Improving the voir dire process: A prospective juror's perspective

While the U.S. legal system takes pains to treat jury trial defendants fairly, it tends to treat prospective jurors unfairly, as if their time had little value. Citizens might view performing this important civic duty more favorably if the court made better use of their time. This article studies the voir dire phase of jury selection in California courts and investigates how it can be made more pleasant and productive from a prospective juror's perspective. A discrete-event computer simulation model of the process was built to test an alternative approach to handling jurors during voir dire, in which some are permitted to leave the courtroom and use their out-of-courtroom time as they wish, e.g., working or reading. Jurors can be recalled to the courtroom as needed via text messaging or paging, so the approach should be relatively easy to implement. Experiments performed with the model found that, depending on trial characteristics, jurors allowed to leave the courtroom would likely would recoup an average of one to 4 h, collectively recover dozens to hundreds of hours, comprising 15–30% of the total time spent by all jurors at the courthouse for voir dire, and potentially save several thousand dollars in lost income.     

Using simulation as a tool in selecting a retirement age under defined benefit pension plans

This paper examines how simulation modeling can be used to select a retirement age under defined benefit pension plans. This approach construes the variables affecting pension benefits as probabilistic variables. Simulations are then run to generate probabilistic values for the real value of pension benefits for alternative retirement ages. By construing variables affecting pension benefits as probability distributions, this approach reflects the uncertainty facing individuals contemplating retirement. By generating estimates of retirement benefits as probability distributions rather than as single deterministic values, the model provides individuals with a more realistic and complete frame of reference for making the retirement decision. The author is grateful to an anonymous referee and Joachim Zietz. JEF editor, for helpful comments.     

Estimating causal effects of credit decisions

In principle, making credit decisions under uncertainty can be approached by estimating the potential future outcomes that will result from the various decision alternatives. In practice, estimation difficulties may arise as a result of selection bias and limited historic testing. We review some theoretical results and practical estimation tools from observation study design and causal modeling, and evaluate their relevance to credit decision problems. Building on these results and tools, we propose a novel approach for estimating potential outcomes for credit decisions with multiple alternatives based on matching on multiple propensity scores. We demonstrate the approach and discuss results for risk-based pricing and credit line increase problems. Among the strengths of our approach are its transparency about data support for the estimates and its ability to incorporate prior knowledge in the extrapolative inference of treatment-response curves.     

Using decision modeling to examine management consensus: A study of a maintenance management control system

Using a decision modeling approach, this research examined differences across levels of management in a large construction company concerning the criteria for a maintenance management control system. The six criteria were cost efficiency, productivity improvement, materials management, unification of work control, planning and scheduling, and information and retrieval. A decision-making exercise was constructed that asked the respondent to make recommendations concerning the approval or disapproval of 32 hypothetical maintenance management systems. Among the 60 managers who completed the exercise, there was evidence of significant differences across levels of management concerning the criteria for a maintenance management control system. Additionally, distortions were observed between what the managers said was important and what their decisions indicated. These data demonstrate that the decision modeling approach presented can be effectively used to examine the degree of consensus within an organization.     

An agent-based modeling optimization approach for understanding behavior of engineered complex adaptive systems

The objective of this study is to present a formal agent-based modeling (ABM) platform that enables managers to predict and partially control patterns of behaviors in certain engineered complex adaptive systems (ECASs). The approach integrates social networks, social science, complex systems, and diffusion theory into a consumer-based optimization and agent-based modeling (ABM) platform. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). Furthermore, the modeling and solution methodology address shortcomings in previous ABM and Transactive Energy (TE) approaches and advances our ability to model and understand ECAS behaviors through computational intelligence. The mathematical approach is a non-convex consumer-based optimization model that is integrated with an ABM in a game environment.     

Expected utility theory with non-commutative probability theory

In this paper, we extend von Neumann and Morgenstern’s expected utility approach to a non-commutative probability theory. We introduce a new representation of the decision maker’s set of events which extends the canonical representation. We reformulate von Neumann and Morgenstern’s approach to modeling decision maker behavior by non-commutative probability theory. We introduce a set of preference axioms similar to von Neumann and Morgenstern’s axioms, and show that they lead to a generalization of the expected utility theorem. Our generalization allows for decision makers to make an intuitive distinction between representations of a set of events. We find that this methodology enables several paradoxes and inconsistencies in traditional expected utility theory (e.g., Allais paradox, etc.) to be solved or better understood.     

