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Context effects: the proportional difference model and the reflection of preference
Claudia Gonzalez-Vallejo1, Aaron A Reid, Joel Schiltz
1Department of Psychology, 200 Porter Hall, Ohio University, Athens, OH 45701, US. Gonzalez@ohiou.edu
Summary
This study explored the reflection effect in decision-making using a stochastic choice model. Findings show risk attitudes shift between gains and losses, influenced by individual thresholds and savings, with the model outperforming prospect theory.
Area of Science:
- Decision Science
- Behavioral Economics
- Cognitive Psychology
Background:
- The reflection effect describes how risk preferences reverse between gains and losses.
- Existing models like cumulative prospect theory explain decision-making under risk.
- A stochastic model of choice offers an alternative framework for analyzing attribute trade-offs.
Purpose of the Study:
- To investigate the reflection effect using a stochastic model of choice.
- To examine how individual thresholds influence risk attitudes in gain and loss domains.
- To compare the explanatory power of the stochastic model against cumulative prospect theory.
Main Methods:
- Utilized a stochastic model of choice incorporating a difference variable and an individual threshold.
- Conducted two experiments manipulating gain/loss situations and participant savings.
- Applied model testing to compare goodness-of-fit with cumulative prospect theory.
Main Results:
- Risk attitudes varied with stimuli and individual thresholds, particularly between gain and loss scenarios.
- Lower thresholds in loss situations indicated a general risk-taking tendency.
- Increased savings led to lower individual thresholds, suggesting altered risk-taking behavior.
Conclusions:
- The stochastic model provides a robust framework for understanding the reflection effect.
- Individual thresholds and situational factors significantly modulate risk-taking behavior.
- The tested stochastic model demonstrated superior fit compared to cumulative prospect theory for the observed data.