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From the Cover: Going Beyond Panaceas Special Feature: Panaceas and diversification of environmental policy

机译:从封面:超越万能药专题:万能药和环境政策的多样化

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摘要

We consider panacea formation in the framework of adaptive learning and decision for social–ecological systems (SESs). Institutions for managing such systems must address multiple timescales of ecological change, as well as features of the social community in which the ecosystem policy problem is embedded. Response of the SES to each candidate institution must be modeled and treated as a stochastic process with unknown parameters to be estimated. A fundamental challenge is to design institutions that are not vulnerable to capture by subsets of the community that self-organize to direct the institution against the overall social interest. In a world of episodic structural change, such as SESs, adaptive learning can lock in to a single institution, model, or parameter estimate. Policy diversification, leading to escape from panacea traps, can come from monitoring indicators of episodic change on slow timescales, minimax regret decision making, active experimentation to accelerate model identification, mechanisms for broadening the set of models or institutions under consideration, and processes for discovery of new institutions and technologies for ecosystem management. It is difficult to take all of these factors into account, but the discipline that comes with the attempt to model the coupled social–ecological dynamics forces policy makers to confront all conceivable responses. This process helps induce the modesty needed to avoid panacea traps while supporting systematic effort to improve resource management in the public interest.
机译:我们在适应性学习和社会生态系统(SESs)决策框架内考虑灵丹妙药的形成。用于管理此类系统的机构必须解决生态变化的多个时间尺度,以及生态系统政策问题所在的社会社区的特征。必须对SES对每个候选机构的响应进行建模,并将其视为具有未知参数的随机过程进行估计。一个根本的挑战是设计一个不易被自组织的社区子集捕获的机构,以指导该机构违背整体社会利益。在诸如SES之类的突发性结构变化世界中,适应性学习可以锁定单个机构,模型或参数估计。政策多样化可能导致摆脱万灵药陷阱,这可能来自于在缓慢的时间尺度上监控情节变化的指标,最小最大遗憾决策,积极尝试以加快模型识别,扩展正在考虑的模型或机构的机制以及发现过程用于生态系统管理的新机构和技术。很难将所有这些因素都考虑在内,但是,试图对社会生态动态耦合进行建模的学科迫使政策制定者面对所有可能的应对措施。这个过程有助于引起避免万灵药陷阱所需的谦虚,同时支持系统性的努力来改善公共利益的资源管理。

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