If stimulus responses are linearly related to squared distances between stimulus scale values and person scores along a latent continuum, (a) the stimulus × stimulus correlation matrix will display a simplex-like pattern, (b) the signs of first-order partial correlations can be specified in an empirically testable manner, and (c) the variables will have a semicircular, two-factor structure. Along the semicircle, variables will be ordered by their positions on the latent dimension. The above results suggest procedures for examining the appropriateness of the model and procedures for ordering the stimuli. Applications to developmental and attitudinal data are discussed.

In this paper, modern statistics is considered as a branch of psychometrics and the question of how the central problems of statistics can be resolved using psychometric methods is investigated. Theories and methods developed in the fields of test theory, scaling, and factor analysis are related to the principle problems of modern statistical theory and method. Topics surveyed include assessment of probabilities, assessment of utilities, assessment of exchangeability, preposterior analysis, adversary analysis, multiple comparisons, the selection of predictor variables, and full-rank ANOVA. Reference is made to some literature from the field of cognitive psychology to indicate some of the difficulties encountered in probability and utility assessment. Some methods for resolving these difficulties using the Computer-Assisted Data Analysis (CADA) Monitor are described, as is some recent experimental work on utility assessment.

Using Carroll's external analysis, several studies have found that unfolding models account for more, although seldom significantly more, variance in preferences than Tucker's vector model. In studies of sociometric ratings and political preferences, the unfolding model again rarely outpredicted the vector model by a significant amount. Yet on cross-validation, the unfolding model consistently accounted for more variance. Results suggest that sometimes significance tests are less sensitive than cross-validation procedures to the small but consistent superiority of the unfolding model. Future researchers may wish to use significance tests and cross-validation techniques in comparing models.

A quadratic programming algorithm is presented for fitting Carroll’s weighted unfolding model for preferences to known multidimensional scale values. The algorithm can be applied directly to pairwise preferences; it permits nonnegativity constraints on subject weights; and it provides a means of testing various preference model hypotheses. While basically metric, it can be combined with Kruskal’s monotone regression to fit ordinal data. Monte Carlo results show that (a) adequacy of “true” preference recovery depends on the number of data points and the amount of error, and (b) the proportion of data variance accounted for by the model sometimes only approximately reflects “true” recovery.

We formulate a centrally planned portfolio selection problem with the investor and the manager having S-shaped utilities under a recently popular first-loss contract. We solve for the closed-form optimal portfolio, which shows that a first-loss contract can sometimes behave like an option contract. We propose an asymptotic approach to investigate the portfolio. This approach can be adopted to illustrate economic insights, including the fact that the portfolio under a convex contract becomes more conservative when the market state is better. Furthermore, we discover a means of Pareto improvement by simultaneously considering the investor’s utility and increasing the manager’s incentive rate. This is achieved by establishing the collection of Pareto points of a single contract, proving that it is a strictly decreasing and strictly concave frontier, and comparing the Pareto frontiers of different contracts. These results may be helpful for the illustration of risk choices and the design of Pareto-optimal contracts.

Survey experiments are an important tool to measure policy preferences. Researchers often rely on the random assignment of policy attribute levels to estimate different types of average marginal effects. Yet, researchers are often interested in how respondents trade-off different policy dimensions. We use a conjoint experiment administered to more than 10,000 respondents in Germany, to study preferences over personal freedoms and public welfare during the COVID-19 crisis. Using a pre-registered structural model, we estimate policy ideal points and indifference curves to assess the conditions under which citizens are willing to sacrifice freedoms in the interest of public well-being. We document broad willingness to accept restrictions on rights alongside sharp heterogeneity with respect to vaccination status. The majority of citizens are vaccinated and strongly support limitations on freedoms in response to extreme conditions—especially, when they vaccinated themselves are exempted from these limitations. The unvaccinated minority prefers no restrictions on freedoms regardless of the severity of the pandemic. These policy packages also matter for reported trust in government, in opposite ways for vaccinated and unvaccinated citizens.

We propose a constructed-choice model for general decision making. The model departs from utility theory and prospect theory in its treatment of multiple goals and it suggests several different ways in which context can affect choice.

It is particularly instructive to apply this model to protective decisions, which are often puzzling. Among other anomalies, people insure against non-catastrophic events, underinsure against catastrophic risks, and allow extraneous factors to influence insurance purchases and other protective decisions. Neither expected-utility theory nor prospect theory can explain these anomalies satisfactorily. To apply this model to the above anomalies, we consider many different insurance-related goals, organized in a taxonomy, and we consider the effects of context on goals, resources, plans and decision rules.

The paper concludes by suggesting some prescriptions for improving individual decision making with respect to protective measures.

A company with $n$ geographically widely dispersed sites seeks insurance that pays off if $m$ out of the $n$ sites experience rarely occurring catastrophes (e.g., earthquakes) during a year. This study describes an adaptive dynamic strategy that enables an insurance company to offer the policy with smaller loss probability than more conventional static policies induce, but at a comparable or lower premium. The strategy accomplishes this by periodically purchasing reinsurance on individual sites. Exploiting rarity, the policy induces zero loss with probability one if no more than one quake occurs during any review interval. The policy also may induce a profit if $m$ or more quakes occur in an interval if no quakes have occurred in previous intervals. The study also examines the benefit of more than one reinsurance policy per active site. The study relies on expected utility to determine indifference premiums and derives an upper bound on loss probability independent of premium.

