Journal of Economic Methodology 17 (2):171-183 (2010)
|Abstract||Contrary to what is claimed by Gul and Pesendorfer (2008), in this paper I argue that neuroscience and economics can meet in ways that speak to the interests of economists. As Bernheim (2009) argues, economists seem to be primarily interested in novel models that link ?traditional? environmental variables (such as prices and taxes) to choice behavior in a more accurate way than existing models. Neuroscience might be helpful here, since especially computational neuroscience is also in the business of mapping environmental variables on to behavior. Given that experimental findings seem to show that choice behavior displays great context-sensitivity, I discuss two tentative ways in which neuroscience might be helpful. Neuroscience might be able to identify a multitude of environmental variables and the choice algorithms in the brain that they activate. Going this way might lead to novel models that differ markedly from standard economic models. Alternatively, neuroscience might be able to provide more theoretical guidance as to how individuals model the situations they are in. In principle, this route might leave standard economic models largely intact while improving their predictive record.|
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
|Through your library||Configure|
Similar books and articles
Don Ross (2011). Estranged Parents and a Schizophrenic Child: Choice in Economics, Psychology and Neuroeconomics. Journal of Economic Methodology 18 (3):217-231.
Nicholas Maxwell (1985). Methodological Problems of Neuroscience. In David Rose & Vernon Dobson (eds.), Models of the Visual Cortex. New York: John Wiley & Sons.
M. Chirimuuta (2013). Extending, Changing, and Explaining the Brain. Biology and Philosophy 28 (4):613-638.
Jennifer Mundale & William P. Bechtel (1996). Integrating Neuroscience, Psychology, and Evolutionary Biology Through a Teleological Conception of Function. Minds and Machines 6 (4):481-505.
William P. Bechtel & Jennifer Mundale (1996). Integrating Neuroscience, Psychology, and Evolutionary Biology Through a Teleological Conception of Function. Minds And Machines 6 (4):481-505.
Roberto Fumagalli (2011). On the Neural Enrichment of Economic Models: Tractability, Trade-Offs and Multiple Levels of Description. Biology and Philosophy 26 (5):617-635.
M. Colombo & P. Series (2012). Bayes in the Brain--On Bayesian Modelling in Neuroscience. British Journal for the Philosophy of Science 63 (3):697-723.
David Michael Kaplan (2011). Explanation and Description in Computational Neuroscience. Synthese 183 (3):339-373.
Jason Shepard & Shane Reuter (2012). Neuroscience, Choice, and the Free Will Debate. American Journal of Bioethics - Neuroscience 3 (3):7-11.
Anthony Landreth & John Bickle (2008). Neuroeconomics, Neurophysiology and the Common Currency Hypothesis. Economics and Philosophy 24 (3):419-429.
Jacqueline Anne Sullivan (2009). The Multiplicity of Experimental Protocols: A Challenge to Reductionist and Non-Reductionist Models of the Unity of Neuroscience. Synthese 167 (3):511 - 539.
Chris Eliasmith (forthcoming). Computational Neuroscience. In Paul R. Thagard (ed.), Philosophy of Psychology and Cognitive Science. Elsevier.
Gualtiero Piccinini (2006). Computational Explanation in Neuroscience. Synthese 153 (3):343-353.
Pete Mandik & Andrew Brook (2007). The Philosophy and Neuroscience Movement. Analyze and Kritik 26.
Added to index2012-02-20
Total downloads3 ( #213,130 of 722,745 )
Recent downloads (6 months)1 ( #60,247 of 722,745 )
How can I increase my downloads?