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We consider a principal who periodically offers a fixed and costly nonmonetary reward to agents to incentivize them to invest effort over the long run. An agent’s output, as a function of his effort, is a priori uncertain and is worth a fixed per-unit value to the principal. The principal’s goal is to design an attractive reward policy that specifies how the rewards are to be given to an agent over time based on that agent’s past performance. This problem, which we denote by [Formula: see text], is motivated by practical examples from both academia (e.g., a reduced teaching load) and industry (e.g., “Supplier of the Year” awards). The following “limited-term” (LT) reward policy structure has been quite popular in practice. The principal evaluates each agent periodically; if an agent’s performance over a certain (limited) number of periods in the immediate past exceeds a predefined threshold, then the principal rewards him for a certain (limited) number of periods in the immediate future. When agents’ outputs are deterministic in their efforts, we show that there always exists an optimal policy that is an LT policy and also, obtain such a policy. When agents’ outputs are stochastic, we show that the class of LT policies may not contain any optimal policy of problem [Formula: see text] but is guaranteed to contain policies that are arbitrarily near optimal. Given any [Formula: see text], we show how to obtain an LT policy whose performance is within ϵ of that of an optimal policy. This guarantee depends crucially on the use of sufficiently long histories of the agents’ outputs. We also analyze LT policies with short histories and derive structural insights on the role played by (i) the length of the available history and (ii) the variability in the random variable governing an agent’s output. We show that the average performance of these policies is within 5% of the optimum, justifying their popularity in practice. We then introduce and analyze the class of “score-based” reward policies; we show that this class is guaranteed to contain an optimal policy and also, obtain such a policy. Finally, we analyze a generalization in which the principal has a limited number for rewards in any given period and show that the class of score-based policies, with modifications to accommodate the limited availability of the rewards, continues to contain an optimal solution for the principal. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4482 .

Data-science algorithms and models changed the way we search for information on products and services, make payments, procure and trade, and along the way, changed how firms use individual-level consumer data, and how business transactions are created, documented, regulated, and analyzed. The emergence of data-intensive environments and algorithms also challenges management science research in many ways. It affects how knowledge is organized, produced, and assessed; how we search for answers to management questions, analyze information or validate insights and findings; and it challenges how we discover, design, describe, motivate, and replicate solutions. More importantly, it demands new theory to enable the development of models that exploit the type and volume of data available.

It is a fact that people make decisions and have been making decisions for a very long time. Contrary to what some of us who are interested in decision-making may like to believe, most people do not take seriously the existence of theories which purport to set their thinking and feeling right. They claim to know their own value system and what they want. They may wonder how anyone else can know well enough to tell them how best to organize their thinking in order to make better choices. Yet, research has shown that complex decisions are beyond the capacity of the brain to synthesize intuitively and efficiently. Since decision making is a natural characteristic of people, how do we describe what they do so that an ordinary mortal can understand what we are saying? We do not wish to legislate the method with which people should make decisions, but only to describe it even when it is prescribed by some method. In the process, we may learn things that can help people make better decisions. How? The Analytic Hierarchy Process (AHP) (Forman et al., Harker 1986, Harker and Vargas 1987, Saaty 1986, 1988a, b, Saaty and Vargas 1987, Xu 1988, Golden et al. 1989, Saaty and Alexander 1989) is a theory of measurement. When applied in decision making it assists one to describe the general decision operation by decomposing a complex problem into a multi-level hierarchic structure of objectives, criteria, subcriteria and alternatives. The AHP provides a fundamental scale of relative magnitudes expressed in dominance units to represent judgments in the form of paired comparisons. A ratio scale of relative magnitudes expressed in priority units is then derived from each set of comparisons. An overall rcatio scale of priorities is then synthesized to obtain a ranking of the alternatives. From its axioms to its procedures, the AHP has turned out to be historically and theoretically a different and independent theory of decision making from utility theory. Much as a dialogue evolved in mathematics around the consistency of different geometries and around absolute and relative space and time in physics, both to dispel absolute notions, those who believe that only utility theory can tell us the absolute truth about man's decision-making might take a close look at the AHP. It has found varied and serious applications. It also has a particular way of generating ratio scales and dealing with inconsistency in judgment that have contributed to its effectiveness in resource allocation and in the setting of priorities by a group of decision makers. Utility theory is a normative process. The AHP as a descriptive theory encompasses procedures leading to outcomes as would be ranked by a normative theory. But it must go beyond to deal with outcomes not accounted for by the demanding assumptions of a normative theory. We must briefly describe the AHP to enable the reader to see that a practicable theory based on ratio scales need not dilute itself to satisfy expectations of people who derive their understanding from a theory based on interval scales. This is particularly true if the rival theory, in aspiring for generality, also makes unrealistic assumptions, for example about the transitivity and consistency of preferences and the difficult use of lotteries

Organizations today have access to enormous data sets, and sophisticated business analytical tools are needed to harness the tremendous potential of these data to improve day-to-day decision making. To encourage further developments in this rapidly growing area of interdisciplinary research, Management Science will publish a special issue dedicated to business analytics. Applications of business analytics extend to nearly all managerial functions in an organization. Using microlevel data, mathematical models can become more realistic, be validated more easily, and match more closely true behavioral phenomena. Through business analytics, historical data can be used to discover relationships among important elements in an organization's environment, make more formal statistical inferences regarding the structure or strength of these relationships, and improve actions taken by the organization. Because these relationships are often not static, the entire cycle of identification, estimation, prediction, and decision making is repeated frequently and on an ongoing basis, leading to real-time and highly targeted data-driven decisions. We envision business analytics applied to many domains, including, but surely not limited to: digital market design and operation; network and social-graph analysis; pricing and revenue management; targeted marketing and customer relationship management; fraud and security; sports and entertainment; retailing to healthcare to financial services to many other industries. We seek novel modeling and empirical work which includes, among others, probability modeling, structural empirical models, and/or optimization methods. Submission: Please submit your manuscript online via http://mc.manuscriptcentral.com/ms You must select "Special Issue" as the Manuscript Type in Step 1 and select one of the issue's four coeditors in Step 5. Manuscripts will generally be assigned to one of the Associate Editors for this issue (see below; please recommend three in Step 4), but you may also recommend guest Associate Editors.

