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Decades of research suggest that information exchange in groups and organizations can reliably improve judgment accuracy in tasks such as financial forecasting, market research, and medical decision making. However, we show that improving the accuracy of numeric estimates does not necessarily improve the accuracy of decisions. For binary-choice judgments, also known as classification tasks—for example, yes/no or build/buy decisions—social influence is most likely to grow the majority vote share, regardless of the accuracy of that opinion. As a result, initially, inaccurate groups become increasingly inaccurate after information exchange, even as they signal stronger support. We term this dynamic the "crowd classification problem." Using both a novel data set and a reanalysis of three previous data sets, we study this process in two types of information exchange: (1) when people share votes only, and (2) when people form and exchange numeric estimates prior to voting. Surprisingly, when people exchange numeric estimates prior to voting, the binary-choice vote can become less accurate, even as the average numeric estimate becomes more accurate. Our findings recommend against voting as a form of decision making when groups are optimizing for accuracy. For those cases where voting is required, we discuss strategies for managing communication to avoid the crowd classification problem. We close with a discussion of how our results contribute to a broader contingency theory of collective intelligence. This paper was accepted by Lamar Pierce, organizations. [ABSTRACT FROM AUTHOR]

In a paper published in Management Science in 2015, Stewart, Reimers, and Harris (SRH) demonstrated that shapes of utility and probability weighting functions could be manipulated by adjusting the distributions of outcomes and probabilities on offer as predicted by the theory of decision by sampling. So marked were these effects that, at face value, they profoundly challenge standard interpretations of preference theoretic models in which such functions are supposed to reflect stable properties of individual risk preferences. Motivated by this challenge, we report an extensive replication exercise based on a series of experiments conducted as a quasi-adversarial collaboration across different labs and involving researchers from both economics and psychology. We replicate the SRH effect across multiple experiments involving changes in many design features; importantly, however, we find that the effect is also present in designs modified so that decision by sampling predicts no effect. Although those results depend on model-based inferences, an alternative analysis using a model-free comparison approach finds no evidence of patterns akin to the SRH effect. On the basis of simulation exercises, we demonstrate that the SRH effect may be a consequence of misspecification biases arising in parameter recovery exercises that fit imperfectly specified choice models to experimental data. Overall, our analysis casts the SRH effect in an entirely new light. This paper was accepted by Yuval Rottenstreich, judgment and decision making [ABSTRACT FROM AUTHOR]

We introduce random projection, an important dimension-reduction tool from machine learning, for the estimation of aggregate discrete-choice models with high-dimensional choice sets. Initially, high-dimensional data are projected into a lower-dimensional Euclidean space using random projections. Subsequently, estimation proceeds using cyclical monotonicity moment inequalities implied by the multinomial choice model; the estimation procedure is semiparametric and does not require explicit distributional assumptions to be made regarding the random utility errors. Our procedure is justified via the Johnson–Lindenstrauss lemma—the pairwise distances between data points are preserved through random projections. The estimator works well in simulations and in an application to a supermarket scanner data set. This paper was accepted by Juanjuan Zhang, marketing. [ABSTRACT FROM AUTHOR]

Oftentimes, close competitors carry partially overlapping assortments in seeming contradiction to the principle of maximum differentiation. One of the justifications of such practice is that an overlapping assortment with competitive prices on the common products may prevent further consumer search and therefore could be useful even when profits from the overlapping products do not justify the costs of carrying them. In this paper, we examine the validity of this intuition and show that such strategy may indeed be optimal when consumers are uncertain about prices they might find elsewhere and face shopping costs for discovery of all prices. Specifically, we show that the (larger) assortment with product overlap may signal a 'competitive' price of the relatively unique product and prevent further consumer search for a lower price on it. An implication of this finding is that a consumer may rationally behave as if she likes a larger assortment even if the assortment is enlarged by adding products the consumer has no interest in. Furthermore, we show that the optimal pricing strategy may include pricing of common products or products with known costs at a loss, which provides a novel explanation of loss-leader pricing. Data, as supplemental material, are available at . This paper was accepted by Pradeep Chintagunta, marketing. [ABSTRACT FROM AUTHOR]

In a stochastic interdiction game a proliferator aims to minimize the expected duration of a nuclear weapons development project, and an interdictor endeavors to maximize the project duration by delaying some of the project tasks. We formulate static and dynamic versions of the interdictor's decision problem where the interdiction plan is either precommitted or adapts to new information revealed over time, respectively. The static model gives rise to a stochastic program, whereas the dynamic model is formalized as a multiple optimal stopping problem in continuous time and with decision-dependent information. Under a memory-less probabilistic model for the task durations, we prove that the static model reduces to a mixed-integer linear program, whereas the dynamic model reduces to a finite Markov decision process in discrete time that can be solved via efficient value iteration. We then generalize the dynamic model to account for uncertainty in the outcomes of the interdiction actions. We also discuss a crashing game where the proliferator can use limited resources to expedite tasks so as to counterbalance the interdictor's efforts. The resulting problem can be formulated as a robust Markov decision process. Keywords: interdiction game; PERT network; Markov decision process; robust optimization History: Received April 15, 2013; accepted March 28, 2014, by Dimitris Bertsimas, optimization. Published online in Articles in Advance September 23, 2014., 1. Introduction Since its inception in 1968, a total of 190 states have joined the Treaty on the Non-Proliferation of Nuclear Weapons, whose objective is to prevent the spread of [...]

