High Stakes Decision Making: Normative,Descriptive and Prescriptive Considerations

This paper reviews the state of the art of research on individual decision-making in high-stakes, low-probability settings. A central theme is that resolving high-stakes decisions optimally poses a formidable challenge not only to naïve decision makers, but also to users of more sophisticated tools, such as decision analysis. Such decisions are difficult to make because precise information about probabilities is not available, and the dynamics of the decision are complex. When faced with such problems, naïve decision-makers fall prey to a wide range of potentially harmful biases, such as failing to recognize a high-stakes problem, ignoring the information about probabilities that does exist, and responding to complexity by accepting the status quo. A proposed agenda for future research focuses on how the process and outcomes of high-stakes decision making might be improved.     

Decision making under conditions of conflict

A real-world application is employed to explain three general types of decision situations that can arise under conditions of conflict. In addition, meaningful connections and relationships among these areas of decision making are clearly pointed out. To permit useful decision technologies to be employed by practitioners for better understanding and for resolving a variety of actual decision problems, a range of flexible decision support systems is discussed. Subsequently, interesting research developments contained in the upcoming sequence of 12 articles on decision making under conflict are summarized and compared. The research articles not only present unique approaches to decision making involving multiple participants, each of whom may have multiple objectives, but suggest a variety of challenging research problems to be investigated in the future.     

Provision of Step-Level Public Goods with Uncertain Provision Threshold and Continuous Contribution

Common to most theoretical and empirical research on public goods is the assumption that the parameters of the game are common knowledge. Recent theoretical and empirical studies have questioned this assumption by arguing that many public goods situations are characterized by uncertainty regarding various aspects of the situation. In particular, Suleiman (1997) argued that members of production groups of step-level public goods are often uncertain about the value of the provision threshold. For this type of uncertainty he proposed three distinct models to account for the individual's contribution.The present study reports the results of an experiment designed primarily to test and contrast the predictions derived from two of these models – a subjective expected utility model and a cooperative model – regarding the effects of threshold uncertainty on contribution for the provision of step-level public goods. Other goals of the study were to test the joint effect of the threshold uncertainty level, and its mean (low vs. high), on contribution, and to examine the effect of threshold uncertainty on the individuals' estimates regarding the contributions of other group members.The results show that the effect of threshold uncertainty is moderated by the threshold mean: Contribution to the public good increased as a function of uncertainty for the lower threshold mean, and decreased (though not significantly) for the higher threshold mean. In contrast, for the two threshold means the subjects' estimates of the mean and variability of others' contribution increased with threshold uncertainty.The models' comparison revealed that the cooperative model was superior to the subjective expected utility model. This result adds to a substantial body of research on social dilemmas showing that under conditions of social (strategic) uncertainty, group members tacitly coordinate their choice behavior by anchoring their decisions on rules of fairness.     

Challenging Group Support Systems Research: The Case for Strategic Decision Making

Strategic decision making (SDM) often occurs in groups that can benefit from the use of group support systems (GSS). However, no comprehensive review of this logical intersection has been made. We explore this intersection by viewing GSS research through the lens of SDM. First, SDM is broadly characterized and a model of GSS-mediated SDM is produced. Second, we review empirical GSS research linking these findings to the characteristics of SDM. We conclude that GSS research has not produced sufficient knowledge about group history, heterogeneity, member experience, task type, time pressure, technology or tool effects, and decision consensus for a favorable evaluation of SDM in GSS groups. SDM in GSS groups challenges researchers to study the effects of group processes such as those just mentioned in a context that involves ongoing and established groups, political activity, and a multiplicity of tasks.     

Applying Multiattribute-Based Group Decision Making Techniques in Complex Equipment Selection Tasks

A practicing industrial engineer, even an experienced decision maker, needs help in the sense that there is a necessity to use more logical or analytical decision support tools, especially when dealing with control and instrumentation projects which are often worth millions of dollars. In this article we propose an algorithm which can support the process of group decision making relating to industrial automation, especially involving the selection of control and instrumentation equipment. The aim of this article is to look only at the algorithm's application and how it is applied. To this end, two test cases are used as examples: (1) selecting a local area network for installation in an academic environment; and (2) selecting an integrated control system for a real-world pulp and paper mill. Obtained results show that the algorithm leads to a satisfactory solution. A software form of the algorithm is being programmed for use as a decision support tool.     

Dynamic Influences on Individual Choice Behavior

Research examining the process of individual decision making over time isbriefly reviewed. We focus on two major areas of work in choice dynamics:research that has examined how current choices are influenced by the historyof previous choices, and newer work examining how choices may be made toexploit expectations about options available in the future. A central themeof the survey is that if a general understanding of choice dynamics is toemerge, it will come through the development of boundedly-rational models ofdynamic problem solving that lie on the interface between economics andpsychology.     

Decision Neuroscience

This article presents an introduction to and analysis of an emerging area of research, namely decision neuroscience, whose goal is to integrate research in neuroscience and behavioral decision making. The article includes an exposition of (1) how the exponential accumulation of knowledge in neuroscience can potentially enrich research on decision making, (2) the range of techniques in neuroscience that can be used to shed light on various decision making phenomena, (3) examples of potential research in this emerging area, and (4) some of the challenges readers need to be cognizant of while venturing into this new area of research. The genesis of this workshop session and article was a meeting that Dipankar Chakravarti, Antoine Bechara, and Baba Shiv had one balmy Iowa City summer afternoon in 2003. We coined the term Decision Neuroscience to describe the emerging stream of research outlined in this article.     

