could be mandated to offset the attributed emissions. We adopt a cooperative game-theoretic model and propose the nucleolus mechanism to apportion upstream emission responsibilities in a fossil fuel supply chain, represented by a directed tree, wherein the nodes correspond to various entities in the supply chain such as extractors, distributors, refineries, and end consumers. The nucleolus allocation avoids the distortionary effects of double counting and exhibits a certain consistency property that is especially important in a regulatory context wherein fossil fuel supply chains span multiple legal jurisdictions. We develop a polynomial-time algorithm to compute the nucleolus and further prove that it arises as the unique subgame perfect equilibrium allocation of a noncooperative game induced by two easily stated and verifiable policies, thereby providing an implementation framework. We then demonstrate the strong Nash stability of the nucleolus mechanism subject to the two policies, study its sensitivity to parameter changes, and characterize it on the basis of fairness considerations. Furthermore, under the common assumption that the emissions allocated to a firm and the resulting financial penalties do not impact the revenues from the firm's core operations, we also provide lower-bound guarantees on the welfare gains it delivers to firms in the fossil fuel supply chain and on the incentives it offers such firms to adopt emission abatement technologies. Finally, we contextualize our discussion with a case study on a proposed expansion of the Trans Mountain pipeline in Western Canada. This paper was accepted by Chung Piaw Teo, optimization. [ABSTRACT FROM AUTHOR]

The article presents an interregional analysis of the North American newsprint industry in the U.S. and Canada. The manufacture of newsprint is divided into three stages: harvesting the wood, making it into pulp and converting the pulp into paper. The article describes a model of the North American newsprint industry capable of predicting the effect of changes in variables such as income, population, and wood price, on regional production and prices. The model belongs to the class of interregional activity analysis. Manufacturing costs include labor, materials, administration, and depreciation. Pulpwood, residues, and recycled newspaper are the primary products used in making newsprint. The basic relationship relating consumption of newsprint per capita to income per capita and price is assumed to be constant over time and across regions. Therefore, shifts of the demand equations over time are due entirely to changes in regional populations and incomes. The usefulness of the model is its ability to quickly provide projections for a wide range of assumptions, or scenarios, that an analyst may want to investigate.

The ongoing shortage of pharmaceutical drugs critically threatens public health. With increasing industry consolidation, operational disruptions at a firm can lead to a nationwide shortage of life-saving drugs. In 2012, the U.S. Food and Drug Administration mandated all manufacturers to report any manufacturing interruption that can potentially cause shortages. The goal of the mandate was to mitigate drug shortages by enhancing operational transparency in the pharmaceutical industry. Subsequently, other countries such as Canada have also begun mandating reporting of interruptions to alleviate drug shortages. We leverage the policy changes in the United States and Canada to understand the impact of mandated reporting induced operational transparency on alleviating the extent of drug shortages. Using the data on time-to-recovery for individual drug-shortage incident and annual-days-of-shortage for each drug, we find that the new policy alleviates drug shortages, but its effectiveness is contingent upon the prevailing level of competition in the product category. Although the intervention is not as impactful under a monopoly, the mandate is most effective under a duopoly, and its impact wanes as competition intensifies. In the absence of the mandate-induced transparency, competition does not necessarily alleviate shortages, but with the regulation, competition can relieve drug shortages. Our results potentially offer healthcare providers and policymakers the impetus to alleviate drug shortages by mandating interruption reporting and improving operational transparency. This paper was accepted by Charles Corbett, operations management. [ABSTRACT FROM AUTHOR]

This paper reports on the 40-year experience of academia-industry interfacing in the operations research (OR) field in Montréal. We focus on five spin off companies that academic entrepreneurs from the CIRRELT and the GERAD created between 1976 and 2003: INRO Consultants, GIRO, AD OPT, Omega Optimisation/Planora, and ExPretio. The importance of university spin offs for knowledge transfer is well documented in fields such as biology and nanotechnology; however, few papers have studied university spin offs in OR. Yet, OR has an enormous impact on society, and university spin off firms play a key role in the diffusion of research to the world of practitioners. In this paper, we tell the story of five companies created by academics from two world-renowned OR research centers based in Montréal, and we derive lessons about academia-industry interfacing in the OR field. By so doing, we hope to improve our understanding of the creation of fruitful relationships between academics and OR practitioners. [ABSTRACT FROM AUTHOR]