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Recently, a growing number of Operations Management researchers have started to investigate the interface between the two fields leveraging the institutional richness of Operations Management research, and proposing ways to operationally hedge operational and competitive exposures not effectively handled through typical financial hedging contracts. It is now timely to compile state-of-theart research at the interface of Finance and Operations Management in a special issue of Management Science. Such research will capitalize on the richness of the two fields while exploiting gaps left in their interfaces, with clear objectives: • contributing to the theory of at least one of the fields, while rigorously representing knowledge of the other. • creating managerial insights, practical knowledge and/or tools that general managers and risk management practitioners can use in their decisions and applications. The goal of this call for papers is to include research that shows conditions under which a tighter integration of both functions within and across enterprises leads to higher value creation, while at the same time advances the knowledge and creates tools for enterprise risk management. We solicit contributions spanning a diverse set of topics and using rigorous methodological approaches as appropriate to the question under study (theoretical modeling, conceptual frameworks, empirical, survey research, experimental etc.). Topics we are interested in, but not limited to: • Joint financing and capacity-investment choices • Integrated financial and operational hedging • Risk issues in commodity procurement • Real-options valuation and hedging in operations management • Global risk-management in manufacturing • Supply-chain contracting • Cross-functional approaches to managing large project risks • Risk measures, performance evaluation and incentives • Managing disruption and catastrophic risks in global supply chains: Building the Resilient Enterprise • Insurance and supply chain risks • Managing global outsourcing risks • Cross-functional approaches to managing new product and technology development risks • Managing operational risks in the service sector (banking, entertainment, travel, rentals etc.) • Operationalizing supply-chain risk management • Risk aversion in supply-chain management • Revenue management: financial implications of capacity-planning and dynamic pricing strategies • Risk considerations in revenue management • Special topics in sectors such as financial services and energy and commodity trading Submitted papers should feature original research contributions, display the rigor emphasized in the editorial statement of Management Science, clearly articulate their managerial relevance, and obey the journal’s author guidelines. The review process will be handled by the guest editors and an ad-hoc set of associate editors representing the fields of Operations and Supply Chain Management, Operations Research, Financial Economics and Applied Finance, and Financial Engineering. Submissions are due by July 31, 2007. Publication of the special issue is expected in early 2009. Paper provisions and submission guidelines are available at http://mansci.pubs.informs.org/.

In this paper, we consider the yet-uncharted assortment optimization problem under the exponomial choice model, in which the objective is to determine the revenue-maximizing set of products that should be offered to customers. Our main algorithmic contribution comes in the form of a fully polynomial-time approximation scheme, showing that the optimal expected revenue can be efficiently approached within any degree of accuracy. We synthesize several ideas related to approximate dynamic programming, intended to construct a compact discretization of the continuous state space by keeping track of “key statistics” in rounded form and by operating with a suitable bit precision complexity. We complement this result by a number of NP-hardness reductions to natural extensions of this problem. Moreover, we conduct empirical and computational evaluations of the exponomial choice model and our solution method. Focusing on choice models with a simple parametric structure, we provide new empirical evidence that the exponomial choice model can achieve higher predictive accuracy than the multinomial logit (MNL) choice model on several real-world data sets. We uncover that this predictive performance correlates with certain characteristics of the choice instance—namely, the entropy and magnitude of choice probabilities. Finally, we leverage fully ranked preference data to simulate the expected revenue of optimal assortments prescribed using the fitted exponomial and MNL models. On semisynthetic data, the exponomial-based approach can lift revenues by 3%–4% on average against the corresponding MNL benchmark. This paper was accepted by Chung Piaw Teo, optimization. Funding: The research of Danny Segev on this project was supported by the Israel Science Foundation [Grants 148/10 and 1407/20]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4492 .

Decision makers often ask experts to forecast a future state. Experts, however, can be biased. In the economics and psychology literature, one extensively studied behavioral bias is called herding. Under strong levels of herding, disclosure of public information may lower forecasting accuracy. This result, however, is derived only for point forecasts. In this paper, we consider experts’ probabilistic forecasts under herding, find a closed-form expression for the first two moments of a unique equilibrium forecast, and show that the experts report too similar locations and inflate the variance of their forecasts because of herding. Furthermore, we show that the negative externality of public information no longer holds. In addition to reacting to new information as expected, probabilistic forecasts contain more information about the experts’ full beliefs and interpersonal structure. This facilitates model estimation. To this end, we consider a one-shot setting with one forecast per expert and show that our model is identifiable up to an infinite number of solutions based on point forecasts but up to two solutions based on probabilistic forecasts. We then provide a Bayesian estimation procedure for these two solutions and apply it to economic forecasting data collected by the European Central Bank and the Federal Reserve Bank of Philadelphia. We find that, on average, the experts invest around 19% of their efforts into making similar forecasts. The level of herding shows an increasing trend from 1999 to 2007 but drops sharply during the financial crisis of 2007–2009 and then rises again until 2019. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The electronic companion and data are available at https://doi.org/10.1287/mnsc.2022.4487 .

This paper studies the investor’s investment decision in a standard financial contracting model. The investor chooses among entrepreneurs with heterogeneous production technologies who generate different probability distributions of cash flow under effort. I provide a complete characterization of optimal contracts, agency rents, and the investor’s investment decision. Differences in entrepreneurs’ production technologies imply differences in optimal contracts and agency rents across equally productive entrepreneurs, which biases and potentially distorts the investor’s investment decision. The results provide a complete characterization of entrepreneurs’ financial constraints in the presence of heterogeneous production technologies and uncover a fundamental link between production technologies, financial contracts, and financial constraints. This paper was accepted by Gustavo Manso, finance. Funding: This work was supported by the Swedish House of Finance.

This paper shows that investments based on deep learning signals extract profitability from difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular, excluding microcaps, distressed stocks, or episodes of high market volatility considerably attenuates profitability. Machine learning-based performance further deteriorates in the presence of reasonable trading costs because of high turnover and extreme positions in the tangency portfolio implied by the pricing kernel. Despite their opaque nature, machine learning methods successfully identify mispriced stocks consistent with most anomalies. Beyond economic restrictions, deep learning signals are profitable in long positions and recent years and command low downside risk. This paper was accepted by Kay Giesecke, finance. Funding: D. Avramov acknowledges the Israel Science Foundation (Grant 288/18) for financial support. S. Cheng acknowledges the General Research Fund of the Research Grants Council of Hong Kong [Project 14502318] for financial support. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4449 .

