Human decision-makers show various cognitive limitations in terms of “bounded rationality” [1]. Among these limitations is that decision-makers usually do not know the entire solution space in advance but have to search stepwise for new options in the hope to identify superior solutions. For capturing this kind of search behavior and to represent experiential learning, models in the domains of economics and managerial science often employ greedy algorithms and, in particular, hill-climbing algorithms [2]. However, based on experimental evidence, it has been argued that hill-climbing algorithms may be inappropriate representations of managerial search behavior for decision problems of any complexity [3]. With this, also the question arises in how far results of models relying on hill-climbing algorithms may hold if other and possibly more realistic representations of search behavior are implemented.
Against this background, the paper suggests to capture managerial behavior in agent-based models following Herbert A. Simon's concept of satificing [3] which was found to be a relevant representation of human search behavior (e.g., [4]): Satisficing means a process of sequential search for options until a satisfactory level of utility is achieved; what is regarded satisfactory is captured in the aspiration level which – shaped by the difficulty of the decision problem – may be subject to adaptation over time as well as the maximum number of options searched. In particular, a satisficing algorithm is proposed and contrasted to hill-climbing algorithms via the example of an agent-based simulation model based on the framework of NK fitness landscapes [5] which allows to conveniently control for the complexity of the decision-problem. In the model, decision-makers collaboratively search for superior performance to a multi-dimensional binary decision problem.
The results suggest that the models' behavior may remarkably differ depending on whether search behavior is captured by a hill-climbing or a satisficing algorithm in combination with the complexity of the decision problem. Hence, one may infer that further research is required to assess the effects of the representation of managerial behavior on the results of agent-based models in managerial science.
References
[1] H.A. Simon, A behavioral model of rational choice. Quarterly Journal of Economics, 69 (1955) 99-118.
[2] O. Baumann, J. Schmidt, N. Stieglitz, Effective search in rugged performance landscapes: A review and outlook. Journal of Management, 45 (2019) 285-318.
[3] W.M. Tracy, D.G. Markovitch, L.S. Peters et al., Algorithmic representations of managerial search behavior. Computational Economics, 49 (2017) 343-361.
[4] W. Güth, Satisficing and (un)bounded rationality: A formal definition and its experimental validity. Journal of Economic Behavior & Organization, 73 (2010) 308-316.
[5] S.A. Kauffman, S. Levin, Towards a general theory of adaptive walks on rugged landscapes, Journal of Theoretical Biology, 128(1987) 11-45.