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Line 2: Baron, J. (2006). Thinking and Deciding. https://doi.org/10.1017/cbo9780511840265</ref> Cognitive biases can lead to both [[bias]] and [[noise]] in forecasting. The existence of cognitive biases is a central concern for making better forecasts. A cognitive bias is not ''necessarily'' a deviation from the correct answer (though it can be), but a deviation from ~~normativity~~normative reasoning. For example, since Bayes Theorem is a normative model, any deviation from it would be considered a cognitive bias regardless of the true state of the world. Therefore, to say that someone is wrong because they exhibited a cognitive bias would be an example of the [[Fallacy Fallacy]]. A cognitive bias does not mean someone is wrong, it only means they did not reason according to how a normative model said they should have reasoned. Thus a cognitive bias is one possible source of [[bias]] and [[noise]] in forecasts, but is not the same as saying one is ''wrong'', though certainly deviations from using normative models can lead to errors in judgment. This distinction is important because in forecasting we often do not know what the correct answer is, but can still identify cognitive biases in our reasoning. Since a cognitive bias is merely a directional deviation from normativity, and there is no widespread agreement on the set of models considered normative, a comprehensive list of biases is not possible even in theory. And indeed, in practice the list of biases is always growing as researchers discover new ways in which people do not reason as the researchers think they ought to reason. An example list of biases, and the normative model from which they deviate, is listed below.

Cognitive Biases: Difference between revisions