This debate contrasts the rational economic agent standard for decision making versus one grounded in human social needs and motivations.
Examples: There are numerous investigations of incidents determining that human error was central to highly negative potential or actual real-world outcomes, in which manifestation of cognitive biases is a plausible component.
An illustrative selection, recounted in multiple studies:[1][2][3][4][5][6][7][8][9][10] An increasing number of academic and professional disciplines are identifying means of cognitive bias mitigation.
Practitioners tend to treat deviations from what a rational agent would do as evidence of important, but as yet not understood, decision-making variables, and have as yet no explicit or implicit contributions to make to a theory and practice of cognitive bias mitigation.
Game theory, a discipline with roots in economics and system dynamics, is a method of studying strategic decision making in situations involving multi-step interactions with multiple agents with or without perfect information.
However, Daniel Kahneman and others have authored recent articles in business and trade magazines addressing the notion of cognitive bias mitigation in a limited form.
One approach to mitigation originally suggested by Daniel Kahneman and Amos Tversky, expanded upon by others, and applied in real-life situations, is reference class forecasting.
Nonetheless, this approach has merit as part of a cognitive bias mitigation protocol when the process is applied with a maximum of diligence, in situations where good data is available and all stakeholders can be expected to cooperate.
A concept rooted in considerations of the actual machinery of human reasoning, bounded rationality is one that may inform significant advances in cognitive bias mitigation.
Originally conceived of by Herbert A. Simon[39] in the 1960s and leading to the concept of satisficing as opposed to optimizing, this idea found experimental expression in the work of Gerd Gigerenzer and others.
One line of Gigerenzer's work led to the "Fast and Frugal" framing of the human reasoning mechanism,[40] which focused on the primacy of 'recognition' in decision making, backed up by tie-resolving heuristics operating in a low cognitive resource environment.
Studies and anecdotes reported in popular-audience media[13][20][41][42] of firefighter captains, military platoon leaders and others making correct, snap judgments under extreme duress suggest that these responses are likely not generalizable and may contribute to a theory and practice of cognitive bias mitigation only the general idea of domain-specific intensive training.
[45] This discipline explicitly challenges the prevalent view that humans are rational agents maximizing expected value/utility, using formal analytical methods to do so.
[13][46] In this view, System 1 is the 'first line' of cognitive processing of all perceptions, including internally generated 'pseudo-perceptions', which automatically, subconsciously and near-instantaneously produces emotionally valenced judgments of their probable effect on the individual's well-being.
Evolutionary psychology practitioners emphasize that our heuristic toolkit, despite the apparent abundance of 'reasoning errors' attributed to it, actually performs exceptionally well, given the rate at which it must operate, the range of judgments it produces, and the stakes involved.
While this notion must remain speculative until further work is done, it appears to be a productive basis for conceiving options for constructing a theory and practice of cognitive bias mitigation.
While this approach can produce effective responses to critical situations under stress, the protocols involved must be viewed as having limited generalizability beyond the domain for which they were developed, with the implication that solutions in this discipline may provide only generic frameworks to a theory and practice of cognitive bias mitigation.
Challenges to realizing this potential: accumulating the considerable amount of appropriate real world 'training sets' for the neural network portion of such models; characterizing real-life decision-making situations and outcomes so as to drive models effectively; and the lack of direct mapping from a neural network's internal structure to components of the human reasoning mechanism.
Other initiatives aimed directly at a theory and practice of cognitive bias mitigation may exist within other disciplines under different labels than employed here.