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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Manipulability of comparative tests.

Wojciech Olszewski¹, Alvaro Sandroni

¹Department of Economics, Northwestern University, 2001 Sheridan Road, Evanston, IL 60208, USA. wo@northwestern.edu

Proceedings of the National Academy of Sciences of the United States of America

|March 18, 2009

Summary

Uninformed experts can create convincing false forecasts that may pass statistical tests. This challenges the ability of data alone to discredit strategic but unknowing forecasters.

Area of Science:

Decision theory
Probability theory
Statistical inference

Background:

Experts often claim knowledge of future event probabilities.
Assessing expert reliability is challenging when true probabilities are unknown.
Existing tests aim to control Type I errors in expert evaluation.

Purpose of the Study:

To investigate if uninformed experts can produce forecasts that evade rejection by statistical tests.
To determine the limitations of data-driven evaluation for strategic, uninformed forecasters.

Main Methods:

Simulating a testing scenario where experts provide probability forecasts for all future events.
Analyzing the conditions under which uninformed experts' false forecasts can pass a test designed to control Type I errors.

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Considering scenarios with strategic experts who may not possess true knowledge.

Main Results:

Uninformed experts can generate false forecasts that are likely to pass the test, irrespective of future data.
The testing procedure, even with controlled Type I error, may fail to discredit strategic, uninformed experts.
Observed data may be insufficient to definitively identify and reject experts lacking genuine predictive ability.

Conclusions:

Statistical tests controlling Type I error may not be sufficient to debunk uninformed but strategic experts.
The effectiveness of data in discrediting experts depends on their actual knowledge, not just their strategic forecasting.
Further research is needed on methods to reliably assess expert claims in situations of uncertainty.