We propose a structural model of correlated learning with indirect inference to explain late-mover advantages. Our model focuses on a class of products with the following two features: (i) products that build on a common fundamental technology (e.g., computer processor, car, smartphone, etc.) and (ii) that consumers can observe some product attributes of a product (e.g., CPU clock speed, horsepower of a car engine, screen size of a smartphone, etc.), but when making their purchase decisions, consumers are not sure how efficiently the product can translate its observed attributes to performing tasks that they care about. For products that base on a similar technology, it is plausible that consumers use the information signals of one product's technological efficiency to help them update their belief about another product's technological efficiency within the same product category. As a result, a late entrant could benefit from the information spillover generated by an early entrant. We apply our framework to the statin market in Canada, where drugs rely on a similar mechanism to reduce the cholesterol level. In our model, patients/doctors can observe a statin's efficacy in reducing the cholesterol level, but they are uncertain about how effectively it can convert its cholesterol-reducing ability to reducing heart disease risks. Our estimation results show that the combination of correlated learning and informative and persuasive detailing explain the success of the two late entrants in the statin market: Lipitor and Crestor. This paper was accepted by Matthew Shum, marketing. [ABSTRACT FROM AUTHOR]

This paper studies consumers' revealed privacy choice in online display advertising using real-world transaction data from an ad exchange. We study consumer privacy choice in the context of online display advertising, where advertisers track consumers' browsing to improve ad targeting. In 2010, the American ad industry self-regulated by implementing the AdChoices program: consumers could opt out of online behavioral advertising via a dedicated website, which can be reached by clicking the overlaid AdChoices icons on ads. We examine the real-world uptake of AdChoices using transaction data from an ad exchange. Though consumers express strong privacy concerns in surveys, we find that only 0.23% of American ad impressions arise from users who opted out of online behavioral advertising. We also find that opt-out user ads fetch 52% less revenue on the exchange than comparable ads for users who allow behavioral targeting. These findings are broadly consistent with evidence from the European Union and Canada, where industry subsequently implemented the AdChoices program. We calculate that the inability to behaviorally target opt-out users results in a loss of about $8.58 in ad spending per American opt-out consumer, which is borne by publishers and the exchange. We find that opt-out users tend to be more technologically sophisticated, though opt-out rates are also higher in older and wealthier American cities. These results inform the privacy policy discussion by illuminating the real-world consequences of an opt-out privacy mechanism. [ABSTRACT FROM AUTHOR]

Every few years, the city of Edmonton, Canada must review and evaluate changes to its electoral district boundaries. The review process that was completed in 2009 resulted in modifying the district plan from a six-ward system with two council members in each to a single-member 12-ward system. The authors of this paper designed the redistricting plan. This paper describes the algorithm we applied to solve the problem and the decision support system we used. The algorithm is based on a multicriteria mathematical model, which is solved by a tabu search heuristic embedded within a geographic information system (GIS)-based decision support system. The resulting district plan meets districting criteria, including population balance, contiguity, compactness, respect for natural boundaries, growth areas, and integrity of communities of interest. This plan was formally approved as a city bylaw and used in the municipal elections in 2010. [ABSTRACT FROM AUTHOR]

We study how misaligned language between the investor and the firm contributes to the underweighting of foreign securities in an international portfolio. In particular, we document a significant U.S. institutional investor bias against firms located in Quebec relative to firms located in the rest of Canada (ROC). The differential bias is surprising given that (i) Quebec and the other Canadian provinces share the same nationality, federal law, stock exchange, and accounting standards; (ii) their regulatory filings are prepared in English and French; and (iii) U.S. institutional investors are sophisticated and located close to Quebec and the ROC. We also examine Quebec firms with different levels of French versus English online presences as well as those with CEOs who have U.S. work experience or board members or financial analysts who reside in the United States. We find that each factor affects the relative underweighting of investment in Quebec versus the ROC. Finally, we contrast the holdings of institutional investors located in the United Kingdom and France to bolster our conclusion that incongruent languages contribute to the underweighting of Quebec firms relative to firms in the ROC. This paper was accepted by Suraj Srinivasan, accounting. [ABSTRACT FROM AUTHOR]

