Abstract
In general, two paradigms deal with the categorization of decision theories: the descriptive and normative theories. Descriptive theories are based on empirical observations and on experimental studies of choice behaviors. But the normative theories specifically assume a rational decision-maker who follows well-defined preferences of behaviors as well as obeys certain axioms of rational processes. The axiomatic approach plays an important role in formulating these theories of decision making. In this process, theories of decision making are often formulated in terms of deterministic axioms. But these axioms do not necessarily account for the stochastic variation that attends empirical data. Moreover, a rigorous description of the decision process is provided only through real/time perception. Then it is possible to avail the real-time decisions by repetitive application of the fundamental cognitive process. In such a situation, the Bayesian framework provides readily applicable statistical procedures where typical inference questions are addressed. This framework offers readily applicable statistical procedures, and it is possible to address many typical inference questions. But, in many cases, the applicability of algebraic axioms comes into question concerning viability, especially, when the application connected to empirical data arises. Again, the probabilistic approach to decision making needs to be investigated properly in order to study the empirical data. In such cases, where typical inference questions are addressed, the Bayesian framework provides readily applicable statistical procedures. Attempt has been made to study the various aspects of the Bayesian approach to analyze the observational data found in the new empirical findings on decision making.
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