This paper seeks to demonstrate the impact of the gig economy on product quality in seemingly unrelated local industries through the labor market. Our empirical context is the quality of service for restaurants in the city of Austin, and we examine how they were impacted by the exogenous exit and reentry of rideshare platforms, Uber and Lyft, because of regulatory changes. We leverage these exogenous shocks and combine them with sentiment-analyzed data from Yelp reviews that capture how customers assess the quality of service at each restaurant. We show that, compared with control cities, customers in Austin become more negative about service quality when Uber and Lyft are present in the city. Additionally, we use rich data on employee turnover and wages to demonstrate that service staff turnover increases in Austin when Uber and Lyft are present compared with the control cities. We also conduct several additional studies and robustness checks that are all congruent with our hypothesis that Uber and Lyft lower the quality of service in Austin restaurants by raising their staff turnover. Together, these results suggest significant ramifications of the gig economy on the broader industries through the labor market. This paper was This paper was accepted by Matt Shum, marketing. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4481 .

This paper exploits a major food safety crisis to estimate a full demand model for the unsafe product and its substitutes, recovering consumers’ preference parameters for different product characteristics. Counterfactual exercises quantify the relevance of different mechanisms driving consumers’ responses, such as changes in safety perceptions, idiosyncratic taste, product characteristics, and price. We find that consumers’ reactions are limited by their preferences for the product’s observable and unobservable characteristics. Because of the costs associated with switching from the affected product, the decline in demand following a product-harm crisis tends to understate the true weight of such events in consumers’ utility. We find that unobservable taste is a crucial driver of consumers’ responses. Our counterfactual exercises illustrate that the demand would have declined further if consumers had had access to a closer substitute. For an accurate assessment of product-harm crises, managerial strategies should therefore account for how different demand drivers bind consumers’ substitution patterns. This paper was accepted by Matthew Shum, marketing. Funding: This work was supported by the Ministerio de Ciencia e Innovación [Grants ECO2008-01116 and ECO2010-15052], the Ministerio de Economía y Competitividad [Grant ECO2013-43011-P], and the Deutsche Forschungsgemeinschaft [Grant CRC TR 224]. R. Ferrer received support from [Grant ECO2016-76998-P], and the Severo Ochoa Programme for Centers of Excellence in R&D [SEV-2015-0563].

This paper examines an innovation in capital formation that has spurred contentious debate: the Umbrella Partnership Corporation (“Up-C”) IPO. Advisors and underwriters argue that the Up-C deal structure is a driver of post-IPO value and, thus, is a value-enhancing means of raising capital that may be one solution to concerns regarding the drop in the number of publicly traded companies. Consistent with these claims, recent research suggests that organizing soon-to-be public businesses as pass-through entities (as is the case for Up-Cs) leads to superior future performance. Yet, broadening the analysis to consider abnormal stock performance and post-IPO litigation of a larger and more recent sample of exclusively Up-C IPOs, we conclude just the opposite. While the Up-C deal structure increases IPO valuations and predicts positive post-IPO operating performance, the return performance of Up-C IPOs indicates that Up-C deals harm public shareholders. Further, despite their superior earnings performance, Up-C IPOs face a significantly higher rate of post-IPO litigation as compared with non-Up-C IPOs. Because IPO investors seemingly do not anticipate the myriad ways in which the Up-C deal structure might facilitate opportunism by pre-IPO owners, they frequently turn to litigation as an ex-post settling-up mechanism. Consequently, our paper offers the first empirical evidence of downsides associated with the Up-C deal structure for public shareholders and, in so doing, affords the rarity of having academic evidence lead (as opposed to respond to) a controversial debate. This paper was This paper was accepted by Victoria Ivashina, finance. Funding: The authors gratefully acknowledge the financial support of the NYU Stern School of Business, the J. Reuben Clark Law School, and the Marriott School of Business. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2022.4454 .

Assortment optimization concerns the problem of selling items with fixed prices to a buyer who will purchase at most one. Typically, retailers select a subset of items, corresponding to an "assortment" of brands to carry, and make each selected item available for purchase at its brand-recommended price. Despite the tremendous importance in practice, the best method for selling these fixed-price items is not well understood, as retailers have begun experimenting with making certain items available only through a lottery. In this paper we analyze the maximum possible revenue that can be earned in this setting, given that the buyer's preference is private but drawn from a known distribution. In particular, we introduce a Bayesian mechanism design problem where the buyer has a random ranking over fixed-price items and an outside option, and the seller optimizes a (randomized) allocation of up to one item. We show that allocations corresponding to assortments are suboptimal in general, but under many commonly-studied Bayesian priors for buyer rankings such as the MNL and Markov Chain choice models, assortments are in fact optimal. Therefore, this large literature on assortment optimization has much greater significance than appreciated before -- it is not only computing optimal assortments; it is computing the economic limit of the seller's revenue for these fixed-price substitute items. We derive several further results -- a more general sufficient condition for assortments being optimal that captures choice models beyond Markov Chain, a proof that Nested Logit choice models cannot be captured by Markov Chain but can be partially captured by our condition, and suboptimality gaps for assortments when our condition does not hold. Finally, we show that our mechanism design problem provides the tightest-known LP relaxation for assortment optimization under the ranking distribution model., Full version of EC2022 paper. Forthcoming in Management Science

This paper analyzes how the degree of task-related team diversity influences the team outcome. The focus is on a novel benefit of team diversity that does not depend on complementary skills or mutual learning. We consider a multiagent setting where the employer makes use of career competition to motivate the team. Our analysis reveals incentive effects that do not exist in the well-studied case of teams of size 2. In teams with more than two members, diversity may have a positive effect on the efforts of some members although diversity leads to unbalanced career competition. As a consequence, expected profits are higher under a moderate degree of diversity compared with a situation with completely homogeneous agents. This result shows that the employer prefers a positive degree of diversity for pure incentive reasons. This paper was accepted by Suraj Srinivasan, accounting. Funding: This work was supported by the Deutsche Forschungsgemeinschaft [Grants EXC 2126/1–390838866 and EXC-2047/1–390685813].

