{"title":"Stochastic Modeling of Non-linear Terrorism Dynamics","authors":"Jakub Drmola, Tomáš Hubík","doi":"10.1515/jhsem-2020-0029","DOIUrl":null,"url":null,"abstract":"Abstract Modeling terrorism is both necessary and difficult. While the necessity comes from the all too obvious real-world pressures our society is facing, the difficulty stems from the underlying complexity of the phenomena itself – there are many variables to account for, they are hard to measure, and the relationships between them are confounding. Since modeling terrorism is at its most onerous when it comes to predicting specific attacks, their timing and scale, we opted to work around this using observed probabilistic distribution and integrate power laws into our system dynamics model. After evaluating thousands of simulations runs, this allows us to replicate historical data as well as produce prognostic scenarios, while maintaining what we believe to be authentic behavior. Compromises need to be made, but we believe that this approach can be useful for systems highly dependent on events or parameters which we are unable to predict but whose distributions are known.","PeriodicalId":46847,"journal":{"name":"Journal of Homeland Security and Emergency Management","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Homeland Security and Emergency Management","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1515/jhsem-2020-0029","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PUBLIC ADMINISTRATION","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Modeling terrorism is both necessary and difficult. While the necessity comes from the all too obvious real-world pressures our society is facing, the difficulty stems from the underlying complexity of the phenomena itself – there are many variables to account for, they are hard to measure, and the relationships between them are confounding. Since modeling terrorism is at its most onerous when it comes to predicting specific attacks, their timing and scale, we opted to work around this using observed probabilistic distribution and integrate power laws into our system dynamics model. After evaluating thousands of simulations runs, this allows us to replicate historical data as well as produce prognostic scenarios, while maintaining what we believe to be authentic behavior. Compromises need to be made, but we believe that this approach can be useful for systems highly dependent on events or parameters which we are unable to predict but whose distributions are known.
期刊介绍:
The Journal of Homeland Security and Emergency Management publishes original, innovative, and timely articles describing research or practice in the fields of homeland security and emergency management. JHSEM publishes not only peer-reviewed articles, but also news and communiqués from researchers and practitioners, and book/media reviews. Content comes from a broad array of authors representing many professions, including emergency management, engineering, political science and policy, decision science, and health and medicine, as well as from emergency management and homeland security practitioners.