Concepts,Theory, and Techniques PRINCIPLES OF MULTIOBJECTIVE OPTIMIZATION*

This paper attempts to isolate and analyze the principal ideas of multiobjective optimization. We do this without casting aspersions on single-objective optimization or championing any one multiobjective technique. We examine each fundamental idea for strengths and weaknesses and subject two—efficiency and utility—to extended consideration. Some general recommendations are made in light of this analysis. Besides the simple advice to retain single-objective optimization as a possible approach, we suggest that three broad classes of multiobjective techniques are very promising in terms of reliably, and believably, achieving a most preferred solution. These are: (1) partial generation of the efficient set, a rubric we use to unify a wide spectrum of both interactive and analytic methods; (2) explicit utility maximization, a much-overlooked approach combining multiattribute decision theory and mathematical programming; and (3) interactive implicit utility maximization, the popular class of methods introduced by Geoffrion, Dyer, and Feinberg [24] and extended significantly by others.     

INTERVENTIONS INTO GROUP PROCESS: AN APPROACH TO IMPROVING THE EFFECTIVENESS OF GROUPS*

Some new directions for research on group effectiveness are proposed. These include: (a) explicit recognition of group interaction process as the moderator of input-output relationships in groups; (b) research which focuses on the functions of group interaction in influencing group performance; and (c) use of experimental interventions which create new, non-typical patterns of interpersonal behavior in groups as an approach to studying group effectiveness. The results of two studies based on these proposals are summarized. One study examines the effectiveness of implicit vs. explicit discussion of group task “strategies”; the other addresses the effects of implicit (“traditional”) vs. explicit (“adaptive”) approaches to the maintenance of internal social relationships in work groups. Implications of the research are drawn both for future research on group effectiveness, and for the design of interventions aimed at improving the performance of on-going groups in organizations.     

Review of Empirical Studies in Multiobjective Mathematical Programming: Subject Reflection of Nonlinear Utility and Learning

Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade-offs among conflicting objectives must be reconciled. Interactive multiobjective programming provides a means for decision makers to learn what these trade-offs involve, while the mathematical program generates solutions that seek improvement of the implied utility of the decision maker. A variety of multiobjective programming techniques have been presented in the multicriteria decision-making literature. This study reviews published studies with human subjects where some of these techniques were applied. While all of the techniques have the ability to support decision makers under conditions of multiple objectives, a number of features in applying these systems have been tested by these studies. A general evolution of techniques is traced, starting with methods relying upon linear combinations of value, to more recent methods capable of reflecting nonlinear trade-offs of value. Support of nonlinear utility and enhancing decision-maker learning are considered.     

SIMULATION OF INFORMATION CHOICE

Beginning with the information economics framework and a multi-period decision model [15], this paper considers the use of computer simulation methods within an information system choice environment. Actual decision behavior is replaced by optimal decision rules, and simulation is used to evaluate the effects of parameter changes in the environmental model. Simulation is shown to be functional (1) in estimating the value of alternative information structures within a fifteen period decision model and (2) in providing sensitivity and statistical data which would be useful both for different decision maker utility functions and for a variety of information system design questions.     

Multiobjective investment planning under uncertainty

In this paper we consider the relevance of various planning methods and decision criteria to multiobjective investment planning under uncertainty. Assuming that a natural reaction to uncertainty is to operate so as to leave open as many good options as possible (as opposed to maximizing subjective expected utility) we argue that the planning process should concentrate on analyzing the effects of the initial decision, and that for this exercise the classical methods of mixed integer programming are inappropriate. We demonstrate how the technique of dynamic programming can be extended to take account of multiple objectives and use dynamic programming as a framework in which we analyze the robustness of an initial decision in the face of various types of uncertainty. In so doing we also analyze the risks involved in both the planning and decision making functions.     

AN EXPERIMENTAL EVALUATION OF THE RELATIONSHIP OF UNCERTAINTY IN THE ENVIRONMENT TO INFORMATION USED BY DECISION MAKERS*

This paper discusses the relationship between uncertainty in a decision making environment and the desired characteristics of information used for decision making. The work is aimed at providing a better understanding of the variables that affect the design of Management Information Systems. An experiment was conducted using an inventory simulator. The subjects could decide on inventory control variables and the amount and type of information to be used in monitoring system performance. The demand for inventory was an external random variable under the control of the experimenter. The experiment investigated how different demand variances affected the decision information used, the decisions made, and the resulting decision effectiveness (cost.) It was found that decision review frequency was not affected by demand variability. However, age and degree of summary of information used were greatly affected. Subjects exposed to high variability used data with a shorter history and a higher level of detail than those exposed to low variability. It was also found that the number of reports used increased from the low to middle variance group, then decreased at very high variance. Subjects tended to “give-up” on their information system at high variance, and they relied on additional safety stock to prevent frequent stockouts. Finally, the correlation between the information used and decision effectiveness as measured by cost was low. This result was contrary to the subjects' beliefs that more and better information produced “better” decisions. It indicates that although variability may strongly affect preferences for different types of information, the information used may not in turn affect decision performance.     

