BoundedRationality_TheAdaptiveToolbox.pdf

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Abdolkarim Sadrieh et al.
issue that should precede such discussions. However, we refrain from offering a
general and rigorous definition of complexity, for two reasons. First, such a definition
poses a challenge that is clearly out of the range of this paper. Second, in
our view, the nature of complexity will be quite obvious in many of the applications
we discuss (e.g., linear cost functions are much more easily understood
than nonlinear ones, etc.). Usually it suffices to assume that simpler rules rely on
fewer contingencies than more complex ones.
The rest of the chapter is organized as follows. We begin by asking why it is
that the decision-making tools are simple rules, for animals as well as for humans.
We will pay special attention to the role of evolution in this context. Next
we examine the relation of the heuristics in the adaptive toolbox to the artifacts
in the decision environment. We then ask how the hypotheses about the nature
and significance of these rules and heuristics should be devised and validated.
Finally, we try to find classifications for the decision situations by examining
which of the rules can be most appropriately used to model decision making, in
what types of situations. The final section is devoted to exploring the effect of
social interaction on the modeling of the tools in the adaptive toolbox.
DID EVOLUTION PRODUCE SIMPLE RULES
AND HEURISTICS?
To understand why evolution may support simple rules, two aspects of the matter
must be examined. On one hand, tasks that seem difficult for human beings to
perform may be easily performed by evolution. This is mainly due to the fact that
the process of mutation and selection is quite different as a "problem-solving
strategy" from cognitive problem solving by human beings. Experiments with
human subjects, for example, have shown the "winner's curse" to persist even
after many trials with complete information feedback (Ball et al. 1991;
Bazerman and Samuelson 1983; Selten et al., in preparation). It seems extremely
difficult to learn the relatively simple deliberation of conditional probabilities
that leads to the resolution of this "adverse selection" problem. There are
many examples in biology, however, showing that such an adverse selection
would quickly lead to the extinction of the winner's curse in an evolutionary setting
(see the example of the navigation of the Cataglyphis and other examples in
Hammerstein, this volume.)
On the other hand, difficulties that seem easy to overcome by human standards
may be left unresolved by evolution. Two major obstacles for the evolutionary
process can be pointed out. First, evolution by mutation and selection
can only go from one stage to the next gradually. The system cannot "jump" to
any arbitrary better state. Thus, if the performance of a task can only be enhanced
via a whole set of mutations that must come simultaneously, this enhancement
is not likely to evolve. Second, effective selection only takes place if
fitness differences are substantial. Finally, unlike human decision makers,