Search

In this article, the author discusses various topics related to publication of the journal. Topics include changes in research areas of management science such as analytics; criteria for submission of research papers to the journal, such as research relevant to the management science community; and different departments related to selection of papers, like departments of Behavioral Economics, and Judgment and Decision Making.

This paper discusses the selection and design of heuristic procedures for solving one-dimensional roll trim problems. These are well-defined combinatorial problems which are too large to optimize. Five different roll trim problems from the paper and film industries are described along with the heuristic procedures that have been used successfully to improve the quality of the solutions generated. The paper concludes with some general guidelines for developing "industrial-grade" heuristic problem solving procedures. [ABSTRACT FROM AUTHOR]

The article focuses on the editorial objectives of the journal. The Accounting Department publishes research on managerial decisions and accounting. The Decision Analysis Department publishes articles on scientific knowledge and decision making. The Finance Department publishes information pertaining to decision makers in management. The Business Strategy Department focuses on locating periodicals that publish research related to the field of management science. The Marketing Department strives to publish articles that pertain to various managerial decisions.

The paper by Huber [6] stimulates further comment. While his conclusions that cognitive styles research has not served and will not serve as a satisfactory basis for operational DSS design guidelines appear justified, some additional points need to be made. This paper looks at several of Huber's arguments more closely and suggests a potentially valuable role for cognitive style awareness in the DSS development process. [ABSTRACT FROM AUTHOR]

The article discusses editorial policy for management science research papers submitted to the journal, citing the aim of improving the understanding of how decisions are and should be made, and mentions research topics like individual and group decisions, and negotiations.

The rapid adoption of artificial intelligence (AI) technologies by many organizations has recently raised concerns that AI may eventually replace humans in certain tasks. In fact, when used in collaboration, machines can significantly enhance the complementary strengths of humans. Indeed, because of their immense computing power, machines can perform specific tasks with incredible accuracy. In contrast, human decision makers (DMs) are flexible and adaptive but constrained by their limited cognitive capacity. This paper investigates how machine-based predictions may affect the decision process and outcomes of a human DM. We study the impact of these predictions on decision accuracy, the propensity and nature of decision errors, and the DM's cognitive efforts. To account for both flexibility and limited cognitive capacity, we model the human decision-making process in a rational inattention framework. In this setup, the machine provides the DM with accurate but sometimes incomplete information at no cognitive cost. We fully characterize the impact of machine input on the human decision process in this framework. We show that machine input always improves the overall accuracy of human decisions but may nonetheless increase the propensity of certain types of errors (such as false positives). The machine can also induce the human to exert more cognitive efforts, although its input is highly accurate. Interestingly, this happens when the DM is most cognitively constrained, for instance, because of time pressure or multitasking. Synthesizing these results, we pinpoint the decision environments in which human-machine collaboration is likely to be most beneficial. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4744. [ABSTRACT FROM AUTHOR]

Exhibiting altruism toward and cooperativeness with others is a key ingredient for successful work relationships and managerial decision making. Rising political polarization creates a hazard because it ruptures this fabric and impedes the interaction of employees, especially across political isles. This paper's focus is to examine various behavioral-, belief-, and norm-based layers of (non)strategic decision making that are plausibly affected by polarization. I quantify this phenomenon via five preregistered studies in the context of Donald J. Trump, comprising 15 well-powered behavioral experiments and a diverse set of over 8,600 participants. To capture the pervasiveness of polarization, I contrast the findings with various political and nonpolitical identities. Overall, I consistently document strong heterogeneous effects: ingroup-love occurs in the perceptional domain (how close one feels toward others), whereas outgroup-hate occurs in the behavioral domain (how one helps/harms/cooperates with others). The rich setting also enables me to examine the mechanisms of observed intergroup conflict, which can be attributed to one's grim expectations regarding cooperativeness of the opposing faction, rather than one's actual unwillingness to cooperate. For the first time, the paper also tests whether popular behavioral interventions (defaults and norm-nudges) can reduce the detrimental impact of polarization in the contexts studied here. The tested interventions improve prosociality but are ineffective in closing the polarization gap. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by Deutsche Forschungsgemeinschaft [Grant EXC 2126/1– 390838866]. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4701. [ABSTRACT FROM AUTHOR]

The normative solution concepts of game theory try to provide a clear mathematical characterization of what it means to act rationally in a game where all players expect each other to act rationally. Kadane and Larkey reject the use of these normative solution concepts. Yet, this amounts to throwing away an important piece of information to the effect that the players are rational and expect each other to be rational. Even in situations where the players do not expect each other to act with complete rationality, normative game theory can help them heuristically to formulate reasonable expectations about the other players' behavior. [ABSTRACT FROM AUTHOR]