激活陪审选择权的进路:规制与救济——以申请陪审为视角

陪审制度由指定陪审和申请陪审两部分构成,与指定陪审相比,申请陪审更能发挥陪审制度的功能和作用。司法实践中,指定陪审占到了适用陪审的绝大多数案件,申请陪审却基本处于空白状态,主要原因还是制度上的粗疏与司法实践的怠慢。陪审选择权的逻辑结构应当包含三层内容,即选择诉讼程序的权利、是否申请陪审的权利、选择陪审员的权利。     

Structure of teams—A cooperative game theory approach

One key task for HR departments and managers is to design the structure of teams in the firm. Two main dimensions when carrying out this decision are the size of the teams and the combination of specialized expertise within the team. In this article, we apply cooperative game theory to model this decision. First, we introduce production games. These games are an enhancement of team games and allow the modeling of teams. Together with the coalition structure approach of cooperative game theory, we analyze two standard situations of team theory.     

A multiobjective modeling approach to locating incinerators

The location of hazardous material incineration facilities is an important problem due to the environmental, social, and economic impacts that they impose. The costs associated with the facilities and the risks placed on nearby populations are important concerns as are the distributions of these costs and risks. This paper introduces a mixed-integer, multiobjective programming approach to identify the locations and capacities of such facilities. The approach incorporates a Gaussian dispersion model and a multiobjective optimization model in a GIS-based interactive decision support system that planners can access via the Internet. The proposed approach is demonstrated via a case study in central Portugal where the national government has decided to locate a large facility for the incineration of hazardous industrial waste. Due to intense local and national opposition, construction of the facility has been delayed. The system has been designed so that it can be used by decision makers with no special training in dispersion modeling, multiobjective programming, or GIS.     

Adjusting inconsistency through learning in group decision-making, and its application to China's MBA recruiting interview

In group decision-making, because of limitations on individual knowledge and information bases, or because of the existing decision rule, an individual decision maker may not be capable of evaluating selected alternatives. Such circumstances can lead to inconsistencies across group decision matrices. These inconsistencies are difficult to remedy under existing approaches. Based on Rough Set Theory, we thus propose a new approach that integrates two types of learning techniques. It first applies a machine-learning procedure that extracts possible alternatives from other decision makers that are currently not included in a given decision maker's alternative set. It then applies a group knowledge-learning model to determine corresponding attribute values of those newly learned alternatives in meeting a group's consistency requirement. Efficacy of the approach is illustrated by its application to China's MBA recruiting interview.     

Decision making with imprecise probabilistic information

We develop an axiomatic approach to decision under uncertainty that explicitly takes into account the information available to the decision maker. The information is described by a set of priors and a reference prior. We define a notion of imprecision for this informational setting and show that a decision maker who is averse to information imprecision maximizes the minimum expected utility computed with respect to a subset of the set of initially given priors. The extent to which this set is reduced can be seen as a measure of imprecision aversion. This approach thus allows a lot of flexibility in modelling the decision maker attitude towards imprecision. In contrast, applying Gilboa and Schmeidler [J. Math. Econ. 18 (1989) 141] maxmin criterion to the initial set of priors amounts to assuming extreme pessimism.     

基于群决策的供应商选择方法研究

针对供应链管理下的供应商选择问题,给出了一种多属性群决策方法。在采用主观赋权法确定专家权重和客观赋权法确定属性权重基础上,引入专家权重客观调整方法,使决策结果更合理。最后以算例说明该方法可行性和有效性。     

A simplified approach to subjective expected utility

I provide a novel simplified approach to Savage’s theory of subjective expected utility. Such an approach is based on abstract integral representation theorems in the space of measurable functions. The advantage of such an approach is that these results can be used to easily obtain variations on Savage’s theorem, such as representations with state-dependent utility or probability measures that can have atoms. Finally, I discuss how such an approach can be used in other settings such as decision making under ambiguity.     

The effects of ambiguity on entrepreneurship

We incorporate ambiguity (Knightian uncertainty) into a classic model of entrepreneurship to analyze, among other things, its effects on the optimal level of business startups, the relation between total assets and the size of the entrepreneurial investment, the effects of increasing ambiguity on developing new ventures, and the decision to self‐select into entrepreneurship for an indifferent decision maker. We first show that, under the monotone‐likelihood ratio property, the introduction of ambiguity negatively affects the optimal entrepreneurial investment, something that is consistent with most experimental evidence about entrepreneurial choice under ambiguity. Then, we show that the classical explanations for the positive correlation between total assets and business startups based on decreasing absolute risk aversion preferences and prudent behavior can be challenged when ambiguity is incorporated into the analysis, and we provide the conditions that guarantee that the traditional comparative static result under risk is replicated under ambiguity. We also show that increases in ambiguity aversion reduce entrepreneurial activities. Finally, we discuss our results under alternative ways of modeling ambiguity.     

