Estimating ambiguity preferences and perceptions in multiple prior models: Evidence from the field

Area of Science:

• Behavioral Economics
• Decision Theory
• Psychology

Background:

• Decision-making under ambiguity is a key area in behavioral economics.
• Understanding how individuals evaluate uncertain prospects is crucial for economic and financial modeling.
• Existing models often struggle to capture the nuances of ambiguity attitudes across different domains.

Purpose of the Study:

• To develop and apply a tractable method for estimating multiple prior models of decision-making under ambiguity.
• To measure ambiguity attitudes in both gain and loss domains within a representative U.S. population sample.
• To identify the best-fitting model for choices made under ambiguity and analyze demographic influences.

Main Methods:

• Development of a novel method to estimate prior models of decision-making under ambiguity.
• Empirical measurement of ambiguity attitudes using a representative U.S. population sample.
• Statistical analysis and model comparison, including the α-MaxMin model, MaxMin, and MaxMax.

Main Results:

• Ambiguity aversion is prevalent for moderate-to-high likelihood gains, while ambiguity seeking dominates low likelihoods and losses.
• The α-MaxMin model, with parameters for ambiguity aversion and perceived ambiguity, best explains choices in the gain domain.
• Demographic factors like gender and education influence ambiguity aversion and perceived ambiguity; reflection is observed in the loss domain.

Conclusions:

• The α-MaxMin model provides a robust framework for understanding decision-making under ambiguity.
• Ambiguity attitudes exhibit domain-specific patterns (gains vs. losses) and are influenced by individual characteristics.
• The findings have implications for economic and financial analyses of preferences and perceptions under uncertainty.