This paper addresses primarily two questions. First, when (and why) should a company's strategy be developed by its CEO versus by some outside analyst or other insider? Second, how does strategy interact with vision (in the sense of a strong belief about the right course of action)? The paper studies these questions using a functional definition of strategy as "the smallest set of choices to optimally guide other choices." Among other things, the paper shows that strategy formulation by the CEO leads to both better strategy and better execution, and that a strategist's vision may improve execution. In the process, the paper also identifies criteria that make a decision strategic and derives explanations why strategies often reflect the background of the strategist. This paper was accepted by Bruno Cassiman, business strategy. [ABSTRACT FROM AUTHOR]

This paper analyzes a dynamic relational contract for employees with reciprocal preferences. A model of a long-term employment relationship is developed that implies that generous upfront wages activate the norm of reciprocity and then are more important when an employee is close to retirement. In earlier stages, direct incentives promising a bonus in exchange for effort are more effective. Hence, direct and reciprocity-based incentives reinforce each other and should be used in combination. Moreover, a more competitive labor market may increase the utilization of reciprocity-based incentives. This paper was accepted by Axel Ockenfels, behavioral economics and decision analysis. Funding: This work was supported by Hardegg'sche Stiftung, the Deutsche Forschungsgemeinschaft [CRC TRR 190], and the Austrian Science Fund [Grant P 33307-G]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4657. [ABSTRACT FROM AUTHOR]

In environments with impending regime shifts, previous research studies probability judgments of decision makers (DMs) regarding a regime shift and finds evidence for the system neglect hypothesis: In unstable environments with precise signals, DMs exhibit relatively more underreaction in their probability judgments, and in stable environments with noisy signals, they exhibit relatively more overreaction. In the present paper we focus on pricing decisions and study how risk attitudes, as well as probability judgments regarding a regime shift, affect these decisions. We develop a model that integrates prospect theory with the system neglect hypothesis and compare changes in empirical pricing decisions to those of a Bayesian risk-neutral agent. We find a pattern of under- and overreaction in pricing decisions that mimics the system neglect hypothesis in probability judgments only when the DM's prior probability of a regime shift is low; the system neglect pattern vanishes when the DM's prior probability of a regime shift is high. We argue that in the case of low priors, changes in risk attitude in reaction to signals indicative of a regime shift amplify system neglect in probability judgments. In the case of high priors, on the other hand, changes in risk attitude dampen the effect of system neglect. We test our model predictions in experimental insurance markets and offer various extensions to check for the robustness of our findings. Finally, we discuss implications for managerial practice in organizational contexts that are characterized by continuous change. This paper was accepted by Yuval Rottenstreich, behavioral economics and decision analysis. Funding: This work was funded by Fundação para a Ciência e a Tecnologia (UIDB/00124/2020, UIDP/00124/2020 and Social Sciences DataLab - PINFRA/22209/2016), POR Lisboa and POR Norte (Social Sciences DataLab, PINFRA/22209/2016). Furthermore, it was supported by the Ministerio de Ciencia e Innovación España [10.13039/501100011033, Grants ECO2014-52925-P, PID2019-111512RB-I00-HMDM, and HDL-HS-280218]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4661. [ABSTRACT FROM AUTHOR]

Decision making on the job is becoming increasingly important in the labor market, in which there is an unprecedented rise in demand for workers with problem-solving and critical-thinking skills. This paper investigates how indoor air quality affects the quality of strategic decision making based on data from official chess tournaments. Our main analysis relies on a unique data set linking the readings of air-quality monitors inside the tournament room to the quality of 30,000 moves, each of them objectively evaluated by a powerful artificial intelligence–based chess engine. The results show that poor indoor air quality hampers players' decision making. We find that an increase in the indoor concentration of fine particulate matter (PM 2.5 ) by 10 μ g / m 3 (corresponding to 75% of a standard deviation in our sample) increases a player's probability of making an erroneous move by 26.3%. The decomposition of the effects by different stages of the game shows that time pressure amplifies the damage of poor air quality to the players' decisions. We implement a number of robustness checks and conduct a replication exercise with analogous move-quality data from games in the top national league showing the strength of our results. The results highlight the costs of poor air quality for highly skilled professionals faced with strategic decisions under time pressure. This paper was accepted by Prof. Yan Chen, behavioral economics and decision analysis. Funding: The authors gratefully acknowledge the financial support from the Graduate School of Business and Economics at Maastricht University as well as the Institute of Labor Economics Bonn. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4643. [ABSTRACT FROM AUTHOR]

This article presents an update to an article entitled "Analysis of a Decision Making Investigation," by Kenneth D. MacKenzie, published in the December 1970 issue of the journal "Management Science."