基于Fuzzy理论的区域配送中心选址模型对比分析

目前已有的区域配送中心数学选址模型及算法以定量分析为主，计算复杂，其实用性受现实条件限制较多。本文引入Fuzzy理论来探讨两种Fuzzy选址模型的建立及决策过程，通过相互对比各自运用中的优点和局限性，提出了改进模型的方法及实际评价过程中须注意问题，从而提高选址决策水平。     

Planning for education in the health services: a multicriterion approach.

Administrators are often confronted with problems for which there exist several distinct measures of success. Such problems can be expressed in terms of linear programming models with several linear “criterion” functions instead of a single objective function. Although a variety of techniques are available for the solution of multicriterion problems, there exists a need for one which does not assume technical sophistication on the part of the decision maker and which provides valid solutions with minimum effort. “Efficient Manifold Presentation”, the approach used here, is based on the concept that the ideal solution must be a Pareto optimal solution. A method for finding an expression for the finite set of all Pareto optimal solutions to a linear program with multiple linear criteria is presented. Two processes are involved; first, the discovery of all Pareto optimal vertices of the feasible region, and secondly a grouping of these into sets each of which defines a convex polyhedron of Pareto optimal possibilities. Alternate versions of the second process are suggested for use under varying circumstances. An example of the applicability of the method for modeling enrollment and staffing policy in an educational institution is provided.     

Making tough choices: A policy capturing approach to evaluating the tradeoffs in sustainable supplier development initiatives

This research explores the tradeoffs that decision makers are willing to make when implementing development initiatives to improve supplier sustainability performance. We employ a policy capturing methodology in which respondents are forced to choose between multiple potential sustainable supplier development initiatives. Data were collected from traditional and executive MBA students. The results suggest that when confronted with tradeoffs, decision makers value improvements in supplier cost savings and injury reduction equally, which was somewhat unexpected. Further, both improvements in supplier cost savings and injury reduction were valued over supplier emissions performance. Because we measure individual tradeoff preferences, multi-level regression analysis was used to better understand the impact of respondent value structure regarding sustainably developing suppliers. Our findings suggest a hierarchy of tradeoff preferences for decision makers as they pertain to sustainable supplier development. As the pressure to ensure supply chain sustainability increases, more firms will engage in sustainable supplier development. The outcomes of the choices they make when choosing between initiatives, and how managers make these choices, will be of increasing interest in both industry and academia. This research answers previous calls for further examination of decision maker tradeoff preferences in sustainable supply chain development.     

Location choice for direct foreign investment in new hospitals in China by using ANP and TOPSIS

This study models location choices for foreign direct investments in new hospitals in China as an multi-criteria decision-making (MCDM) problem and designs develops a multidirectional relationship decision model combines the techniques of analytic network process (ANP) and technique for order performance based on similarity to the ideal solution (TOPSIS). This study discusses applying ANP to the relative weighting of multiple assessment criteria. The TOPSIS approach is employed to rank 15 counties without the Zhoushan of China’s Yangtze River Delta (YRD) region in terms of their overall performance under the decision model. To illustrate how the proposed approach is applied to the problem of selecting locations for new hospitals in China an empirical study of a real case is performed. This study demonstrates the effectiveness and feasibility of the proposed assessment procedure.     

Decision analysis using targets instead of utility functions

A common precept of decision analysis under uncertainty is the choice of an action which maximizes the expected value of a utility function. Savage's (1954) axioms for subjective expected utility provide a normative foundation for this principle of choice. This paper shows that the same set of axioms implies that one should select an action which maximizes the probability of meeting an uncertain target. This suggests a new perspective and an alternate target-based language for decision analysis. We explore the implications and the advantages of this target-based approach for both individual and group decision-making.     

A fuzzy bi-level method for modeling age-specific migration

Modeling and forecasting international migration are significant research areas since migration forecasts are vital in decision making and policy design regarding economy, security, society, and resource allocation. The methods for modeling and forecasting migration rely on strict subjective or statistical assumptions which may not always be met. In addition, lack of a universally accepted definition of the term “migrant” and the ambiguities in data due to recording and collection systems result in inconsistencies and vagueness in migration modeling. Considering these, in this paper, a fuzzy bi-level age-specific migration modeling method is proposed. The bi-level structure embedded in the model makes use of the well-known Lee-Carter method as well as fuzzy regression, singular value decomposition technique, and hierarchical clustering to reflect the general characteristics of the country of concern together with the distinct emigration and immigration behaviors of the age groups. Bayesian time series models are fitted to the time-variant fuzzy parameters obtained through the proposed method to forecast future migration values. The proposed method is applied on female and male age-specific emigration and immigration counts of Finland for 1990–2010 period and Germany for 1995–2012 period, and the future values are forecasted for 2011–2025 and 2013–2025 respectively. The method is compared with an existing Bayesian approach and the numerical findings display that the proposed fuzzy method is superior to the existing one in modeling and forecasting age-specific migration values within significantly narrower prediction intervals.