Sorted-pareto dominance and qualitative notions of optimality

Pareto dominance is often used in decision making to compare decisions that have multiple preference values – however it can produce an unmanageably large number of Pareto optimal decisions. When preference value scales can be made commensurate, then the Sorted-Pareto relation produces a smaller, more manageable set of decisions that are still Pareto optimal. Sorted-Pareto relies only on qualitative or ordinal preference information, which can be easier to obtain than quantitative information. This leads to a partial order on the decisions, and in such partially-ordered settings, there can be many different natural notions of optimality. In this paper, we look at these natural notions of optimality, applied to the Sorted-Pareto and min-sum of weights case; the Sorted-Pareto ordering has a semantics in decision making under uncertainty, being consistent with any possible order-preserving function that maps an ordinal scale to a numerical one. We show that these optimality classes and the relationships between them provide a meaningful way to categorise optimal decisions for presenting to a decision maker

The effects of Δ9-tetrahydrocannabinol on the dopamine system

Δ(9)-tetrahydrocannabinol (THC), the main psychoactive ingredient in cannabis, is a pressing concern to global mental health. Patterns of use are changing drastically due to legalisation, availability of synthetic analogues (‘spice’), cannavaping and aggrandizements in the purported therapeutic effects of cannabis. Many of THC’s reinforcing effects are mediated by the dopamine system. Due to complex cannabinoid-dopamine interactions there is conflicting evidence from human and animal research fields. Acute THC causes increased dopamine release and neuron activity, whilst long-term use is associated with blunting of the dopamine system. Future research must examine the long-term and developmental dopaminergic effects of the drug

Surface Fitting using Multicriteria Optimization Techniques

The quality of freeform surfaces is one of the major topics of CAD/CAM. Aesthetic and technical demands require the construction of high quality surfaces with strong shape conditions. Quality diminishing properties like dents or flat points have to be eliminated while approximation conditions must hold at the same time. Our approach combines quality and approximation criteria to a nonlinear multicriteria optimization problem and achieves an automatic approximation and fitting process

Surface Fitting using Multicriteria Optimization Techniques

The quality of freeform surfaces is one of the major topics of CAD/CAM. Aesthetic and technical demands require the construction of high quality surfaces with strong shape conditions. Quality diminishing properties like dents or flat points have to be eliminated while approximation conditions must hold at the same time. Our approach combines quality and approximation criteria to a nonlinear multicriteria optimization problem and achieves an automatic approximation and fitting process