Laboratory and field studies of time preference find that discount rates are much greater in the short run than in the long run. Hyperbolic discount functions capture this property. This paper presents simulations of the savings and asset allocation choices of households with hyperbolic preferences. The behavior of the hyperbolic households is compared to the behavior of exponential households. The hyperbolic households borrow much more frequently in the revolving credit market. The hyperbolic households exhibit greater consumption income comovement and experience a greater drop in consumption around retirement. The hyperbolic simulations match observed consumption and balance sheet data much better than the exponential simulations.

Decision-making requires cognitive operations, including information acquisition and information processing. Economists assume that agents act as if they were choosing these (costly) operations optimally. But models of optimal cognition pose significant conceptual challenges. Such models are generally intractable. Only very simple settings admit analytic solutions. Moreover, even computational (i.e., numerical) tractability fails as the complexity of the problem increases. In addition, models of optimal cognition suffer from the infinite regress problem: if cognition is costly, then optimizing cognition is also costly, leading one to optimize the optimization, and so on ad infinitum (John Conlisk, 1996;Barton L. Lipman, 1991;Herbert Simon, 1955).Instead of trying to model optimal cognition, we study a partially myopic and tractable alternative. The directed cognition model uses approximate option-value calculations to direct cognition to mental activities with high shadow values (Gabaix and Laibson, 2005). The current paper applies the directed cognition model to a problem of information acquisition, or search. In this context, the model assumes the following iterative search structure: At each decision point, agents act as if their next set of search operations were their last opportunity for search.Such decision-making, although partially myopic, nevertheless helps agents focus on information that is likely to be useful and ignore information that is likely to be redundant. The directed cognition model is also tractable. The model can be computationally solved in highly complex settings. The model does not suffer from the curse of dimensionality or the infinite regress problem.The current paper experimentally evaluates the directed cognition model. We find that laboratory behavior matches the predictions of the directed cognition algorithm. We begin with a relatively simple choice problem for which it is possible to compute optimal choices, and show that the directed cognition model outperforms rationality. We then turn to a complex (and

Decision-making requires cognitive operations, including information acquisition and information processing. Economists assume that agents act as if they were choosing these (costly) operations optimally. But models of optimal cognition pose significant conceptual challenges. Such models are generally intractable. Only very simple settings admit analytic solutions. Moreover, even computational (i.e., numerical) tractability fails as the complexity of the problem increases. In addition, models of optimal cognition suffer from the infinite regress problem: if cognition is costly, then optimizing cognition is also costly, leading one to optimize the optimization, and so on ad infinitum (John Conlisk, 1996;Barton L. Lipman, 1991;Herbert Simon, 1955).Instead of trying to model optimal cognition, we study a partially myopic and tractable alternative. The directed cognition model uses approximate option-value calculations to direct cognition to mental activities with high shadow values (Gabaix and Laibson, 2005). The current paper applies the directed cognition model to a problem of information acquisition, or search. In this context, the model assumes the following iterative search structure: At each decision point, agents act as if their next set of search operations were their last opportunity for search.Such decision-making, although partially myopic, nevertheless helps agents focus on information that is likely to be useful and ignore information that is likely to be redundant. The directed cognition model is also tractable. The model can be computationally solved in highly complex settings. The model does not suffer from the curse of dimensionality or the infinite regress problem.The current paper experimentally evaluates the directed cognition model. We find that laboratory behavior matches the predictions of the directed cognition algorithm. We begin with a relatively simple choice problem for which it is possible to compute optimal choices, and show that the directed cognition model outperforms rationality. We then turn to a complex (and

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