We analyze ecosystem management under 'unmeasurable' Knightian uncertainty or ambiguity which, given the uncertainties characterizing ecosystems, might be a more appropriate framework relative to the classic risk case (measurable uncertainty). This approach is used as a formal way of modelling the precautionary principle in the context of least favorable priors and maxmin criteria. We provide biodiversity management rules which incorporate the precautionary principle. These rules take the form of either minimum safety standards or optimal harvesting under precautionary approaches.
We analyze ecosystem management under 'unmeasurable' Knightian uncertainty or ambiguity which, given the uncertainties characterizing ecosystems, might be a more appropriate framework relative to the classic risk case (measurable uncertainty). This approach is used as a formal way of modelling the precautionary principle in the context of least favorable priors and maxmin criteria. We provide biodiversity management rules which incorporate the precautionary principle. These rules take the form of either minimum safety standards or optimal harvesting under precautionary approaches.
Optimal portfolio rules are derived under uncertainty aversion by formulating the portfolio choice problem as a robust control problem. The robust portfolio rule indicates that the total holdings of risky assets as a proportion of the investor's wealth could increase as compared to the holdings under the Merton rule, which is the standard risk aversion case. In particular, with two risky assets and one risk-free asset, we show that uncertainty aversion could lead to an increase in the holdings of the one risky asset, accompanied by a reduction in the holdings of the other risky asset. Furthermore, in the optimal robust portfolio the investor may increase the holdings of the asset for which there is or less ambiguity, and reduce the holdings of the asset for which there is more ambiguity, a result that might provide an explanation of the home bias puzzle. 1 We would like to thank William Brock and Angelos Kanas for helpful comments and suggestions.
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