Commentary is presented for the article "Remarks on the Analytic Hierarchy Process," by James S. Dyer, published in the March 1, 1990 issue of "Management Science." The author discusses the merits of various theories of decision making. He notes one in particular, the analytic hierarchy process (AHP), which is a theory of measurement designed to aid decision makers in deconstructing complex problems into multiple levels of subproblems that have a hierarchical structure. According to the author, as a theory, AHP exists independently of utility theory.

Trust is an important element of many financial transactions. Yet, the financial industry has been struggling with public mistrust. One explanation for this could be the selection of individuals who wish to work in and get job offers from the financial industry. In this paper, we examine the selection into the financial industry based on social preferences. We identify the social preferences of business and economics students, and, for more than six years, follow up on their early career choices and their job placement after graduation. Students eager to work in the financial industry behave in a substantially less trustworthy manner and show less willingness to cooperate than those with other career plans. The job market does not alleviate this selection. Those subjects who find their first permanent job in finance behave in significantly less trustworthy manner in a trust game than those working in other industries. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: Financial support was provided from the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany's Excellence Strategy [Grant EXC 2126/1–390838866], the University of Cologne [Hans Kelsen Prize], and the Center for Financial Studies. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4562. [ABSTRACT FROM AUTHOR]

Given the ranking of competitors, how should the prize endowment be allocated? This paper introduces and axiomatically studies the prize allocation problem. We focus on consistent prize allocation rules satisfying elementary solidarity and fairness principles. In particular, we derive several families of rules satisfying anonymity, order preservation, and endowment monotonicity, which all fall between the equal division rule and the winner-takes-all rule. Our results may help organizers to select the most suitable prize allocation rule for rank-order competitions. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: A. Y. Kondratev is supported by HSE University [Basic Research Program and "Priority 2030" Program]. [ABSTRACT FROM AUTHOR]

Models of statistical discrimination typically assume that employers make rational inference from (education) signals. However, there is a large amount of evidence showing that most people do not update their beliefs rationally. We use a model and two experiments to show that employers who are conservative, in the sense of signal neglect, discriminate more against disadvantaged groups than Bayesian employers. We find that such non-Bayesian statistical discrimination deters high-ability workers from disadvantaged groups from pursuing education, further exacerbating initial group inequalities. Excess discrimination caused by employer conservatism is especially important when signals are very informative. Out of the overall hiring gap in our data, around 40% can be attributed to Bayesian statistical discrimination, a further 40% is due to non-Bayesian statistical discrimination, and the remaining 20% is unexplained or potentially taste-based. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: F. Mengel thanks the European Research Council for financial support [Starting Grant 805017]. P. Campos-Mercade acknowledges funding from the Danish National Research Foundation [Grant DNRF134 (CEBI)]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4824. [ABSTRACT FROM AUTHOR]

We exploit a unique data set from the Israeli Professional Football Leagues that provides high-frequency direct measures of players' effort to estimate effort peer effects in a high-skill collaborative team task. Using two complementary identification strategies, we find robust evidence of substantial positive peer effects. Our findings highlight that effort spillovers play an important role in team production and that even a change in just one worker's effort can substantially influence team effort and thus performance. Moreover, we present suggestive evidence that behavioral considerations are a relevant mechanism for creating peer effects even in highly skilled teams of workers. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4811. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We initiate the study of incentive-compatible forecasting competitions in which multiple forecasters make predictions about one or more events and compete for a single prize. We have two objectives: (1) to incentivize forecasters to report truthfully and (2) to award the prize to the most accurate forecaster. Proper scoring rules incentivize truthful reporting if all forecasters are paid according to their scores. However, incentives become distorted if only the best-scoring forecaster wins a prize, since forecasters can often increase their probability of having the highest score by reporting more extreme beliefs. In this paper, we introduce two novel forecasting competition mechanisms. Our first mechanism is incentive compatible and guaranteed to select the most accurate forecaster with probability higher than any other forecaster. Moreover, we show that in the standard single-event, two-forecaster setting and under mild technical conditions, no other incentive-compatible mechanism selects the most accurate forecaster with higher probability. Our second mechanism is incentive compatible when forecasters' beliefs are such that information about one event does not lead to belief updates on other events, and it selects the best forecaster with probability approaching one as the number of events grows. Our notion of incentive compatibility is more general than previous definitions of dominant strategy incentive compatibility in that it allows for reports to be correlated with the event outcomes. Moreover, our mechanisms are easy to implement and can be generalized to the related problems of outputting a ranking over forecasters and hiring a forecaster with high accuracy on future events. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by the European Research Council [Grant ERC StG 307036] and the National Science Foundation [Grant CCF-1445755]. [ABSTRACT FROM AUTHOR]

About 400 publications, working papers and books are included in this bibliography on Stochastic Dominance. It contains an exhaustive listing of papers that are either basic contributions to this subject or primarily concerned with applications of the Stochastic Dominance concepts. It also contains selective listing of papers from finance, economics, mathematics, mathematical physics, mathematical psychology, operations research and statistics literature to illustrate the wide applicability of Stochastic Dominance concepts. [ABSTRACT FROM AUTHOR]

An editorial is presented on the intersection of accounting with risk management, data science, finance, entrepreneurship and marketing. Topics mentioned include the integration of mathematical theory with data analysis related to accounting, the submission of accounting papers in the journal and the importance of an appropriate fast track process for accounting.

