Metanormativity: Solving questions about moral and empirical uncertainty

Ethic@: An International Journal for Moral Philosophy 19 (3):790-810 (2020)
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Abstract

How can someone reconcile the desire to eat meat, and a tendency toward vegetarian ideals? How should we reconcile contradictory moral values? How can we aggregate different moral theories? How individual preferences can be fairly aggregated to represent a will, norm, or social decision? Conflict resolution and preference aggregation are tasks that intrigue philosophers, economists, sociologists, decision theorists, and many other scholars, being a rich interdisciplinary area for research. When trying to solve questions about moral uncertainty a meta understanding of the concept of normativity can help us to develop strategies to deal with norms themselves. 2nd-order normativity, or norms about norms, is a hierarchical way to think about how to combine many different normative structures and preferences into a single coherent decision. That is what metanormativity is all about, a way to answer: what should we do when we don’t know what to do? In this study, we will review a decision-making strategy dealing with moral uncertainty, Maximization of Expected Choice-Worthiness. This strategy, proposed by William MacAskill, allows for the aggregation and inter-theoretical comparison of different normative structures, cardinal theories, and ordinal theories. In this study, we will exemplify the metanormative methods proposed by MacAskill, using has an example, a series of vegetarian dilemmas. Given the similarity to this metanormative strategy to expected utility theory, we will also show that it is possible to integrate both models to address decision-making problems in situations of empirical and moral uncertainty. We believe that this kind of ethical-mathematical formalism can be useful to help develop strategies to better aggregate moral preferences and solve conflicts.

Author's Profile

Nythamar De Oliveira
Pontifical Catholic University of Rio Grande do Sul

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