TY - CHAP
T1 - Neuroeconomics
T2 - Formal models of decision making and cognitive neuroscience
AU - Rustichini, Aldo
PY - 2009
Y1 - 2009
N2 - In economic analysis, decision theory is developed with a purely axiomatic method. The theory proceeds by first defining a set of choices that a subject (the decision-maker, DM) faces. A choice is a finite set of options that are offered to the DM; a decision is the selection of one of these options. The observed data are pairs of choices offered and decisions taken: it is possible to collect these data experimentally asking a real DM to pick one out of two options, under the condition that the object selected is actually delivered to him or her. For neuroeconomics and any research program that tries to determine how decisions are implemented, the utility function is the most interesting object. This function ties observed behavior with a simple one-dimensional quantity, the utility of the option, and predicts that the decision between two options is taken by selecting the option with the highest utility. However, if interested in determining the neural correspondents of the objects one has introduced, one must first know whether these objects are unique. For example, one may formulate the hypothesis that the decision is taken depending on some statistics of the firing rate of a group of neurons associated with each of the options.
AB - In economic analysis, decision theory is developed with a purely axiomatic method. The theory proceeds by first defining a set of choices that a subject (the decision-maker, DM) faces. A choice is a finite set of options that are offered to the DM; a decision is the selection of one of these options. The observed data are pairs of choices offered and decisions taken: it is possible to collect these data experimentally asking a real DM to pick one out of two options, under the condition that the object selected is actually delivered to him or her. For neuroeconomics and any research program that tries to determine how decisions are implemented, the utility function is the most interesting object. This function ties observed behavior with a simple one-dimensional quantity, the utility of the option, and predicts that the decision between two options is taken by selecting the option with the highest utility. However, if interested in determining the neural correspondents of the objects one has introduced, one must first know whether these objects are unique. For example, one may formulate the hypothesis that the decision is taken depending on some statistics of the firing rate of a group of neurons associated with each of the options.
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U2 - 10.1016/B978-0-12-374176-9.00004-X
DO - 10.1016/B978-0-12-374176-9.00004-X
M3 - Chapter
AN - SCOPUS:80054015741
SN - 9780123741769
SP - 33
EP - 46
BT - Neuroeconomics
PB - Elsevier Inc.
ER -