Do people have well-defined social preferences waiting to be applied when making decisions? Or do they have to construct social decisions on the spot? If the latter, how are those decisions influenced by the way in which information is acquired and evaluated? These temporal dynamics are fundamental to understanding how people trade off selfishness and prosociality in organizations and societies. Here, we investigate how the temporal dynamics of the choice process shape social decisions in three studies using response times and mouse tracking. In the first study, participants made binary decisions in mini-dictator games with and without time constraints. Using mouse trajectories and a starting time drift diffusion model, we find that, regardless of time constraints, selfish participants were delayed in processing others' payoffs, whereas the opposite was true for prosocial participants. The independent mouse trajectory and computational modeling analyses identified consistent measures of the delay between considering one's own and others' payoffs (self-onset delay, SOD). This measure correlated with individual differences in prosociality and predicted heterogeneous effects of time constraints on preferences. We confirmed these results in two additional studies, one a purely behavioral study in which participants made decisions by pressing computer keys, and the other a replication of the mouse-tracking study. Together, these results indicate that people preferentially process either self or others' payoffs early in the choice process. The intrachoice dynamics are crucial in shaping social preferences and might be manipulated via nudge policies (e.g., manipulating the display order or saliency of self and others' outcomes) for behavior in managerial or other contexts. This paper was accepted by Yan Chen, behavioral economics and decisions analysis. Funding: F. Chen acknowledges support from the National Natural Science Foundation of China [Grants 71803174 and 72173113]. Z. Zhu acknowledges support from the Ministry of Science and Technology [Grant STI 2030-Major Projects 2021ZD0200409]. Q. Shen acknowledges support from the National Natural Science Foundation of China [Grants 71971199 and 71942004]. I. Krajbich acknowledges support from the U.S. National Science Foundation [Grant 2148982]. This work was also supported by the James McKeen Cattell Fund. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4732. [ABSTRACT FROM AUTHOR]

Endogenous response time data are increasingly becoming available to applied researchers of economic choices. However, the usefulness of such data for preference estimation is unclear. Here, we adapt a sequential sampling model—previously validated to jointly explain subjects' choices and response times in laboratory experiments—to model users' responses to video advertisements on mobile devices in a field setting. Our estimates of utility correlate positively with out-of-sample measures of ad engagement, thus providing external validation of the value of incorporating endogenous response time information into a choice model. We then use the model estimates to assess the effectiveness of manipulating attention toward an advertisement at the beginning of a decision. Counterfactual simulations predict that making an ad "nonskippable" (requiring users to watch some portion of the ad)—as is the practice of some online platforms (e.g., YouTube)—generates only modest increases in click-through rates and revenue. This paper was accepted by David Simchi-Levi, behavioral economics and decision analysis. Funding: R. Webb work was supported by Social Sciences and Humanities Research Council [Grant 430-2019-00246]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4738. [ABSTRACT FROM AUTHOR]

In a large-scale natural field experiment, we partnered with a microlending company in Kyrgyzstan that asked over 180,000 of its clients for donations to social projects as a form of corporate philanthropy. In a 2 × 2 design, we explored two main (preregistered) hypotheses about giving by the poor. First, based on a conjecture that the poor are more price sensitive than the rich and in contrast to previous studies, we hypothesize that matching incentives induce crowding in of out-of-pocket donations. Second, we hypothesize that our population cares about their proximity to the charitable project. We find evidence in favor of the former hypothesis but not the latter. Previous studies of charitable giving focus on middle- or high-income earners in Western countries, neglecting the poor, although the lowest-income groups are often shown to contribute substantial shares of their income to charitable causes. Our results challenge the evidence in the extant literature but are in line with our theoretical model. The implications for fundraising managers are that the optimal design of fundraising campaigns crucially depends on the targeted groups and that donation matching is successful in stimulating participation in poorer populations. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: Financial support was from the Deutsche Forschungsgemeinschaft [Grant 280092119] and the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Grant 100018_189152]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4702. [ABSTRACT FROM AUTHOR]

