Normative and Descriptive in Decision Theory

 

Normative and descriptive

Normative decision theory is concerned with identification of optimal decisions where optimality is often determined by considering an ideal decision maker who is able to calculate with perfect accuracy and is in some sense fully rational. The practical application of this prescriptive approach (how people ought to make decisions) is called decision analysis and is aimed at finding tools, methodologies, and software ( decision support system) to help people make better decisions.

In contrast, Positive or descriptive decision theory is concerned with describing observed behaviors often under the assumption that the decision-making agents are behaving under some consistent rules. These rules may, for instance, have a procedural framework  elimination by aspects model or an axoimatic framework (e.g. stochastic transitive axioms), reconciling the Von-neumann morgenstran axioms with behavioral violations of the expected utility hypothesis, or they may explicitly give a functional form for time inconsistent utility functions ( quasi hyperbolic discounting).

The prescriptions or predictions about behavior that positive decision theory produces allow for further tests of the kind of decision-making that occurs in practice. In recent decades, there has also been increasing interest in "behavioral decision theory", contributing to a re-evaluation of what useful decision-making requires