دانلود مقاله ISI انگلیسی شماره 78576
عنوان فارسی مقاله

مدل سازی محاسباتی بر اساس عامل پاسخ معرض خطر اجتماعی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
78576 2016 14 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Agent-based computational modelling of social risk responses
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : European Journal of Operational Research, Volume 251, Issue 3, 16 June 2016, Pages 1029–1042

کلمات کلیدی
OR در تجزیه و تحلیل مشکل اجتماعی؛ سیستم های چند عامله - مدیریت ریسک
پیش نمایش مقاله
پیش نمایش مقاله مدل سازی محاسباتی بر اساس عامل پاسخ معرض خطر اجتماعی

چکیده انگلیسی

A characteristic aspect of risks in a complex, modern society is the nature and degree of the public response – sometimes significantly at variance with objective assessments of risk. A large part of the risk management task involves anticipating, explaining and reacting to this response. One of the main approaches we have for analysing the emergent public response, the social amplification of risk framework, has been the subject of little modelling. The purpose of this paper is to explore how social risk amplification can be represented and simulated. The importance of heterogeneity among risk perceivers, and the role of their social networks in shaping risk perceptions, makes it natural to take an agent-based approach. We look in particular at how to model some central aspects of many risk events: the way actors come to observe other actors more than external events in forming their risk perceptions; the way in which behaviour both follows risk perception and shapes it; and the way risk communications are fashioned in the light of responses to previous communications. We show how such aspects can be represented by availability cascades, but also how this creates further problems of how to represent the contrasting effects of informational and reputational elements, and the differentiation of private and public risk beliefs. Simulation of the resulting model shows how certain qualitative aspects of risk response time series found empirically – such as endogenously-produced peaks in risk concern – can be explained by this model.

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