Decisions under Risk | ScienceGate

Choosing solutions under risk and uncertainty requires the consideration of several factors. One of the main factors in choosing a solution is modeling the decision maker’s attitude to risk. The expected utility theory was the first approach that allowed to correctly model various nuances of the attitude to risk. Further research in this area has led to the emergence of even more effective approaches to solving this problem. Currently, the most developed theory of choice with respect to decisions under risk conditions is the cumulative prospect theory. This paper presents the development history of various extensions of the original expected utility theory, and the analysis of the main properties of the cumulative prospect theory. The main result of this work is a fuzzy version of the prospect theory, which allows handling fuzzy values of the decisions (prospects). The paper presents the theoretical foundations of the proposed version, an illustrative practical example, and conclusions based on the results obtained.

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Cognitive Processes in Decisions Under Risk are not the Same as in Decisions Under Uncertainty

Frontiers in Neuroscience ◽

10.3389/fnins.2012.00105 ◽

2012 ◽

Vol 6 ◽

Cited By ~ 42

Author(s):

Kirsten G. Volz ◽

Gerd Gigerenzer

Keyword(s):

Cognitive Processes ◽

Decisions Under Uncertainty ◽

Decisions Under Risk

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DECISION ANALYSIS WITH MULTIPLE OBJECTIVES IN A FRAMEWORK FOR EVALUATING IMPRECISION

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s021848850500362x ◽

2005 ◽

Vol 13 (05) ◽

pp. 495-509 ◽

Cited By ~ 16

Author(s):

ARON LARSSON ◽

JIM JOHANSSON ◽

LOVE EKENBERG ◽

MATS DANIELSON

Keyword(s):

Decision Tree ◽

Utility Function ◽

Evaluation Method ◽

Convex Sets ◽

Utility Functions ◽

Probability Measures ◽

Decisions Under Risk ◽

Closed Intervals ◽

Pros And Cons ◽

Tree Evaluation

We present a decision tree evaluation method for analyzing multi-attribute decisions under risk, where information is numerically imprecise. The approach extends the use of additive and multiplicative utility functions for supporting evaluation of imprecise statements, relaxing requirements for precise estimates of decision parameters. Information is modeled in convex sets of utility and probability measures restricted by closed intervals. Evaluation is done relative to a set of rules, generalizing the concept of admissibility, computationally handled through optimization of aggregated utility functions. Pros and cons of two approaches, and tradeoffs in selecting a utility function, are discussed.

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Are decisions under risk malleable?

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.96.19.10927 ◽

1999 ◽

Vol 96 (19) ◽

pp. 10927-10932 ◽

Cited By ~ 8

Author(s):

C. Fong ◽

K. McCabe

Keyword(s):

Decisions Under Risk

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Investigation of the Stochastic Choice under Risk using Experimental Data

Gadjah Mada International Journal of Business ◽

10.22146/gamaijb.34446 ◽

2020 ◽

Vol 22 (2) ◽

pp. 137

Author(s):

Yudistira Permana ◽

Giovanni Van Empel ◽

Rimawan Pradiptyo

Keyword(s):

Expected Utility ◽

Goodness Of Fit ◽

Random Utility ◽

Utility Model ◽

Stochastic Choice ◽

Decisions Under Risk ◽

Empirical Performance ◽

Maximum Likelihood Technique ◽

Better Than ◽

The Eu

This paper extends the analysis of the data from the experiment undertaken by Pradiptyo et al. (2015), to help explain the subjects’ behaviour when making decisions under risk. This study specifically investigates the relative empirical performance of the two general models of the stochastic choice: the random utility model (RUM) and the random preference model (RPM) where this paper specifies these models using two preference functionals, expected utility (EU) and rank-dependent expected utility (RDEU). The parameters are estimated in each model using a maximum likelihood technique and run a horse-race using the goodness-of-fit between the models. The results show that the RUM better explains the subjects’ behaviour in the experiment. Additionally, the RDEU fits better than the EU for modelling the stochastic choice.

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