The dynamic assignment of patients to exam days in order to manage daily variations in demand and capacity is a long-standing open research area in appointment scheduling. In particular, the dynamic assignment of advance appointments has been considered to be especially challenging because of its high dimensionality. We consider a canonical model of dynamic advance scheduling with two patient classes: an urgent demand class, which must be served on the day of arrival, and a regular demand class, which can be served at a future date. Patients take the earliest appointments offered and do not differentiate among providers. We derive a surprising characterization of an optimal policy and an algorithm to compute the policy exactly and efficiently. These are, to our knowledge, the first analytical results for the dynamic advance assignment of patients to exam days. We introduce the property of successive refinability, which allows advance schedules to be easily computable and under which there is no cost to the system to making advance commitments to patients. We allow multiple types of capacity to be considered and both demand and capacity to be nonstationary and stochastic. [ABSTRACT FROM AUTHOR]

We elicited the prospect theory components (utility, probability weighting, and loss aversion) when consequences are expressed as the time dedicated to a specific task or activity. A similar elicitation was performed for monetary consequences to allow an across-attribute (time/money) comparison of the elicited components (at the individual level). We obtained less concave utility and smaller loss aversion for time than for money. Moreover, while the probability weighting was predominantly inverse S-shaped for both attributes, it was less sensitive to probabilities and more elevated for time than for money. This finding implies more optimism for gains and more pessimism for losses. [ABSTRACT FROM AUTHOR]

We study how corporate governance affects firm value through the decision of whether to fire or retain the chief executive officer (CEO). We present a model in which weak governance-which prevents shareholders from controlling the board-protects inferior CEOs from dismissal, while at the same time insulates the board from pressures by biased or uninformed shareholders. Whether stronger governance improves retain/replace decisions depends on which of these effects dominates. We use our theoretical framework to assess the effect of governance on the quality of firing and hiring decisions using data on the CEO dismissals of large U.S. corporations during 1994-2007. Our findings are most consistent with a beneficent effect of weak governance on CEO dismissal decisions, suggesting that insulation from shareholder pressure may allow for better long-term decision making. [ABSTRACT FROM AUTHOR]

The nature of utility is controversial. Whereas decision theory commonly assumes that utility is context specific, applied and empirical decision analysis typically assumes one unifying concept of utility applicable to all decision problems. This controversy has hardly been addressed empirically because of the absence of methods to measure utility outside the context of risk. We introduce a method to measure utility over time and compare utility under risk and utility over time. We distinguish between gains and losses and also measure loss aversion. In two experiments we found that utility under risk and utility over time differed and were uncorrelated. Utility under risk was more curved than utility over time. Subjects were loss averse both for risk and for time, but loss aversion was more pronounced for risk. Loss aversion over risk and time were uncorrelated. This suggests that loss aversion, although important in both decision contexts, is volatile and subject to framing. [ABSTRACT FROM AUTHOR]

One of the main assumptions in research on designing supply contracts is that decision makers act in a way that maximizes their expected profit. A number of laboratory experiments demonstrate that this assumption does not hold. Specifically, faced with uncertain demand, decision makers place orders that systematically deviate from the expected profit maximizing levels. We have added to this body of knowledge by demonstrating that ordering decisions also systematically depend on individual contract parameters and by developing a behavioral model that captures this systematic behavior. We proceed to test our behavioral model using laboratory experiments and use the data to derive empirical model parameters. We then test our approach in out-of-sample validation experiments that confirm that, indeed, contracts designed using the behavioral model perform better than contracts designed using the standard model. [ABSTRACT FROM AUTHOR]

