Risk aversion and adaptive management: Insights from a multi-armed bandit model of invasive species risk

Michael R. Springborn

Journal of Environmental Economics and Management, 2014, vol. 68, issue 2, 226-242

Abstract: This article explores adaptive management (AM) for decision-making under environmental uncertainty. In the context of targeting invasive species inspections of agricultural imports, I find that risk aversion increases the relative value of AM and can increase the rate of exploratory action. While calls for AM in natural resource management are common, many analyses have identified modest gains from this approach. I analytically and numerically examine the distribution of outcomes from AM under risk neutrality and risk aversion. The inspection decision is framed as a multi-armed bandit problem and solved using the Lagrangian decomposition method. Results show that even when expected gains are modest, asymmetry in the distribution of outcomes has important implications. Notably, AM can serve to buffer against large losses, even if the most likely outcome is a small loss.

Keywords: Adaptive management; Approximate dynamic programming; Multi-armed bandit; Decision-making under uncertainty; Bayesian learning; Invasive species; Risk aversion (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeeman:v:68:y:2014:i:2:p:226-242

DOI: 10.1016/j.jeem.2014.05.004

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