Showing papers on "R-CAST published in 1994"

A primer on decision making : how decisions happen

Abstract: Building on lecture notes from his course at Stanford University, James G. March provides an introduction to decision making, a central human activity, fundamental to individual, group, organizational, and societal life. March draws on research from all the disciplines of social and behavioural science to show decision making in its broadest context. By emphasizing how decisions are actually made - as opposed to how they should be made - he enables those involved in the process to understand it both as observers and as participants. In addition, March explains key concepts of vital importance to decision makers, such as limited rationality, history-dependent rules, and ambiguity, and weaves these ideas into a full depiction of decision making.

Multiple Objective Decision Making

Abstract: Decision making is the process of selecting a possible course of action from all available alternatives. In many cases, multiplicity of criteria for judging the alternatives is pervasive. Often the decision maker wants to attain more than one objective or goal in selecting a course of action, while satisfying constraints dictated by environment, processes, and resources.

User Perceptions of Decision Support Effectiveness: Two Production Planning Experiments *

Abstract: Decision support systems continue to be very popular in business, despite mixed research evidence as to their effectiveness. We hypothesize that what-if analysis, a prominent feature of most decision support systems, creates an “illusion of control” causing users to overestimate its effectiveness. Two experiments involving a production planning task are reported which examine decision makers' perceptions of the effectiveness of what-if analysis relative to the alternatives of unaided decision making, and quantitative decision rules. Experiment 1 found that almost all subjects believed what-if analysis was superior to unaided decision making, although using what-if analysis had no significant effect on performance. Experiment 2 found that decision makers were indifferent between what-if analysis and a quantitative decision rule which, if used, would have led to significant cost savings. Thus, what-if analysis did create an illusion of control: decision makers perceived performance differences where none existed, and did not detect large differences when they were present. In both experiments, decision makers exhibited difficulty realizing that their positive beliefs about what-if analysis were exaggerated. Such misjudgments could lead people to continue using what-if analysis even when it is not beneficial and to avoid potentially superior decision support technologies.

Decision Making in Action

Abstract: The importance of decision-making to safety in complex, dynamic environments like mission control centers and offshore installations has been well established. NASA-ARC has a program of research dedicated to fostering safe and effective decision-making in the manned spaceflight environment. Because access to spaceflight is limited, environments with similar characteristics, including aviation and nuclear power plants, serve as analogs from which space-relevant data can be gathered and theories developed. Analyses of aviation accidents cite crew judgement and decision making as causes or contributing factors in over half of all accidents. A similar observation has been made in nuclear power plants. Yet laboratory research on decision making has not proven especially helpful in improving the quality of decisions in these kinds of environments. One reason is that the traditional, analytic decision models are inappropriate to multidimensional, high-risk environments, and do not accurately describe what expert human decision makers do when they make decisions that have consequences. A new model of dynamic, naturalistic decision making is offered that may prove useful for improving decision making in complex, isolated, confined and high-risk environments. Based on analyses of crew performance in full-mission simulators and accident reports, features that define effective decision strategies in abnormal or emergency situations have been identified. These include accurate situation assessment (including time and risk assessment), appreciation of the complexity of the problem, sensitivity to constraints on the decision, timeliness of the response, and use of adequate information. More effective crews also manage their workload to provide themselves with time and resources to make good decisions. In brief, good decisions are appropriate to the demands of the situation. Effective crew decision making and overall performance are mediated by crew communication. Communication contributes to performance because it assures that all crew members have essential information, but it also regulates and coordinates crew actions and is the medium of collective thinking in response to a problem. This presentation will examine the relations between leadership, communication, decision making and overall crew performance. Implications of these findings for spaceflight and training for offshore installations will be discussed.

Administrators Solving the Problems of Practice: Decision-Making Concepts, Cases, and Consequences

Abstract: 1. Introduction. 2. Decision Making: Optimizing and Satisficing. 3. Decision Making: Muddling and Scanning. 4. Decision Making: Garbage and Politics. 5. Using the Best Model: Practice Cases. 6. Shared Decision Making: A Comprehensive Model. 7. Shared Decision Making: A Simplified Model. 8. Decision Making: Final Cases

Toward a Peer Review Process for Medical Decision Analysis Models

Abstract: This paper presents a framework for a peer review process for medical decision analysis models. This framework is based on the collective experience of the members of the Inter-PORT Decision Modeling work group, a team of decision analysis experts comprising members from each of the Patient Outcomes Research Teams (PORTs), sponsored by the Agency for Health Care Policy and Research. Important general principles of correct model structure include choice of model type, perspective of the analysis, choice of utility scheme and identification of strategies that should be included and events that should be modeled. In addition, a set of rules for correct decision model structure may help to identify common errors. Although not necessarily exhaustive, this scheme provides an approach by which a reviewer may judge the adequacy of a decision model presented for publication or as the basis for a health policy.

