The Downside of Heterogeneity: How Established Relations Counteract Systemic Adaptivity in Tasks Assignments

Casiraghi, Giona; Zingg, Christian; Schweitzer, Frank

doi:10.3390/e23121677

Cited by 8 publications

(7 citation statements)

References 35 publications

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“…Using non-network approaches, the authors of [39] characterised the interaction structure spatially by detecting locations in an o ce building where employees frequently interact. In other works, the task redistribution between software developers was studied with agent-based models [12,30]. The key di erence to this work is that all approaches mentioned above study the interaction structure between individuals.…”

Section: Related Workmentioning

confidence: 99%

Detecting and Optimising Team Interactions in Software Development

Zingg¹,

Gernler²,

Arzig³

et al. 2023

Preprint

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The functional interaction structure of a team captures the preferences with which members of di erent roles interact. This paper presents a data-driven approach to detect the functional interaction structure for software development teams from traces team members leave on development platforms during their daily work. Our approach considers di erences in the activity levels of team members and uses a block-constrained con guration model to compute interaction preferences between members of di erent roles. We apply our approach in a case study to extract the functional interaction structure of a product team at the German IT security company genua GmbH . We subsequently validate the accuracy of the detected interaction structure in interviews with ve team members. Finally, we show how our approach enables teams to compare their functional interaction structure against synthetically created benchmark scenarios. Speci cally, we evaluate the level of knowledge di usion in the team and identify areas where the team can further improve. Our approach is computationally e cient and can be applied in real time to manage a team's interaction structure.

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Section: Related Workmentioning

confidence: 99%

Detecting and Optimising Team Interactions in Software Development

Zingg¹,

Gernler²,

Arzig³

et al. 2023

Preprint

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show abstract

“…It helps understanding cascading effects from removing a specific agent. Centralization [179] takes the concentration of interactions in a few agents into account, which increases the systemic risk if these agents fail [34] (see also Figure 17). Betweenness preference [128] and Eigengap [112] indicate communication bottlenecks and identify gate keepers.…”

Section: Quantifying Social Resiliencementioning

confidence: 99%

Modeling social resilience: Questions, answers, open problems

Schweitzer¹,

Andres²,

Casiraghi³

et al. 2023

Preprint

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Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, non-equilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) delimitation, i.e., narrowing down the target systems, (ii) conceptualization, i.e., identifying how to approach social organizations, (iii) formal representation using a combination of agent-based and network models, (iv) operationalization, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the robustness of social organizations and their adaptivity, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.

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“…Their integration into the collective may improve over time, as can be 27/54 measured by their coreness [58,151,155]. If core nodes leave, this may trigger cascades of other nodes leaving as empirical and simulation studies have demonstrated [34,60].…”

Section: Dynamics Of Social Organizationsmentioning

confidence: 99%

“…It helps understanding cascading effects from removing a specific agent. Centralization [179] takes the concentration of interactions in a few agents into account, which increases the systemic 32/54 risk if these agents fail [34] (see also Figure 17). Betweenness preference [128] and Eigengap [112] indicate communication bottlenecks and identify gate keepers.…”

Section: Quantifying Social Resiliencementioning

confidence: 99%

Modeling Social Resilience: Questions, Answers, Open Problems

Schweitzer

Andres

Casiraghi

et al. 2022

Advs. Complex Syst.

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Resilience denotes the capacity of a system to withstand shocks and its ability to recover from them. We develop a framework to quantify the resilience of highly volatile, nonequilibrium social organizations, such as collectives or collaborating teams. It consists of four steps: (i) delimitation, i.e., narrowing down the target systems, (ii) conceptualization, i.e., identifying how to approach social organizations, (iii) formal representation using a combination of agent-based and network models, (iv) operationalization, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the robustness of social organizations and their adaptivity, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.

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The Downside of Heterogeneity: How Established Relations Counteract Systemic Adaptivity in Tasks Assignments

Cited by 8 publications

References 35 publications

Detecting and Optimising Team Interactions in Software Development

Detecting and Optimising Team Interactions in Software Development

Modeling social resilience: Questions, answers, open problems

Modeling Social Resilience: Questions, Answers, Open Problems

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