It is generally acknowledged that sunk cost bias leads to suboptimal decisions, such as escalation of commitment. Some researchers, however, suggest that sunk cost bias can be beneficial when consumers have self-control problems. In this paper we explore the case when consumers with sunk cost bias have time-inconsistent preferences and, therefore, suffer from self-control problems. We experimentally demonstrate that sunk costs can make subjects better off by inducing higher effort. We then develop an analytical model to explore the implications of sunk cost bias for firm’s pricing strategy. We find that, in the presence of sunk cost bias, higher prices can lead to higher experienced quality. We show that sunk cost bias can sometimes improve firm’s profits, lead to lower prices, and increase welfare. Our results suggest that, when consumers use a product for multiple periods, pricing policies such as 0% financing, which are often viewed as exploitative, can instead lead to lower total prices, higher profits, and higher welfare. This paper was accepted by Matthew Shum, marketing. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4479 .

Facial information is essential in daily life, but relatively little is known about whether seeing a face improves people’s decision quality. This experimental paper studies the loan-approval decisions based on the historical cash-loan data with real repayment outcomes and exogenously varies whether and how a borrower’s facial information is provided. We find that facial information does not improve subjects’ decisions, despite the fact that it can predict repayment behavior in a machine-learning algorithm. This is because subjects have various biases in evaluating facial photos, and they rely excessively on facial information in making the loan-approval decisions. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: J. Meng received financial support from the National Natural Science Foundation of China [Grants 71103003, 71471004, and 71822301]. Supplemental Material: Data files and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4436 .

This paper studies a pricing problem for a single-server queue where customers arrive according to a Poisson process. For each arriving customer, the service provider announces a price rate and system wait time. In response, the customer decides whether to join the queue, and, if so, the duration of the service time. The objective is to maximize either the long-run average revenue or social welfare. We formulate this problem as a continuous-time control model whose optimality conditions involve solving a set of delay differential equations. We develop an innovative method to obtain the optimal control policy, whose structure reveals interesting insights. The optimal dynamic price rate policy is not monotone in the wait time. That is, in addition to the congestion effect (the optimal price rate increases in the wait time), we find a compensation effect, meaning that the service provider should lower the price rate when the wait time is longer than a threshold. Compared with the prevalent static pricing policy, our optimal dynamic pricing policy improves the objective value through admission control, which, in turn, increases the utilization of the server. In a numerical study, we find that our revenue-maximizing pricing policy outperforms the best static pricing policy, especially when the arrival rate is low, and customers are impatient. Interestingly, the revenue-maximizing policy also improves social welfare over the static pricing policy in most of the tested cases. We extend our model to consider nonlinear pricing and heterogeneous customers. Nonlinear pricing may improve the revenue significantly, although linear pricing is easier to implement. For the hetergeneous customer case, we obtain similar policy insights as our base model. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4474 .

One of the most commanding powers of the U.S. Patent and Trademark Office (USPTO) is to compel inventions into secrecy, withholding patent rights and prohibiting disclosure, to prevent technology from leaking to foreign competitors. This paper studies the impacts of compulsory secrecy on firm invention and the wider innovation system. In World War II, USPTO issued secrecy orders to more than 11,000 patent applications, which it rescinded en masse at the end of the war. Compulsory secrecy caused implicated firms to shift their patenting away from treated classes, with effects persisting through at least 1960. It also restricted commercialization and impeded follow-on innovation. Yet it appears it was effective at keeping sensitive technology out of public view. The results provide insight into the effectiveness of compulsory secrecy as a regulatory strategy and into the roles, and impacts, of formal intellectual property in the innovation system. This paper was accepted by Toby Stuart, entrepreneurship and innovation. Funding: This work was supported by Harvard Business School (Division of Research and Faculty Development) and the National Bureau of Economic Research (Innovation Policy Grant). This material is based upon work supported by the National Science Foundation [Grant 1951470]. Supplemental Material: Data and the online appendices are available at https://doi.org/10.1287/mnsc.2022.4457 .

In this paper, we study the learning problem in contextual search, which is motivated by applications such as crowdsourcing and personalized medicine experiments. In particular, for a sequence of arriving context vectors, with each context associated with an underlying value, the decision maker either makes a query at a certain point or skips the context. The decision maker will only observe the binary feedback on the relationship between the query point and the value associated with the context. We study a probably approximately correct learning setting, where the goal is to learn the underlying mean value function in context with a minimum number of queries. To address this challenge, we propose a trisection search approach combined with a margin-based active learning method. We show that the algorithm only needs to make [Formula: see text] queries to achieve an ε-estimation accuracy. This sample complexity significantly reduces the required sample complexity in the passive setting where neither sample skipping nor query selection is allowed, which is at least [Formula: see text]. This paper was accepted by J. George Shanthikumar, data science. Funding: X. Chen and Q. Liu were supported by the National Science Foundation [Grant IIS-1845444].

In many markets, buyers sign advance contracts before actual decisions on transactions or consumptions are made. Therefore, a buyer may have private information on expected payoff at the contracting stage, and as time moves on, new information on other components of payoff may arrive. However, prior information can be losable, forgettable, or unattended. In this paper, we investigate how limited memory may influence the optimal design of contracts for sequential screening. Despite memory loss, the buyer can make ex post inference about her initially informed type from the chosen contract. As ex ante screening facilitates subsequent retrospection, the chosen contract can serve as a self-reminding instrument. This would yield an endogenous demand for separation in ex ante contract choice. In response, distortions in the optimal contract design can be either mitigated or intensified, leading to improved or undermined social welfare, respectively. As a result, the equilibrium buyer surplus can be higher than that under perfect memory. We also show that the buyer can exhibit the so-called flat-rate bias, even though her preference is time consistent and perfectly predicted. In addition, as memory can be perfectly recovered from the equilibrium contract choice, investing on any other memory-improving instrument is redundant. Moreover, the buyer’s demand for screening can induce her to choose dominated refund contract. Nevertheless, when dominance must be obeyed, the seller may offer a menu of refund contracts with two-way distortions. This paper was accepted by Matthew Shum, marketing. Funding: The financial support offered by the Hong Kong RGC General Research Fund [Grant 14500519] is acknowledged.

