Forecasting the adoption and spread of a community-based marine management initiative using agent-based models DOI Creative Commons
Andreas Christ Sølvsten Jørgensen, Thomas Pienkowski, Matt Clark

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 17, 2024

Abstract While many successful initiatives for conserving nature exist, efforts to take them scale have been inadequate. Moreover, conservation science currently lacks a systematic methodology determining if or when interventions will reach effective scales and how programmatic decisions affect the scaling process. This paper presents modelling framework that aims address both issues by operationalizing Diffusion of Innovations theory local knowledge using agent-based Bayesian inference. By applying our existing data on spatiotemporal adoption community-based marine management initiative in Fiji, we demonstrate approach can identify mechanisms govern observed patterns. In this case, relative advantage intervention, village social networks, perceived stand out as important drivers adoption. Using identified causal processes, forecast business-as-usual counterfactual future scenarios hence inform policy. Finally, highlight importance making detailed predictions. We structure step-by-step guide, highlighting possible limitations. Thus, besides presenting case study, serves template practitioners researchers better model process other interventions.

Language: Английский

Forecasting the adoption and spread of a community-based marine management initiative using agent-based models DOI Creative Commons
Andreas Christ Sølvsten Jørgensen, Thomas Pienkowski, Matt Clark

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 17, 2024

Abstract While many successful initiatives for conserving nature exist, efforts to take them scale have been inadequate. Moreover, conservation science currently lacks a systematic methodology determining if or when interventions will reach effective scales and how programmatic decisions affect the scaling process. This paper presents modelling framework that aims address both issues by operationalizing Diffusion of Innovations theory local knowledge using agent-based Bayesian inference. By applying our existing data on spatiotemporal adoption community-based marine management initiative in Fiji, we demonstrate approach can identify mechanisms govern observed patterns. In this case, relative advantage intervention, village social networks, perceived stand out as important drivers adoption. Using identified causal processes, forecast business-as-usual counterfactual future scenarios hence inform policy. Finally, highlight importance making detailed predictions. We structure step-by-step guide, highlighting possible limitations. Thus, besides presenting case study, serves template practitioners researchers better model process other interventions.

Language: Английский

Citations

4