As providers of health care, we face increasing demand on our limited, indeed diminishing, resources. Economic appraisal of our interventions means assessing the trade-off between effectiveness, efficiency and equity. When rationing becomes inevitable, calculation of utility values is a valuable decision-making tool. This paper reviews objective measures of patient benefit, such as quality of life, and focuses on their application within otolaryngology.

Engineering risk methods and tools account for and make decisions about risk using an expected-value approach. Psychological research has shown that stakeholders and decision makers hold domain-specific risk attitudes that often vary between individuals and between enterprises. Moreover, certain companies and industries (e.g., the nuclear power industry and aerospace corporations) are very risk-averse whereas other organizations and industrial sectors (e.g., IDEO, located in the innovation and design sector) are risk tolerant and actually thrive by making risky decisions. Engineering risk methods such as failure modes and effects analysis, fault tree analysis, and others are not equipped to help stakeholders make decisions under risk-tolerant or risk-averse decision-making conditions. This article presents a novel method for translating engineering risk data from the expected-value domain into a risk appetite corrected domain using utility functions derived from the psychometric Engineering Domain-Specific Risk-Taking test results under a single-criterion decision-based design approach. The method is aspirational rather than predictive in nature through the use of a psychometric test rather than lottery methods to generate utility functions. Using this method, decisions can be made based upon risk appetite corrected risk data. We discuss development and application of the method based upon a simplified space mission design in a collaborative design-center environment. The method is shown to change risk-based decisions in certain situations where a risk-averse or risk-tolerant decision maker would likely choose differently than the expected-value approach dictates.

Those with responsibility for the assets of institutional investors have a fiduciary duty to attempt to earn the best possible risk adjusted returns and to comply with ethical standards. A satisfactory resolution of these, and other, conflicting demands requires a coherent intellectual framework. Such a framework can be based on a traditional scheme that analyses the various components of profit in terms of the requirements of justice. The framework provides a basis for discussing the major challenges facing the institutional investors. These relate to their role in rational asset selection, effective corporate governance, job creation and the minimisation of environmental impact.

The paper compares and contrasts the theories and methodologies of financial economics with generalisations drawn from practical experience of institutional investment management. A Dynamic Equilibrium Model is put forward as a much more realistic theoretical framework than the Sharpe Diagonal Model, a downside theory of risk is developed from sports analogies, and a detailed model of human behaviour, incorporating benchmark levels of ‘unintelligent’, ‘intelligent’, ‘optimal’ and ‘rational’ behaviour, is suggested as a replacement for the very restrictive simplifying assumptions of existing theories. On the basis of a classification system derived from parallels with the history of astronomy, it is concluded that, as a scientific framework, the current theories of financial economics are seriously incomplete, the two crucial areas of incompleteness being the absence of a downside theory of risk and the absence of a realistic model of human behaviour under conditions of risk and uncertainty.

The design of even the simplest product requires thousands of decisions. Yet few of these decisions are supported with methods on paper or on computers. Is this because engineering design decisions do not need support or is it because techniques have yet to be developed that are usable on a wide basis? In considering this question a wide range of decision problem characteristics need to be addressed. In engineering design some decisions are made by individuals, others by teams – some are about the product and others about the processes that support the product – some are based on complete, consistent, quantitative data and others on sparse, conflicting, qualitative discussions. To address the reasons why so little support is used and the characteristics of potentially useful decision support tools, a taxonomy of decision characteristics is proposed. This taxonomy is used to classify current techniques and to define the requirements for an ideal engineering design decision support system.

Current design practices mandate that engineering designs be evaluated based on multiple attributes, e.g., cost, power, and area. For multiattribute design problems, generation and evaluation of the Pareto optimal set guarantees the optimal design will be found, but is not practical for a large class of problems. Iterative techniques can be applied to most problems, but sacrifice optimality. In this paper, we introduce a new technique that extends the set of design problems that can be solved optimally. By first constructing an imprecise value function, the number of nondominated alternatives that must be generated is reduced. We describe an implementation based on combinatorial optimization and constraint satisfaction which achieves additional performance gains by decomposing the value function to identify dominated design-variable assignments. Test results indicate that our approach extends the set of problems that can be solved optimally.

The paper analyses the questions: Should – or should not – an individual buy insurance? And if so, what insurance coverage should he or she prefer? Unlike classical studies of optimal insurance coverage, this paper analyses these questions from a bonus-malus point of view, that is, for insurance contracts with individual bonus-malus (experience rating or no-claim) adjustments. The paper outlines a set of new statements for bonus-malus contracts and compares them with corresponding classical statements for standard insurance contracts. The theoretical framework is an expected utility model, and both optimal coverage for a fixed premium function and Pareto optimal coverage are analyzed. The paper is an extension of another paper by the author, see Holtan (2001), where the necessary insight to – and concepts of – bonus-malus contracts are outlined.

Constraints imposed on premium calculation principles are studied under one aspect of competitive market theory: the impossibility of systematic arbitrage. Principles based on second moments or utility theory are shown to lead to arbitrage possibilities, while some other principles do not.