Security, Potential, Goal Achievement, and Risky Choice Behavior

2014 ◽  
Author(s):  
Isacco Piccioni
Keyword(s):  
Choice Behavior ◽  
Risky Choice ◽  
Goal Achievement

Multiattribute Risky Choice Behavior: The Editing of Complex Prospects.

1982 ◽  
Author(s):  
John W. Payne ◽  
Dan J. Laughhunn ◽  
Roy Crum
Keyword(s):  
Choice Behavior ◽  
Risky Choice

The effect of time pressure on risky choice behavior

1981 ◽  
Vol 47 (2) ◽  
pp. 89-104 ◽  
Author(s):  
Hasida Ben Zur ◽  
Shlomo J. Breznitz
Keyword(s):  
Choice Behavior ◽  
Time Pressure ◽  
Risky Choice

Translation of Gambles and Aspiration Level Effects in Risky Choice Behavior

1980 ◽  
Vol 26 (10) ◽  
pp. 1039-1060 ◽  
Author(s):  
John W. Payne ◽  
Dan J. Laughhunn ◽  
Roy Crum
Keyword(s):  
Choice Behavior ◽  
Risky Choice ◽  
Aspiration Level

scholarly journals Challenge Theory: The Structure and Measurement of Risky Binary Choice Behavior

2020 ◽  
Author(s):  
Samuel Shye ◽  
Ido Haber
Keyword(s):  
Choice Behavior ◽  
Gambling Behavior ◽  
Risky Choice ◽  
Lower Probability ◽  
Binary Choice ◽  
Measurement Scales ◽  
Risky Decision ◽  
Concept Space ◽  
The One ◽  
Gain Loss

Challenge Theory (Shye & Haber 2015; 2020) has demonstrated that a newly devised challenge index (CI) attributable to every binary choice problem predicts the popularity of the bold option, the one of lower probability to gain a higher monetary outcome (in a gain problem); and the one of higher probability to lose a lower monetary outcome (in a loss problem). In this paper we show how Facet Theory structures the choice-behavior concept-space and yields rationalized measurements of gambling behavior. The data of this study consist of responses obtained from 126 student, specifying their preferences in 44 risky decision problems. A Faceted Smallest Space Analysis (SSA) of the 44 problems confirmed the hypothesis that the space of binary risky choice problems is partitionable by two binary axial facets: (a) Type of Problem (gain vs. loss); and (b) CI (Low vs. High). Four composite variables, representing the validated constructs: Gain, Loss, High-CI and Low-CI, were processed using Multiple Scaling by Partial Order Scalogram Analysis with base Coordinates (POSAC), leading to a meaningful and intuitively appealing interpretation of two necessary and sufficient gambling-behavior measurement scales.

scholarly journals The Wisdom of Model Crowds

2021 ◽  
Author(s):  
Lisheng He ◽  
Pantelis P. Analytis ◽  
Sudeep Bhatia
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Choice Behavior ◽  
Predictive Performance ◽  
Choice Models ◽  
Risky Choice ◽  
Risky Decision Making ◽  
Risky Decision ◽  
Performance Bound ◽  
Individual Level

A wide body of empirical research has revealed the descriptive shortcomings of expected value and expected utility models of risky decision making. In response, numerous models have been advanced to predict and explain people’s choices between gambles. Although some of these models have had a great impact in the behavioral, social and management sciences, there is little consensus about which model offers the best account of choice behavior. In this paper, we conduct a large-scale comparison of 58 prominent models of risky choice, using 19 existing behavioral datasets involving more than 800 participants. This allows us to comprehensively evaluate models in terms of individual-level predictive performance across a range of different choice settings. We also identify the psychological mechanisms that lead to superior predictive performance and the properties of choice stimuli that favor certain types of models over others. Second, drawing on research on the wisdom of crowds, we argue that each of the existing models can be seen as an expert that provides unique forecasts in choice predictions. Consistent with this claim, we find that crowds of risky choice models perform better than individual models and thus provide a performance bound for assessing the historical accumulation of knowledge in our field. Our results suggest that each model captures unique aspects of the decision process, and that existing risky choice models offer complementary rather than competing accounts of behavior. We discuss the implications of our results on theories of risky decision making and the quantitative modeling of choice behavior.

