Knowledge management in decision making and decision support

A knowledge-oriented view of decisions and decision making is introduced, as a complement to classical perspectives and as a contribution of under-standing computer-based possibilities for relaxing strains on decision makers. This perspective includes a model of knowledge management activities performed by a decision maker and a taxonomy of knowledge types. It leads to a characterization of decision-support-system purpose, traits, and potentials that offers a basis for new research into computer-based possibilities for knowledge management. Clyde W. Holsapple holds the Rosenthal Endowed Chair in Management Information Systems and is Professor of Decision Science and Information Systems at the College of Business and Economics, University of Kentucky.     

Response modes, decision processes and decision outcomes

Until recently, research on riskless multiattribute decision making has generally ignored a distinction among response modes, i.e. the type of response required of the decision maker. This neglect persisted in spite of the results of early studies on preference reversals in gambles, which showed a distinct difference between the decision outcomes of choice and those of judgment. This article examines the effects of response modes in relation to two explanations proposed in recent response mode studies: the compatibility hypothesis and the response mode continuum. An experiment was conducted with a process tracing technique (information boards), in which 104 subjects made decisions on the quality of student papers submitted for publication. Four response modes were compared: selecting, rejecting, classifying, and ranking of alternatives. In addition, information load was manipulated. Response mode influenced both the decision process and the decision outcome. The results are interpreted as supporting the response mode continuum idea, but not the compatibility hypothesis. A classification of response modes is proposed.     

Information about expert decision and post‐decision distortion of facts of own decision

Cognitive representations of decision problems are dynamic. During and after a decision, evaluations and representations of facts change to support the decision made by a decision maker her‐ or himself (Svenson, 2003). We investigated post‐decision distortion of facts (consolidation). Participants were given vignettes with facts about two terminally ill patients, only one of whom could be given lifesaving surgery. In Study 1, contrary to the prediction, the results showed that facts were distorted after a decision both by participants who were responsible for the decisions themselves and when doctors had made the decision. In Study 2 we investigated the influence of knowledge about expert decisions on a participant's own decision and post‐decisional distortion of facts. Facts were significantly more distorted when the participant's decision agreed with an expert's decision than when the participant and expert decisions disagreed. The findings imply that knowledge about experts' decisions can distort memories of facts and therefore may obstruct rational analyses of earlier decisions. This is particularly important when a decision made by a person, who is assumed to be an expert, makes a decision that is biased or wrong.     

Group decision process and incrementalism in organizational decision making

In two studies examining resource allocation, support is found for the notion that group decisions are affected in systematic ways depending on whether or not there was individual consideration of the problem before meeting as a group. Specifically, compared to no prior consideration groups, prior consideration groups (1) escalate their commitment more in progress (i.e., ongoing) decisions, and (2) are less willing to concentrate resources on a single project in adoption (i.e., resource utilization) decisions. The findings challenge the blanket assertion that promoting divergent views in a group decision context is always related to better decisions.     

Causal decision theory

On the dominant view of vagueness, if it is vague whether Harry is bald, then it is unsettled, not merely epistemically, but metaphysically, whether Harry is bald. In other words, vagueness is a type of indeterminacy. On the standard alternative, vagueness is a type of ignorance: if it is vague whether Harry is bald, then, even though it is metaphysically settled whether Harry is bald, we cannot know whether Harry is bald. On my view, vagueness is neither a type of indeterminacy nor a type of ignorance. Rather, it is sui generis.     

Computer-augmented decision making

The principal judgmental components of multiattribute decision making are examined here with specific reference to how these components can be captured electronically. Once captured, a function, rule, or algorithm may be executed for the integration of this information and the selection of the optimal alternative(s). Two kinds of algorithms are discussed: one based on linear models, the other on fuzzy-set theory and ratio scaling. With on-line support and certain assumptions about human biases (which lead to nonoptimal decisions), the quality of decisions can be enhanced considerably. The principal concerns are with end-user acceptance of computer augmented decisions.     

