Convex Cones, Assessment Functions, Balanced Attributes

We investigate a class of polyhedral convex cones, with $$R^k_+$$ (the nonegative orthant in $$\mathbb {R}^k$$ ) as a special case. We start with the observation that for convex cones contained in $$R^k_+$$ , the respective cone efficiency is inconsistent with Pareto efficiency, the latter being deeply rooted in economics, decision theory, and multiobjective optimization theory. Despite that, we argue that convex cones contained in $$R^k_+$$ and the respective cone efficiency are also relevant to these domains. To demonstrate this, we interpret polyhedral convex cones of the investigated class in terms of assessment functions, i.e., functions that aggregate multiple numerical attributes into single numbers. Further, we observe that all assessment functions in the current use share the same limitation; that is, they do not take explicitly into account attribute proportionality. In consequence, the issue of attribute balance (meaning balance of attribute values) escapes them. In contrast, assessment functions defined by polyhedral convex cones of the investigated class, contained in $$R^k_+$$ , enforce attribute balance in a more dominant manner. However, enforcing attribute balance is, in general, inconsistent with the well-established paradigm of Pareto efficiency. We give a practical example where such inconsistency is meaningful.