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引用次数: 0
摘要
目的我们认为,有关如何搜索以及如何在不同认知模式之间切换的一个基本问题在于影响学习和探索动态的决策规则。我们研究了这些决策规则背后的搜索逻辑,并提出了概念性的提示,这些提示可以通过心理或计算来帮助管理者做出决策。通过应用多臂匪徒(MAB)建模来模拟代理与动态环境的互动,我们比较了在不同环境条件配置下选定的 MAB 算法的模式和性能。首先,基于启发式的简单探索策略在环境变化小、备选方案少的条件下效果良好。第二,结合简单启发式和去偏差启发式的探索策略适用于大多数动态和复杂的决策环境。第三,基于不确定性的探索策略更适用于环境不可预测性较高的情况,因为它能更有效地识别偏离的模式。研究局限/启示本研究为战略决策中使用算法开发新概念以及启发式和算法智能相结合的新兴研究做出了贡献。原创性/价值我们借鉴了机器学习和认知心理学研究的观点,通过比较三种人机对话算法的不同搜索逻辑,展示了不同探索策略在不同动态环境配置下的适用性。
Managerial decision-making: exploration strategies in dynamic environments
Purpose
We argue that a fundamental issue regarding how to search and how to switch between different cognitive modes lies in the decision rules that influence the dynamics of learning and exploration. We examine the search logics underlying these decision rules and propose conceptual prompts that can be applied mentally or computationally to aid managers’ decision-making.
Design/methodology/approach
By applying Multi-Armed Bandit (MAB) modeling to simulate agents’ interaction with dynamic environments, we compared the patterns and performance of selected MAB algorithms under different configurations of environmental conditions.
Findings
We develop three conceptual prompts. First, the simple heuristic-based exploration strategy works well in conditions of low environmental variability and few alternatives. Second, an exploration strategy that combines simple and de-biasing heuristics is suitable for most dynamic and complex decision environments. Third, the uncertainty-based exploration strategy is more applicable in the condition of high environmental unpredictability as it can more effectively recognize deviated patterns.
Research limitations/implications
This study contributes to emerging research on using algorithms to develop novel concepts and combining heuristics and algorithmic intelligence in strategic decision-making.
Practical implications
This study offers insights that there are different possibilities for exploration strategies for managers to apply conceptually and that the adaptability of cognitive-distant search may be underestimated in turbulent environments.
Originality/value
Drawing on insights from machine learning and cognitive psychology research, we demonstrate the fitness of different exploration strategies in different dynamic environmental configurations by comparing the different search logics that underlie the three MAB algorithms.
期刊介绍:
■In-depth studies of major issues ■Operations management ■Financial management ■Motivation ■Entrepreneurship ■Problem solving and proactivity ■Serious management argument ■Strategy and policy issues ■Tactics for turning around company crises Management Decision, considered by many to be the best publication in its field, consistently offers thoughtful and provocative insights into current management practice. As such, its high calibre contributions from leading management philosophers and practitioners make it an invaluable resource in the aggressive and demanding trading climate of the Twenty-First Century.