Known Unknowns in Judgment and Choice: Experimental Data on Overconfidence and Investment

Daniel J. Walters's dissertation data from the University of California, Los Angeles investigates how inferences about missing information impact judgments and choices. The research includes studies on overconfidence related to neglecting unknown evidence and on investment choices influenced by perceptions of epistemic versus aleatory uncertainty. The dataset likely contains experimental results from surveys and process-tracing techniques.

Use Cases

• Modeling overconfidence calibration based on consideration of unknown evidence described in Chapter 1.
• Analyzing investment strategy preferences based on perceptions of epistemic vs. aleatory uncertainty described in Chapter 2.
• Testing debiasing techniques like 'consider the unknowns' versus 'consider the alternative' as outlined in the dissertation.
• Studying the relationship between information search behavior and willingness to pay for financial advice as investigated with investors and advisors.

Strengths

• Data is derived from multiple studies involving laypeople, experienced investors, and financial advisors, suggesting varied participant pools.
• Research employs process-tracing techniques and experimental prompts, indicating structured data collection methods.
• Findings are detailed across three distinct chapters investigating specific facets of judgment and choice.

Limitations

• Row count, column definitions, and sample data are unavailable, limiting suitability assessment.
• Description metadata is limited; actual data quality and structure require manual inspection after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Daniel J. Walters, University of California, Los Angeles

Collection Method

Experimental studies and surveys involving process-tracing techniques and participant prompts.