By Itzhak Gilboa, David Schmeidler
Gilboa and Schmeidler offer a brand new paradigm for modeling choice making lower than uncertainty. Case-based determination conception means that humans make judgements by means of analogies to prior instances: they generally tend to decide on acts that played good some time past in related occasions, and to prevent acts that played poorly. The authors describe the final idea and its courting to making plans, repeated selection difficulties, inductive inference, and studying. They spotlight its mathematical and philosophical foundations and examine it to anticipated software idea in addition to to rule-based systems.
"This textual content is a superb advent to optimization theory." Richard A. Chechile, magazine of Mathematical Psychology
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Extra resources for A Theory of Case-Based Decisions
As we will see in Chapter 7, adding cases to memory is but the simplest form of learning. Yet, it appears to be the basis for other forms of learning as well. 3 Aspirations and satisﬁcing The presentation of the elementary formula above highlights the distinction between desirable outcomes, whose utility values are positive, and undesirable ones, to which negative utility values are attached. While this distinction is rather intuitive, classical decision theory typically ﬁnds it redundant: higher utility values are considered more desirable, or, equivalently, less undesirable.
Moreover, it is argued, people are often not aware of the way they make decisions, and their verbal introspective account of the decision process may be more confusing than helpful. As long as they behave as if they follow the theory, we should be satisﬁed. One can hardly take issue with this approach if the underlying axioms are tested and proved true, or if the theory generates precise quantitative predictions that conform to reality. But economics often uses a theory such as expected utility maximization in cases where its axioms cannot be tested directly, and its predictions are at best qualitative.
By contrast, case-based decision theory is probably the most natural description of decision making when the decision problem is amorphous and there are insufﬁcient data to analyze it properly. We defer a more detailed discussion of CBDT and EUT until Chapter 4, following the axiomatic derivation of CBDT in Chapter 3. 5 Comments Subjective similarity As will be shown in Chapter 3, the similarity function in our model may be derived from observed preferences. Since different people may have different preferences, we should expect that the derived similarity functions would also differ across individuals.
A Theory of Case-Based Decisions by Itzhak Gilboa, David Schmeidler