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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Bayesian Revision vs. Information Distortion.

J Edward Russo^1,2

¹SC Johnson College of Business, Cornell University, Ithaca, NY, United States.

Frontiers in Psychology

|September 14, 2018

Summary

This summary is machine-generated.

Information distortion, a bias where new data favors existing beliefs, undermines Bayesian calculus. This unrecognized bias impacts decision-making in forecasting and statistical inference.

Keywords:

Bayesian calculus connectionism desirability bias forecasting information distortion likelihood updating rationality statistical inference

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Area of Science:

Cognitive Psychology
Decision Science
Bayesian Statistics

Background:

The Bayesian calculus provides a rational framework for updating beliefs with new evidence.
Information distortion, a bias where evidence is evaluated to favor pre-existing conclusions, challenges this framework.
This bias is often unrecognized and can be subtle, impacting rational decision-making.

Purpose of the Study:

To highlight the phenomenon of information distortion and its impact on Bayesian reasoning.
To explain why information distortion is widespread and often goes unrecognized.
To illustrate the potential negative consequences of this bias in professional contexts.

Main Methods:

Conceptual analysis of information distortion within the framework of Bayesian probability.
Review of cognitive and psychological factors contributing to the bias.
Examination of professional applications where information distortion can occur, such as forecasting and statistical inference.

Main Results:

Information distortion invalidates the independence assumption of Bayesian calculus by allowing prior beliefs to influence the evaluation of new data.
Individuals are typically unaware of their own susceptibility to this bias.
Partial mitigation strategies, like simultaneous data presentation, may reduce the impact of information distortion.

Conclusions:

Information distortion poses a significant threat to the rational application of Bayesian methods.
The desire for cognitive coherence can lead individuals to unconsciously distort information, compromising objective analysis.
Recognizing and addressing information distortion is crucial for improving accuracy in forecasting, consumer choice prediction, and experimental data interpretation.