Social reference points have been identified to be important determinants of individuals' welfare. We investigate the consequences of social reference points for risk taking in a laboratory experiment. In the main treatments, risk-taking subjects observe the predetermined earnings of peer subjects when making a risky choice. We exogenously manipulate peers' earnings and find a significant treatment effect: decision makers make less risk-averse choices in the case of larger peers' earnings. The treatment effect is consistent with an application of prospect theory to social reference points and cannot be explained by reference points based on counterfactual information, anchoring, and experimenter demand effects. In additional analyses, we show that diminishing sensitivity seems to play an important role in subjects' risky choices. We explore also whether inequity aversion and expectations-based reference points can account for our findings and conclude that they do not provide plausible alternative explanations for them. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This research was financially supported by the Bonn Graduate School of Economics and the Center for Economics and Neuroscience in Bonn. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4698. [ABSTRACT FROM AUTHOR]

Reputation feedback systems assign feedback scores to traders with the aim of separating them according to reliability. There is now a substantial amount of literature on what these mechanisms do well and not so well. Conspicuously absent is a theoretical framework to guide thinking on improving these systems. Here we construct a prototype market, stylized and conceptual in nature, but also suitable for laboratory testing. We use the prototype to examine competing approaches to eliciting feedback from traders. Using entropy to benchmark informativeness, we show that the informativeness of feedback elicitation approaches depends on the ability to solve a coordination problem such that traders use a common mapping to turn experiences into ratings. In theory, different approaches can be about equally informative although the map to coordinate on is more ambiguous in some cases. We then test the approaches in the laboratory. The resulting data find that the most informative feedback is associated with the elicitation methods where the solution to the coordination problem is least ambiguous. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: Financial support from the Deutsche Forschungsgemeinschaft (German Research Foundation) through the Research Unit "Design & Behavior" [FOR 1371] is gratefully acknowledged. The authors thank the Dutch national e-infrastructure for support of the SURF Cooperative. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4694. [ABSTRACT FROM AUTHOR]

This study tests the hypothesis that competitive strategic interactions foster overconfidence. We experimentally compare a strategic environment in which players have an incentive to overstate their own ability to deter competitors and avoid competition with a nonstrategic environment in which these incentives are removed. Subsequently, we measure the participants' confidence. Overconfidence persists in the former environment but vanishes in the latter. We provide evidence for three mechanisms that contribute to the persistence of overconfidence. First, participants who win uncontested update their confidence as if they had won in an actual competition. Second, by contrast, participants who do not compete do not update their confidence, thus creating an asymmetry in updating. Third, inflated ability messages are "contagious" because they affect positively how their receivers update their confidence. We provide empirical evidence on the role of these mechanisms to explain the Dunning–Kruger effect and gender differences in confidence. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4688. [ABSTRACT FROM AUTHOR]

Many studies show that time preference data from experiments and surveys are related to field behavior. Time preference measures in these studies typically depend simultaneously on utility curvature, the level of impatience, and the change in the level of impatience. Thus, these studies do not allow one to establish which of these three components drive(s) the field behavior of interest. Of these components, the change in the level of impatience is theoretically thought to be the main driver of time inconsistencies and self-control problems. To test this theoretical presumption, one has to measure the change in the level of impatience independently from utilities and levels of impatience. This paper introduces a measure of the degree of decreasing impatience, the DI-index. It measures the change of impatience independently from the level of impatience and independently from utility. It can also be used to test various discounting models. An experiment finds no correlation between the degree of decreasing impatience and self-reported self-control problems in daily life, suggesting that changing impatience is not the sole driver of self-control problems. Data and the supplementary material are available at https://doi.org/10.1287/mnsc.2017.3015. This paper was accepted by Manel Baucells, decision analysis. [ABSTRACT FROM AUTHOR]

This Note points out a minor error in a technique developed by Soyster and Lev [1] for determining if a linear goal can be substituted for a fractional goal in a goal programming problem. A revised formulation is provided. [ABSTRACT FROM AUTHOR]

This paper studies how the presence of peers and different peer assignment rules—self-selection versus random assignment—affect individual performance. Using a framed field experiment, we find that the presence of a randomly assigned peer improves performance by 28% of a standard deviation (SD), whereas self-selecting peers induces an additional 15%–18% SD improvement in performance. Our results document peer effects in multiple characteristics and show that self-selection changes these characteristics. However, a decomposition reveals that variations in the peer composition contribute only little to the performance differences across peer assignment rules. Rather, we find that self-selection has a direct effect on performance. This paper was accepted by Yan Chen, decision analysis. Funding: Funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through CRC TR 224 (projects A01 and A02) is gratefully acknowledged. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2021.4267. [ABSTRACT FROM AUTHOR]