In the United States, end-stage liver disease patients join a waiting list and then make accept/reject decisions for transplantation as deceased-donor organs are offered to them over time. These decisions are largely influenced by the patient's prospect for future offers, which can be ascertained most accurately by knowing the entire composition of the waiting list. Under the current transplantation system, however, the United Network for Organ Sharing (UNOS), in an effort to strike a balance between privacy and transparency, only publishes an aggregated version of the waiting list. However, it is not clear whether the published information is good enough (compared with perfect information) to help patients make optimal decisions that maximize their individual life expectancies. We provide a novel model of this accept/reject problem from an individual patient's perspective using a partially observed Markov decision process (POMDP) framework, which incorporates the imperfect waiting list information as published currently into the patient's decision making. We analyze structural properties of this model. In particular, we establish conditions that guarantee a monotone value function and a threshold-type optimal policy with respect to the partially observable rank state that captures the imperfect waiting list information. Furthermore, we develop an improved solution methodology to solve a generic POMDP model. This solution method guarantees, for any fixed grid, the best possible approximation to the optimal value function by solving linear programs to compute the optimal weights used for the approximation. Finally, we compare, in a clinically driven numerical study, the results of this model with those of an existing Markov decision process model that differs from our model in assuming the availability of perfect waiting list information. This comparison allows us to assess the quality of the published imperfect information as measured by a patient's so-called price of privacy (i.e., the opportunity loss in expected life days due to a lack of perfect waiting list information). Previous work estimates a significant loss in a patient's life expectancy, on average, when the patient has no waiting list information compared with full information. In this paper, we find that the currently published partial information is nearly sufficient to eliminate this loss, resulting in a negligible price of privacy and supporting current UNOS practice [ABSTRACT FROM AUTHOR]

Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called adaptive design optimization, adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate expected utility, weighted expected utility, original prospect theory, and cumulative prospect theory models. [ABSTRACT FROM AUTHOR]

In diagnostic services, agents typically need to weigh the benefit of running an additional test and improving the accuracy of diagnosis against the cost of delaying the provision of services to others. Our paper analyzes how to dynamically manage this accuracy/congestion trade-off. To that end, we study an elementary congested system facing an arriving stream of customers. The diagnostic process consists of a search problem in which the service provider conducts a sequence of imperfect tests to determine the customer's type. We find that the agent should continue to perform the diagnosis as long as her current belief that the customer is of a given type falls into an interval that depends on the congestion level as well as the number of performed tests thus far. This search interval should shrink as congestion intensifies and as the number of performed tests increases if additional conditions hold. Our study reveals that, contrary to diagnostic services without congestion, the base rate (i.e., the prior probability of the customer type) has an effect on the agent's search strategy. In particular, the optimal search interval shrinks when customer types are more ambiguous a priori, i.e., as the base rate approaches the value at which the agent is indifferent between types. Finally, because of congestion effects, the agent should sometimes diagnose the customer as being of a given type, even if test results indicate otherwise. All these insights disappear in the absence of congestion. Key words: service operations; queueing theory; dynamic programming [ABSTRACT FROM AUTHOR]

A number of recent papers have examined the environmental and genetic sources of individual differences in economic and financial decision making. Here we contribute to this burgeoning literature by extending it to a number of key behavioral anomalies that are thought to be of importance for consumption, savings, and portfolio selection decisions. Using survey-based evidence from more than 11,000 Swedish twins, we demonstrate that a number of anomalies such as, for instance, the conjunction fallacy, default bias, and loss aversionare moderately heritable. In contrast, our estimates imply that variation in common environment explains only a small share of individual differences. We also report suggestive evidence in favor of a shared genetic architecture between cognitive reflection and a subset of the studied anomalies. These results offer some support for the proposition that the heritable variation in behavioral anomalies is partly mediated by genetic variance in cognitive ability. Taken together with previous findings, our results underline the importance of genetic differences as a source of heterogeneity in economic and financial decision making. [ABSTRACT FROM AUTHOR]

Intertemporal decision making under risk involves two dimensions: time preferences and risk preferences.This paper focuses on the impact of time on risk preferences, independent of the intertemporal trade-off of outcomes, i.e., time preferences. It reports the results of an experimental study that examines how delayed resolution and payment of risky options influence individual choice. We used a simple experimental design based on the comparison of two-outcome monetary lotteries with the same delay. Raw data clearly reveal that subjects become more risk tolerant for delayed lotteries. Assuming a prospect theory--like model under risk, we analyze the impact of time on utility and decision weights, independent of time preferences. We show that the subjective treatment of outcomes (i.e., utility) is not significantly affected by time. In fact, the impact of time is completely absorbed by the probability weighting function. The effect of time on risk preferences was found to generate probabilistic optimism resulting in a higher risk tolerance for delayed lotteries. [ABSTRACT FROM AUTHOR]

The mean-variance approach is an influential theory of decision under risk proposed by Markowitz (Markowitz, H. 1952. Portfolio selection. J. Finance 7(1) 77-91). The mean-variance approach implies violations of first-order stochastic dominance not commonly observed in the data. This paper proposes a new model in the spirit of the classical mean-variance approach without violations of stochastic dominance. The proposed model represents preferences by a functional U (L) - p • r (L) , where U (L) denotes the expected utility of lottery L, p ε [-1, 1 is a subjective constant, and r (L) is the mean absolute (utility) semideviation of lottery L. The model comprises a linear trade-off between expected utility and utility dispersion. The model can accommodate several behavioral regularities such as the Allais paradox and switching behavior in Samuelson's example. [ABSTRACT FROM AUTHOR]

