Economists routinely make predictions in environments where data is unavailable, relying on evidence from related but distinct contexts. For example, an economist at a development agency may need to predict diffusion of microfinance takeup in one Indian village given data on diffusion in others. Or an economist at an insurance company may need to predict willingness-to-pay for certain insurance plans given data on willingness-to-pay for others.

We propose a new way to quantify the restrictiveness of an economic model, based on how well the model fits simulated, hypothetical data sets. The data sets are drawn at random from a distribution that satisfies some application-dependent content restrictions (such as that people prefer more money to less). Models that can fit almost all hypothetical data well are not restrictive. To illustrate our approach, we evaluate the restrictiveness of popular behavioral models in two experimental settings---certainty equivalents and initial play---and explain how restrictiveness reveals new insights about each of the models.

Nash equilibrium takes optimization as a primitive, but suboptimal behavior can persist in simple stochastic decision problems. This has motivated the development of other equilibrium concepts such as cursed equilibrium and behavioral equilibrium. We experimentally study a simple adverse selection (or 'lemons') problem and find that learning models that heavily discount past information (i.e. display recency bias) explain patterns of behavior better than Nash, cursed or behavioral equilibrium. Providing counterfactual information or a record of past outcomes does little to aid convergence to optimal strategies, but providing sample averages ('recaps') gets individuals most of the way to optimality. Thus recency effects are not solely due to limited memory but stem from some other form of cognitive constraints. Our results show the importance of going beyond static optimization and incorporating features of human learning into economic models.