A key feature of online gaming, which serves as an important measure of consumer engagement with a game, is level progression, wherein players make play-or-quit decisions at each level of the game. Understanding users' level-progression behavior is, therefore, fundamental to game designers. In this paper, we propose a dynamic model of consumer level-progression decisions to shed light on the underlying motivational drivers. We cast the individual play-or-quit decisions in a dynamic framework with forward-looking players and consumer learning about the evolution patterns of their operation efficiencies (defined as the average score earned per operation for passing a level). We develop a boundedly rational approach to model how individuals form predictions of their own operation efficiency and playing utility. This new approach allows researchers to flexibly capture players' over/unbiased/underestimation tendencies and risk-averse/-neutral/-seeking preferences—two features that are particularly relevant when modeling game-playing behavior. We develop an algorithm for estimating such a dynamic model and apply our model to level-progression data from individual players with one online game. We find that players in the sample tend to overestimate their operation efficiency as their predicted values are significantly higher than the mean estimates inferred from their playing history with their completed levels. Furthermore, players are found to be risk-seeking with a moderate amount of uncertainty. We uncover two segments of players labeled as "experiencers" versus "achievers"—the former tend to derive a higher utility from the playing process, and the latter are more goal-oriented and derive a higher benefit from completing the entire game. Two counterfactual simulations demonstrate that the proposed model can help adjust the uncertainty level and configure a more effective level-progression point schedule to better engage players and improve the game developer's revenue. This paper was accepted by David Simchi-Levi, marketing. [ABSTRACT FROM AUTHOR]

This paper presents the results of two natural field experiments at a dental clinic. Guided by a simple theoretical model, we exogenously vary deadlines and associated rewards for arranging checkup appointments. Our data show strong and systematic effects of deadlines on patients' behavior. Imposing deadlines induces patients to act earlier and at a persistently higher frequency than without a deadline. We further document that individuals systematically respond to deadlines even if these are not tied to explicit rewards. Several of our findings suggest that individuals' responses to deadlines are shaped by limitations in memory and attention. Our results illustrate that deadlines can be a powerful management tool to encourage timely task completion and to increase the cost effectiveness of performance-contingent rewards. This paper was accepted by Axel Ockenfels, behavioral economics and decision analysis. [ABSTRACT FROM AUTHOR]

Film studios occasionally withhold movies from critics before their release. Because the unreviewed movies tend to be below average in quality, this practice provides a useful setting in which to test models of limited strategic thinking: Do moviegoers seem to realize that no review is a sign of low quality? A companion paper showed that in a set of all widely released movies in 2000-2009, cold opening produces a significant 20%-30% increase in domestic box office revenue, which is consistent with moviegoers overestimating quality of unreviewed movies (perhaps due to limited strategic thinking). This paper reviews those findings and provides two models to analyze this data: an equilibrium model and a behavioral cognitive hierarchy model that allows for differing levels of strategic thinking between moviegoers and movie studios. The behavioral model fits the data better, because moviegoer parameters are relatively close to those observed in experimental subjects. These results suggests that limited strategic thinking rather than equilibrium reasoning may be a better explanation for naïve moviegoer behavior. [ABSTRACT FROM AUTHOR]

Managerial models for practice have undergone remarkable growth in the past 50 years. My paper on decision calculus, published in 1970, was both a progress report and a prescription for improvement. This commentary describes why I wrote the paper, my perception of why it has been considered influential, and a brief overview of what has happened since. The overview starts by tracing the trends and ideas of the 1960s into the 1970s. The 1970s blend easily into the era of decision support systems (DSS). Starting late in the decade and still continuing, DSSs have evolved rapidly due to an explosion of data, increased computer power, and advances in modeling methods. I review highlights of this evolution from the point of view of managerial models, giving special emphasis to marketing, since that has been my window on this world. The good news is that more managers than ever are using models. The bad news is that many managers do not even realize they are using models! (But we should ask whether this is really bad.) The commentary concludes with thoughts about the future of managerial models. The selection of my paper as one of the most influential in the first 50 years of Management Science was a significant honor for which I am grateful. I take it as an indication by the members of INFORMS of the importance to our field of the issues addressed. [ABSTRACT FROM AUTHOR]

This paper introduces the index τ = ln β / ln δ as a measure of time inconsistency and vulnerability to self-control problems in the quasi-hyperbolic, beta-delta (β , δ) discounting model. We provide a preference foundation for τ and, consequently, a revealed preference definition of failed self-control. The τ index is independent of utility and has an intuitive interpretation as the maximum number of future selves who can disagree with the current self with respect to uniform deviations from an intertemporal plan. The index is also computable for continuous discount functions after an appropriate mapping of functions onto the (β , δ) family. The τ index thus provides a common yardstick for comparing temporal inconsistency across different functional forms. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. [ABSTRACT FROM AUTHOR]

