Functional Representation of the Intentional Bounded Rationality of Decision-Makers: A Laboratory to Study the Decisions a Priori
Metadatos
Mostrar el registro completo del ítemEditorial
MDPI
Materia
Intended bounded rationality Beliefs Logic Maximum entropy
Fecha
2022-02-26Referencia bibliográfica
Sáenz-Royo, C.; Chiclana, F.; Herrera-Viedma, E. Functional Representation of the Intentional Bounded Rationality of Decision- Makers: A Laboratory to Study the Decisions a Priori. Mathematics 2022, 10, 739. [https://doi.org/10.3390/math10050739]
Patrocinador
Spanish Government ECO2013-48496-C4-3-R MTM2016-77642-C2-2-P; Gobierno de Aragon; European Social Fund [CREVALOR]; Spanish Government PID2019-10380RBI00/AEI/10.13039/501100011033; Andalusian Government Project P20_00673Resumen
The judgments of decision-makers are frequently the best way to process the information
on complex alternatives. However, the performances of the alternatives are often not observable in
their entirety, which prevents researchers from conducting controlled empirical studies. This paper
justifies a functional representation that, due to its good predictive results, has been widely used
ad hoc in studies in different branches of knowledge; it formalizes aspects of the human mental
structure that influence the ability of people to decide and the intentional bounded rationality, and
it subsequently analyzes how the reliability of decision-makers is affected by the difficulty of the
problem and the expertise and beliefs of the decision-maker. The main research objective of this
paper is to derive explicitly a general functional form that represents the behavior of a decision-maker
linked to their way of thinking. This functional form allows a laboratory to be created to study a priori
the performance of human decisions, i.e., the probability of choosing each of the alternatives, once the
returns of the alternatives, the level of expertise, and the initial beliefs of the decision-maker are known
exogenously. This laboratory will allow (1) the evaluation of decision support techniques; (2) the
creation of agent-based models that anticipate group performances due to individual interactions;
and (3) the development of other investigations based on statistical simulations.