This paper provides novel evidence suggesting that securities class action lawsuits, a central pillar of the U.S. litigation and corporate governance system, can constitute an obstacle to valuable corporate innovation. We first establish that valuable innovation output makes firms particularly vulnerable to costly low-quality class action litigation. Exploiting judge turnover in federal courts, we then show that changes in class action litigation risk affect the value and number of patents filed, suggesting firms take into account that risk in their innovation decisions. A new perspective we provide is that innovation success, not only innovation failure, can increase firms’ securities class action litigation risk. This paper was accepted by Victoria Ivashina, finance. Funding: E. Kempf gratefully acknowledges financial support from the James S. Kemper Foundation at the University of Chicago Booth School of Business. Supplemental Material: Data and the internet appendix are available at https://doi.org/10.1287/mnsc.2022.4388 .

In this paper, we consider a container leasing firm that has elementary and premium containers, which are downward substitutable and for use by elementary contract customers (ECCs), premium contract customers (PCCs), as well as walk-in customers (WICs). ECCs can be satisfied by elementary containers or premium ones at discounted prices while PCCs only accept premium containers. WICs can be satisfied by any type of container at different prices. The objective is to maximise the expected total rental revenue by managing its limited capacity. We formulate this problem as a discrete-time Markov Decision Process and show the submodularity and concavity of the value function. Based on this, we show that the optimal policy can be characterised by a series of rationing thresholds, a series of substitution thresholds and a priority threshold, all of which depend on the system states. We further give conditions under which the optimal policy can be simplified. Numerical experiments are conducted to show the impact of the substitution of two items on the revenue, to compare the performance of the optimal policy with those of the commonly used policies and to investigate the influence of arrival rates on the optimal policy. Last, we extend the basic model to consider different rental durations, ECCs’ acceptance behaviour and endogenous prices for WICs. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: This work was supported by National Natural Science Foundation of China/Research Grants Council of Hong Kong Joint Research Scheme [Grant 71661167009, N_PolyU531/16], National Natural Science Foundation of China [Grants 72171016, 71831001], and British Academy\Leverhulme Small Research [Grant SRG19\190059]. This work was also supported by Zhejiang Shuren University Research [Grant KXJ0121605] and Beijing Logistics Informatics Research Base. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022.4425 .

Understanding the drivers of market concentration in the generic pharmaceutical industry is essential to guaranteeing the availability of low-cost generics. In this paper, we develop a structural model to capture the multiple determinants governing manufacturers’ entry decisions; in particular, we focus on how manufacturing complexity and the regulatory environment for generics approval affect concentration in drug markets. We estimate the model using data collated from six disparate sources. We find that manufacturing complexity, as reflected in the number of active ingredients, for example, significantly reduces the likelihood of generics entry. Moreover, the delay in the review process for the generics applications significantly affects the number of firms entering a market. Our policy simulations suggest that a shortened drug approval review time significantly increases the average number of entrants per market and reduces the fraction of markets with no generics entry; however, a notable portion of markets would still lack any generic competition. This paper was accepted by Jayashankar Swaminathan, operations management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4423 .

In this issue, we reproduce 10 of the most important papers published in Management Science from 1954 to 2003. Each paper is followed by a commentary by the author(s) and other scholars that offers insights into the background, creation, or subsequent impact of the paper.

The Becker-DeGroot-Marschak (BDM) mechanism is frequently used in experimental research to measure willingness-to-pay (WTP). The mechanism is very appealing given its incentive compatibility property. However, a growing body of experimental evidence finds that WTP is sensitive to the underlying price distribution of the BDM mechanism. The literature suggests that these findings can be rationalized only if subjects have context-dependent preferences. Under context-dependent preferences, the use of the BDM mechanism momentarily changes subjects’ preferences; subjects do not have incentives to report their intrinsic valuation, which is what the BDM attempts to measure, but instead they report the amount they are willing to pay after their preferences have momentarily changed. This lack of incentive compatibility would raise serious concerns about the validity of the BDM as an elicitation procedure; indeed, virtually all studies that use the BDM mechanism choose that elicitation procedure precisely for its incentive compatibility property. In this paper, we propose that an assumption of context dependence is not necessary to rationalize the aforementioned experimental findings. We provide an alternative plausible theory and experimental evidence to support it. Our theory relies on the premise that subjects need to exert effort to learn their preferences. We find that the price distribution of the BDM mechanism influences the subject’s incentives to exert effort. The results presented here are particularly appealing to the experimental research: our theory is consistent with the experimental findings that WTP is sensitive to the price distribution of the BDM mechanism while preserving the incentive compatibility property. This paper was accepted by Duncan Simester, marketing. Funding: F. Sguera acknowledges the support from the Portuguese Foundation of Science and Technology [Project UIDB/00407/2020 (CUBE)]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.4409 .

The “right-to-repair” (RTR) movement calls for government legislation that requires manufacturers to provide repair information, tools, and parts so that consumers can independently repair their own products with more ease. The initiative has gained global traction in recent years. Repair advocates argue that such legislation would break manufacturers’ monopoly on the repair market and benefit consumers. They further contend that it would reduce the environmental impact by reducing e-waste and new production. Yet the RTR legislation may also trigger a price response in the product market as manufacturers try to mitigate the profit loss. This paper employs an analytical model to study the pricing, welfare, and environmental implications of RTR. We find that, as the RTR legislation continually lowers the independent repair cost, manufacturers may initially cut the new product price and then raise it. This nonmonotone price adjustment may further induce a nonmonotone change in consumer surplus, social welfare, and the environmental impact. Strikingly, the RTR legislation can potentially lead to a lose–lose–lose outcome that compromises manufacturer profit, reduces consumer surplus, and increases the environmental impact despite repair being made easier and more affordable. This paper was accepted by Charles Corbett, operations management. Funding: Chen Jin gratefully acknowledges the Singapore Ministry of Education Academic Research Fund [Tier 1, Grant R-253-000-144-133]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4401 .

