{"title":"事故因果模型的制图:重新映射建模景观。","authors":"Takaharu Igarashi, Karen Marais","doi":"10.1111/risa.70084","DOIUrl":null,"url":null,"abstract":"<p><p>Accident causation models are abstractions of the real world that explicitly or implicitly underlie our perception and understanding of accidents. As the continuous advancement of our dynamic society demands us to keep updating our understanding of accidents, this review article provides an overview of the landscape of accident causation models to aid our endless journey of expanding the collective endeavor of accident modeling. Our review of existing literature reviews identified several issues in the labels and categorizations of models, including logical inconsistency and ambiguous definitions, that prevent the comparison and analysis of models from a neutral and objective standpoint. To offer a structured perspective for revealing what was actually done in the past and what can be further explored in accident modeling practices, we developed an alternative map of modeling approaches based on their underlying assumptions and geometric structures of graphical representations. This article further provides individual reviews of 30 models, focusing particularly on how the assumptions and structures of the models inform the derivation of remedial and preventive measures. It concludes with a discussion on unexplored research paths that the alternative map directs us to investigate, indicating that our journey toward a safer world is far from complete.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cartography of Accident Causation Models: Remapping the Modeling Landscape.\",\"authors\":\"Takaharu Igarashi, Karen Marais\",\"doi\":\"10.1111/risa.70084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Accident causation models are abstractions of the real world that explicitly or implicitly underlie our perception and understanding of accidents. As the continuous advancement of our dynamic society demands us to keep updating our understanding of accidents, this review article provides an overview of the landscape of accident causation models to aid our endless journey of expanding the collective endeavor of accident modeling. Our review of existing literature reviews identified several issues in the labels and categorizations of models, including logical inconsistency and ambiguous definitions, that prevent the comparison and analysis of models from a neutral and objective standpoint. To offer a structured perspective for revealing what was actually done in the past and what can be further explored in accident modeling practices, we developed an alternative map of modeling approaches based on their underlying assumptions and geometric structures of graphical representations. This article further provides individual reviews of 30 models, focusing particularly on how the assumptions and structures of the models inform the derivation of remedial and preventive measures. It concludes with a discussion on unexplored research paths that the alternative map directs us to investigate, indicating that our journey toward a safer world is far from complete.</p>\",\"PeriodicalId\":21472,\"journal\":{\"name\":\"Risk Analysis\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Risk Analysis\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/risa.70084\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risk Analysis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/risa.70084","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Cartography of Accident Causation Models: Remapping the Modeling Landscape.
Accident causation models are abstractions of the real world that explicitly or implicitly underlie our perception and understanding of accidents. As the continuous advancement of our dynamic society demands us to keep updating our understanding of accidents, this review article provides an overview of the landscape of accident causation models to aid our endless journey of expanding the collective endeavor of accident modeling. Our review of existing literature reviews identified several issues in the labels and categorizations of models, including logical inconsistency and ambiguous definitions, that prevent the comparison and analysis of models from a neutral and objective standpoint. To offer a structured perspective for revealing what was actually done in the past and what can be further explored in accident modeling practices, we developed an alternative map of modeling approaches based on their underlying assumptions and geometric structures of graphical representations. This article further provides individual reviews of 30 models, focusing particularly on how the assumptions and structures of the models inform the derivation of remedial and preventive measures. It concludes with a discussion on unexplored research paths that the alternative map directs us to investigate, indicating that our journey toward a safer world is far from complete.
期刊介绍:
Published on behalf of the Society for Risk Analysis, Risk Analysis is ranked among the top 10 journals in the ISI Journal Citation Reports under the social sciences, mathematical methods category, and provides a focal point for new developments in the field of risk analysis. This international peer-reviewed journal is committed to publishing critical empirical research and commentaries dealing with risk issues. The topics covered include:
• Human health and safety risks
• Microbial risks
• Engineering
• Mathematical modeling
• Risk characterization
• Risk communication
• Risk management and decision-making
• Risk perception, acceptability, and ethics
• Laws and regulatory policy
• Ecological risks.