Flexibly-bounded Rationality and Marginalization of Irrationality Theories for Decision Making
This work addresses decision-making challenges for fields like economics or AI, but appears incremental as it builds on existing bounded rationality theories.
The paper tackles the problem of decision-making under imperfect information and human inconsistency by extending bounded rationality to flexibly-bounded rationality, using advanced information analysis and AI to expand rationality bounds, and proposes marginalization of irrationality to address satisficing in irrational contexts.
In this paper the theory of flexibly-bounded rationality which is an extension to the theory of bounded rationality is revisited. Rational decision making involves using information which is almost always imperfect and incomplete together with some intelligent machine which if it is a human being is inconsistent to make decisions. In bounded rationality, this decision is made irrespective of the fact that the information to be used is incomplete and imperfect and that the human brain is inconsistent and thus this decision that is to be made is taken within the bounds of these limitations. In the theory of flexibly-bounded rationality, advanced information analysis is used, the correlation machine is applied to complete missing information and artificial intelligence is used to make more consistent decisions. Therefore flexibly-bounded rationality expands the bounds within which rationality is exercised. Because human decision making is essentially irrational, this paper proposes the theory of marginalization of irrationality in decision making to deal with the problem of satisficing in the presence of irrationality.