We consider periodic review perishable inventory systems with a fixed product lifetime. Unsatisfied demand can be either lost or backlogged. The objective is to minimize the long-run average holding, penalty, and outdating cost. The optimal policy for these systems is notoriously complex and computationally intractable because of the curse of dimensionality. Hence, various heuristic replenishment policies are proposed in the literature, including the base-stock policy, which raises the total inventory level to a constant in each review period. Whereas various studies show near-optimal numerical performances of base-stock policies in the classic system with zero replenishment lead time and a first-in-first-out issuance policy, the results on their theoretical performances are very limited. In this paper, we first focus on this classic system and show that a simple base-stock policy is asymptotically optimal when any one of the product lifetime, demand population size, unit penalty cost, and unit outdating cost becomes large; moreover, its optimality gap converges to zero exponentially fast in the first two parameters. We then study two important extensions. For a system under a last-in-first-out or even an arbitrary issuance policy, we prove that a simple base-stock policy is asymptotically optimal with large product lifetime, large unit penalty costs, and large unit outdating costs, and for a backlogging system with positive lead times, we prove that our results continue to hold with large product lifetime, large demand population sizes, and large unit outdating costs. Finally, we provide a numerical study to demonstrate the performances of base-stock policies in these systems. This paper was accepted by Victor Martinez de Albéniz, operations management. Funding: J. Bu was partially supported by a Hong Kong Polytechnic University Start-up Fund for New Recruits [Grant P0039585]. X. Gong was partially supported by a Chinese University of Hong Kong (CUHK) Direct Grant [Grant 4057147] and the Hong Kong Research Grants Council (RGC) General Research Fund [Grant CUHK14500120]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4400 .

This paper examines startups’ positioning within technological cycles. We use patent text to measure whether innovation pertains to a technological area that is rapidly evolving or stable. We show that innovation in rapidly evolving areas (i.e., early in the cycle) substitute for existing technologies, whereas innovation in stable areas (i.e., later in the cycle) complement them. Our new measure is distinct from existing characterizations of innovation and is economically important. We find that startups in rapidly evolving areas tend to exit via initial public offering, thus remaining independent, consistent with technological substitution. In contrast, startups in stable areas tend to sell out, consistent with technological complementarity and synergies. This paper was accepted by Gustavo Manso, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4362 .

In this paper, we study how an on-demand service platform could improve its performance through managing user conduct. In such a platform, service providers may reject certain platform-proposed service requests, and their responses, in turn, incentivize users to adjust their conduct. We develop an evolutionary game theory model of user conduct and provider responses that shows that the platform could improve user conduct through either setting a low wage for service providers or implementing priority matching. Building upon these results, we further model providers and users joining and leaving the platform by once again utilizing the evolutionary game theory approach. We find that wage setting alone is a blunt instrument to improve platform performance via managing user conduct, whereas supplementing the wage decision with priority matching could overcome its limitations and serve as an effective strategy to further improve platform performance in terms of growth and profitability. This finding suggests that matching prioritization could be an important strategy for managing platforms with user and provider heterogeneities. In addition, our analysis and results also demonstrate the potential of the evolutionary game theory approach for analyzing the impact of pricing and matching decisions on the performance of large markets. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4391 .

The impact of product reviews on consumer purchasing behavior is empirically well documented. This can create perverse incentives for firms to offer reviewers side payments (“bribes”) in exchange for biased reviews for their products. The presence of bribes distorts the information in reviews and leads to detrimental effects on consumer utility. This paper builds a two-sided reputation model where a reviewer can inflate her reviews in exchange for bribes. If the reviewer accepts bribes and misrepresents her reviews, then she builds her reputation as an inaccurate reviewer and makes consumers less likely to follow her recommendations. This in turn makes firms no longer interested in offering her a bribe. Can the reviewer retain influence over consumers’ purchasing decisions while simultaneously accepting bribes and misrepresenting her reviews? We provide a characterization of the environments that allow this kind of manipulation and show that regulatory policies that aim to reduce bribes can lead to undesirable outcomes. Finally, we show that eliminating bribes from the market can increase the welfare of all market participants, even for those firms who would have otherwise bribed in exchange for more favorable reviews. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4376 .

This paper investigates how the pricing policy of a revenue-maximizing monopolist is influenced by the social learning dynamics of customers who use online reviews to estimate the quality of the product. A salient feature of our problem is that the customers’ willingness to pay, and hence the demand function, evolves over time in conjunction with the online reviews. The monopolist strives to maximize its total expected revenue over a finite horizon by adjusting prices in response to these dynamics. The revenue maximization problem is studied using two different review models: a quality-based review model, where customers report their experienced quality, and a value-based review model, where reviews internalize experienced quality as well as the purchase price. To formulate the problem in tractable form, we derive a fluid model that provides a good approximation of the system dynamics when the volume of sales is large. This formulation lends itself to key structural insights into the interactions between optimal pricing policies and review dynamics. In particular, we identify critical time scales and social learning regimes that sharply separate the efficacy of dynamic pricing vis-à-vis fixed-price strategies. Furthermore, we demonstrate the impact of the quality-based and value-based review models on key structural properties of the optimal pricing policies. These structural insights are also elucidated in an illustrative simulation study based on data from an online marketplace. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: Data and the e-companion are available at https://doi.org/10.1287/mnsc.2022.4387 .

This paper examines how a manager uses voluntary disclosure to influence corporate control by a short-term shareholder. Because a short-term shareholder intervenes excessively, the manager’s disclosure strategy is determined by the trade-off between excessive and insufficient intervention. In equilibrium, when shareholder short-termism is not too high, the manager discloses both good and bad news and withholds intermediate news. Alternatively, when shareholder short-termism is high, the manager only discloses good news and withholds bad news. In both equilibria, withholding information is value-enhancing for the nondisclosing firms. We also show that the likelihood of disclosure weakly decreases as the shareholder is more short-term-oriented. Moreover, nondisclosing firms are more likely to face shareholder intervention than disclosing firms. This paper was accepted by Brian Bushee, accounting. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4357 .

