Modeling social resilience: Questions, answers, open problems
Schweitzer, Frank, Andres, Georges, Casiraghi, Giona, Gote, Christoph, Roller, Ramona, Scholtes, Ingo, Vaccario, Giacomo, Zingg, Christian
–arXiv.org Artificial Intelligence
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) \emph{delimitation}, i.e., narrowing down the target systems, (ii) \emph{conceptualization}, .e., identifying how to approach social organizations, (iii) formal \emph{representation} using a combination of agent-based and network models, (iv) \emph{operationalization}, i.e. specifying measures and demonstrating how they enter the calculation of resilience. Our framework quantifies two dimensions of resilience, the \emph{robustness} of social organizations and their \emph{adaptivity}, and combines them in a novel resilience measure. It allows monitoring resilience instantaneously using longitudinal data instead of an ex-post evaluation.
arXiv.org Artificial Intelligence
Dec-31-2022
- Country:
- Asia > Singapore (0.04)
- South America > Colombia
- Meta Department > Villavicencio (0.04)
- Oceania > Australia
- Queensland (0.04)
- North America > United States
- District of Columbia > Washington (0.04)
- Louisiana > Orleans Parish
- New Orleans (0.04)
- California > San Francisco County
- San Francisco (0.04)
- Europe
- Austria > Vienna (0.14)
- United Kingdom > England
- Cambridgeshire > Cambridge (0.04)
- Oxfordshire > Oxford (0.04)
- Switzerland > Zürich
- Zürich (0.14)
- Poland > Lublin Province
- Lublin (0.04)
- Germany > Bavaria
- Lower Franconia > Würzburg (0.04)
- Genre:
- Research Report
- New Finding (0.46)
- Experimental Study (0.45)
- Research Report
- Industry:
- Government (1.00)
- Information Technology (0.93)
- Technology:
- Information Technology
- Communications > Networks (1.00)
- Software (0.92)
- Data Science > Data Mining (0.92)
- Artificial Intelligence
- Representation & Reasoning > Agents (1.00)
- Machine Learning > Statistical Learning (1.00)
- Information Technology