Practitioners and academics alike have argued that a firm's interest in its short-term capital market valuation (short-termism) is harmful to the firm's long-term profit. Their argument is intuitive—when a firm exhibits short-termism, its decision making will cater to the short term at the expense of the long term. Some practitioners claim that the adverse effects of increased short-termism become particularly acute in competitive markets. However, there is little academic research that examines the compound effect of short-termism and competition on a firm's operational choices and long-term profit. In this paper, we provide a rigorous analytical model to examine how short-termism and competition can interact to induce a firm to distort its capacity investment and affect its long-term profit. We model a firm that has private information about its market demand, which can be either high or low and is captured by the firm's type. Signaling this private information can influence both the investor's valuation and the competitor's entry decision. We find that operational distortion can emerge not only when short-termism is large, but also when it is small. In fact, when short-termism is sufficiently small, both firm types will distort their operational decision by underinvesting in a pooling outcome. We show, however, that the impact of short-termism on a firm's long-term profit is not monotonically negative, and a certain level of short-termism can be beneficial to a firm's long-term profit. The positive impact of incremental short-termism is robust to alternative modeling assumptions, including the form of competition and the investor's risk profile. This paper was accepted by Victor Martínez-de-Albéniz, operations management. [ABSTRACT FROM AUTHOR]

There is an ongoing debate regarding the degree to which a forecaster's ability to draw correct inferences from market signals is real or illusory. This paper attempts to shed light on the debate by examining how personal characteristics do or do not affect forecaster success. Specifically, we investigate the role of fluid intelligence, manipulativeness, and theory of mind on forecast accuracy in experimental asset markets. We find that intelligence improves forecaster performance when market mispricing is low, manipulativeness improves forecaster performance when mispricing is high, and the degree to which theory of mind skills matter depends on both the level of mispricing and how information is displayed. All three of these results are consistent with hypotheses derived from the previous literature. Additionally, we observe that male forecasters outperform female forecasters after controlling for intelligence, manipulativeness, and theory of mind skills as well as risk aversion. Interestingly, we do not find any evidence that forecaster performance improves with experience across markets or within markets. This paper was accepted by Axel Ockenfels, behavioral economics and decision analysis. [ABSTRACT FROM AUTHOR]

Ignorance enables individuals to act immorally. This is well known in policy circles, in which there is keen interest in lowering moral ignorance. In this paper, we study how the demand for moral ignorance responds to monetary incentives and how the demand curve for ignorance reacts to social norm messages. We propose a simple behavioral model in which individuals suffer moral costs when behaving selfishly in the face of moral information. In several experiments, we find that moral ignorance decreases by more than 30 percentage points with small monetary incentives, but we find no significant change with social norm messages, and we document strong persistence of ignorance across moral contexts. Our findings indicate that rather simple messaging interventions may have limited effects on ignorance. In contrast, changes in incentives could be highly effective. This paper was accepted by Yan Chen, behavioral economics and decision analysis. [ABSTRACT FROM AUTHOR]

This paper reports two experiments in which attitudes toward temporal risk resolution is elicited from choices between two-outcome lotteries that pay out at some future fixed date and can be resolved either now or later. We show that matching probabilities provides a simple method to measure attitudes toward temporal resolution—via the utility scale—under the recursive expected utility of Kreps and Porteus. We also analyze our data using a general recursive model that can reveal attitudes toward temporal risk resolution through the utility scale and/or the probability weighting scale. In terms of goodness of fit, as well as of prediction accuracy, our results point to a better performance of the probability weighting approach. More specifically, we show that individuals become less sensitive and more pessimistic with respect to winning probabilities when lotteries are resolved later rather than now. This paper was accepted by George Wu, decision analysis. [ABSTRACT FROM AUTHOR]

In this paper, we study a regretful seller's problem of selling a fixed number of goods over a finite and known time horizon. The seller engages in counterfactual thinking to compare her selected price with other forgone alternatives. If a forgone alternative (ex post) generates a better outcome than the selected one, then the seller experiences regret. We characterize the pricing decision of a regretful seller and find that, although regret leads the seller to set a price that is lower than that set by an unbiased seller, the regretful seller employs decision policies whose structure is similar to those of the unbiased seller: the price decreases with the remaining inventory and increases with the time-to-go. Interestingly, we find that the seller who has a greater number of goods does not necessarily receive greater revenue. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. [ABSTRACT FROM AUTHOR]

Ensuring fairness in algorithmic decision making is a crucial policy issue. Current legislation ensures fairness by barring algorithm designers from using demographic information in their decision making. As a result, to be legally compliant, the algorithms need to ensure equal treatment. However, in many cases, ensuring equal treatment leads to disparate impact particularly when there are differences among groups based on demographic classes. In response, several "fair" machine learning (ML) algorithms that require impact parity (e.g., equal opportunity) at the cost of equal treatment have recently been proposed to adjust for the societal inequalities. Advocates of fair ML propose changing the law to allow the use of protected class-specific decision rules. We show that the proposed fair ML algorithms that require impact parity, while conceptually appealing, can make everyone worse off, including the very class they aim to protect. Compared with the current law, which requires treatment parity, the fair ML algorithms, which require impact parity, limit the benefits of a more accurate algorithm for a firm. As a result, profit maximizing firms could underinvest in learning, that is, improving the accuracy of their machine learning algorithms. We show that the investment in learning decreases when misclassification is costly, which is exactly the case when greater accuracy is otherwise desired. Our paper highlights the importance of considering strategic behavior of stake holders when developing and evaluating fair ML algorithms. Overall, our results indicate that fair ML algorithms that require impact parity, if turned into law, may not be able to deliver some of the anticipated benefits. This paper was accepted by Kartik Hosanagar, information systems. [ABSTRACT FROM AUTHOR]