Explicit and Implicit Trust Within Safety Culture

Safety culture is an important topic for managers in high-hazard industries because a deficient safety culture has been linked to organizational accidents. Many researchers have argued that trust plays a central role in models of safety culture but trust has rarely been measured in safety culture/climate studies. This article used explicit (direct) and implicit (indirect) measures to assess trust at a UK gas plant. Explicit measures assessed trust by asking workers to consider and state their attitude to attitude objects. Implicit measures assessed trust in a more subtle way by using a priming task that relies on automatic attitude activation. The results show that workers expressed explicit trust for their workmates, supervisors, and senior managers, but only expressed implicit trust for their workmates. The article proposes a model that conceptualizes explicit trust as part of the surface levels of safety culture and implicit trust as part of the deeper levels of safety culture. An unintended finding was the positive relationship between implicit measures of trust and distrust, which suggests that trust and distrust are separate constructs. The article concludes by considering the implications for safety culture and trust and distrust in high-hazard industries.     

THE SEU CALCULUS: EFFECTS OF RESPONSE MODE,SEX, AND SEX ROLE ON UNCERTAIN DECISIONS *

Subjects were instructed on how to use simple subjective probability and utility scales, and they were asked to actively role-play a decision maker in seven risk-dilemma situations. Each scenario provided subjects with specific subjective expected utility (SEU) information for both a certain and uncertain decision alternative, but left out one critical SEU component. Subjects supplied either the lowest probability or the lowest utility for success that they found necessary before they would select the uncertain over the certain alternative in each dilemma. Three experiments examined: (a) the degree to which Ss' estimations deviated from a pattern predicted by SEU models; (b) differences in choice patterns induced by response format variations (e.g., probability vs. utility estimation); (c) the effects of sex of S; and (d) the effects of the sex-role framing of the decision problems. Ss generally chose in accord with SEU maximization principles and did so with decreasing deviations from theoretical values as practice over situations increased (Experiments I, II and III). Decisions were initially more conservative on items requesting probability estimates (Experiment I), but this effect washed out over situations. Sex differences were revealed (Experiments I and III), but in limited fashion. Rather, a replicable (Experiments I, II and III) sex-by-sex role appropriateness by response format interaction was found, in which females responded “rationally” under both probability and utility estimation conditions and under both role sets (male and female). Males, however, responded extremely conservatively under female-framed, probability estimate conditions. Ss' choices were stable over a three-week interval (Experiment III).     

AN ADAPTIVE MULTIVARIATE APPROACH TO TIME SERIES FORECASTING

In recent years, time series analysts have shifted their interest from univariate to multivariate forecasting approaches. Among them, the Box-Jenkins transfer function process and the state space method have received the most attention. This paper presents a simplified approach that embodies some desirable features of existing methods. It stresses empirical analysis, has a unified modeling structure, is easily applicable, and is adaptive to changes without necessitating prior information on the evolution of a system under study. The core of the method relies on the Carbone-Longini adaptive estimation procedure (AEP). Results of a comparative study based on the well-known Lydia E. Pinkham data and the Box-Jenkins sales/leading indicator data illustrate the merits of multivariate AEP in improving forecasting accuracy while simplifying the analysis process. Subject Area: Forecasting.     

INTEGRATION IN MANUFACTURING: AN INFORMATION THEORETIC PERSPECTIVE

Integration is a buzzword in manufacturing strategies for global competitiveness. However, some fundamental questions have yet to be answered scientifically; namely, what is integration, why integrate, and what should be integrated? How do existing integration models approach the problems of integration and to what extent are they successful? A theory-based model of information requirements for integrated manufacturing is needed to answer these questions properly. Such a model can unify hitherto narrowly defined and domainoriented approaches. We suggest a paradigm of parallel formulation and use it to formulate the information requirements for integration, including data and knowledge classes, decision spaces, and logical interactions.     