An exploratory study into a methodology for the examination of decision making by nurses in the clinical area

Abstract: This study was undertaken to explore decision making in the clinical area and why it is that some nurses make seemingly irrational decisions. In the past simulations and decision frames have been used by psychologists and clinician researchers to examine decision making, although none of these methods were reported to have been used in the clinical area. In the study 11 subjects were observed in the clinical area for 2 hours each and a total of 18 decisions were made by these subjects. Each observation was followed by an interview in which the decisions were further explored and three simulations and four decision framed questions were also employed as comparative tools. The subjects were also asked about their decision making strategies. The method of using observation to collect data was effective and the effect of experience was highlighted as one of the main factors influencing decision making. The other methods proved useful as tools to examine particular aspects of decision making. The effect of experience on decision making has wide reaching effects on future nurse education, as well as having professional implications in the area of autonomy, accountability and responsibility. Further research in this area is then recommended.

Neural networks for decision support: problems and opportunities

Abstract: Neural networks offer an approach to computing which — unlike conventional programming — does not necessitate a complete algorithmic specification. Furthermore, neural networks provide inductive means for gathering, storing, and using, experiential knowledge. Incidentally, these have also been some of the fundamental motivations for the development of decision support systems in general. Thus, the interest in neural networks for decision support is immediate and obvious. In this paper, we analyze the potential contribution of neural networks for decision support, on one hand, and point out at some inherent constraints that might inhibit their use, on the other. For the sake of completeness and organization, the analysis is carried out in the context of a general-purpose DSS framework that examines all the key factors that come into play in the design of any decision support system.

Decision support systems: scope and potential

Abstract: The paper addresses the scope of decision support systems starting from the three words, decision, support, and system, that make up the term. Based on a decision-theory point of view, the notion of decision and related cognitive concepts are discussed, particularly in connection with the idea of supporting human decision makers in making decisions. The aspect of systems brings in automatic devices that assist the decision maker on various levels of cognitive sophistication. This aspect includes a connection to artificial intelligence whenever a high degree of sophistication is required. With a view to these connections with AI, the paper takes a model-oriented approach, aiming at an integration of the modelling necessities from decision theory, from human-machine interfaces, from application domains, and from system Eigen models as the core of every advanced architecture in this field.

Understanding managers' strategic decision-making process

Abstract: This goal of this paper is to establish a research agenda that will lead to a stream of research that closes the gap between actual and normative strategic managerial decision making. We start by distinguishing strategic managerial decision making (choices) from other choices. Next, we propose a conceptual model of how managers make strategic decisions that is consistent with the observed gap between actual and normative decision making. This framework suggests a series of interesting issues, both descriptive and prescriptive in nature, about the strategic decision-making process that define our proposed research agenda.

An active intelligent decision support system—architecture and simulation

Abstract: Development and implementation of decision support systems to support intelligent decision making is an area of research that has gained in importance in recent years. Due to the increased complexity of decision making, active involvement of the user and the computer in an intelligent way is necessary in the decision process. This paper presents issues in the design of an active intelligent decision support system (IDSS), develops an architectural model based on cooperative distributed problem solving, and performs a simulation of the system using object oriented programming for an example application in airfleet control.

The Prescriptive Orientation of Decision Making: A Synthesis of Decision Analysis, Behavioral Decision Making, and Game Theory

Abstract: In this presentation I would like, perhaps in a missionary style, to draw your attention to three broad concerns I have about the directions of research and practice of decision making, concerns that need to be addressed.

Using case-based reasoning for decision support

Abstract: Case-based reasoning is one of the most preferred method for problem solving and decision making in complex and dynamically changing situations. Case-based reasoning solves problems by relating some previously solved problems or experiences to a current, unsolved problem in a way that facilitates the search for an acceptable solution. In this paper we propose a computational case-based reasoning model, and investigate its feasibility to decision support. A performance comparison among two computational models, including ours, and a symbolic model is also conducted. We view our model not as a replacement technology, but rather as a complementary technology to the symbolic approaches for the purpose of supporting unstructured-oriented decisions. >

Computerized support systems for emergency decision making

Abstract: In all fields of human society, occasional emergencies are almost inevitable. Once an emergency occurs, rapid and proper decision making is required. The purpose of this paper is to explore the design and development of computerized support systems for emergency decision making (EDM). First the characteristics of EDM problems are examined. Then, in view of limited human computer rationality, requirements for a computerized support system for EDM are determined. A conceptual structure for knowledge-based distributed emergency decision support systems is proposed. Finally, a prototype system for safety protection and disaster response in coal mines, developed using the proposed structure, is briefly described.

Decision making and decision aiding: Defining the process, it representations, and support

Abstract: We present a formalized account of decision making as a multistep process that involves several classes of participating entities. The purpose of this article is to lay the foundations for a conceptual framework in which decision support systems can be placed. A series of increasingly formal representations of the decision problem are developed, from a mental model conceived by the decision maker to a knowledge base that may be used in a decision support system. The reformulations of the decision problem lead us to contemplate different forms of support: for mental models, for formal models (this includes supporting measurement and representation), for solution, and for communication.

