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Wisdom is a very difficult construct to work with in research and practice. One reason for this is that wise people can deal with metaphysical questions and experience spiritual phenomena, both of which are hard to measure meaningfully. Although metaphysical and spiritual matters are not imponderable, they have significant measurement problems that are also part of the shortcomings of standard social science statistical frameworks. A second reason is that for many wisdom theorists, wisdom is context-dependent because wisdom is defined by and responds to what its context presents to it. We can therefore argue that wisdom is essentially context, which in quantum physics is theorised as a superposition of random variables that interact. This chapter, therefore, ponders the ‘immeasurable’ from the perspective of quantum-like social science and quantum theory to render wisdom, including its spirituality component, in formal mathematical models. The mathematical formalism of quantum physics allows for the presence of metaphysical phenomena in its ontological foundations and its mathematical models. This chapter, therefore, also presents an argument for understanding wisdom from the superposition perspective and, in particular, the internal interactions between random variables contained within it. If the challenge of measuring wisdom as a nondeterministic system is met, we may finally have an opportunity to measure wisdom in ways that embrace wisdom's complex ontology. A third reason is that wisdom depends on people making first-person subjective judgements. Subjectivity is central to many interpretations of quantum theory, and we can borrow the analytical formalism used in quantum physics for wisdom research. Finally, the chapter discusses future approaches to empirical wisdom research that adopt quantum-like social science methods.

In this chapter, the interplay between the development of the discipline, the development of the field of study, and the emergence of professional fields is examined using the example of mathematics. In connection with the formation of the modern research university, mathematics has emerged as an independent scientific discipline and as an independent field of study. In the process, mathematics attains a high degree of formalization and internal coherence. This is the basis for the penetration of mathematicians into more and more professional fields, even outside science. Real problems or real facts are reduced to aspects that are amenable to mathematical modeling by treating them as quantifiable parameters. As mathematics expands as a field of study, more and more professional sectors become applications of mathematical models. As a consequence, more mathematical fields of study are differentiating themselves, specializing in these application fields. This chapter analyzes this dynamic and its preconditions.

This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.