An evolutionary approach to support decision making with linear decision models

In this paper we consider decision problems that can be described as linear decision models. These models have been traditionally solved using linear programming, fuzzy linear programming, multiple-objective linear programming or ‘what-if’ analysis. Using these approaches, one encounters a number of difficulties. We propose an ‘evolutionary approach’ to overcome these difficulties. In the proposed approach the decision maker does not have to precisely specify the model (i.e. the objective functions, the RHS values, etc.) at the beginning of the solution procedure. In fact, the model evolves as the solution procedure proceeds.     

The decision tree polytope and its application to sequential decision problems

This paper describes a new mathematical programming approach to sequential decision problems that have an underlying decision tree structure. The approach, based upon a characterization of strategies as extreme points of a 0–1 polytope called the ‘decision tree polytope’, is particularly suited to the direct examination of risk-return and other tradeoffs amongst strategies. However, it can also be used for conventional utility maximization if a utility function is available. Further, the approach requires no algorithmic development—it can be implemented using commercially available algebraic modeling software and can solve large problems. A related, and already known, approach can be used for some more general Markov decision problems. © 1998 John Wiley & Sons, Ltd.     

Multialternative decision field theory: a dynamic connectionist model of decision making

The authors interpret decision field theory (J. R. Busemeyer & J. T. Townsend, 1993) as a connectionist network and extend it to accommodate multialternative preferential choice situations. This article shows that the classic weighted additive utility model (see R. L. Keeney & H. Raiffa, 1976) and the classic Thurstone preferential choice model (see L. L. Thurstone, 1959) are special cases of this new multialternative decision field theory (MDFT), which also can emulate the search process of the popular elimination by aspects (EBA) model (see A. Tversky, 1969). The new theory is unique in its ability to explain several central empirical results found in the multialternative preference literature with a common set of principles. These empirical results include the similarity effect, the attraction effect, and the compromise effect, and the complex interactions among these three effects. The dynamic nature of the model also implies strong testable predictions concerning the moderating effect of time pressure on these three effects.     

Inferences from decision difficulty

We propose that people infer the relative attractiveness of the choice alternatives from decision difficulty. A difficult decision signifies that the alternatives are close to each other in attractiveness, and an easy decision signifies that the alternatives are remote from each other in attractiveness. In Study 1, observers used reported decision difficulty to infer preferences of the decision maker. Studies 2-4 showed that inferences about the source of one’s own decision difficulty may affect a decision maker’s preferences. Study 4 integrates the notion of inferences from decision difficulty with dissonance theory, showing that in repeatable decisions difficulty reduces post-decisional spreading of alternatives, as predicted by our model, whereas with one-time decisions, difficulty enhances post-decisional spreading of alternatives, as predicted by dissonance theory.     

Numeracy and decision making

A series of four studies explored how the ability to comprehend and transform probability numbers relates to performance on judgment and decision tasks. On the surface, the tasks in the four studies appear to be widely different; at a conceptual level, however, they all involve processing numbers and the potential to show an influence of affect. Findings were consistent with highly numerate individuals being more likely to retrieve and use appropriate numerical principles, thus making themselves less susceptible to framing effects, compared with less numerate individuals. In addition, the highly numerate tended to draw different (generally stronger or more precise) affective meaning from numbers and numerical comparisons, and their affective responses were more precise. Although generally helpful, this tendency may sometimes lead to worse decisions. The less numerate were influenced more by competing, irrelevant affective considerations. Analyses showed that the effect of numeracy was not due to general intelligence. Numerical ability appears to matter to judgments and decisions in important ways.     

Against causal decision theory

Proponents of causal decision theories argue that classical Bayesian decision theory (BDT) gives the wrong advice in certain types of cases, of which the clearest and commonest are the medical Newcomb problems. I defend BDT, invoking a familiar principle of statistical inference to show that in such cases a free agent cannot take the contemplated action to be probabilistically relevant to its causes (so that BDT gives the right answer). I argue that my defence does better than those of Ellery Eells and Richard Jeffrey; and that it applies, where necessary, to other types of Newcomb problem.