Searching far away from the lamp-post: An agent-based model
通过实验室实验研究人类在组合任务中的问题解决行为，发现人们不仅进行局部和随机搜索，还会基于问题结构进行模型化搜索；随后校准基于主体的模型分析实验发现，并讨论对组织搜索的启示。
This article presents insights from a laboratory experiment on human problem solving in a combinatorial task. I rely on a hierarchical rugged landscape to explore how human problem-solvers are able to detect and exploit patterns in their search for an optimal solution. Empirical findings suggest that solvers do not engage only in local and random distant search, but as they accumulate information about the problem structure, solvers make ‘model-based’ moves, a type of cognitive search. I then calibrate an agent-based model of search to analyse and interpret the findings from the experimental setup and discuss implications for organizational search. Simulation results show that, for non-trivial problems, performance can be increased by a low level of persistence, that is, an increased likelihood to quickly abandon unsuccessful paths.