Can an organization avoid blame for an unpopular action when an adviser advises it to do it? We present experimental evidence suggesting this is the case—advice to be selfish substantially decreases punishment of being selfish. Further, this result is true despite advisers’ misaligned incentives, known to all: Through a relational contract incentive, advisers are motivated to tell the decision makers what they want to hear. Through incentivized elicitations, we find suggestive evidence that advice moves punishment by affecting beliefs of how necessary the selfish action was. In follow-up treatments, however, we show advice does not decrease punishment solely through a beliefs channel. Advice not only changes beliefs about what happened, but also the perceived morality of it. Finally, in treatments in which advisers are available, the data suggest selfish decision makers act more selfishly. This paper was accepted by Axel Ockenfels, behavioral economics.

This paper introduces the formal study of process flexibility to the novel domain of sports analytics. In baseball, positional flexibility is the analogous concept to process flexibility from manufacturing. We study the flexibility of players (plants) on a baseball team who produce innings-played at different positions (products). We develop models and metrics to evaluate expected and worst-case performance under injury risk (capacity uncertainty) with continuous player-position capabilities. Using Major League Baseball data, we quantify the impact of flexibility on team and individual performance and explore the player chains that arise when injuries occur. We discover that top teams can attribute at least one to two wins per season to flexibility alone, generally as a result of long subchains in the infield or outfield. The least robust teams to worst-case injury, those whose performance is driven by one or two star players, are over four times as fragile as the most robust teams. We evaluate several aspects of individual flexibility, such as how much value individual players bring to their team in terms of average and worst-case performance. Finally, we demonstrate the generalizability of our framework for player evaluation by quantifying the value of potential free agent additions and uncovering the true “MVP” of a team. Data are available at https://doi.org/10.1287/mnsc.2017.3004 . This paper was accepted by Vishal Gaur, operations management.

In classical inverse linear optimization, one assumes that a given solution is a candidate to be optimal. Real data are imperfect and noisy, so there is no guarantee that this assumption is satisfied. Inspired by regression, this paper presents a unified framework for cost function estimation in linear optimization comprising a general inverse optimization model and a corresponding goodness-of-fit metric. Although our inverse optimization model is nonconvex, we derive a closed-form solution and present the geometric intuition. Our goodness-of-fit metric, ρ, the coefficient of complementarity, has similar properties to R 2 from regression and is quasi-convex in the input data, leading to an intuitive geometric interpretation. While ρ is computable in polynomial time, we derive a lower bound that possesses the same properties, is tight for several important model variations, and is even easier to compute. We demonstrate the application of our framework for model estimation and evaluation in production planning and cancer therapy. This paper was accepted by Yinyu Ye, optimization.

What motivates the rich and powerful to exhibit generosity? We explore this important question in a large field experiment. We solicit donations from 32,174 alumni of an Ivy League university, including thousands of rich and powerful alumni. Consistent with past psychology research, we find that the rich and powerful respond dramatically, and differently than others, to being given a sense of agency over the use of donated funds. Gifts from rich and powerful alumni increase by 100%–350% when they are given a sense of agency. This response arises primarily on the intensive margin with no effect on the likelihood of donating. Results suggest that motivating the rich and powerful to act may require tailored interventions. This paper was accepted by Uri Gneezy, behavioral economics.

Governments use consumer incentives to promote green technologies (e.g., solar panels and electric vehicles). Our goal in this paper is to study how policy adjustments over time will interact with production decisions from the industry. We model the interaction between a government and an industry player in a two-period game setting under uncertain demand. We show how the timing of decisions affects the risk sharing between the government and the supplier, ultimately affecting the cost of the subsidy program. In particular, we show that when the government commits to a fixed policy, it encourages the supplier to produce more at the beginning of the horizon. Consequently, a flexible subsidy policy is on average more expensive, unless there is a significant negative demand correlation across time periods. However, we show that the variance of the total sales is lower in the flexible setting, implying that the government’s additional spending reduces the adoption level uncertainty. In addition, we show that for flexible policies, the supplier is better off in terms of expected profits, whereas the consumers can either benefit or not depending on the price elasticity of demand. Finally, we test our insights with a numerical example calibrated on data from a solar subsidy program. This paper was accepted by Gad Allon, operations management.

