title: Panaceas and diversification of environmental policy.
abstract: 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.
authors: Department of Economics, University of Wisconsin, Madison, WI 53706, USA.
authors: William A
authors: WA
authors: Brock
authors: Stephen R
authors: SR
authors: Carpenter
mesh_terms: D000818:Animals; D003247:Conservation of Natural Resources; D004463:Ecology; D017753:Ecosystem; D004777:Environment; D004784:Environmental Monitoring; D006801:Humans; D011050:Policy Making; D011640:Public Policy; D013269:Stochastic Processes; D013997:Time Factors