Socio-Economic Physics


Humanity is confronted with increasing social and economic challenges. Several of them already escalated towards large scale crises, including burning climate crisis, risk of mass extinction in conjunction with ongoing rapid biodiversity loss, a sequence of financial and economic crises over the past decades, increasing loss of societal stability and democratic structures, and a currently grappling global pandemic. All of these challenges are centrally driven by or result in emergent collective phenomena that originate from a combination of individual behaviors, population-wide incentives (or the lack thereof) and multiple nonlinear feedback loops. To address any one of these challenges, we need to understand the basic mechanisms underlying the core collective phenomena. A fundamental understanding, in turn, requires us to collect and integrate a vast spectrum of heterogeneous data — from individual activity and ways to change it to overall economic activity of countries –, and to use, combine and further develop quantitative and qualitative evaluation tools from nonlinear time series analysis, statistical physics theory, nonlinear dynamical systems, computational modeling, network dynamics, control theory, agent-based simulations, and many more realms. Integrating data and bringing them to use for enabling understanding, predicting and consequently adjusting the collective dynamics of socioeconomic systems is at the core of Socio-Economic Physics.

Within NFDI4Phys we aim for establishing research frameworks that not only enable the organized access and easy re-use of existing data in new contexts but also their exchange and integration into multiple, often strongly overlapping research areas. We thus hope to exchange insights from the various inter- and transdisciplinary areas of and beyond physics faced with related problems, including extreme data heterogeneity, interacting research project directions that lean towards two or more of multiple different disciplines and other factors that are rooted in the self-organizing nature of systems we observe or evaluate data from. In summary, complex socioeconomic problems can only be studied and attacked with a transdisciplinary portfolio of tools including nonlinear dynamics, computational modeling, network dynamics, control theory and agent-based simulations.

Use cases

  • SocEcP01 Prof. Dr. Marc Timme
  • SocEcP02 Prof. Dr. Jan Nagler
  • SocEcP03 Dr. Jens Christian Claussen
  • SocEcP04 Dr. Maik Boltes

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