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. 2021 Mar 8;16(3):e0247034.
doi: 10.1371/journal.pone.0247034. eCollection 2021.

Variety-seeking, learning and performance

Gino Cattani  1 , Ji-Hyun Kim  2
Affiliations

Variety-seeking, learning and performance

Gino Cattani et al. PLoS One. .

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Learning on a two-peaked landscape with limited learning targets.
(A) Performance landscape indicating there are two knowledge sets at two endpoints denoted by R1 and R2. (B) A contour plot view illustrating how learning unfolds under focused exploration (i.e., a small σ value; σ indicates search boundary). H and L indicates a high and a low performer within σ. (C) A contour point view in the case of broad exploration.
Fig 2
Fig 2. Effects of search restriction (σ): An equal peak case.
The shades represent 95% confidence intervals. μ (initial dispersion of individuals) is set to be 0.1.
Fig 3
Fig 3. Search restriction and intertemporal variation in performance.
Colors in data plot and color bars represent Hamming distance between the source and the learning target. (A) σ = 0.3 for a single Individual (B) σ = 1.0 for a single Individual (C) σ = 0.3 for average of 100 individuals (D) σ = 1.0 for average of 100 individuals.
Fig 4
Fig 4. Distribution after convergence.
On the horizontal axis, we indicate the proportion of individuals who converge to one peak (we picked the R1 peak.). On the vertical axis, we indicate the frequency of each proportion out of 100 independent runs. (A) σ = 0.3 (B) σ = 0.83 (C) σ = 1.0.
Fig 5
Fig 5. Transition to one-peak convergence.
On the vertical axis, we report the absolute value of the difference between the number of individuals on one peak and the number of individuals on the other, divided by the entire number of individuals. If this ratio is 0, it means that the number of individuals on each peak is the same. For example, if 50 out of 100 individuals are on one peak and the other 50 are on the other peak, the value is |50–50|/100 = 0. As the ratio approaches 1, this means that there are more individuals on one peak than on the other. When the value is equal to 1 (i.e., |0–100|/100 = 1), all individuals are located only on one peak.
Fig 6
Fig 6. Effects under high initial dispersion of individual knowledge (μ = 0.5).
The shades represent 95% confidence intervals.
Fig 7
Fig 7. Effects under unequal peak case.
The shades represent 95% confidence intervals.
Fig 8
Fig 8. Number of peaks and learning consequences.
(A) Performance difference after learning; negative values indicate a performance decline (B) Number of times the learning individual experiences non-negative performance changes out of 1,000 learning events.
See this image and copyright information in PMC

References

    1. Dahlander L, O’Mahony S, Gann DM. One foot in, one foot out: how does individuals’ external search breadth affect innovation outcomes? Strategic Management Journal. 2016;37(2):280–302.
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    1. Miller KD, Zhao M, Calantone RJ. Adding interpersonal learning and tacit knowledge to March’s exploration-exploitation model. Academy of Management Journal. 2006;49(4):709–722.
    1. Winter SG, Cattani G, Dorsch A. The value of moderate obsession: Insights from a new model of organizational search. Organization Science. 2007;18(3):403–419.

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