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doi: 10.3934/jimo.2019064

## Does the existence of "talented outliers" help improve team performance? Modeling heterogeneous personalities in teamwork

 System Dynamics Group, Sloan School of Management, MIT, Cambridge, MA 02139, USA

* Corresponding author: Tianyi Li

Received  November 2018 Revised  January 2019 Published  May 2019

Personality heterogeneity is an important topic in team management. In many working groups, there exists certain type of people that are talented but under-disciplined, who could occasionally make extraordinary contributions for the team, but often have less satisfactory overall performance. It is interesting to investigate whether the existence of such people in the team does help improve the overall team performance, and if it does so, what are the conditions for their existence to be positive, and through which channel their benefits for the team are manifested. This study proposes an analytical model with a simple structure that sets up an environment to study these questions. It is shown that: (1) feedback learning could be the mechanism through which outliers' benefits to the team are established, and thus could be a prerequisite for outliers' positive existence; (2) different types of teamwork settings have different outlier-positivity conditions: a uniform round-wise punishment for teamwork failures could be the key idea to encourage outliers' existence; for two specific types of teamwork, teamwork that highlights assistance in interactions are more outliers-friendly than teamwork that consists internal competitions. These results well match empirical observations and may have further implications for managerial practice.

Citation: Tianyi Li. Does the existence of "talented outliers" help improve team performance? Modeling heterogeneous personalities in teamwork. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2019064
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Summary of the model and major assumptions. Teamwork is conducted in a multi-round game setting. In the team, normal players (empty nodes) and "talented" outliers (the solid node) behave differently (orange box). Outliers have worse average performance but a greater performance potential than normal players. Moreover, unlike normal players, outliers do not adjust his performance according to feedbacks from past interactions
Testing two utility types and two distributions of individual performance. Left: $\Delta Q_g$ for individual MC runs; right: average $\Delta Q_g$ for all 200 MC runs. Utility function: equation (3): U1; equation (14): U2. Performance distribution: uniform: D1; Gaussian: D2. $\{\gamma, m, K, L\}$ is chosen such that $H<0$ (blue; D1U1) and $H'>0$ (red; D1U2). Proposition 3 is demonstrated since $\Delta Q_g(\mathit{\boldsymbol{D\mathit{1}U\mathit{2}}})>0>\Delta Q_g(\mathit{\boldsymbol{D\mathit{1}U\mathit{1}}})$. The Gaussian distribution of individual performance is more stringent for outlier's positive existence than the uniform distribution
The significance of the feedback learning mechanism as a potential prerequisite for the positivity condition of outliers' existence. Left: no feedback. MC simulation results are consistent with equation (7). Right: the feedback mechanism activated. Inset: average all-round team performance as a function of the number of outliers in the team $N_o$. Results show that $N_o = 1$ produces the best outcome of teamwork under this parameter set, which satisfies $H'>0$
Feedback learning from multiple past rounds. Results show $\Delta Q_g$ (as in Figure 2) as a function of $w$, which ranges from 1 to 5. The results from a few individual runs are plotted together, for all four combinations of $U$ and $D$. No conclusion could be drawn from this test
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