February 26, 2018

Clarifying The Scientific Type of Knowledge

To target the right things in our theorizing, we have to know what the New Statecraft will look like. What kind of knowledge are we going for?

We have a hypothesis. The hypothesis has three major claims:

  1. That true sociology, and statecraft based on it, can be properly scientific in the structure and type of the knowledge.

  2. That any actualized discipline of statecraft will by necessity postively promote some complete and coherent Social Order, a sort of Pattern Language of civilization. And that such a Social Order is what we need.

  3. That the new Social Order should be conceived of as a set of interlinked Social Technologies, explained and justified on a scientific basis.

We won’t here deal with #2 and #3. We will save them for later. Here we will clarify #1.

By “scientific” here we do not mean the modernist idea that society can be “rationally” planned from the top down by packing everything into highly legible rectangular structures. Nor do we mean the “empirical” statistical approach that came to dominate the social sciences in the 1930s and after.

By “scientific” we mean that our body of knowledge and social technologies will be phrased in terms of:

  1. Key concepts and laws which correspond to real “measurable” features and regularities of reality. We don’t here mean things like “interest rate”, but rather things like “this person’s state of mind”, “that person’s empire (region of coordinated resources)”, and “this concrete social arrangement”. Which concepts and laws ultimately have logical relation to the laws in other areas of science.

  2. Mathematical structures of deduction and description. Not necessarily number, which is not often a useful concept in highly non-linear domains like society, but things which can be precisely defined with a deductive toolbox related to the rest of mathematics.

As we go, our understanding of what a proper science looks like in this area will improve, but for now the operating hypothesis is that we should be driving towards this kind of knowledge.

Other kinds of knowledge that we believe are inadequate for this task are:

  1. Ideological, which is to say a set of memes which are compelling to human psychology. We will need an ideology, but it too should be an engineered technology to accomplish our ends, not our primary working medium.

  2. Mystical, where the knowledge itself is a scred artifact not to be understood in detail, but just used as mysterious guidance. For example, mystical thinking would be if we take Julius Evola’s ideas, or some traditional system of knowledge about the Golden Age and Kali Yuga, too seriously, and find ourselves unwilling to fully own the ideas and critique them and upgrade them.

  3. Statistical, which is what social science has been doing for the last 80 years or so, where the focus is on accumulating lots of measurements of questionable correlations to try to get at underlying cause and effect without any guiding depth of theory.

  4. Rationalistic A-Priori, which is what the later Mises-derived Austrian economists claim to be doing. This is a problem because it has no formal allowance for being corrected by reality, no ability to extend its knowledge beyond the absolutely provable, and no ability to evaluate technology except as moral certainties derived purely a-priori. The sensible parts of this, like logical deduction, fit perfectly fine in the scientific framework, and the claims to “synthetic a-priori” epistemic status are wholly unnecessary.

  5. Heuristical, where we build a large body of pre-scientific rules of thumb and common phenomena and working technologies, related to each other only roughly and by metaphor, rather than by rigorously shared concepts, laws, and underlying logic. This is a necessary stage of knowledge, but inadequate as final destination.

In our research up to this point, we seem to do a mix of ideological, proto-scientific, and hueristical reasoning, with a trace of mystical thrown in usually for fun and metaphor. The aim in this project is to push towards a scientific type of knowledge.

We should comment that science, especially engineering science, makes no claims to a-priori knowledge, and no claims to analytic knowledge. Both of those are both metaphysically contentious and not actually necessary. Science does its math as logical deduction on physical-referent propositions, and holds the test of reality, rather than reasonableness to the mind, as the ultimate test.

These should not be taken as positive metaphysical claims, but as self-imposed limitations on the kind of claims allowed in a body of scientific knowledge. These limitations are made to minimize the “metaphysical footprint” so to speak, so that the claims we do make can be as solid as possible. Nobody but the most radical skeptic denies the synthetic a-posteriori.

(That said, we should in more general circumstances take an epistemically optimistic stance. It is possible to know things by reason and experience. The mind works pretty well especially when trained, the senses mostly percieve reality, etc. This is often denied by modern science fans, with science taking on an almost mystical character as a set of rituals which take place in the temple of academia to produce special knowledge that is much more reliable than one’s own deductions and observations. This modern perception of science is interestingly contrary to the classical “Nullius in Verba” science of the Royal Society.)

So we are trying to drive towards a “scientific” type of knowledge.

In practice, this means as we do our theorizing and make our analyses, we should spend extra energy to invest in the following activities, which pay off over the long term in a more scientific body of knowledge:

  1. Clarify Concepts. Spend time to get very precise about exactly what a concept does and does not refer to, exactly what it means, and exactly how it is related to other concepts.

  2. Make Rigorous Causal Arguments. When we make arguments, get obnoxiously detailed about exactly what is assumed, and which follows from what. Numbered arguments are good here.

  3. Try to Falsify Claims. When we have a claim that we are making, treat it as a hypothesis. Find counterexamples. Try hard to break it. If counterexamples can be found, discard or fix the claim. When discarded, come up with a better one.

  4. Make Claims of Totality. When a theory has held up so far, especially a new and suprising theory, there is still a temptation to treat it as a heuristic guideline, not a total law. To generate truly solid theory, we have to treat our theories like total laws, even if it seems crazy. Apply a law absolutely in all circumstances, seeking falsification, until its lack of falsification becomes shockingly noticable, or it is falsified.

  5. Aim For General Laws. When making claims, look for ways that multiple hard laws or examples can be unified by a single principle. See if the single more general principle holds up in new areas.

There are other key things to do to generate science, but these will get us started. To follow rule #4, we would say “These and no others are the necessary and sufficient techniques of the Scientific Method”, but we are not here attempting to produce a definitive method of Science.

This whole article is an example of #1, conceptual clarification, on the idea of scientific knowledge.

This is all on the level of the individual theorist. There will be additional things to consider and do when dealing with a group of theorists (as we must), which we are not addressing here.

Edited and curated by Wolf Tivy

Comments? Email comments@newstatecraft.org