From a Regret to an Expected Utility Model: a learning process

Giuliano Giusti · Fabio Zagonari

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Decision-Making and Behavioral Economics Economic and Environmental Valuation Experimental Behavioral Economics Studies

Abstract

This paper identifies in a feed-forward neural network the mathematical algorithm which can catch the learning process highlighted by econometric works that makes people assess the satisfaction arising in each single contingency so that they are better depicted in their decision making by an Expected Utility rather than by a Regret Model. Evidence from experimental economics are also accounted for, since the network does not manage to extrapolate the former from the latter model when probabilities are extreme.

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