The Wisdom of Model Crowds

2021 ◽  
Author(s):  
Lisheng He ◽  
Pantelis P. Analytis ◽  
Sudeep Bhatia
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Choice Behavior ◽  
Predictive Performance ◽  
Choice Models ◽  
Risky Choice ◽  
Risky Decision Making ◽  
Risky Decision ◽  
Performance Bound ◽  
Individual Level

A wide body of empirical research has revealed the descriptive shortcomings of expected value and expected utility models of risky decision making. In response, numerous models have been advanced to predict and explain people’s choices between gambles. Although some of these models have had a great impact in the behavioral, social, and management sciences, there is little consensus about which model offers the best account of choice behavior. In this paper, we conduct a large-scale comparison of 58 prominent models of risky choice, using 19 existing behavioral data sets involving more than 800 participants. This allows us to comprehensively evaluate models in terms of individual-level predictive performance across a range of different choice settings. We also identify the psychological mechanisms that lead to superior predictive performance and the properties of choice stimuli that favor certain types of models over others. Moreover, drawing on research on the wisdom of crowds, we argue that each of the existing models can be seen as an expert that provides unique forecasts in choice predictions. Consistent with this claim, we find that crowds of risky choice models perform better than individual models and thus provide a performance bound for assessing the historical accumulation of knowledge in our field. Our results suggest that each model captures unique aspects of the decision process and that existing risky choice models offer complementary rather than competing accounts of behavior. We discuss the implications of our results on theories of risky decision making and the quantitative modeling of choice behavior. This paper was accepted by Yuval Rottenstreich, behavioral economics and decision analysis.

Multiattribute Risky Choice Behavior: The Editing of Complex Prospects

1984 ◽  
Vol 30 (11) ◽  
pp. 1350-1361 ◽  
Author(s):  
John W. Payne ◽  
Dan J. Laughhunn ◽  
Roy Crum
Keyword(s):  
Choice Behavior ◽  
Risky Choice

scholarly journals Note—Further Tests of Aspiration Level Effects in Risky Choice Behavior

1981 ◽  
Vol 27 (8) ◽  
pp. 953-958 ◽  
Author(s):  
John W. Payne ◽  
Dan J. Laughhunn ◽  
Roy Crum
Keyword(s):  
Choice Behavior ◽  
Risky Choice ◽  
Aspiration Level

scholarly journals Travelers’ Bi-Attribute Decision Making on the Risky Mode Choice with Flow-Dependent Salience Theory

2021 ◽  
Vol 13 (7) ◽  
pp. 3901
Author(s):  
Xiangfeng Ji ◽  
Xiaoyu Ao
Keyword(s):  
Choice Behavior ◽  
Mode Choice ◽  
User Equilibrium ◽  
Risky Choice ◽  
Objective Probability ◽  
Equilibrium Solutions ◽  
Alternative Approach ◽  
The Mean ◽  
Risky Option

The purpose of this paper is to provide new insights into travelers’ bi-attribute (travel time and travel cost) risky mode choice behavior with one risky option (i.e., the highway) and one non-risky option (i.e., the transit) from the long-term planning perspective. In the classical Wardropian User Equilibrium principle, travelers make their choice decisions only based on the mean travel times, which might be an unrealistic behavioral assumption. In this paper, an alternative approach is proposed to partially remedy this unrealistic behavioral assumption with flow-dependent salience theory, based on which we study travelers’ context-dependent bi-attribute mode choice behavior, focusing on the effect of travelers’ salience characteristic. Travelers’ attention is drawn to the bi-attribute salient travel utility, and then the objective probability of each state for the risky world is distorted in favor of this bi-attribute salient travel utility. A long-term bi-attribute salient user equilibrium will be achieved when no traveler can improve their bi-attribute salient travel utility by unilaterally changing the choice decisions. Conditions for the existence and uniqueness of the bi-attribute salient user equilibrium are presented, and based on the equilibrium results, we analyze travelers’ risk attitudes in this bi-attribute risky choice problem. Finally, numerical examples are conducted to examine the sensitivity of equilibrium solutions to the input parameters, which are cost difference and salience bias.

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