JUDGES: a descriptive group decision support system for the ranking of items

Abstract: A set of tools for group decision support are presented. Decision problems involving several decision makers, here-after called judges, that have to rank several alternatives, are considered. The toolbox is called JUDGES. It includes the four following procedures: • - a hierarchical representation of the judges allows to display the existing conflicts between groups of judges, • - enhanced box-plots representations of the alternatives are generated in order to detect those that are responsible for the major conflicts, • - specific advice is issued to each judge in order to reach more easily a consensus, • - a general framework for a pairwise group preference structure is proposed, and can be used to finalise the decision. These procedures are embedded in an interactive software, implemented on micro-computer, which currently simulates the use on a network. Actual network implementation is foreseen in the near future. Several applications are presented and future developments are discussed.

Reliability of an automated decision support system for behavioral treatment planning: preliminary results from the mental retardation-expert

Abstract: Automated decision support systems can provide inexperienced individuals with expert advice. But while a computer will always arrive at the same conclusion from a given set of data, human users bring individual variability to decision making. This study, therefore, examined the reliability of the decision support component of an expert system, the Mental Retardation-Expert (MR-E), which assists clinicians who treat problem behaviors of individuals with mental retardation or developmental disabilities. Four clinicians independently used MR-E to obtain consultations on 31 separate cases abstracted from the published literature. They reached a high level of agreement on functional hypotheses regardless of clinical experience, familiarity with MR-E, use of MR-E's basic or advanced modes, or consecutive number of consultations obtained.

An integrated approach to dynamic decision-making under uncertainty

Abstract: Decision making is often complicated by the dynamic and uncertain information involved. This work unifies and generalizes the major approaches to modeling and solving a subclass of such decision problems. The relevant approaches include semi-Markov decision processes, dynamic decision modeling, and decision-theoretic planning. An analysis of current decision frameworks establishes a unifying task definition and a common vocabulary; the exercise also identifies the trade-off between model transparency and solution efficiency as their most significant limitation. Insights gained from the analysis lead to a new methodology for dynamic decision making under uncertainty. The central ideas involved are multiple perspective reasoning and incremental language extension. Multiple perspective reasoning supports different information visualization formats for different aspects of dynamic decision modeling. Incremental language extension promotes the use of translators to enhance language ontology and facilitate practical development. DynaMoL is a language design that adopts the proposed paradigm; it differentiates inferential and representational support for the modeling task from the solution or computation task. The language is evaluated on a prototype implementation, via a comprehensive case study in medicine. The results demonstrate practical promise of the framework.

Design and Implementation of Model-based Decision Support Systems

Abstract: Decision making often requires the analysis of large amount of data and complex relations. Computerized tools designed and implemented for such purposes are called Decision Support Systems (DSS). A DSS, which is typically a problem specific tool, usually helps in the evaluation of consequences of given decisions and may advise what decision would be the best for achieving a given set of goals. In such cases, an analysis of a mathematical model can support rational decision making. The paper provides an overview of the methodology of the design and deals with practical aspects related to implementations of model-based decision support systems. In particular, different approaches to the analysis of a model using simulation and optimization are summarized. Various optimization techniques are discussed in this context, including multi-criteria optimization used for a model analysis. The paper summarizes also problems of hardware selection and of software development. Modular software tools applicable to DSSs, including a tool for data interchange, are characterized. Selected issues of implementations of modular solvers and of applications of artificial neural nets to decision support are also presented.

Decision making in complex systems

Abstract: Individuals, organizations, and governments are often expected to make decisions of far-reaching consequences. Judgment and decision-making capabilities are important facets of human intelligence. Systematic studies of these topics have commenced only in the 1960s. Simultaneous developments in computer hardware and software and in fields such as artificial intelligence have given impetus to the study of human decision making from descriptive, normative, and prescriptive points of view. Realworld decision problems are often unstructured and difficult to formulate. There are multiple objectives, distributed decision makers and difficulties in acquiring different types of knowledge needed for problem solving. Human knowledge is often available in natural language with its inherent ambiguity and vagueness. While a human being has only “bounded rationality,” his intuition and common sense enable him to make good decisions in using qualitative nonnumerical information in narrow domains of expertise such as medical diagnosis. He has to be supported by decision aids when confronted with situations in complex systems. In this paper, we briefly review decision making in complex systems from the point of view of intelligent decision support systems, which applications to the project management task.

Tactical Decision Making Under Stress: Preliminary Results and Lessons Learned

Abstract: : This paper presents preliminary results of ongoing research into tactical decision making under stress (TADMUS). A description will be given of (1) the general methodological approach; (2) the development of the performance measures and issues related to their development; (3) lessons learned in the planning and conducting of this research; and (4) types of errors typically made and their implications for the development of a decision support system (DSS) using a naturalistic model of decision making. Data from ten team responses to scenarios run in the Decision-Making Evaluation Facility for Tactical Teams (DEFTT) Laboratory will be discussed. Discussion of these results will include a description of the TapRoot Incident Investigation System, the approach used to analyze the data to identify errors. Ways in which these errors can be mitigated by the DSS will also be discussed.