Michael Springborn

Learning in a noisy environment: Adaptive management of ecosystem services under uncertainty

Michael Springborn and Jim Sanchirico
Department of Environmental Science and Policy, University of California, Davis
Day 1 15:10-15:50

In this paper we extend the deterministic mangrove fishery, ecosystem service model of Sanchirico and Springborn (2011) to incorporate both risk (irreducible uncertainty) and uncertainty that is reduced over time through learning. We demonstrate how to handle multilevel (hierarchical) models of the natural system—which are increasingly relied upon to more realistically describe complex ecological processes—but are “inconvenient” in the sense that the decision-maker’s post-observational updated beliefs about the nature of the system do not follow a convenient closed (conjugate) form. To facilitate this we develop a method for approximating the decision-maker’s posterior beliefs using a Kullback–Leibler divergence approach. The full management model describes optimal management of both a harvestable resource (fish) and the ecosystem on which it may depend (mangroves) as a function of the state of the system, which includes both fish and mangrove stocks as well as current information or beliefs about mechanics. We examine how passive and active learning strategies change management of a fishery, and demonstrate the economic gains from incorporating a learning approach into standard resource management.


Michael Springborn

Assistant Professor. I work in the areas of resource and environmental economics. I typically focus on problems involving decision-making under uncertainty which may feature environmental risk and incorporate learning strategies such as adaptive management. The methods I use include econometrics with Bayesian inference, Bayesian learning processes, dynamic control and general equilibrium models. Recent and current projects include: combined bioeconomic and quantitative genetic models for salmon biodiversity management; estimating and mitigating invasive species risk from international trade; adaptive management of environmental risk; econometrics for decision-making applied to screening of potentially invasive species imports; analysis of greenhouse gas control policies under uncertainty.