Cognitive bias

A cognitive bias is a systematic pattern of deviation from norm or rationality in judgment. Individuals create their own "subjective reality" from their perception of the input. An individual's construction of reality, not the objective input, may dictate their behavior in the world. Thus, cognitive biases may sometimes lead to perceptual distortion, inaccurate judgment, illogical interpretation, and irrationality. While cognitive biases may initially appear to be negative, some are adaptive. They may lead to more effective actions in a given context. Furthermore, allowing cognitive biases enables faster decisions which can be desirable when timeliness is more valuable than accuracy, as illustrated in heuristics. Other cognitive biases are a "by-product" of human processing limitations, resulting from a lack of appropriate mental mechanisms (bounded rationality), the impact of an individual's constitution and biological state (see embodied cognition), or simply from a limited capacity for information processing. A continually evolving list of cognitive biases has been identified over the last six decades of research on human judgment and decision-making in cognitive science, social psychology, and behavioral economics. The study of cognitive biases has practical implications for areas including clinical judgment, entrepreneurship, finance, and management. The notion of cognitive biases was introduced by Amos Tversky and Daniel Kahneman in 1972 and grew out of their experience of people's innumeracy, or inability to reason intuitively with the greater orders of magnitude. Tversky, Kahneman, and colleagues demonstrated several replicable ways in which human judgments and decisions differ from rational choice theory. Tversky and Kahneman explained human differences in judgment and decision-making in terms of heuristics. Heuristics involve mental shortcuts which provide swift estimates about the possibility of uncertain occurrences. Heuristics are simple for the brain to compute but sometimes introduce "severe and systematic errors.

It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons

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Biases in Information Selection and Processing: Survey Evidence from the Pandemic

Biases in Information Selection and Processing: Survey Evidence from the Pandemic

Exploiting the Signal-Leak Bias in Diffusion Models

It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons