Abstract
According to the variance hypothesis, variety-seeking or exploration is a critical condition for improving learning and performance over time. Extant computational learning models support this hypothesis by showing how individuals who are exposed to diverse knowledge sources are more likely to find superior solutions to a particular problem. Yet this research provides no precise guidelines about how broadly individuals should search. Our goal in this paper is to elucidate the conditions under which variety-seeking in organizations is beneficial. To this end, we developed a computational model in which individuals learn as they interact with other individuals, and update their knowledge as a result of this interaction. The model reveals how the type of learning environment (performance landscape) in which the learning dynamic unfolds determines when the benefits of variety-seeking outweigh the costs. Variety-seeking is performance-enhancing only when the knowledge of the chosen learning targets (i.e., individuals to learn from) provide useful information about the features of the performance landscape. The results further suggest that superior knowledge might be available locally, i.e., in the proximity of an individual’s current location. We also identify the point beyond which variety-seeking causes a sharp performance decline and show how this point depends on the type of landscape in which the learning dynamic unfolds and the degree of specialization of individual knowledge. The presence of this critical point explains why exploration becomes very costly. The implications of our findings for establishing the boundaries of exploration are discussed.
Highlights
According to the variance hypothesis, variety-seeking or exploration is a critical condition for improving learning and performance over time [1]
Extant computational learning models support this hypothesis by showing how individuals who are exposed to diverse knowledge sources are more likely to find superior solutions to a particular problem [2,3,4,5]
In the S1 Appendix, we show how the results of the simulation when an NK modeling approach is used still support the variance hypothesis: on average broad exploration leads to higher performance or superior solutions to a given problem-solving situation without incurring costs
Summary
According to the variance hypothesis, variety-seeking or exploration is a critical condition for improving learning and performance over time [1]. While some best practices (or superior solutions) can be learned and transferred across different locations, others are location-specific, i.e., valuable for one Variety-seeking, learning and firm performance location but of little value or even harmful to another In this case, the performance landscape can be modeled as a multi-peak landscape in which variety-seeking might prove very costly due to the frictions that make the sharing of superior knowledge less viable. We further identify the point beyond which variety-seeking causes a sharp performance decline and show how this point depends on the type of landscape in which individuals learn from each other and their degree of specialization The presence of this critical point explains why, as individuals change the scope of their search strategy, exploration becomes very costly. We review extant computational research on learning and recent empirical studies that examine the impact of variety in knowledge on the effectiveness of search, i.e., the ability to identify superior solutions to a particular problem. The concluding section draws out the general implications of the simulation results and identifies model extensions that represent viable avenues for future research
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