ABSTRACT: Proper linear models are those in which predictor variables are given weights in such a way that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regression analysis. Research summarized in Paul Meehl's book on clinical versus statistical prediction—and a plethora of research stimulated in part by that book—all indicates that when a numerical criterion variable (e.g., graduate grade point average) is to be predicted from numerical predictor variables, proper linear models outperform clinical intuition. Improper linear models are those in which the weights of the predictor variables are obtained by some nonoptimal method; for example, they may be obtained on the basis of intuition, derived from simulating a clinical judge's predictions, or set to be equal. This article presents evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors. In fact, unit (i.e., equal) weighting is quite robust for making such predictions. The article discusses, in some detail, the application of unit weights to decide what bullet the Denver Police Department should use. Finally, the article considers commonly raised technical, psychological, and ethical resistances to using linear models to make important social decisions and presents arguments that could weaken these resistances.

Elicitation is a key task for subjectivist Bayesians. While skeptics hold that it cannot (or perhaps should not) be done, in practice it brings statisticians closer to their clients and subjectmatter-expert colleagues. This paper reviews the state-of-the-art, reflecting the experience of statisticians informed by the fruits of a long line of psychological research into how people represent uncertain information cognitively, and how they respond to questions about that information. In a discussion of the elicitation process, the first issue to address is what it means for an elicitation to be successful, i.e. what criteria should be employed? Our answer is that a successful elicitation faithfully represents the opinion of the person being elicited. It is not necessarily “true ” in some objectivistic sense, and cannot be judged that way. We see elicitation as simply part of the process of statistical modeling. Indeed in a hierarchical model it is ambiguous at which point the likelihood ends and the prior begins. Thus the same kinds of judgment that inform statistical modeling in general also inform elicitation of prior distributions.

The paper describes a new preference method and its use in multiobjective optimisation. These preferences are developed with a goal to reduce the cognitive overload associated with the relative importance of a certain criterion within a multiobjective design environment involving large numbers of objectives. Their successful integration with several genetic algorithm-based design search and optimisation techniques (weighted sums, weighted Pareto, weighted coevolutionary methods, and weighted scenarios) are described and theoretical results relating to complexity and sensitivity of the algorithm are presented and discussed. Its usefulness has been demonstrated in a real-world project of conceptual airframe design (a joint project with British Aerospace Systems).

accuracy issues more directly. Moreover, this research attracts a great deal of attention because of what many take to be its dismal implications for the accuracy of human social reasoning. These implications are illusory, however, because an error is not the same thing as a "mistake. " An error is a judgment of an experimental stimulus that departs from a model of the judgment process. If this model is normative, then the error can be said to represent an incorrect judgment. A mistake, by contrast, is an incorrect judgment of a real-world stimulus and therefore more difficult to determine. Although errors can be highly informative about the process of judgment in general, they are not necessarily relevant to the content or accuracy of particular judgments, because errors in a laboratory may not be mistakes with respect to a broader, more realistic frame of reference and the processes that produce such errors might lead to correct decisions and adaptive outcomes in real life. Several examples are described in this article. Accuracy issues cannot be addressed by research that concentrates on demonstrating error in relation to artificial stimuli, but only by research that uses external, realistic criteria for accuracy. These criteria might include the degree to which judgments agree with each other and yield valid predictions of behavior. The accuracy of human social judgment is a topic of obvious

Pairs of items describing objectionable behaviors were rated for their overall morality. Contrary to additive or constant-weight averaging models, the ratings were demonstrated to depend upon the range as well as the average scale value of the component behaviors. A range model accounted for more than half of the variance left unexplained by the additive models. One interpretation of the range effect postulates that each component stimulus produces a distribution of values. The value of the stimulus combination is assumed to be the mean value in the overlap of the component distributions, which is closer to the item with the narrower dispersion. How immoral is it to both "pocket the tip the previous customer left for the waitress " and "poison your neighbor's dog whose barking bothers you? " Anderson (1968b) has suggested that 5s combine information by averaging psychological values associated with each component stimulus. Thus, 5 independently assesses the morality of pocketing the tip and the morality of poisoning the dog and then averages his two assessments to arrive at an overall rating. According to this view, the psychological value of the whole is simply an average of the psychological values of the parts. The theory is a special case of a general additive model (Rosenberg, 1968), which can be written: + C [1] where St!...&... „ is the psychological value of the combination of n stimuli, Sk is the psychological value of stimulus k, sa and

views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research or the U.S. government. Validating Computational Models The use of computational models in the social sciences has grown quickly in the past decade. For many these models represent a bewildering and possibly intimidating approach to examining data and developing social and organizational theory. Few researchers have had courses or personal experience in the development and building of computational models and even fewer have an understanding of how to validate such models. And while many papers extort the relative advantages and disadvantages of the computational approach, and many call for the validation of such models, few provide insight into how to validate such models and the issues involved in validation. This paper represents an attempt at redressing this oversight. An overview is provided of computational modeling in the social sciences,

This paper uses the original Technology Acceptance Model (TAM) and its extension models to explain the factors affecting the use of the mobile Internet services in Korea. Based on data collected from a questionnaire survey, we show that social influence and self-efficacy variables significantly affect perceived usefulness and perceived ease of use, respectively. The data also reveal that both perceived usefulness and ease of use explain a significant percentage of the variations in the attitude toward using the mobile Internet, which in turn influences the actual usage frequency.

this paper. Correspondence should be addressed to M. J. Dennis, Madsen Center, Augustana College, 2001 S. Summit Ave., Sioux Falls, SD 57197 (e-mail: dennis@inst.augie . edu)

This section reviews a specialty within the field of decision-making known as dynamic decision-making. Dynamic decisions are characterized by a decision-maker choosing among various actions at different points in time in order to control and optimize performance of a dynamic stochastic system. Realistic examples include fighting fires, navigational control, battlefield decisions, medical emergencies, and so on. The section has four parts: The first reviews basic theory concerning optimal decision principles in a dynamic context; the second summarizes empirical approaches to the study of human performance on dynamic decision tasks; the third presents theoretical models that describe how humans learn to control dynamic systems; and the last discusses methodological issues arising from the study of complex decisions including differences between field versus laboratory research.

Advisors vary in quality. People should make more use of better advisors: they should weight their advice more heavily. They should also assess them as providing more useful advice: they should express greater confidence in their advice by estimating that it has a higher probability of being correct. We discuss whether someone who is good at using advice will be good at assessing it (or vice versa). Performance in these tasks may be dissociated because they depend on different underlying cognitive processes. This issue of whether there is a dissociation between use of advice and assessment of its usefulness has implications for the development of automated systems designed to provide users with expertise and decision support. We review three areas of research relevant to the relation between use of advice and assessment of its usefulness. Then we summarize findings of Harvey, Harries, and Fischer (1998) indicating that people are better at assessing the usefulness of advice than at using it. Implications for systems development are discussed.