The COVID-19 pandemic has killed millions and gravely disrupted the world’s economy. A safe and effective vaccine was developed remarkably swiftly, but as of yet, uptake of the vaccine has been slow. This paper explores one potential explanation of delayed adoption of the vaccine, which is data privacy concerns. We explore two contrasting regulations that vary across U.S. states that have the potential to affect the perceived privacy risk associated with receiving a COVID-19 vaccine. The first regulation—an “identification requirement”—increases privacy concerns by requiring individuals to verify residency with government approved documentation. The second regulation—“anonymity protection”—reduces privacy concerns by allowing individuals to remove personally identifying information from state-operated immunization registry systems. We investigate the effects of these privacy-reducing and privacy-protecting regulations on U.S. state-level COVID-19 vaccination rates. Using a panel data set, we find that identification requirements decrease vaccine demand but that this negative effect is offset when individuals are able to remove information from an immunization registry. Our results remain consistent when controlling for CDC-defined barriers to vaccination, levels of misinformation, vaccine incentives, and states’ phased distribution of vaccine supply. These findings yield significant theoretical and practical contributions for privacy policy and public health. This paper was accepted by David Simchi-Levi, information systems. Supplemental Material: Data and the e-companion are available at https://doi.org/10.1287/mnsc.2022.4580 .

This paper studies outcomes of the deferred acceptance algorithm in large random matching markets where priorities are generated either by a single lottery or by independent lotteries. In contrast to prior work, my model permits students to submit lists of varying lengths and schools to vary in their popularity and number of seats. In a limiting regime where the number of students and schools grow while the length of student lists and number of seats at each school remain bounded, I provide exact expressions for the number of students who list l schools and match to one of their top k choices, for each [Formula: see text]. These expressions provide three main insights. First, there is a persistent tradeoff between using a single lottery and independent lotteries. For students who submit short lists, the rank distribution under a single lottery stochastically dominates the corresponding distribution under independent lotteries. However, the students who submit the longest lists are always more likely to match when schools use independent lotteries. Second, I compare the total number of matches in the two lottery systems, and find that the shape of the list length distribution plays a key role. If this distribution has an increasing hazard rate, then independent lotteries match more students. If it has a decreasing hazard rate, the comparison reverses. To my knowledge, this is the first analytical result comparing the size of stable matchings under different priority rules. Finally, I study the fraction of assigned students who receive their first choice. Under independent lotteries, this fraction may be arbitrarily small, even if schools are equally popular. Under a single lottery, we provide a tight lower bound on this fraction which depends on the ratio r of the popularity of the most to least popular school. When each school has a single seat, the fraction of assigned students who receive their first choice is at least [Formula: see text]. This guarantee increases to [Formula: see text] as the number of seats at each school increases. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4338 .

This paper examines the effect of the expiry of recording copyright on the supply of music—in the form of rereleases, availability on streaming platforms, and concert performances—by artists popular in the United Kingdom in the 1960s. We find that recording copyright expiry has different effects on a song’s availability in different distribution channels. The lapsing of copyright leads to a large increase in the number of rereleases in physical formats, holding constant artist, age, and year fixed effects. However, when a song’s original recording copyright expires, it becomes less likely to be performed in concert. Moreover, copyright status is not associated with differences in availability on the digital streaming platform Spotify. These results show that copyright expiry has nuanced effects on availability and can lead to different and even opposite effects on availability of a product across different distribution channels. They also show that within the context of digital distribution, the impact of copyright on availability differs based on the business model of a platform. This paper was accepted by Chris Forman, information systems. Funding: J. McKeon thanks Boston University’s Undergraduate Research Opportunities Program for funding. M. MacGarvie and J. Watson thank the National Bureau of Economic Research (Digitization Small Grant) for funding. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4343 .

This paper studies the prevalence of potential anticompetitive effects of vertical mergers using a novel data set on U.S. and international buyer-seller relationships and across a large range of industries. We find that relationships are more likely to break when suppliers vertically integrate with one of the buyers’ competitors than when they vertically integrate with an unrelated firm. This relationship holds for both domestic and cross-border mergers and for domestic and international relationships. It also holds when instrumenting mergers using exogenous downward pressure on the supplier’s stock prices, suggesting that reverse causality is unlikely to explain the result. In contrast, the relationship vanishes when using rumored or announced but not completed integration events. Firms experience a substantial drop in sales when one of their suppliers integrates with one of their competitors. This sales drop is mitigated if the firm has alternative suppliers in place. These findings are consistent with anticompetitive effects of vertical mergers, such as vertical foreclosure, rising input costs for rivals, or self-foreclosure. This paper was accepted by Joshua Gans, business strategy. Funding: This work was supported by a Banque de France/Sciences Po Partnership. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4363 .

Medicare does not pay for a skilled nursing facility (SNF) unless a fee-for-service patient has stayed in the hospital for at least three days. This Medicare reimbursement rule, or the “three-day rule,” provides full coverage for the first 20 days and partial coverage for days 21–100 for skilled nursing care provided at any Centers for Medicare and Medicaid Services-approved SNF. In this paper, we study how this Medicare reimbursement rule affects patient routing to SNFs and whether an SNF discharge reduces patients’ 30-day hospital readmission rates. Data analysis shows that Medicare patients are more likely to be discharged to an SNF rather than home after the three-day cutoff, and SNF discharges increase hospital readmission rates for Medicare day 3 patients. This perverse effect is driven by infection-related readmissions and is more likely to occur when local SNFs have lower occupancy rates and higher deficiency citations than the median SNF of the same state-year. Back-of-the-envelope calculation suggests that the three-day rule may have generated an extra Medicare cost of $71 million to $345 million per year due to the overuse of SNFs and the subsequent rise in hospital readmissions. Replacing the three-day rule with a machine-learning algorithm mimicking private insurers would help. This paper was accepted by Stefan Scholtes, healthcare management. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4316 .

