4.3 Article

Navigating inconsistent preferences: A multimethod approach to making informed decisions

期刊

出版社

WILEY
DOI: 10.1111/csp2.469

关键词

Chesapeake Bay; conservation; decision analysis; elicitation; multicriteria analysis

向作者/读者索取更多资源

This study introduces a decision-analytic framework for prioritizing conservation strategies by combining direct and indirect preference-elicitation methods and analyzing inconsistencies between them. Results indicate that understanding potential bias in the direct method and clarifying assumptions in the indirect method can reduce inconsistencies, providing useful insights for decision-making.
This study presents a decision-analytic framework for prioritizing conservation strategies. The framework is based on combining direct and indirect preference-elicitation methods and analyzing inconsistencies between the methods. A case study with The Nature Conservancy's Chesapeake Bay (The United States) agriculture team is presented. Participants evaluated six strategies to engage with agribusinesses, farmers, and farm landowners and increase adoption of nutrient and soil conservation and stream and wetland restoration activities. Impact, feasibility, and risk criteria and performance measures were developed to compare the strategies. Participants individually evaluated the strategies using a multimethod approach. One method included direct ranking based on an intuitive assessment of the strategies. The second method included indirect ranking based on swing weighting and multicriteria analysis. Some participants made adjustments to reduce inconsistencies between the methods. Results show that final rankings were more consistent than initial rankings. Inconsistencies can be reduced by understanding potential bias in the direct method and clarifying assumptions in the indirect method. This study provides evidence that a multimethod approach can deliver useful insights to inform decisions.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据