Nonprofits regularly use conditional "thank you" gifts to entice prospective donors to give, yet experimental evidence suggests that their effects are mixed in practice. This paper uses multiple laboratory experiments to test when and why thank you gifts vary in effectiveness. First, we demonstrate that although gifts often increase donations to charities that donors did not rate highly, many of the same gifts had no effects or negative effects for charities that prospective donors already liked. We replicate these findings in a second experiment that uses a different range of charity and gift options as well as different measures of participant perceptions of a charity. We also find that making gifts optional, as is common in fundraising campaigns, does not eliminate these negative gift effects. In additional experiments, we directly test for donor motives using self-report and priming experiments. We find that thank you gifts increase (decrease) the weight that donors place on self-interested (prosocial) motives, leading to changes in donation patterns. Altogether, our results suggest that practitioners may find gifts more useful when appealing to donors not already familiar with or favorably inclined to their charity, such as during donor acquisition campaigns. They may be less useful when appealing to recent donors or others who already favor the charity, in part because the gift may activate mindsets or norms that emphasize self-interested motives instead of more prosocial, other-regarding motives. This paper was accepted by Yan Chen, decision analysis. [ABSTRACT FROM AUTHOR]

In nearly every betting market, gamblers overvalue the riskiest bets. This "longshot bias" is usually explained by a tendency to overweight low-probability events. We offer a novel explanation: contrast effects enhance the attractiveness of longshots because gambles presented in terms of their payoffs are easier to compare along the payoff dimension than along the probability dimension. Our analysis of historical betting odds suggests that contrasts between gamble payoffs can explain the longshot bias. Laboratory experiments confirm those contrast effects and show that the longshot bias disappears when gamblers consider bets in isolation or when winning probabilities are easier to compare. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: The authors thank the University of Chicago's Center for Decision Research and Richard Thaler for funding. Supplemental Material: The online supplement and data are available at https://doi.org/10.1287/mnsc.2023.4684. [ABSTRACT FROM AUTHOR]

Affirmative action policies are often implemented through reserve systems. In this study, we demonstrate that reserve systems face widespread misunderstanding by the public. This misunderstanding can lead individuals to support policies that ineffectively pursue their interests. To establish these claims, we present 1,013 participants in the Understanding America Study with choices between pairs of reserve systems. Participants are members of the group receiving affirmative action and are financially incentivized to choose the system that maximizes their chance of admission. Using this data, we apply a novel approach to identifying the rate of uptake of different decision rules used by participants. We find that participants rarely use a fully optimal decision rule. In contrast, we find that many choices—40% in our primary estimates—are rationalized by a nearly correct decision rule, with errors driven solely by failing to appreciate the importance of processing order. Failing to account for processing order causes individuals to fail to distinguish between two policies that achieve different degrees of affirmative action: policies that provide nonbinding minimum guarantees of the number of spaces allocated and policies that provide spaces over-and-above what would be allocated absent a reserve. Confusion about the importance of processing order helps to explain otherwise surprising decisions made in field applications of reserve systems. We discuss implications for managers and policy makers who are trying to implement reserve systems and who are accountable to the public. This paper was accepted by Axel Ockenfels, behavioral economics and decision analysis. Funding: The authors thank the National Science Foundation and the Wharton Behavioral Laboratory for financial support. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4669. [ABSTRACT FROM AUTHOR]

We study the joint implementation of reservation and de-reservation policies in India that has been enforcing comprehensive affirmative action since 1950. The landmark judgment of the Supreme Court of India in 2008 mandated that whenever the OBC category (with 27% reservation) has unfilled positions, they must be reverted to general category applicants in admissions to public schools without specifying how to implement it. We disclose the drawbacks of the recently reformed allocation procedure in admissions to technical colleges and offer a solution through "de-reservation via choice rules." We propose a novel priority design—Backward Transfers (BT) choice rule—for institutions and the deferred acceptance mechanism under these choice rules (DA-BT) for centralized clearinghouses. We show that DA-BT corrects the shortcomings of existing mechanisms. By formulating India's legal requirements and policy goals as formal axioms, we show that the DA-BT mechanism is unique for the concurrent implementation of reservation and de-reservation policies. This paper was accepted by Yan Chen, behavioral economics and decision analysis. [ABSTRACT FROM AUTHOR]