In this paper, we investigate toll setting as a policy tool to regulate the use of roads for dangerous goods shipments. We propose a mathematical formulation as well as a solution method for the hazardous materials toll problem. Based on a comparative analysis of proposed mathematical models, we show that toll policies can be more effective than the popular network design policies that identify road segments to be closed for vehicles carrying hazardous materials. We present a summary of computational experiments on a problem instance from Western Ontario, Canada. [ABSTRACT FROM AUTHOR]

This paper analyzes a waiting line system that is motivated by the operations of border-crossing stations between the United States and Canada. There are two main conflicting goals in such a system: high security level, which often leads to a longer line; and good customer service, which requires a shorter line. Thus, unlike other queueing systems, maintaining the average queue length within a certain range is the primary objective. This is achieved using a staffing policy, called "congestion-based staffing," or CBS, where the number of servers (inspection booths) is adjusted according to the queue length during a planning period. We first present an exact benchmark model of Markovian type based on the matrix-geometric solution. For practical CBS policies, we develop a set of closed-form formulas for the major performance measures based on regenerative cycle analysis and fluid limit approximation. Numerical examples show that these approximation formulas are simple, accurate, and robust for practitioners to use in designing CBS policies. [ABSTRACT FROM AUTHOR]

In this paper the authors present an interpretive discussion of the scope for management scientists to make a more effective contribution to improving the performance of the Canadian economy, by active involvement in management and decision making in Canadian institutions, both private and public. Initially the paper surveys the evidence on the differences in economic performance between Canada and the United States, outlining the methods used, and the results. Some factors turn out to be relatively unimportant in the differences, while others are quite crucial. In the broad area of manufacturing, the gap in economic performance is particularly wide. Tariffs in Canada and other countries have become important contributions to the existing product diversity, short production runs, high cost and low productivity in relation to performance in other countries. A further important area is the pattern of decision making in Canada which has led to a survival strategy and resistance to change rather than innovation, creativity and risk taking. This tends to perpetuate the lower levels of productivity found to exist. In the light of this analysis of economic factors and decision making, the paper makes some broad recommendations on the role of management scientists, the need for change in management style, and changes in tariffs that would accelerate change and would recognize the importance of interdependence for more efficient performance. The paper concludes on the theme "The Time is Now!". [ABSTRACT FROM AUTHOR]

The conditions under which interdisciplinary research helps improve research performance are explored in a heterogeneous sample of 67 ongoing academic interdisciplinary teams from the United States and Canada. We conclude that interdisciplinary research is more appropriate for very difficult research questions and at early stages of the research process. Interdisciplinary research was found to inhibit the outputs of articles, books, papers, and technical reports when the research had a clearly identified client prior to the start of the project. Interdisciplinary research was not found to be more helpful for either applied or pure research. It was not found to be helpful either at the concluding stage of research or when used throughout the research process. [ABSTRACT FROM AUTHOR]

This paper describes our experience constructing a spreadsheet implementation of a dynamic ammunition requirements model for Canadian Army training. Each year, the Canadian Army offers a variety of training courses and expends approximately $40 million in ammunition costs to run these courses. It was budgeting over $60 million, and this created a number of organizational problems. The Canadian Army needed a planning tool to bring budgeted expenditures into line with actual expenditures. Our 14 MB spreadsheet implementation accomplished this objective, and the Canadian Army is very pleased with the result. [ABSTRACT FROM AUTHOR]

This paper discusses decomposition of a multiregional economic equilibrium model that is characterized by a cost minimizing, linear programming (LP) model of the supply side and a vector-valued function that gives demand prices as functions of the quantities demanded. Our motivation is to ease model development and maintenance by a solution method that links separately developed regional models only when a consistent multiregion solution is desired. A heuristic strategy is described to extend any existing (LP) decomposition principle to a procedure for decomposing an equilibrium model by region. This strategy is applied to extend Dantzig-Wolfe decomposition to the multiregional economic equilibrium model, and several theoretical results are derived for the resulting algorithm. The central result is a proof of asymptotic convergence, under usefully general conditions. The extended Dantzig-Wolfe procedure is illustrated with an existing, two-region model of Canadian energy supplies and demands. [ABSTRACT FROM AUTHOR]

