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PurposeWe 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/approachBy 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.FindingsWe 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/implicationsThis study contributes to emerging research on using algorithms to develop novel concepts and combining heuristics and algorithmic intelligence in strategic decision-making.Practical implicationsThis 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/valueDrawing 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.

In any model the values of estimates for various parameters are obtained from different sources each with its own level of uncertainty. When the probability distributions of the estimates are obtained as opposed to point values only, the measurement uncertainties in the parameter estimates may be addressed. However, the sources used for obtaining the data and the models used to select appropriate distributions are of differing degrees of uncertainty. A hierarchy of different sources of uncertainty based upon one's ability to validate data and models empirically is presented. When model parameters are aggregated with different levels of the hierarchy represented, this implies distortion or degradation in the utility and validity of the models used. Means to identify and deal with such heterogeneous data sourcesare explored, and a number of approaches to addressing this problem is presented. One approach, using Range/confidence Estimates coupled with an Information Value Analysis Process, is presented as an example.