This paper introduces time-tradeoff (TTO) sequences as a general tool to analyze intertemporal choice. We give several applications. For empirical purposes, we can measure discount functions without requiring any measurement of or assumption about utility. We can quantitatively measure time inconsistencies and simplify their qualitative tests. TTO sequences can be administered and analyzed very easily, using only pencil and paper. For theoretical purposes, we use TTO sequences to axiomatize (quasi-)hyperbolic discount functions. We demonstrate the feasibility of measuring TTO sequences in an experiment, in which we tested the axiomatizations. Our findings suggest rejections of several currently popular discount functions and call for the development of new ones. It is especially desirable that such discount functions can accommodate increasing impatience. [ABSTRACT FROM AUTHOR]

This paper is an update of a paper that five of us published in 1992. The areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields. [ABSTRACT FROM AUTHOR]

In purchase situations where attribute information is either missing or difficult to judge, a well-known heuristic that consumers use to form evaluations is the halo effect. The psychology literature has widely considered the halo a reflection of consumers' inability to discriminate between different attributes and have therefore labeled it the "halo error" or the "logical error." The objective of this paper is to offer a rationale for the halo effect. We use a decision-theory framework to show that the halo is consistent with the goal of minimizing estimation risk. Contrary to conventional wisdom, we demonstrate that a decision using the halo has lower estimation risk compared to not using the halo heuristic. Therefore, using the halo results in utility maximization and is indicative of rational behavior. [ABSTRACT FROM AUTHOR]

The article presents abstracts of management science research. They include "A queueing analysis to determine how many additional beds are needed for the detention and removal of illegal aliens," "Public evacuation decisions and hurricane track uncertainty," and "A Path-based approach for hazmat transport network design."

Objectives have long been considered a basis for sound decision making. This research examines the ability of decision makers to generate self-relevant objectives for consequential decisions. In three empirical studies, participants consistently omitted nearly half of the objectives that they later identified as personally relevant. More surprisingly, omitted objectives were perceived to be almost as important as those generated by participants on their own. These empirical results were replicated in a real-world case study of strategic decision making at a high-tech firm. Overall, our research suggests that decision makers are considerably deficient in utilizing personal knowledge and values to form objectives for the decisions they face. [ABSTRACT FROM AUTHOR]

This experiment tested two behavioral properties of risky decision making--gain-loss separability (GLS) and coalescing. Cumulative prospect theory (CPT) implies both properties, but the transfer of attention exchange (TAX) model violates both. Original prospect theory satisfies GLS but may or may not satisfy coalescing, depending on whether editing rules are assumed. A configural form of CPT proposed by Wu and Markle [Wu, G., A. B. Markle. 2004. An empirical test of gain-loss separability in prospect theory. Working Paper 06-25-04, Graduate School of Business, University of Chicago] violates GLS, but satisfies coalescing. New tests were designed and conducted to test these theories against specific predictions of a TAX model. This model used parameters estimated from previous data, together with simple new assumptions to extend TAX to gambles with negative and mixed consequences. Contrary to all three forms of prospect theory, systematic violations of both coalescing and GLS were observed. Violations of GLS were confirmed by analyses of individual data patterns by means of an error model in which each choice can have a different rate of error. Without estimating any parameters from the new data, the TAX model predicted the majority choices in the new data fairly well, correctly predicting when modal choices would violate GLS, when they would satisfy it, and when indifference would be observed. [ABSTRACT FROM AUTHOR]

We study the global diffusion of ISO 9000 and ISO 14000 certification using a network diffusion framework. We start by investigating the presence and nature of contagion effects by defining alternative cross-country networks and testing their relative strength. Second, we study how the rate of diffusion differs between the two standards and between early- and later-adopting countries. Third, we identify which countries had more influence on diffusion than others. Empirically, we build a diffusion model which includes several possible cross-country contagion effects and then use Bayesian methods for estimation and model selection. Using country by year data for 56 countries and nine years, we find that accounting for cross-country influences improves both the fit and the prediction accuracy of our models. However, the specific cross-country contagion mechanism is different across the two standards. Diffusion of ISO 9000 is driven primarily by geography and bilateral trade relations, whereas that of ISO 14000 is driven primarily by geography and cultural similarity. We also find that the diffusion rate of ISO standards is higher for later-adopting countries and for the later ISO 14000 standard. We discuss several implications of our findings for the global diffusion of management standards. [ABSTRACT FROM AUTHOR]

