BoundedRationality_TheAdaptiveToolbox.pdf

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Preferential Choice and Adaptive Strategy Use 13 5
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Figure 8.1 Accuracy (measured in terms of the percentage obtained of the weighted
additive value) and the average effort (in total EIPs) for five decision strategies (WADD:
weighted adding; EQW: equal weighting; LEX: lexicographic; RC: random choice;
EBA: elimination-by-aspects) as determined through computer simulations. A line is
used to connect the "pure" strategies for each environment that offer nondominated combinations
of accuracy and effort.
option chosen. Examples of such simulation work can be found in Johnson and
Payne(1985), Payne etal. (1988), Bettmanetal. (1993), and Payne et al. (1996).
Figure 8.1 illustrates typical results from those simulations for five "pure"
strategies: WADD, LEX, EBA, EQW, and random choice (RC). This figure
shows the accuracy (measured in terms of the percentage attained of the WADD
value) and the effort (in total EIPs) for the five strategies. The accuracy and effort
values for the decision strategies are plotted for two distinct decision environments.
There are a number of important conclusions about various decision
strategies that can be drawn from our simulations.
First, as illustrated in Figure 8.1, there are decision environments in which
heuristic choice rules provide excellent accuracy and substantial savings in cognitive
effort. For instance, the LEX rule achieves roughly 90% of the accuracy of
the WADD rule with only about 40% of the effort in environments. Thus, the
use of a heuristic to solve a preferential choice problem makes sense in that environment
for a decision maker concerned with the two goals of accuracy and effort
minimization.
Second, also illustrated in Figure 8.1, the most efficient heuristics in terms of
accuracy and effort vary across task environments. For example, the LEX rule is
better in environment B and less effective in environment A than alternative
heuristics. Thus, a decision maker who wanted to achieve both a reasonably high
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