Preference-Order Recursion for Finding Relevant Pure,Admissible and Optimal Statistical Decision Functions*

A preference-order recursion algorithm for obtaining a relevant subset of pure, admissible (non-dominated, efficient) decision functions which converges towards an optimal solution in statistical decision problems is proposed. The procedure permits a decision maker to interactively express strong binary preferences for partial decision functions at each stage of the recursion, from which an imprecise probability and/or utility function is imputed and used as one of several pruning mechanisms to obtain a reduced relevant subset of admissible decision functions or to converge on an optimal one. The computational and measurement burden is thereby mitigated significantly, for example, by not requiring explicit or full probability and utility information from the decision maker. The algorithm is applicable to both linear and nonlinear utility functions. The results of behavioral and computational experimentation show that the approach is viable, efficient, and robust.     

Consumer preference analysis: A data-driven multiple criteria approach integrating online information

Multiple criteria approaches can assist the product manager to know the consumer preferences in the context of e-commerce. Consumer preference analysis explains what aspects of a product affect and how they affect a consumer’s purchasing decision. This issue plays an important role in e-commerce platforms from its relevance in marketing decisions such as advertisements, recommendations and promotions. In this regard, we propose a data-driven multiple criteria decision aiding (MCDA) approach to integrate online information, such as explicit (e.g., reviews and ratings) and implicit (e.g., clicks and purchases) feedback from consumers. However, MCDA approaches present a critical challenge that even an experienced product manager could find it difficult to pre-define the criteria on which a product is evaluated. To address this issue, our proposed approach first utilizes text-mining techniques to assist the product manager identify the criteria, and then determines and collects the relative importance of the criteria and their values. Given the criteria information, we use a sampling process to provide two indices, the consumer preference index and rank acceptability index. The first index helps in prioritizing the pairwise comparisons of products, while the second one helps in deriving a default ranking list for first-time-registered consumers. We record the products viewed by consumers and generate their preference information in the form of pairwise comparisons for analyses within an aggregation-disaggregation paradigm. We also provide a representative value function to help the product manager gain insight into the preferences. Finally, we describe how a real-world application including the product manager and consumers exploits the proposed approach on an e-commerce platform to take a large step toward aiding more realistic and data-driven multiple criteria decision making.     

Implicit Attitudes Toward Nuclear Power and Mobile Phone Base Stations: Support for the Affect Heuristic

The implicit association test (IAT) measures automatic associations. In the present research, the IAT was adapted to measure implicit attitudes toward technological hazards. In Study 1, implicit and explicit attitudes toward nuclear power were examined. Implicit measures (i.e., the IAT) revealed negative attitudes toward nuclear power that were not detected by explicit measures (i.e., a questionnaire). In Study 2, implicit attitudes toward EMF (electro-magnetic field) hazards were examined. Results showed that cell phone base stations and power lines are judged to be similarly risky and, further, that base stations are more closely related to risk concepts than home appliances are. No differences between experts and lay people were observed. Results of the present studies are in line with the affect heuristic proposed by Slovic and colleagues. Affect seems to be an important factor in risk perception.     

Examining the Relationship Between Affect and Implicit Associations: Implications for Risk Perception

It has been suggested that affect may play an important role in risk perception. Slovic et al. argued that people make use of the “affect heuristic” when assessing risks because it is easier and more efficient to rely on spontaneous affective reactions than to analyze all available information. In the present studies, a single category implicit association test (SC‐IAT) to measure associations evoked by different hazards was employed. In the first study, we tested the extent to which the SC‐IAT corresponds to the theoretical construct of affect in a risk framework. Specifically, we found that the SC‐IAT correlates with other explicit measures that claim to measure affect, as well as with a measure of trust, but not with a measure that captures a different construct (subjective knowledge). In the second study, we addressed the question of whether hazards that vary along the dread dimension of the psychometric paradigm also differ in the affect they evoke. The results of the SC‐IAT indicated that a high‐dread hazard (nuclear power) elicits negative associations. Moreover, the high‐dread hazard evoked more negative associations than a medium‐dread hazard (hydroelectric power). In contrast, a nondread hazard (home appliances) led to positive associations. The results of our study highlight the importance of affect in shaping attitudes and opinions toward risks. The results further suggest that implicit measures may provide valuable insight into people's risk perception above and beyond explicit measures.     