This case heard before the National Energy Board (NEB) in 1966 and the decision of Lester B. Pearson's government to reject the Trans Canada Pipe Lines-Great Lakes Gas Transmission project in its original form, marks the first occasion where management science had a determining impact on Canadian public policy. It also marked a psychological breakthrough within the NEB's organization as it provided its newly created Operations Research Branch with the essential credibility base in order to effectively influence NEB's decision-making process. This paper describes TCPL's application of 1966 to expand East/West flow of natural gas by construction of a new, larger diameter pipeline, passing in the United States, south of the Great Lakes rather than expanding the TCPL system north of the Great Lakes. In support of its application, Trans Canada filed an extensive study to demonstrate that the Great Lakes proposal would result in lower costs of natural gas transportation than the expansion of its existing system and would open up new markets for Canadian natural gas in the north central region of the U. S. This paper shows how Operations Research Branch dissented with Trans Canada's position and managed to convince NEB's chairman and key branch heads that a number of staff studies had to be performed before arriving at a conclusion. These O-R studies ranged from the assessment of new pipeline technology to the use of various O-R techniques. These O-R studies met with varying degrees of acceptance; however, the contribution that had the most significant impact was a new methodology for comparison of transportation costs via two alternative routes serving different markets. Furthermore, the O-R branch performed differential economic impact studies to take into account the multiplier effects on the Canadian economy of the two projects which indicated an estimated advantage of a billion dollars in favour of expanding the Northern route. In the end a compromise solution which [ABSTRACT FROM AUTHOR]

This paper presents two models which allow long run demand for a new product to be estimated prior to any significant sales history. The specific product studied is residential solar heating in Canada. Both models are based on the concept that consumers react in their purchasing decisions to the inherent package of characteristics in a commodity. The use of survey information to enable specific numerical estimates to be made is exemplified. Directions for future research to improve the usefulness of such models are suggested. [ABSTRACT FROM AUTHOR]

This paper describes a benefit-cost analysis of a proposed $1.4 million runway extension at Kelowna Airport, in Kelowna, British Columbia (Canada's westernmost province). Lengthening the runway as proposed would allow Pacific Western Airlines to operate their Boeing 737 aircraft out of Kelowna with heavier payloads, thereby reducing the occurrence of situations where the combined passenger and cargo demand exceeds the available weight capacity of the aircraft. The problem is complicated by the fact that the maximum payload weight permitted on take-off varies considerably with the air temperature and wind speed and direction at the time of departure. On hot summer days, when the problem is most severe, it is sometimes necessary to remove all of the cargo from the aircraft, and at times, to limit the number of passengers taking the flight to less than the number of seats. An alternative means of achieving the objectives of the runway extension was also considered: lowering the average demand per flight by simply increasing the number of flights out of Kelowna. The "Runway Extension" and "Additional Flights" options were evaluated in terms of their net economic benefits, as compared with the "Do Nothing" option. Net economic benefit is the present value of economic benefits less the present value of economic costa. The economic benefits depend upon the extent to which unsatisfied passenger and cargo demand would be reduced, and on the unit economic cost of unsatisfied passenger and cargo demand, Estimates of the reductions in unsatisfied demand were obtained by means of a probability model that is similar to ones used to solve single-period, stochastic inventory planning problems. The unit economic costs were not estimated; rather, the economic benefits were expressed as linear functions of the unit economic costs of unsatisfied passenger and cargo demand. The economic cost of the "Runway Extension" option was the cost of constructing and maintaining the runway extension; the cost of the "Additional Flights" options was the cost to Pacific Western Airlines of providing additional flights out of Kelowna. The numbers of such additional flights were chosen to produce the same amount of unsatisfied passenger and cargo demand, thus determining which of the three options was preferred on economic grounds. Prior to the study, Transport Canada had formally recommended the proposed runway extension to the Treasury Board (the Cabinet Committee of the Canadian Government that controls government expenditures). The Treasury Board requested the Department to reconsider the project, and as a result, a benefit-cost analysis was undertaken. Because of the conclusions of the study, Transport Canada decided to postpone indefinitely the lengthening of the runway. The runway may be lengthened sometime in the future to accommodate aircraft that require a longer runway than does the Boeing 737, but only if a subsequent analysis shows that the resulting benefits would exceed the costs. Having been successfully applied at Kelowna Airport, the methodology developed in this study has now been adopted by Transport Canada for evaluating proposed runway extensions at other Canadian airports. [ABSTRACT FROM AUTHOR]