Information Collection Strategic Design in Experts-assisted Decision Making Paradigm

In an experts-assisted decision making paradigm, the information collection design becomes a strategic variable under a weak assumption that the final decision is dependent on the design used to collect information as well. As a result, the same information of the experts and the decision maker about the problem can potentially produce different final decisions for different information collection designs. The implication is that a decision maker can strategically select a design which serves his/her objective. This paper uses a Bayesian estimation methodology for combining experts' information with the decision maker's prior. An information collection process is designed by setting constraints on this model. Several designs are developed here using such controlled factors as a one-stage versus a two-stage decision process, experts' rank ordering, and group versus individual lobbying/consultation. An example is provided to illustrate the applicability of the concept. It is shown that the information produced in the process of producing a decision can also give insights into the impacts of the decision maker and the experts on the decision.     

Aggregation of Individual Preference Structures in Ahp-Group Decision Making

This paper presents a new procedure, to which we have given the name Aggregation of Individual Preference Structures (AIPS), whose objective is to deal with multiactor decision making when using Analytic Hierarchy Process (AHP) as the methodological support. This procedure incorporates ideas similar to Borda count methods and transfers to the case of preference structures the principle of aggregation employed in the two approaches traditionally followed in AHP-group decision making (aggregation of individual judgments and aggregation of individual priorities). The new aggregation method allows us to capture: (i) the richness of uncertainty inherent to human beings; (ii) the vision of each decision maker within the context of the problem; (iii) the interdependencies between the alternatives being compared and (iv) the intensities of the preferences that each decision maker gives to these interdependencies. From the preference structure distribution associated to each decision maker, this new approach (AIPS) provides the holistic importance of each alternative and ranking, as well as the most representative preference structure distribution for the group. The knowledge derived from these could be employed as an initial step in the search for consensus, which characterises the negotiation processes followed by the actors involved in the resolution of decisional problems. An earlier version of this paper was presented at the GDN2005 Conference held at Vienna. The work has been partially funded under Research Projects “Electronic Government. Internet-based Complex Decision Making: e-democracy and e-cognocracy” (Ref. PM2004-052) and “Internet-based Complex Decision Making. Decisional Tools for e-cognocracy” (Ref. TSI2005-02511).     

基于消费者接受理论的移动商务成功因素研究

近几年来中外移动商务呈现快速发展势态,以手机为终端的无线接入用户迅猛增加。B2C移动商务的成功主要取决于消费者的接受和使用。为此,从消费者接受问题的相关理论出发,结合B2C移动商务的消费者接受模型,研究B2C移动商务的成功影响因素,并从操作层面上提出相关成功因素的具体内涵。     

Decision Strategy and Structure in Households: A “Groups” Perspective

From a practical perspective, (arguably) most consumer decisions are not made in isolation of the households in which consumers are inserted, yet we commonly treat them econometrically as if they were. The purpose of this workshop was to take some initial steps in defining needed research in household decision making that structurally accounts for goal sharing, utility interdependence, taste heterogeneity, choice set formation, power structures, group size and composition, and so forth. We also considered conditions under which aggregation of tastes, utility and choices might occur and make sense from both behavioral and modeling perspectives.     

Buyer-supplier relational characteristics and joint decision making

This paper explores propositions dealing with the influence of buyer-supplier relational characteristics on their joint decision making. Specifically, the proposed conceptual framework focuses on the link between three relational constructs (trust, commitment, relationship maturity) and two types of joint decision making (strategic and tactical). Strategic decisions are those that “expand the pie” for both parties (integrative decisions), and tactical decisions are those that “divide the pie” between the two parties (distributive decisions). We propose that the effect of relational characteristics on joint decision making is likely to be moderated by the characteristics of the supplier organization (such as team orientation) and its environment (such as competitive intensity). We conclude with a brief discussion of the implications and possible extensions of the theoretical model.     

基于WTO视角的企业技术并购模式及选择研究

本文按照WTO背景下企业技术并购的战略意图,结合并购中目标技术与企业自有技术及产品之间的内在关联性,将技术并购总结为市场进入型、完善升级型、互补增强型、融合创新型、控制发展型和技术防卫型等6种模式。在模式研究的基础上,分析了企业技术能力构成要素,并采用企业技术能力作为一个维度对波士顿分析矩阵模型进行扩展,构建了用以选择技术并购模式的三维并购环境分析决策模型,从理论和实践的角度帮助企业选择适当的模式进行技术并购以获取竞争优势。     

Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making

Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI and examines how each can bring their own strength in organizational decision-making processes typically characterized by uncertainty, complexity, and equivocality. With a greater computational information processing capacity and an analytical approach, AI can extend humans’ cognition when addressing complexity, whereas humans can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision making. This premise mirrors the idea of intelligence augmentation, which states that AI systems should be designed with the intention of augmenting, not replacing, human contributions.