Since the second half of the 20th century, a body of research, by economists such as Maurice Allais[1] and psychologists such as Amos Tversky and Daniel Kahneman,[2] documented a collection of systematic violations of the principles of RCT.
Gerd Gigerenzer[3][4] argues that some observed behavior, although violating RCT principles, has been empirically shown to be rational in some environments.
This idea, that the rationality of an action not only depends on internal criteria (e.g., transitivity) but also on the structure of the environment, was proposed earlier by Herbert A.
[5][6] Simon envisioned rationality as being shaped by a pair of scissors that cuts with two blades – one representing the structure of the task environment, the other the computational capacities of the agent.
Nonetheless, it was found that the take-the-best heuristic can yield more accurate choices than other models of decision-making including multiple linear regression which considers all available information.
According to the theory of ecological rationality, examples of environmental characteristics that lead to the relatively higher accuracy of take-the-best compared to other models, include the (i) scarce or low quality of available information,[10] (ii) high dispersion of validities of the attributes (also called the non-compensatoriness condition),[12][13] and (iii) the presence of options dominating other options, including the conditions of simple and cumulative dominance.
[24] In situations of risk, the accuracy-effort trade-off outlined above implies loss in accuracy as consequence of reducing the complexity of the decision strategy.
An explanation of this finding is offered by the bias-variance dilemma, which is a mathematical formulation of how simplicity (which might looks as ignorance) tends to increase one source of estimation error (bias) but also to decrease another one (variance).
The two notions are related, however Smith predicates the concept to social entities such as markets, which have evolved in a trial-and-error process to reaching an efficient outcome.