In this paper a probabilistic branch and bound procedure is developed for the ambulance location problem. Information on system characteristics under various ambulance assignments and dispatch rules was provided by a digital simulation. The output of the simulation was also used to construct the objective function for the optimal location problem. The procedure was used to determine ambulance locations in Vancouver, Canada. Solutions obtained from the branch and bound procedure were verified by simulation runs. [ABSTRACT FROM AUTHOR]

The whole concept of modelling has caught on, aided immeasurably by the considerable progress in computer technology. Mini-computers, timesharing systems, remote terminals, and graphical displays have placed computer power directly into the hands of the decision maker. This paper presents a corporate decision-aiding system, a family of timeshared models, developed for Xerox of Canada Limited (XCL) in an effort to link the functional areas for communication, planning, and control purposes. Using the approaches of statistical forecasting, heuristics, and simulation, the XCL Decision System assists and guides management all the way from deriving product demand forecasts to simulating the day-to-day operations of the firm to formulating divisional financial statements to preparing corporate operating and medium-range plans. Developed in a short time span and at low cost, the system is used extensively in Canada and has been adopted, in part, by U.S. corporate and regional offices. [ABSTRACT FROM AUTHOR]

The Government of Alberta has undertaken a study to determine the feasibility of supplying natural gas to rural areas of the Province not currently served. The study covers various aspects relevant to the development of an overall plan for rural gasification, but the emphasis is on major factors affecting the economic viability of the venture. We discuss in this paper one segment of the study -- the problem of determining the demand for natural gas in rural areas. Three separate demand schedules, each based on a different methodology but using common data obtained from a survey of rural residents, are estimated. The three demand schedules are analysed and compared in an attempt to establish an overall demand curve for natural gas in rural Alberta. The price elasticity of demand for natural gas is determined from the demand function obtained. [ABSTRACT FROM AUTHOR]

The article presents updates concerning the field of operations research as of 1960. The Seventh International Meeting of the Institute of Management Sciences will be held in New York City from October 19 to 22. The Argentine Operations Research Society had elected officers of its directing council. The Operations Research Association of Bangalore held its Third Annual General Meeting in Bangalore, India in February 27. The Canadian Operations Research Society has elected D. B. DeLury as president.

This paper employs multiattribute value assessment and risk analysis to evaluate the benefits of four alternatives to improve electrical system reliability in British Columbia. A multiattribute value model is constructed as a cost-equivalent function, based on value judgments provided by a group of senior system planners and two senior vice presidents from BC Hydro. Using data drawn from the utility's service records and probabilities elicited from the utility's technical specialists, a risk analysis is developed to estimate the magnitude and duration of outages associated with the various alternatives. The value model and probabilities are combined to estimate the expected equivalent costs of outages over time, for the four alternatives. The influence of this analysis on BC Hydro's transmission system planning is also discussed. [ABSTRACT FROM AUTHOR]

In this paper we use an optimal control model to examine whether early exploitation of the vast Alberta tax sands resource may be justified due to constraints on the availability of construction resources for tax sands plants. We model conventional oil as an exhaustible resource whose unit production cost increases with cumulative production, and tar sands oil as a virtually inexhaustible resource (a backstop) having constant unit production cost initially much higher than that of conventional oil. There is a finite, time-dependent maximum rate of increase in tar sands production capacity. The problem involves a state-space constraint inasmuch as the tar sands production rate (a state variable) cannot exceed the exogeneous demand rate at any time. In a discounted cost formulation for sufficiently large finite, or infinite, planning periods, we show that the optimal policy is to use conventional oil alone until some critical time and then expand tar sands capacity at a maximum rate until it equals total demand, after which all demand is met through tar sands oil alone. We discuss an unexpected "cost overshoot"--the unit cost of conventional oil exceeds that of tar sands oil at the time of transition to total reliance on the tar sands backstop. Using plausible functional forms and parameter values, we conclude that construction of new tar sands plants should be deferred for several years, although other considerations not dealt with in our examination may have a significant effect on this conclusion. [ABSTRACT FROM AUTHOR]

In this paper, we present a general procedure for fitting the weighted l[sub p] norm in order to estimate travel distances in a distribution system. An important finding of our study is that the search for the best-fitting value of the parameter p can be restricted to the interval (0, 2], provided the reference axes of the distance function are oriented to match the directional bias inherent in the transportation network. The advantages of our methodology and particularly the importance of properly aligning the reference axes are demonstrated through a case study of the road system in Southern Ontario. Although attention is restricted to the weighted l[sub p] norm, the concepts presented are readily extended to other distance functions such as the block norms of Ward and Wendell. [ABSTRACT FROM AUTHOR]

