Risk Assessment under Ambiguity: Precautionary Learning vs. Research Pessimism

Risk Assessment under Ambiguity: Precautionary Learning vs. Research Pessimism
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模糊性下的风险评估:预防性学习与研究悲观主义

DOI:
10.11588/heidok.00019999
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发表时间:
2015
期刊:
影响因子:
--
通讯作者:
Wiesenfarth
Wiesenfarth
中科院分区:
--
文献类型:
--
作者:
Goeschl;Wiesenfarth

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负责监管食品安全或新物质等复杂风险的机构经常需要在不明确的情况下就风险评估和风险管理作出决定,即不能为监管行动的可能结果分配概率。社会应该为这些机构制定哪些任务?在当前的讨论中,有两种方法比较突出。一种是指责该机构应用基于预期效用理论的福利经济学。这种方法支持传统的成本-最低成本分析(CBA)。另一种要求应用模糊厌恶决策规则--其中最著名的是最大期望效用(MEU)--以便根据预防原则(PP)建立安全边际。本文件的贡献是对CBA和PP授权如何影响风险评估的监管任务进行相对评估。在我们的简约模型中,决策者可以决定信号的精度,该信号提供了与收益相关的参数的噪声信息。我们发现,MEU在信息获取方面存在着复杂的相互作用,这两股力量被我们称为“预防性学习”和“研究悲观主义”。我们发现,与直觉相反,PP而不是CBA的授权往往会导致监管机构消息灵通。因此,PP可能导致更高的监管错误可能性,例如批准有害的新物质。
Agencies charged with regulating complex risks such as food safety or novel substances frequently need to take decisions on risk assessment and risk management under conditions of ambiguity, i.e. where probabilities cannot be assigned to possible outcomes of regulatory actions. What mandates should society write for such agencies? Two approaches stand out in the current discussion. One charges the agency to apply welfare economics based on expected utility theory. This approach underpins conventional cost-benet analysis (CBA). The other requires that an ambiguity-averse decision-rule - of which maxmin expected utility (MEU) is the best known - be applied in order to build a margin of safety in accordance with the Precautionary Principle (PP). The contribution of the present paper is a relative assessment of how a CBA and a PP mandate impact on the regulatory task of risk assessment. In our parsimonious model, a decision maker can decide on the precision of a signal which provides noisy information on a payoff-relevant parameter. We find a complex interplay of MEU on information acquisition shaped by two countervailing forces that we dub 'Precautionary Learning' and 'Research Pessimism'. We find that - contrary to intuition - a mandate of PP rather than CBA will often give rise to a less informed regulator. PP can therefore lead to a higher likelihood of regulatory mistakes, such as the approval of harmful new substances.
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