Discussing Landscape Compositional Scenarios Generated with Maximization of Non-Expected Utility Decision Models Based on Weighted Entropies

Abstract

Abstract:The search for hypothetical optimal solutions of landscape composition is a major issue in landscape planning and it can be outlined in a two-dimensional decision space involving economic value and landscape diversity, the latter being considered as a potential safeguard to the provision of services and externalities not accounted in the economic value. In this paper, we use decision models with different utility valuations combined with weighted entropies respectively incorporating rarity factors associated to Gini-Simpson and Shannon measures. A small example of this framework is provided and discussed for landscape compositional scenarios in the region of Nisa, Portugal. The optimal solutions relative to the different cases considered are assessed in the two-dimensional decision space using a benchmark indicator. The results indicate that the likely best combination is achieved by the solution using Shannon weighted entropy and a square root utility function, corresponding to a risk-averse behavior associated to the precautionary principle linked to safeguarding landscape diversity, anchoring for ecosystem services provision and other externalities. Further developments are suggested, mainly those relative to the hypothesis that the decision models here outlined could be used to revisit the stability-complexity debate in the field of ecological studies.

Performance of Portfolios Based on the Expected Utility-Entropy Fund Rating Approach

“…Dong et al [ 32 ], Xia et al [ 33 ], and Xia et al [ 34 ] presented several discussions on the EU-E decision model and emphasizing the role of Shannon entropy in the field of decision-making under risk. Casquilho and Rego [ 35 ] used decision models with different utility values combined with weighted entropies, respectively, incorporating rarity factors associated with Gini-Simpson and Shannon measures. In their paper, they provided an example of this decision framework for landscape compositional scenarios in Portugal.…”

Model Projections in Model Space: A Geometric Interpretation of the AIC Allows Estimating the Distance Between Truth and Approximating Models

“…Recent decades have witnessed a remarkable growth of statistical ecology as a discipline, and today, stochastic models of complex ecological processes are the hallmark of the most salient publications in ecology (e.g., Leibold et al, 2004 ; Gravel et al, 2016 ; Zeng and Rodrigo, 2018 ). Entropy and the Kullback-Liebler divergence as instruments of scientific inquiry are now at the forefront of the toolbox of quantitative ecologists, and many exciting new opportunities for their use are constantly being proposed (e.g., Casquilho and Rego, 2017 ; Fan et al, 2017 ; Kuricheva et al, 2017 ; Milne and Gupta, 2017 ; Roach et al, 2017 ; Cushman, 2018 ). One of the most important, but under explored, applications of the Kullback-Liebler divergence remains the study or characterization of the error rates incurred while making model selection according to information criteria ( Taper and Ponciano, 2016b ).…”

Performance of Portfolios Based on the Expected Utility-Entropy Fund Rating Approach

“…Dong et al [31], Xia et al [32], and Xia et al [33] presented several discussions on the EU-E decision model, emphasizing the role of Shannon entropy in the field of decision-making under risk. Casquilho and Rego [34] used decision models with different utility values combined with weighted entropies, incorporating rarity factors associated to Gini-Simpson and Shannon measures. In their paper, they provided an example of this decision framework for landscape compositional scenarios in Portugal.…”