This paper describes an economical method for obtaining speeds, flows and densities from low quality time-lapse film that is unsuitable for the more common data reduction techniques, most of which require vehicle tracking. The method is developed from some very basic ideas and is illustrated in a description of a recent application--a study involving 16-mm time lapse film coverage of a 1400 m long freeway segment in Toronto, Canada. Average speeds were comparable to those obtained by tracking individual vehicles. [ABSTRACT FROM AUTHOR]

This paper examines the problems of routing freight traffic, scheduling train services and allocating classification work between yards on a rail network. It describes a general optimization model which takes into account the interactions between these activities and develops global strategies for their management over a medium term planning horizon. A heuristic algorithm developed to solve this nonlinear, mixed-integer multicommodity flow problem is also presented with some computational results based on an example at the Canadian National Railroads. [ABSTRACT FROM AUTHOR]

This paper reports on visits to 24 national operations-research societies (two in Germany) and to operations researchers in ten other countries. It considers each visit to a society specific-ally, plus some of the visits in countries not having societies. My purpose was to promote closer relations between the OPERATIONS RESEARCH SOCIETY OF AMERICA and operations researchers of other countries. The first visits were made during July-August of 1967,in conjunction with the Operations Research Around-the-World Meetings. Canada was visited in November, Central and South America in December-January, and the remaining countries in Europe during March-April of 1968. These visits included all of the world's major operations-research societies, and some others. [ABSTRACT FROM AUTHOR]

Presents the author's speech given at the First Annual Conference of the Canadian Operational Research Society in Toronto, Ontario in May 1959. Operational research of the early years; Control of the quality of operational research.

A preface is presented for the December 1973 Canadian issue of "Management Science."

Entrepreneurs that were employed by successful industry incumbents prior to founding tend to confer advantages on their new organizations. We propose and then demonstrate a similar "network progeny" effect rooted in the social relationships that form among entrepreneurs. Our analysis of new entrants into the Ontario wine industry shows that prefounding friendship ties of the founders of one especially prominent entrepreneurial firm led to significantly higher ice wine prices. This attests to the promise of a network progeny extension of the parent-progeny account of new firm success. Follow-on analysis indicates that this effect is not attributable to an entrant's ability to make ice wines of superior quality or to it having access to better distribution knowledge. We therefore conclude that having a social tie to this prominent entrepreneurial firm generated reflected prominence that enhanced the valuations and therefore prices of wines made by connected market entrants. [ABSTRACT FROM AUTHOR]

The eight-team Canadian Football League (CFL) features an 18-game regular season played between late June and early November. The regular-season schedule is manually created using an iterative process involving league management, teams, and a television sports broadcaster. We developed a mixed-integer program-driven decision support system that provided league officials with multiple schedule versions relatively quickly. Our approach informed the development of the regular-season schedule by offering league management a neighborhood of potentially solid solutions. We demonstrated the impact of specific schedule requests to decision makers. Such sensitivity analysis was impossible under the previous manual creation method. [ABSTRACT FROM AUTHOR]

The article focuses on the analysis of the relationship between strategic alliance networks such as closure and bridging ties with their performance on investment banks in Canada from 1952 to 1990. It states that four-year moving windows are used in the study to test several hypotheses where the effect of the ties' ages to the performance of banks was identified at the organization level. It adds that the underwriting syndicate memberships of the banks became the bases for their networks. It says that bank performance or its market share grows as its youngest bridging ties reduce and its oldest closure tie develops.

We investigate whether cultural differences between professional decision makers affect financial contracts in a large data set of international syndicated bank loans. We find that more culturally distant lead banks offer borrowers smaller loans at a higher interest rate and are more likely to require third-party guarantees.These effects do not disappear following repeated interaction between borrower and lender and are economically sizable: A one-standard-deviation increase in cultural distance, approximately the distance between Canada and the United States or between Japan and South Korea, is associated with a 6.5 basis point higher loan spread; the loan spread increases by about 23 basis points if the bank-firm match involves culturally more distant parties,for example, from Japan and the United States. We also find that cultural differences not only affect the relation between borrower and lender, but also hamper risk sharing between participant banks and culturally distant lead banks. [ABSTRACT FROM AUTHOR]