Abstract
Social norms are rules and standards of expected behavior that emerge in societies as a result of information exchange between agents. This paper studies the effects of emergent social norms on the performance of teams. We use the \(N\!K\)-framework to build an agent-based model, in which agents work on a set of interdependent tasks and exchange information regarding their past behavior with their peers. Social norms emerge from these interactions. We find that social norms come at a cost for the overall performance, unless tasks assigned to the team members are highly correlated, and the effect is stronger when agents share information regarding more tasks, but is unchanged when agents communicate with more peers. Finally, we find that the established finding that the team-based incentive schemes improve performance for highly complex tasks still holds in presence of social norms.
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Notes
- 1.
The exact choice of the coupled tasks is random with one condition: every task affects and is affected by exactly \(K+C\cdot S\) other tasks.
- 2.
A performance landscape is a matrix of uniform random variables that correspond to every combination of \(1 + K + C \cdot S\) decisions. We generate entire landscapes to find the overall global maximum and normalize our results accordingly, to ensure comparability among different scenarios.
- 3.
See Verel et al. [10] for methodology.
- 4.
In our context linear incentives are as efficient as other contracts inducing non-boundary actions. See [2, p. 1461].
- 5.
We implement our version of the Social Cognitive Optimization algorithm. See Xie et al. [12] for the original version of the algorithm.
- 6.
We use the bidirectional ring network, in which each node is connected to exactly \(D=2\) other nodes with reciprocal unidirectional links, where nodes represent agents and the links represent sharing of information.
- 7.
Levinthal [6] describes situations in which agents switch more than one decision at a time as long jumps and states that such scenarios are less likely to occur, as it is hard or risky to change multiple processes simultaneously.
- 8.
For reliable results, we generate the entire landscapes before the simulation, which is feasible for \(P=4\) given modern computing limitations. Our sensitivity analyses with simpler models without entire landscapes suggest that the results also hold for \(P=5,6,7\).
- 9.
As the performance landscapes are randomly generated, we normalize the team performance by the maximum performance attainable in the current simulation run r to ensure comparability.
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Khodzhimatov, R., Leitner, S., Wall, F. (2021). On the Effect of Social Norms on Performance in Teams with Distributed Decision Makers. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2021. Lecture Notes in Computer Science(), vol 12720. Springer, Cham. https://doi.org/10.1007/978-3-030-80387-2_29
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