The problem of group ranking, also known as rank aggregation, has been studied in contexts varying from sports, to multicriteria decision making, to machine learning, to ranking Web pages, and to behavioral issues. The dynamics of the group aggregation of individual decisions has been a subject of central importance in decision theory. We present here a new paradigm using an optimization framework that addresses major shortcomings that exist in current models of group ranking. Moreover, the framework provides a specific performance measure for the quality of the aggregate ranking as per its deviations from the individual decision-makers' rankings. The new model for the group-ranking problem presented here is based on rankings provided with intensity--that is, the degree of preference is quantified. The model allows for flexibility in decision protocols and can take into consideration imprecise beliefs, less than full confidence in some of the rankings, and differentiating between the expertises of the reviewers. Our approach relaxes frequently made assumptions of: certain beliefs in pairwise rankings; homogeneity implying equal expertise of all decision makers with respect to all evaluations; and full list requirement according to which each decision maker evaluates and ranks all objects. The option of preserving the ranks in certain subsets is also addressed in the model here. Significantly, our model is a natural extension and generalization of existing models, yet it is solvable in polynomial time. The group rankings models are linked to network flow techniques. [ABSTRACT FROM AUTHOR]

This paper proposes an agent competition double-auction mechanism to simplify decision making and promote transactions for the customer-to-customer marketplaces. Under the proposed double-auction mechanism, bidding one's true valuation (private information) is the best strategy for each individual buyer and seller even when shipping costs and sales taxes are different across various possible transactions. The proposed mechanism also achieves budget balance and asymptotic efficiency. Furthermore, these results not only hold for an environment where buyers and sellers exchange identical commodities, but also can be extended to an environment with multiple substitutable commodities. [ABSTRACT FROM AUTHOR]

Organizational coordination has traditionally been viewed from an organizational-design perspective where rules, modalities, and structures are used to meet the information-processing demands of the environment. Fast-response organizations face unique coordination challenges as they operate under conditions of high uncertainty and fast decision making, where mistakes can be catastrophic. Based on an in-depth investigation of the coordination practices of a medical trauma center where fast-response and error-free activities are essential requirements, we develop a coordination-practice perspective that emphasizes expertise coordination and dialogic coordination. We argue that expertise coordination practices (reliance on protocols, community of practice structuring, plug-and-play teaming, and knowledge sharing) are essential to manage distributed expertise and ensure the timely application of necessary expertise. We suggest that dialogic coordination practices (epistemic contestation, joint sensemaking, cross-boundary intervention, and protocol breaking) are time-critical responses to novel events and ensure error-free operation. However, dialogic coordination practices are highly contested because of epistemic differences, reputation stakes, and possible blame apportionment. [ABSTRACT FROM AUTHOR]

This paper examines how the knowledge-based view (KBV) can be applied to firm boundary decisions and the performance implications of those decisions. At the center of the paper is a theoretical and empirical examination of how firms most efficiently organize to solve different types of problems related to technological development, using the semiconductor industry as the empirical setting. Measures that capture important dimensions of performance support the proposition that organization affects performance in problem solving related to knowledge development. Integrated firms realize performance advantages when problem solving in technological development is ill structured and complex, while the same is true for specialized firms when problem solving in technological development is well structured and simple. Performance differences also arise from the presence of scale economies and scope economies. [ABSTRACT FROM AUTHOR]

We explore the extent to which the decisions of participants in a speculative market effectively account for information contained in prices and price movements. The horse race betting market is an ideal environment to explore these issues. A conditional logit model is constructed to determine winning probabilities based on bookmakers' closing prices and the time-indexed movement of prices to the market close. We incorporate a technique for extracting predictors from price (odds) curves using orthogonal polynomials. The results indicate that closing prices do not fully incorporate market price information, particularly information that is less readily discernable by market participants. [ABSTRACT FROM AUTHOR]

We generalize Markowitz analysis to the situations involving an uncertain exit time. Our approach preserves the form of the original problem in that an investor minimizes portfolio variance for a given level of the expected return. However, inputs are now given by the generalized expressions for mean and variance-covariance matrix involving moments of the random exit time in addition to the conditional moments of asset returns. Although efficient frontiers in the generalized and the standard Markowitz case may coincide under certain conditions, we demonstrate that, by means of an example, in general that is not true. In particular, portfolios efficient in the standard Markowitz sense can be inefficient in the generalized sense and vice versa. As a result, an investor facing an uncertain time horizon and investing as if her time of exit is certain would in general make suboptimal portfolio allocation decisions. Numerical simulations show that a significant efficiency loss can be induced by an improper use of standard mean-variance analysis when time horizon is uncertain. [ABSTRACT FROM AUTHOR]

This paper introduces a technique for improving preference assessment by reducing the influence of context on preferential choices. We propose that a decision maker who is exposed to relevant attribute levels will form spontaneous valuations, which will then insulate the decision maker from the effects of context during subsequent preference assessment. Results from three studies supported this hypothesis. Pre-exposure to product attribute levels undermined the impact of attribute priming, decision framing, and asymmetric dominance on preferential choices. A fourth study demonstrated that similar results can be obtained by allowing decision makers to pregenerate lists of attribute levels on their own. [ABSTRACT FROM AUTHOR]