Implicit Leadership and Followership Theories “in the wild”: Taking stock of information-processing approaches to leadership and followership in organizational settings

For over 30 years information-processing approaches to leadership and more specifically Implicit Leadership Theories (ILTs) research has contributed a significant body of knowledge on leadership processes in applied settings. A new line of research on Implicit Followership Theories (IFTs) has re-ignited interest in information-processing and socio-cognitive approaches to leadership and followership. In this review, we focus on organizational research on ILTs and IFTs and highlight their practical utility for the exercise of leadership and followership in applied settings. We clarify common misperceptions regarding the implicit nature of ILTs and IFTs, review both direct and indirect measures, synthesize current and ongoing research on ILTs and IFTs in organizational settings, address issues related to different levels of analysis in the context of leadership and follower schemas and, finally, propose future avenues for organizational research.     

PROCESS IMPROVEMENT: AN EXPLORATORY DATA ANALYSIS APPROACH WITHIN AN INTERVAL-BASED OPTIMIZATION FRAMEWORK

This article revisits an old problem; “systematically explore the information contained in a set of operating data records and find from it how to improve operational performance by taking the appropriate decisions in the space of operating conditions,” thus leading to continuous process improvement. A series of industrial case studies within the framework of the internships in the Leaders for Manufacturing (LFM) program at Massachusetts Institute of Technology led us to a reexamination of the traditional formulations for the above problem. The resulting methodology is characterized by the following features: (1) problem statement and solutions are expressed in terms of hyperrectangles in the decision space, replacing conventional pointwise results; (2) data-driven, nonparametric learning methodologies were advanced to produce the requisite mapping between performance and decisions; (3) operating performance is in essence multifaceted, leading to a multiobjective problem, which is treated as such. The proposed methodology has been applied to a number of industrial examples and in this paper we provide a brief overview only of those that can be discussed in the open literature.     

Performance Assessments of Nuclear Waste Repositories: A Dialogue on Their Value and Limitations

Performance Assessment (PA) is the use of mathematical models to simulate the long-term behavior of engineered and geologic barriers in a nuclear waste repository; methods of uncertainty analysis are used to assess effects of parametric and conceptual uncertainties associated with the model system upon the uncertainty in outcomes of the simulation. PA is required by the U.S. Environmental Protection Agency as part of its certification process for geologic repositories for nuclear waste. This paper is a dialogue to explore the value and limitations of PA. Two skeptics acknowledge the utility of PA in organizing the scientific investigations that are necessary for confident siting and licensing of a repository; however, they maintain that the PA process, at least as it is currently implemented, is an essentially unscientific process with shortcomings that may provide results of limited use in evaluating actual effects on public health and safety. Conceptual uncertainties in a PA analysis can be so great that results can be confidently applied only over short time ranges, the antithesis of the purpose behind long-term, geologic disposal. Two proponents of PA agree that performance assessment is unscientific, but only in the sense that PA is an engineering analysis that uses existing scientific knowledge to support public policy decisions, rather than an investigation intended to increase fundamental knowledge of nature; PA has different goals and constraints than a typical scientific study. The proponents describe an ideal, six-step process for conducting generalized PA, here called probabilistic systems analysis (PSA); they note that virtually all scientific content of a PA is introduced during the model-building steps of a PSA; they contend that a PA based on simple but scientifically acceptable mathematical models can provide useful and objective input to regulatory decision makers. The value of the results of any PA must lie between these two views and will depend on the level of knowledge of the site, the degree to which models capture actual physical and chemical processes, the time over which extrapolations are made, and the proper evaluation of health risks attending implementation of the repository. The challenge is in evaluating whether the quality of the PA matches the needs of decision makers charged with protecting the health and safety of the public.     

Assessing the Utility of Stochastic Employee Movements

This article illustrates the substantial effects employee movements (i.e., movements due to employee promotions, transfers, and separations) have on the value of a human resource intervention. Two methods for estimating differential employee movements are presented and their resulting utility estimates are contrasted and compared. Finally, the advantages and disadvantages of each procedure are discussed as well as the value such information provides to organizational decision makers.     

EMPIRICAL UTILITY FUNCTIONS AND RANDOM DEVICES: AN EXPERIMENT

The primary purpose of the present experiment was to examine the effects on the empirical derivation of executive's utility functions caused by the use of random device analogues. The results indicate that utility functions obtained using a random device analogue imply more risk aversion than when these functions are determined by simulating actual decision situations. Furthermore, there is also evidence that the bias is directly related to the monetary amounts involved.