Costly Information Acquisition: Experimental Analysis of a Boundedly Rational Model

Abstract

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

Use of data on planned contributions and stated beliefs in the measurement of social preferences

“…We find, indeed, that stated beliefs are less accurate in the Plan-Info treatment. This indicates, in line with previous work on cognitive limitations (Costa-Gomes and Crawford, 2006;Gabaix et al, 2006) and information overload (Jacoby et al, 1974;Edmunds and Morris, 2000;Kaiser, 2011), that too much information can actually backfire.…”

On the use of heuristics to approximate competitors’ private information

“…Specifically, we compare the strategies chosen in the Bayesian benchmark, ( *    * − ), in which firms maximize their expected profit to those chosen in the heuristic equilibrium, (   − ), in which firms maximize their profit using only the first moment of the distribution of their competitor's private information. 10 Let   (  ) denote the difference between firm 's heuristic and Bayesian equilibrium strate-…”

Measuring Attention and Strategic Behavior in Games with Private Information

“…Subjects look at the MIN cells [1C] and [S] about 350-700msec each, which is much higher than the 100-150msec they look at non-MIN ("Other") cells. 28 The last column reports the fraction of total lookup that is fixated on the two MIN cells. This fraction is around .6, much higher than a random-looking baseline (which is 2/7 = .28).…”

“…The pioneering work in decision making is on multi-attribute choice (Payne et al, 1993) and advertising (Lohse, 1997). Recent economic studies propose and test 'directed cognition' (Gabaix et al, 2006), sequential search under time pressure (Reutskaja et al, 2008), calibrate accumulator race-to-barrier models (Armel and Rangel, 2008), and look for within-attribute versus within-choice comparisons (Arieli et al, 2009). 7 Wang et al (in press) were the first to combine lookup information and pupil dilation measures, and to use those measures to see how well private information could be predicted from information measures.…”