Stochastic Choice: An Optimizing Neuroeconomic Model.
A model is proposed in which stochastic choice results from noise in cognitive processing rather than random variation in preferences. The mental process used to make a choice is nonetheless optimal, subject to a constraint on available information-processing capacity that is motivated by neurophysi...
| Published in: | American Economic Review Vol. 104; no. 5; pp. 495 - 501 |
|---|---|
| Format: | Article |
| Published: |
American Economic Association
May2014
|
| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=96037310&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 96037310 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00028282 AER jtl: American Economic Review issn: 00028282 maglogo: N pubinfo: dt: May2014 vid: 104 iid: 5 pid: 22 pub: American Economic Association artinfo: ui: 96037310 10.1257/aer.104.5.495 ppf: 495 ppct: 6 formats: tig: atl: Stochastic Choice: An Optimizing Neuroeconomic Model. aug: su: Neuroeconomics Economics Decision making Stochastic processes Algorithms Reaction time sug: subj: Neuroeconomics Economics Decision making Stochastic processes Algorithms Reaction time ab: A model is proposed in which stochastic choice results from noise in cognitive processing rather than random variation in preferences. The mental process used to make a choice is nonetheless optimal, subject to a constraint on available information-processing capacity that is motivated by neurophysiological evidence. The optimal information-constrained model is found to offer a better fit to experimental data on choice frequencies and reaction times than either a purely mechanical process model of choice (the drift-diffusion model) or an optimizing model with fewer constraints on feasible choice processes (the rational inattention model). pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|