Literature DB >> 30884546

Toward Convergence Disaster Research: Building Integrative Theories Using Simulation.

Ali Mostafavi1, N Emel Ganapati2.   

Abstract

Scholars across disciplines use simulation methods as tools to build theories; however, the full potential of simulation methods has not been fully used for building theories in convergence disaster research. Simulation methods could provide four unique opportunities for building theories for convergence disaster research. First, simulation methods could help researchers model the underlying mechanisms of disaster phenomena by enabling integration of qualitative and quantitative data. Second, they could help researchers specify and characterize the mechanisms affecting specific disaster phenomena by facilitating integration of empirical information with existing theoretical elements from different disciplines. Third, simulation methods could enable multilevel understanding of relationships between factors influencing disaster phenomena and emergent behaviors across different levels of analysis (e.g., individual, household, neighborhood, and community levels). Fourth, simulation methods could help researchers integrate theoretical elements on disasters across different disciplines (e.g., engineering, social science, sociology, and epidemiology) for a more convergent understanding of complex relationships affecting resilience at different levels.
© 2019 Society for Risk Analysis.

Keywords:  Convergence research; disasters; simulation; transdisciplinary theory development

Year:  2019        PMID: 30884546     DOI: 10.1111/risa.13303

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  2 in total

1.  Human-centric infrastructure resilience: Uncovering well-being risk disparity due to infrastructure disruptions in disasters.

Authors:  Jennifer S Dargin; Ali Mostafavi
Journal:  PLoS One       Date:  2020-06-18       Impact factor: 3.240

2.  Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data.

Authors:  Cristian Podesta; Natalie Coleman; Amir Esmalian; Faxi Yuan; Ali Mostafavi
Journal:  J R Soc Interface       Date:  2021-04-28       Impact factor: 4.118

  2 in total

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