Recently, the development of ransomware strains and changes in the marketplace for malware have greatly reduced the entry barrier for attackers to conduct large-scale ransomware attacks. In this paper, we examine how this mode of cyberattack impacts software vendors and consumer behavior. When victims face an added option to mitigate losses via a ransom payment, both the equilibrium market size and the vendor’s profit under optimal pricing can actually increase in the ransom demand. Profit can also increase in the scale of residual losses following a ransom payment (which reflect the trustworthiness of the ransomware operator). We show that for intermediate levels of risk, the vendor restricts software adoption by substantially hiking up price. This lies in stark contrast to outcomes in a benchmark case involving traditional malware (non-ransomware) where the vendor decreases price as security risk increases. Social welfare is higher under ransomware compared with the benchmark in both sufficiently low- and high-risk settings. However, for intermediate risk, it is better from a social standpoint if consumers do not have an option to pay ransom. We also show that the expected ransom paid is nonmonotone in risk, increasing when risk is moderate despite a decreasing ransom-paying population. For ransomware attacks on other vectors (beyond patchable vulnerabilities), there can still be an incentive to hike price. However, market size and profits instead weakly decrease in the ransom amount. When studying a generalized model that includes both traditional and ransomware attacks, our results remain robust to a wide range of scenarios, including threat landscapes where ransomware has only a small presence. This paper was accepted by Kartik Hosanagar, information systems. Funding: This work has been supported by the Haskayne School of Business' Dean's Research Grant and by an award from the Georgia Institute of Technology Center of International Business Education & Research as part of its funded research program. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4300 .

When multiple retailers hold inventory to satisfy random demand, retailer inventory-sharing strategies can potentially reduce the supply-demand mismatch and increase overall supply chain performance. In this paper, we experimentally investigate alternative inventory-sharing strategies in a two-tier supply chain with an upstream manufacturer and two downstream retailers. In one setting, retailers act as if they are centralized and use a single quantity to fulfill joint demand. In the other, retailers are decentralized and face separate demands, but they can transfer inventory after demands are realized. In this latter decentralized scenario, we also consider whether the manufacturer or retailers have decision authority over the inventory transfer price. One key result is that when the retailers are decentralized and the manufacturer sets the transfer price, both retailers and the manufacturer earn higher profits than in the centralized retailer strategy, which runs counter to theory. We also find that when retailers are decentralized and set their own transfer price, the most equitable distribution of profits is achieved. In an effort to account for these results, we find that a model of fairness captures decisions well. Overall, by investigating how different inventory-sharing strategies affect the distribution of profits in a two-tier supply chain, our results provide guidance to firms considering how, if at all, they should enter such arrangements. This paper was accepted by Jay Swaminathan, operations management. Funding: The authors acknowledge financial support from Cornell University and the University of Virginia. Supplemental Material: The data files and electronic companion are available at https://doi.org/10.1287/mnsc.2022.4323 .

Firms often engage in price promotion wars to gain market share from their competitors. However, poor customer satisfaction as a result of limited service capacity may significantly impact such a pricing strategy. In this paper, we consider a two-firm price competition model in which customers’ purchase decisions are affected by their anticipation of a poor service encounter. Our equilibrium analysis reveals that firms would be less aggressive in engaging in price cutting when customers care more about service quality and when service capacity is relatively low. Interestingly, when service capacity is close (but not exact) to covering one half of the total market demand, firms would adopt a mixed strategy with randomized pricing, driven by unilateral motives to either capture more market share (by lowering prices) or increase profit margin (by raising prices). We further show that having a superior service capacity presents a competitive advantage for a firm and such advantage can be preemptive. In an extended two-period model that allows for customer switching after a poor service encounter, we find that when service capacity is relatively low, firms may offer deeper price discounts in the first period if customers are forward looking than if they are myopic. Our numerical study confirms that the main qualitative insights obtained in our base model continue to hold when the customer switching behavior is considered. This paper was accepted by Jay Swaminathan, operations management. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4325 .

In this paper, we investigate the demand response of a firm’s existing customers to retail store entry and which aspects of the response are demand-expanding and which cannibalize online sales. The empirical goal of the paper is to separately identify the effects of customer-to-store distance on purchase frequency, channel choice, and expenditure per purchase, using transaction-level data from a multichannel apparel brand. Our identification strategy exploits within-customer variation in distance resulting from store entry during a period of rapid retail expansion. We establish retail distance effects using descriptive regressions before developing a unified structural model that affords rich counterfactual analyses and further controls for product category preferences. We find material effects of decreasing retail store distance on purchase frequency and retail channel choice but not expenditures per purchase. Our structural model ascribes mechanisms to these effects in the form of increased brand consideration, higher retail utility from nonmonetary factors (e.g., reduced driving times), and infungibility of monetary transportation costs with budgets for apparel. Our estimates imply that a 10% reduction in retail store distance increases retail channel expenditures by 1.9% and decreases online channel expenditures by 1.2%, resulting in a 0.4% increase in total expenditures. Through counterfactual experiments, we demonstrate that retail expansion can ultimately limit the ability of the firm to price discriminate across channels because reducing transportation costs weakens the firm’s ability to enforce channel-based segmentation schemes. This paper was accepted by Matthew Shum, marketing. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4308 .

Mature internet advertising platforms offer high-level campaign management tools to help advertisers run their campaigns, often abstracting away the intricacies of how each ad is placed and focusing on aggregate metrics of interest to advertisers. On such platforms, advertisers often participate in auctions through a proxy bidder, so the standard incentive analyses that are common in the literature do not apply directly. In this paper, we take the perspective of a budget management system that surfaces aggregated incentives—instead of individual auctions—and compare first and second price auctions. We show that theory offers surprising endorsement for using a first price auction to sell individual impressions. In particular, first price auctions guarantee uniqueness of the steady-state equilibrium of the budget management system, monotonicity, and other desirable properties, as well as efficient computation through the solution to the well-studied Eisenberg–Gale convex program. Contrary to what one can expect from first price auctions, we show that incentives issues are not a barrier that undermines the system. Using realistic instances generated from data collected at real-world auction platforms, we show that bidders have small regret with respect to their optimal ex post strategy, and they do not have a big incentive to misreport when they can influence equilibria directly by giving inputs strategically. Finally, budget-constrained bidders, who have significant prevalence in real-world platforms, tend to have smaller regrets. Our computations indicate that bidder budgets, pacing multipliers, and regrets all have a positive association in statistical terms. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: D. Panigrahi was supported in part by the National Science Foundation [Awards CCF 1535972, CCF 1750140, and CCF 1955703]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.4310 .

A simple linear model is constructed for production, use and recycling of various paper and related products. This model is used to estimate the maximum feasible recycling rate given the current state of pulp and paper technology. Parametric linear programming is used to investigate the effect of the efficiency of collecting, sorting and transporting waste paper on the fraction recycled.