We propose a parameter estimation method based on what we call the minimum decisional regret principle. We focus on mathematical programming models with objective functions that depend linearly on costs or other parameters. The approach is illustrated for cost estimation in production planning using linear programming models. The method uses past planning data to estimate costs that are otherwise difficult to estimate. We define a monetary measure of distance between observed plans and optimal ones, called decisional regret. The proposed estimation algorithm finds parameter values for which the associated optimal plans are as near as possible to the observed ones on average. Such techniques may be called behavioral estimation because they are based on the observed planning or decision-making behavior of managers or firms. Two numerical illustrations are given. A supporting hyperplane algorithm is used to solve the estimation model. A method is proposed for obtaining range estimates of the parameters when multiple alternative estimates exist. We also propose a new validation approach for this estimation principle, which we call the target-mode agreement criterion. [ABSTRACT FROM AUTHOR]

We investigate the decision heuristics used by experts to forecast that early-stage ventures are subsequently commercialized. Experts evaluate 37 project characteristics and subjectively combine data on all cues by examining both critical flaws and positive factors to arrive at a forecast. A conjunctive model is used to describe their process, which sums "good" and "bad" cue counts separately. This model achieves a 91.8% forecasting accuracy of the experts' correct forecasts. The model correctly predicts 86.0% of outcomes in out-of-sample, out-of-time tests. Results indicate that reasonably simple decision heuristics can perform well in a natural and very difficult decision-making context. [ABSTRACT FROM AUTHOR]

Decision analysis produces measures of value such as expected net present values or expected utilities and ranks alternatives by these value estimates. Other optimization-based processes operate in a similar manner. With uncertainty and limited resources, an analysis is never perfect, so these value estimates are subject to error. We show that if we take these value estimates at face value and select accordingly, we should expect the value of the chosen alternative to be less than its estimate, even if the value estimates are unbiased. Thus, when comparing actual outcomes to value estimates, we should expect to be disappointed on average, not because of any inherent bias in the estimates themselves, but because of the optimization-based selection process. We call this phenomenon the optimizer's curse and argue that it is not well understood or appreciated in the decision analysis and management science communities. This curse may be a factor in creating skepticism in decision makers who review the results of an analysis. In this paper, we study the optimizer's curse and show that the resulting expected disappointment may be substantial. We then propose the use of Bayesian methods to adjust value estimates. These Bayesian methods can be viewed as disciplined skepticism and provide a method for avoiding this postdecision disappointment. [ABSTRACT FROM AUTHOR]

Subjective expected utility theory does not distinguish between attitudes toward uncertainty (ambiguous probabilities) and attitudes toward risk (unambiguous probabilities). Both are explained in terms of nonlinear utility for money rather than properties of events per se, hence, the decision maker displays the same attitude toward all sources of risk and uncertainty. There is ample evidence that real decision makers do not always behave (or even wish to behave) in this way, and instead they often distinguish between risk and uncertainty, as in Ellsberg's (1961) paradox. This paper presents a simple axiomatic model of nonneutral attitudes toward uncertainty and a behavioral test for uncertainty aversion that is applicable even if utility is state dependent. The decision maker may display different degrees of aversion toward gambles on different kinds of events, e.g., being systematically more averse toward gambles on events whose probabilities are more ambiguous. For such a decision maker, the elicitation of preferences among objective gambles may not yield the correct measure of risk aversion for modeling real-world decisions. [ABSTRACT FROM AUTHOR]

Although there has been much attention in recent years on the effects of additive background risks, the same is not true for its multiplicative counterpart. We consider random wealth of the multiplicative form xy, where x and y are statistically independent random variables. We assume that x is endogenous to the economic agent but that y is an exogenous and nontradable background risk that represents a type of market incompleteness. Our main focus is on how the presence of the multiplicative background risk y affects risk-taking behavior for decisions on the choice of x. We extend the results of Gollier and Pratt (1996) to characterize conditions on preferences that lead to more cautious behavior. [ABSTRACT FROM AUTHOR]

This paper tests new "independence" properties to compare three models of risky decision making. According to the rank-affected multiplicative (RAM) weights model, all three properties should be satisfied; according to the transfer of attention exchange (TAX) model, two should be satisfied and one can be violated. However, according to cumulative prospect theory (CPT), all three properties will be violated if the probability weighting function is nonlinear. Although CPT is flexible enough to accommodate violations of these properties, its predicted violations based on previously estimated parameters failed to materialize. In 14 choices for which the CPT model disagreed with TAX, CPT correctly predicted the modal choice in only one case and TAX predicted the modal choice in the other 13. New versions of three other paradoxes were also tested and found to refute CPT. [ABSTRACT FROM AUTHOR]

