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Agent-based computational modelling of social risk responses

机译:基于主体的社会风险应对计算模型

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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. (C) 2015 Elsevier B.V. All rights reserved.
机译:在复杂的现代社会中,风险的一个典型方面是公众反应的性质和程度-有时与风险的客观评估大相径庭。风险管理任务的很大一部分涉及对此响应的预期,解释和反应。我们用于分析紧急情况下公众反应的主要方法之一,即风险框架的社会放大,一直是很少建模的主题。本文的目的是探索如何表示和模拟社会风险放大。风险感知者之间异质性的重要性以及他们的社交网络在塑造风险感知中的作用,使得采用基于代理的方法变得很自然。我们特别研究如何对许多风险事件的一些中心方面建模:在形成风险认知时,行为者对外部行为者的观察要比外部事件更多。行为既遵循风险感知并塑造风险的方式;以及根据对先前通信的响应来构建风险通信的方式。我们展示了可用性级联如何表示这些方面,但是这又如何造成更多问题,如如何表示信息和声誉因素的对比效果,以及私人和公共风险信念的差异。对结果模型的仿真表明,凭经验发现的风险响应时间序列的某些定性方面(例如,风险关注中的内生峰值)可以由该模型解释。 (C)2015 Elsevier B.V.保留所有权利。

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