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SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 19: Social Systems, Opinion and Group Dynamics II
SOE 19.1: Vortrag
Donnerstag, 30. März 2023, 16:45–17:00, ZEU 260
Evidence-based policy-making in sports funding using a data-driven optimization approach — •Jan Hurt1, Liuhuaying Yang1, Johannes Sorger1, Thomas J. Lampoltshammer2, Nike Pulda5, Ursula Rosenpichler5, Stefan Thurner3,1,4, and Peter Klimek3,1 — 1Complexity Science Hub, Vienna, Austria — 2University for Continuing Education Krems, Krems, Austria — 3Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Vienna, Austria — 4Santa Fe Institute, Santa Fe, NM, USA — 5Austria
Many European countries face rising obesity rates among children. Access to sports facilities depends on multiple factors, such as geographic location, proximity to population centers, budgetary constraints, and other socio-economic covariates. Here we show how an optimal allocation of government funds towards sports facilitators (e.g. sports clubs) can be achieved in a data-driven simulation model that maximizes children's access to sports facilities. We find a characteristic sub-linear relationship between the number of active club members and the budget, which depends on the socio-economic conditions of the clubs' districts. In the model, we evaluate different funding strategies. We show that an optimization strategy outperforms a naive approach by up to 115% for 5 million Euros of additional funding to attract children to sports clubs. Our results suggest that the impact of public funding strategies can be substantially increased by tailoring them to regional socio-economic characteristics in an evidence-based and individualized way.