Theorists have long believed that Dutch auctions are strategically equivalent to standard sealed bidding. However, in recent controlled experiments with actual Dutch and sealed-bid Internet auctions of collectibles, the Dutch auctions produced significantly more revenue. We believe that this happened, in part, because the Internet Dutch auctions are a slow process in which bidders incur incremental transaction costs if they delay bidding. This paper presents models of slow Dutch auctions that include these costs and explain this belief. We first present a decision-theoretic model of a slow Dutch auction. While simple, the decision-theoretic model is fairly general and provides the basic intuition underlying our revenue results. We then develop a game-theoretic model of a slow Dutch auction. We derive two symmetric, payoff-equivalent equilibria of the game in the absence of a cost of return and then consider the more general case of costly return. When the cost of return is in an appropriate range, the seller's expected revenue is an increasing function of that cost. [ABSTRACT FROM AUTHOR]

The article discusses the merits of linear programming. According to the author this article was stimulated by a proposition stating that linear programming methods should be extended to include cases of uncertain demands for the issue of optimal allocation of a carrier fleet to airline routes to meet an anticipated demand distribution. The article provides examples of the proposed method, including one on a minmum cost diet developed by a nutritionist and another involving shipping goods to an outlet store in order to meet uncertain demand.

Traditional sensitivity analysis in linear programming usually focuses on variations of one coefficient or term at a time. The tolerance approach was proposed to provide a decision maker with an effective and easy-to-use method to summarize the effects of simultaneous and independent changes in selected parameters. In particular, for variations of the objective function coefficients, the approach gives a maximum-tolerance percentage within which selected coefficients may vary from their estimated values (within a priori limits) while still retaining the same optimal basic feasible solution. Although an optimal solution may cease being optimal for variations beyond the maximum-tolerance percentage, it may still be close to optimal. Herein we characterize the potential loss of optimality for variations beyond the maximum-tolerance percentage as a maximum-regret function. We consider theoretical properties of this function and propose a method to compute a relevant portion of it. [ABSTRACT FROM AUTHOR]

Expert judgment elicitation is often required in probabilistic decision making and the evaluation of risk. One measure of the quality of probability distributions given by experts is calibration--the faithfulness of the probabilities in an empirically verifiable sense. A method of measuring calibration for continuous probability distributions is presented here. A discussion of the impact of using linear rules for combining such judgments is given and an empirical demonstration is given using data collected from experts participating in a large-scale risk study. It is shown by theoretical argument that combining well-calibrated distributions of individual experts using linear rules can only result in reducing calibration. In contrast, it is demonstrated, both by example and empirically, that an equally weighted linear combination of experts who tend to be "overconfident" can produce distributions that are better calibrated than the experts' individual distributions. Using data from training exercises, it is shown that the improvement in calibration is rapid as the number of experts is increased from one to five or six, but there is only modest improvement from increasing the number of experts beyond that point. [ABSTRACT FROM AUTHOR]

This paper develops a new likelihood-based method for the simultaneous estimation of structural demand-and-supply models for markets with differentiated products. We specify an individual-level discrete-choice model of demand and derive the supply side assuming manufacturers compete in prices. The proposed estimation method considers price endogeneity through simultaneous estimation of demand and supply, allows for consumer heterogeneity, and incorporates a pricing rule consistent with economic theory. The basic idea behind the proposed estimation procedure is to simulate prices and choice probabilities by solving for the market equilibrium. By repeating this many times, we obtain an empirical distribution of equilibrium prices and probabilities. The empirical distribution is then smoothed and used in a likelihood procedure to estimate the parameters of the model. The advantage of this method is that it avoids the need to perform a transformation of variables. If the tastes of consumers are independent across market periods, our approach yields maximum likelihood estimates; otherwise, it yields consistent but not fully efficient partial likelihood estimates. [ABSTRACT FROM AUTHOR]

As part of the 50th anniversary of Management Science, the journal is publishing articles that reflect on the past, present, and future of the various subfields the journal represents. In this article, we consider decision analysis research as it has appeared in Management Science. After reviewing the foundations of decision analysis and the history of the journal's decision analysis department, we review a number of key developments in decision analysis research that have appeared in Management Science and offer some comments on the current state of the field. [ABSTRACT FROM AUTHOR]

Using a simulated supply chain experiment based on the well-known "beer game," we examine how changes in order and delivery cycles, availability of shared point-of-sale (POS) information, and the pattern of customer demand affect supply chain efficiency. We find that speeding up cycle time is beneficial, but the sharing of POS information is not necessarily so. Whether or not the sharing of POS information is beneficial depends on the nature of the demand pattern represented by the POS information. If the demand pattern conveys continual change in ultimate downstream customer demand (as does an S-shaped demand pattern), the POS information can distract the upstream decision maker from what is perhaps more immediately relevant information, orders placed by the proximate downstream agent and the supply line. [ABSTRACT FROM AUTHOR]