This paper considers a traditional problem of resource allocation: scheduling jobs on machines. One such recent application is cloud computing; jobs arrive in an online fashion with capacity requirements and need to be immediately scheduled on physical machines in data centers. It is often observed that the requested capacities are not fully utilized, hence offering an opportunity to employ an overcommitment policy, that is, selling resources beyond capacity. Setting the right overcommitment level can yield a significant cost reduction for the cloud provider while only inducing a very low risk of violating capacity constraints. We introduce and study a model that quantifies the value of overcommitment by modeling the problem as bin packing with chance constraints. We then propose an alternative formulation that transforms each chance constraint to a submodular function. We show that our model captures the risk pooling effect and can guide scheduling and overcommitment decisions. We also develop a family of online algorithms that are intuitive, easy to implement, and provide a constant factor guarantee from optimal. Finally, we calibrate our model using realistic workload data and test our approach in a practical setting. Our analysis and experiments illustrate the benefit of overcommitment in cloud services and suggest a cost reduction of 1.5% to 17%, depending on the provider’s risk tolerance. The online appendices are available at https://doi.org/10.1287/mnsc.2018.3091 . This paper was accepted by Yinyu Ye, optimization.

We offer a new anchoring explanation for the ex-day abnormal returns of stock distributions, including stock dividend distributions, splits, and reverse splits. We propose that investors tend to anchor on cum-day prices in valuating ex-distribution stocks, resulting in a positive association between ex-day returns and adjustment factors. We find that this positive return-factor relation exists for all three types of stock distributions and in both the pre- and post-decimalization periods. Furthermore, we find that this positive return-factor relation is more pronounced among events that are more subject to investors’ anchoring propensity, featured by less investor attention, greater arbitrage difficulty, greater valuation uncertainty, less investor sophistication, and higher market sentiment. Last, using brokerage account data, we show that stocks that are traded by investors with more investment experience demonstrate a weaker return-factor relation. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2843 . This paper was accepted by Lauren Cohen, finance.

This research examines how an individual’s place in the status hierarchy affects their willingness to expend effort on group tasks, why this occurs, and a contingency governing this relationship. Among firefighter teams (Study 1), MBA student workgroups (Study 2), and undergraduates in the laboratory (Study 3), we find that the relationship between status and effort, through performance expectations, is contingent on the perceived visibility of one’s efforts (i.e., task visibility). When task visibility is high, greater status leads to higher performance expectations. When task visibility is low or absent, this relationship was not present. Overall, our findings help paint a more complete picture of the relationship between status, performance expectations, and effort in workgroups while also furthering our understanding of the psychological experience of status. Data are available at https://doi.org/10.1287/mnsc.2018.3103 . This paper was accepted by Greta Hsu, organizations.

In addition to setting price discounts, retailers need to decide how to schedule promotion vehicles, such as flyers and TV commercials. Unlike the promotion pricing problem that received great attention from both academics and practitioners, the promotion vehicle scheduling problem was largely overlooked, and our goal is to study this problem both theoretically and in practice. We model the problem of scheduling promotion vehicles to maximize profits as a nonlinear bipartite matching-type problem, where promotion vehicles should be assigned to time periods, subject to capacity constraints. Our modeling approach is motivated and calibrated using actual data in collaboration with Oracle Retail, leading us to introduce and study a class of models for which the boost effects of promotion vehicles on demand are multiplicative. From a technical perspective, we prove that the general setting considered is computationally intractable. Nevertheless, we develop approximation algorithms and propose a compact integer programming formulation. In particular, we show how to obtain a (1 − ε)-approximation using an integer program of polynomial size, and investigate the performance of a greedy procedure, both analytically and computationally. We also discuss an extension that includes cross-term effects to capture the cannibalization aspect of using several vehicles simultaneously. From a practical perspective, we test our methods on actual data through a case study, and quantify the impact of our models. Under our model assumptions and for a particular item considered in our case study, we show that a rigorous optimization approach to the promotion vehicle scheduling problem allows the retailer to increase its profit by 2% to 9%. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2926 . This paper was accepted by Yinyu Ye, optimization.

In many production environments, a fixed network of capacity is shared flexibly between multiple products with random demands. What is the best way to configure the capacity of the production network and to allocate the available capacity to meet predetermined fill rate requirements? We develop a new approach for network capacity configuration and allocation and characterize the relationship between the capacity of the network and the attainable fill rate levels for the products, taking into account the flexibility structure of the network. This builds on a new randomized allocation mechanism to deliver the desired services. We use this theory to investigate the connection between the flexibility structure and capacity configuration. We provide a new perspective to the well-known phenomenon that “long chain is almost as good as the fully flexible network”: for given target fill rates, the required capacity level in a long-chain network is close to that in a fully flexible network and is much lower than a dedicated system. We apply these insights and techniques on problems arising in the design of last-mile delivery operations and in semiconductor production planning, using real data from two companies. This paper was accepted by Terry Taylor, operations management.