An economic agent in my brain? A critical analysis of multiple-self models in neuroeconomics
Marco Stimolo
Review of Social Economy, 2016, vol. 74, issue 4, 329-348
Abstract:
Neuroeconomic multiple-self models describe individuals’ choices as the equilibrium of the interaction amongst neural sites modelled as economic agents. This approach aims at explaining some inter-temporal inconsistency problems and the rejection of unfair offers in ultimatum games. However, the experiments on these models do not provide replicable results. The standard view interprets this problem as due to inadequate econometric techniques. Conversely, this paper shows that the non-replicability problem arises from a conundrum of multiple-self models’ (MSMs) theory. It illustrates how the assumption of neuroeconomic agents is deduced from the revealed preferences theory applied to the neuro-level. Therefore, the paper shows how experiments on MSMs cannot test the assumption of neuroeconomic agents but only the empirical hypotheses that derive from it. This entails that the assumption of neuroeconomic agents is a tautology, which might generate hypotheses that do not robustly identify the neural correlates of behaviour.
Date: 2016
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/00346764.2016.1171380 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:rsocec:v:74:y:2016:i:4:p:329-348
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RRSE20
DOI: 10.1080/00346764.2016.1171380
Access Statistics for this article
Review of Social Economy is currently edited by Wilfred Dolfsma and John Davis
More articles in Review of Social Economy from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().