The development of the information systems (IS) literature in Management Science during the past 50 years reflects the inception, growth, and maturation of several different research streams. The five research streams we identify incorporate different definitions of the managerial problems that relate to IS, the alternate theoretical perspectives and different methodological paradigms to study them, and the levels of the organization at which their primary results impact managerial practice. The decision support and design science research stream studies the application of computers in decision support, control, and managerial decision making. The value of information research stream reflects relationships established based on economic analysis of information as a commodity in the management of the firm. The human-computer systems design research stream emphasizes the cognitive basis for effective systems design. The IS organization and strategy research stream focuses the level of analysis on the locus of value of the IS investment instead of on the perceptions of a system or its user. The economics of information systems and technology research stream emphasizes the application of theoretical perspectives and methods from analytical and empirical economics to managerial problems involving IS and information technologies (IT). Based on a discussion of these streams, we evaluate the IS literature's core contributions to theoretical and managerial knowledge, and make some predictions about the road that lies ahead for IS researchers. [ABSTRACT FROM AUTHOR]

Conflict arises in decision making when the choice alternatives present strong advantages and disadvantages over one another, that is, when the trade-offs involved are large. Conflict affects human response to choice, in particular, it increases decision difficulty and response unreliability. On the other hand, larger trade-offs, i.e., higher conflict, reveal more information about an individual's preferences and mitigate the influence of measurement unreliability on preference model estimation. This suggests, somewhat counterintuitively, that there may exist some optimal level of conflict for efficient measurement of preferences. How to determine this level? This issue is examined from behavioral and analytical angles. We outline a general analysis of the interaction between trade-off size and modeling accuracy, and demonstrate its application on a simple example. The kind of analysis developed here can be conveniently implemented in a computer spreadsheet, and would be especially valuable when large amounts of preference data are to be collected, as in consumer preference studies, experimental research, and contingent valuation surveys. [ABSTRACT FROM AUTHOR]

Decision weights are an important component in recent theories of decision making under uncertainty. To better explain these decision weights, a two-stage approach has been proposed: First, the probability of an event is judged and then this probability is transformed by the probability weighting function known from decision making under risk. We extend the two-stage approach by allowing the probability weighting function to depend on the type of uncertainty. Using this more general approach, properties of decision weights can be attributed to properties of probability judgments and/or to properties of probability weighting. We present an empirical study that shows that it is indeed necessary to allow the probability weighting function to be source dependent. The analysis includes an examination of properties of the probability weighting function under uncertainty that have not been considered yet. [ABSTRACT FROM AUTHOR]

In models of decision making under uncertainty we often are faced with the problem of representing the uncertainties in a form suitable for quantitative models. If the uncertainties are expressed in terms of multivariate continuous distributions, or a discrete distribution with far too many outcomes, we normally face two possibilities either creating a decision model with internal sampling, or trying to find a simple discrete approximation of the given distribution that serves as input to the model. This paper presents a method based on nonlinear programming that can be used to generate a limited number of discrete outcomes that satisfy specified statistical properties. Users are free to specify any statistical properties they find relevant, and the method can handle inconsistencies in the specifications The basic idea is to minimize some measure of distance between the statistical properties of the generated outcomes and the specified properties. We illustrate the method by single- and multiple-period problems The results are encouraging in that a limited number of generated outcomes indeed have statistical properties that are close to or equal to the specifications We discuss how to verify that the relevant statistical properties are captured in these specifications, and argue that what are the relevant properties, will be problem dependent. [ABSTRACT FROM AUTHOR]

This paper proposes a new framework for the solution of interactive multiobjective group decision-making problems with interval parameters. Its novelty stems from a learning phase where decision makers (DMs) explore the structural characteristics of the specific Multiple Criteria Decision Making (MCDM) problem. This provides important and timely feedback to the DMs. Its core consists of four indices and their relationships. The solution framework consists of three stages. In the first, each DM provides the limits of variation for each problem parameter. These are subsequently combined mb a unique interval of variation. Then, the stochastic multiobjective problem is transformed into a deterministic one. In the second stage, DMs use the four MCDM characteristics to familiarize themselves with the problem before expressing their preferences for nondominated solutions. The DMs are then guided through an interactive procedure to find their best nondorninated solutions. In the last stage, all best nondominated solutions provided by the DMs are combined using a twofold approach to find the best-compromise nondominated solution. This final choice represents the opinion of the group of DMs, Our results show that the learning phase is beneficial to DMs in judging the quality of solutions, leading to better informed decisions. [ABSTRACT FROM AUTHOR]