Ignorance is a formidable foe, and to have hope of even modest victories, we economists need to use every resource and every weapon we can muster, including thought experiments (theory), and the analysis of data from nonexperiments, accidental experiments, and designed experiments. We should be celebrating the small genuine victories of the economists who use their tools most effectively, and we should dial back our adoration of those who can carry the biggest and brightest and least-understood weapons. We would benefit from some serious humility, and from burning our ‘Mission Accomplished’ banners. It’s never gonna happen.

Behavioural studies by and large do not necessarily assume that people always behave by the dictates of standard theory, and especially in the early days repeatedly showed deviations etc., but increasingly are concerned with looking at the source of the departures and with their implications, etc.

[I]t would be useful for theory to identify behavior for which the theory cannot account, in the sense that the observations would force the theorist to reconsider. This would ensure that the theory is not performing well by ‘theorizing to the test’ …

Similarly, it would be helpful to have the experimental design indicate which outcomes would be regarded as a failure as well as which would be considered a success. This question appears to be trivial in many cases, with success and failure riding on the statistical significance of an estimated parameter. However, one of the advantages of experimental work is the ability to control the environment and design the tests. This allows us to direct attention away from issues of statistical significance and toward issues of economic importance. The strength of the experiment will often be reflected in the content of this ‘failure’ category. …

[I]t is important that both theoretical models and interpretations of experimental results be precise enough to apply beyond the experimental situation from which they emerge. This allows links to be made that multiply the power of single studies.