Extending the concept of job withdrawal: Identifying, predicting, and understanding adaptation to work

Thesis (B.S.) in Liberal Arts and Sciences -- University of Illinois at Urbana-Champaign, 1987.Bibliography: leaves 42-46.Microfiche of typescript. [Urbana, Ill.]: Photographic Services, University of Illinois, U of I Library, [1987]. 2 microfiches (81 frames): negative

Off the charts: massive unexplained heterogeneity in a global study of ambiguity attitudes

Ambiguity attitudes have been indicated as important determinants of economic outcomes in economic models, but we still know little about the demographic correlates of ambiguity attitudes, or indeed about the universality of patterns found in the West. We analyse the ambiguity attitudes of almost 3000 students across 30 countries. For gains we find ambiguity aversion everywhere, while ambiguity aversion is much weaker for losses. We also find ambiguity attitudes to systematically change with probabilities for both gains and losses, reflecting ambiguity-insensitivity to probabilities. Much of the between-country variation can be explained through a few macroeconomic characteristics. In contrast, we find massive unexplained variation at the individual level, suggesting that individual differences in ambiguity attitudes remain difficult to explain. We also find much unexplained heterogeneity in individual responses to different decision tasks. We conclude by discussing potential issues underlying this heterogeneity, and indicating potential solution

Multi-attribute utility models as cognitive search engines. Judgment and Decision Making

Abstract In optimal stopping problems, decision makers are assumed to search randomly to learn the utility of alternatives; in contrast, in one-shot multi-attribute utility optimization, decision makers are assumed to have perfect knowledge of utilities. We point out that these two contexts represent the boundaries of a continuum, of which the middle remains uncharted: How should people search intelligently when they possess imperfect information about the alternatives? We assume that decision makers first estimate the utility of each available alternative and then search the alternatives in order of their estimated utility until expected benefits are outweighed by search costs. We considered three well-known models for estimating utility: (i) a linear multi-attribute model, (ii) equal weighting of attributes, and (iii) a single-attribute heuristic. We used 12 real-world decision problems, ranging from consumer choice to industrial experimentation, to measure the performance of the three models. The full model (i) performed best on average but its simplifications (ii and iii) also had regions of superior performance. We explain the results by analyzing the impact of the models' utility order and estimation error

How to select Instruments supporting R&D and Innovation by Industry

We present a theoretical framework which allows for the comparison of the effectiveness of tax measures, loans and funding, in supporting industry-oriented research. We estimate for each of the instruments the exact contribution required by a firm to decide on investing in R&D, given the costs and probability of success of the project, and the foreseen change in profit following successful implementation of the research results. We apply Prospect Theory to analyse the risk attitude of the firm. By comparing the contribution required, we identify the instrument which is most effective, and therefore preferred by a government. Our analysis indicates that there exists a critical value for the probability of success of the project for which the modality of the most effective instruments changes. For a probability of success smaller than the critical value, a tax measures offering support only in case of successful completion of the project is preferred. For a probability higher than the critical value, a loan is most effective. The value of the critical probability depends on the perception of risk and loss aversion of the firm involved in the research.R&D, innovation, firms, public policy

Human behaviour.

Preference foundations give necessary and sufficient conditions for a decision model, stated directly in terms of the empirical primitive: the preference relation. For the most popular descriptive model for decision making under risk and uncertainty today, prospect theory, preference foundations have as yet been provided only for prospects taking finitely many values. In applications, however, prospects often are complex and involve infinitely many values, as in normal and log-normal distributions. This paper provides a preference foundation of prospect theory for such complex prospects. We allow for unbounded utility and only require finite additivity of the underlying probability distributions, leaving the restriction to countably additive distributions optional. As corollaries, we generalize previously obtained preference foundations for special cases of prospect theory (rank-dependent utility and Choquet expected utility) that all required countable additivity. We now obtain genuine generalizations of de Finetti's and Savage's finitely additive setups to unbounded utility