Academic Methods

I will explore the validity and ulitity of a variety of methods employed throughout my writings on this site within this topical domain. These include:

• The application of formal and inductive logic.
• The logistics of statistical approximation and their availability to modern rapid deployment (i.e., “quick use”) philosophical programs.
• The relative softness or hardness of various laws within the Beyesian inference formulas.
• Whether there is still any use in Leibniz’s probability theorems.
• To what degree economic game theory can (or ought to) govern the determination of credibility, which comes down to the nature of utility (i.e., the preference of an economic agent manifesting their will [i.e. an “actor” in economic terms]).

Scientific Methods

The scientific method, as Kuhn and others have explained, is always being revised by its own principles, and is there for simultaneously self-verifying and self-cannibalizing. This raises questions for me such as:

• How effectively can we automate the retroactive verification of previous certainty statuses as the controlling method is debugged?
• What are the exact functions, methods, hooks, classes, and variables that make evidentiary custodial practices self-verifying?
• How do different systems of logic, such as S4 and S5, when utilized within an experiment or analysis, transform the tools, capabilities, and limitations of the scientific method?

Research Design

As I refine my approach to harvesting information, synthesizing those harvests into reportable results, and publishing them, I am faced with certain challenges that need to be better defined before they are answered, which are of supreme material relevance to my own process as efficiency becomes only more important as I get older. These issues in need of definition include:

• The appropriate SQL or noSQL approach to data storage, as well as the relevant applications and packages within R and Python, for storing and synthesizing insights at scale.
• What taxonomic system best accommodates growth-at-scale for that can bridge qualitative insights such as philosophy, and quantitative data, while at the same time making their variables available to economic calculators?
• What sort of theses are provable? And, consequently, what sort of questions can I filter out of my research design by classifying them as absurd, tautological, incoherent, apophatic, or inert?
• What role does the academy, and peer-reviewed journals, play in the future of knowledge acquisition and generation? And, to what degree can the slowness of these institutions deserve still to be respected through an attempt to update their methods of inquiry and data storage?