Hostname: page-component-7479d7b7d-qlrfm Total loading time: 0 Render date: 2024-07-12T21:46:42.829Z Has data issue: false hasContentIssue false

Some Methodological Issues in Experimental Economics

Published online by Cambridge University Press:  01 January 2022

Abstract

The growing acceptance and success of experimental economics has increased the interest of researchers in tackling philosophical and methodological challenges to which their work increasingly gives rise. I sketch some general issues that call for the combined expertise of experimental economists and philosophers of science, of experiment, and of inductive-statistical inference and modeling.

Type
Philosophical Issues in Experimental Economics
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bateman, Ian, Kahneman, Daniel, Munro, Alistair, Starmer, Chris, and Sugden, Robert (2005), “Testing Competing Models of Loss Aversion: An Adversarial Collaboration”, Testing Competing Models of Loss Aversion: An Adversarial Collaboration 89:15611580.Google Scholar
Cosmides, Leda, and Tooby, John (1996), “Are Humans Good Intuitive Statisticians after All? Rethinking Some Conclusions of the Literature on Judgment under Uncertainty”, Are Humans Good Intuitive Statisticians after All? Rethinking Some Conclusions of the Literature on Judgment under Uncertainty 58:173.Google Scholar
Friedman, Daniel, and Cassar, Alessandra (2004), Economics Lab: An Intensive Course in Experimental Economics. London: Routledge.Google Scholar
Gigerenzer, Gerd (2000), Adaptive Thinking: Rationality in the Real World. Oxford: Oxford University Press.Google Scholar
Guala, Francesco (2005), The Methodology of Experimental Economics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Kahneman, Daniel, Slovic, Paul, and Tversky, Amos (1982), Judgment under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Kuhn, Thomas (1962), The Structure of Scientific Revolutions. Chicago: University of Chicago Press.Google Scholar
Mayo, Deborah G. (1996), Error and the Growth of Experimental Knowledge. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Mayo, Deborah G. (2008), “How to Discount Double-Counting When It Counts: Some Clarifications”, British Journal of Philosophy of Science, forthcoming.CrossRefGoogle Scholar
Mayo, Deborah G., and Cox, David R. (2006),“Frequentist Statistics as a Theory of Inductive Inference”, in Rojo, Javier (ed.), Optimality: The Second Erich L. Lehmann Symposium, Lecture Notes Monograph Series, vol. 49. Beachwood, OH: Institute of Mathematical Statistics, 7797.CrossRefGoogle Scholar
Novemsky, Nathan, and Kahneman, Daniel (2005), “The Boundaries of Loss Aversion?”, The Boundaries of Loss Aversion? 42:119128.Google Scholar
Schram, Arthur (2005), “Artificiality: The Tension between Internal and External Validity in Economic Experiments”, Artificiality: The Tension between Internal and External Validity in Economic Experiments 12 (2): 225237..Google Scholar
Smith, Vernon (2002), “Method in Experiment: Rhetoric and Reality”, Method in Experiment: Rhetoric and Reality 5 (2): 91110..Google Scholar
Starmer, Chris (1999), “Experiments in Economics: Should We Trust the Dismal Scientists in White Coats?”, Experiments in Economics: Should We Trust the Dismal Scientists in White Coats? 6:130.Google Scholar
Sugden, Robert (2005), “Experiments as Exhibits and Experiments as Tests”, Experiments as Exhibits and Experiments as Tests 12 (2): 291302..Google Scholar
Sugden, Robert (2008), “The Changing Relationship between Theory and Experiment in Economics”, The Changing Relationship between Theory and Experiment in Economics 75 (5), in this issue.Google Scholar