Harstad and Selten (this forum) raise interesting questions about the relative promise of optimization models and bounded-rationality models in making progress in economics. This article builds from their analysis by indicating the potential for using neoclassical (broadly defined) optimization models to integrate insights from psychology on the limits to rationality into economics. I lay out an approach to making (imperfect and incremental) improvements over previous economic theory by incorporating greater realism while attempting to maintain the breadth of application, the precision of predictions, and the insights of neoclassical theory. I then discuss how many human limits to full rationality are, in fact, well understood in terms of optimization.

This article proposes an approach to improving the psychological realism of economics while maintaining its conventional techniques and goals--formal theoretical and empirical analysis using tractable models, with a focus on prediction and estimation. Besides tolerating the imperfections that come with precision, models should aim for two crucial criteria: power and scope. The approach advocated is to develop portable extensions of existing models that embed preexisting theories as parameter values, while introducing the new psychological assumptions as alternative parameter values, and make the model portable by defining it in all cases where existing models make predictions.

A growing literature explores differences in subjective well-being across demographic groups, often relying on surveys with high nonresponse rates. By using the reported number of call attempts made to participants in the University of Michigan's Surveys of Consumers, we show that comparisons among easy-to-reach respondents differ from comparisons among hard-to-reach ones. Notably, easy-to-reach women are happier than easy-to-reach men, but hard-to-reach men are happier than hard-to-reach women, and conclusions of a survey could reverse with more attempted calls. Better alternatives to comparing group sample averages might include putting greater weight on hard-to-reach respondents or even extrapolating trends in responses.