In Stochastic Programming, Statistics or Econometrics, one often looks for the solution of optimization problems of the following form: \begin{equation} \inf_{\theta\in\Theta} \mathbb{E}_{\mathbb{P}_{}} g(\cdot,\theta)=\inf_{\theta\in\Theta} \int_{\mathbb{R}^{q}}g(y,\theta)\mathbb{P}_{}(dy) \end{equation} where $\Theta$ is a Borel subset of $\mathbb{R}^{p}$ and $\mathbb{P}$ is a probability measure defined on $\mathbf{Y}=\mathbb{R}^{q}$ endowed with its Borel $\sigma$-field $\mathcal{B}(\mathbf{Y})$ (but more general spaces can be considered).
The increasing importance of biological sciences for creating value added in many economic sectors contributed to the rise of the now popular term “bioeconomy,” referring to “the set of economic activities relating to the invention, development, production and use of biological products and processes” (OECD, 2009), which are characterized by the accent on the reduction of environmental pollution and the adoption of sustainable practices.
Measurement theory is “a field of study that examines the attribution of values to traits, characteristics, or constructs. Measurement theory focuses on assessing the true score of an attribute, such that an obtained value has a close correspondence with the actual quantity” (APA Dictionary of Psychology, 2nd ed.
Quantifying uniformity of a configuration of points on a space is a topic that is receiving growing attention in computer science, physics and mathematics. The problem has interesting connections with statistics, where several tests of uniformity have been introduced.