  Computational models of managerial search often build on backward-looking
search based on hill-climbing algorithms. Regardless of its prevalence, there
is some evidence that this family of algorithms does not universally represent
managers' search behavior. Against this background, the paper proposes an
alternative algorithm that captures key elements of Simon's concept of
satisficing which received considerable support in behavioral experiments. The
paper contrasts the satisficing-based algorithm to two variants of
hill-climbing search in an agent-based model of a simple decision-making
organization. The model builds on the framework of NK fitness landscapes which
allows controlling for the complexity of the decision problem to be solved. The
results suggest that the model's behavior may remarkably differ depending on
whether satisficing or hill-climbing serves as an algorithmic representation
for decision-makers' search. Moreover, with the satisficing algorithm, results
indicate oscillating aspiration levels, even to the negative, and intense - and
potentially destabilizing - search activities when intra-organizational
complexity increases. Findings may shed some new light on prior computational
models of decision-making in organizations and point to avenues for future
research.
