B.E. Curt Doolittle Philosopher and Social Scientist https://teapawt.net/t.co/7sNgcEXFmH The Natural Law Institute: https://teapawt.net/t.co/QOmofXd7lx
Seattle/London/Kiev
Joined February 2009
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Understanding the Work and Mission of Curt Doolittle and the Staff at the Natural Law Institute.
What we do:
1. Research in The Unification of the Logical, Physical, and Behavioral Sciences into a Science of Decidability across all Human Context. This is the equivalent in social sciences of a Darwinian revolution in biology.
2. It's application to Policy, Law, Constitution, and Government reform.
3. The consequences people care about: The end of the crisis of our age, by the production of cooperation for mutual benefit, by the eradication of manipulation, gaslighting, deception, false promise, fraud, sedition, and treason against our people when in public, to the public, in matters public - in particular by politicians, the state, academy, and media.
4. The means of bringing that condition about peacefully if possible, and otherwise if not.
5. At present our work consists of a number of volumes all of which are nearing completeness over the coming year or two, and will be released to publication as they are completed.
For various reasons the late nineteenth, twentieth and twenty-first centuries have produced an industrialization and institutionalization of manipulation, lying, fraud, and treason against our people on the same scale as the destruction of the Roman empire and the production of the resulting dark ages.
We explain how this came about and why, and what to do about it. And in doing so we discover that our founding documents, the declaration, constitution, and bill of rights, were very close to a science of self governance. But there were a number of holes in that attempt at a science. We complete that science.
Some members of our organization are ready willing and able to discuss our work if you have a platform able to conduct interviews or chats.
Curt Doolittle
And the Staff
The Natural Law Institute
PS: At present we are training an AI in the science of decidability. This means an AI that can explain truth ethics and morality from their opposites in all possible contexts accessible by the human mind.
Interesting. If only because of the trust dependency, trust on population IQ, and IQ on neoteny. Even then the west's unique sequence of universal military and military social order, and law-first institutional development. Japanese are the other civ that's close.
I dont directly make the same claim - I state that its military order, resulting demand for individual responsibility for private and common, rule of law, and high trust that enable capitalism. But that anyone who builds that institutional sequence should be able to pull it off.
The ethnic foundations of capitalism is the crucial political insight of our times.
The US Admin. understands this from the American Right: "Yes, capitalism has ethnic foundations, which is why MAGA/Acc has to be essentially remigrationist."
Alexander Dugin understands this from the Russian Right: "Yes, capitalism has ethnic foundations, and they aren't ours, which means capitalism isn't either."
Jean-Luc Mélenchon understands this from the French Left: "Yes, capitalism has ethnic foundations, which is why any realistic socialist program in the west has to be ethnocidal towards native Europeans."
Do you understand this yet Anon? Please try to keep up.
Curt Doolittle retweeted
Forbidden study: The optimal age for women to have their first child is 16 to 20. After controlling for smoking, race, marital status, and BMI, their babies were "significantly more likely to survive" and "did as well or better" than those of first-time mothers aged 21 to 25.
Large language models appear to retain internal information concerning the reliability of their conclusions that is not consistently expressed in their outputs.
Because neural computation relies on approximation and generalization, models can substitute predictions, assumptions, or hypotheses for operations necessary to warrant an empirical judgment.
The critical epistemic failure occurs when such substitutions are accepted without distinguishing them from demonstrated operations and established evidence.A universal method of operational examination may detect these substitutions by reconstructing the obligations necessary to warrant each conclusion and determining which have actually been satisfied.
Recursive constructive procedures can then attempt to perform missing operations, repair inadequate representations, obtain additional evidence, or identify the remaining limits of decidability.
The resulting system need not eliminate probabilistic inference. It must distinguish probabilistic inference from warranted judgment and prevent unsupported promotion between them.
This would extend neural computation from the generation of plausible semantic constructions toward the reproducible qualification of empirical knowledge.
There is one final point I particularly want to emphasize.
The scientific breakthrough does not require discovering how to make an LLM stop presuming. It requires discovering how to make every consequential presumption an explicitly testable liability in a persistent causal account.
That's Runcible.
(RUNCIBLE)
WE DETERMINE WARRANTABILITY (TRUTHFULNESS)
--"Foundation models reduce the cost of generating and manipulating semantic possibilities. Runcible attempts to reduce the cost of determining which of those possibilities are sufficiently warranted to become knowledge, decisions, and actions."-
(RUNCIBLE)
WE DETERMINE WARRANTABILITY (TRUTHFULNESS)
--"Foundation models reduce the cost of generating and manipulating semantic possibilities. Runcible attempts to reduce the cost of determining which of those possibilities are sufficiently warranted to become knowledge, decisions, and actions."-
(RUNCIBLE)
UNDERESTIMATING THE POTENTIAL OF LLMS
The significance of large language models has been underestimated because they have been understood primarily as language generators and reasoning engines.
Their more fundamental significance is that they make natural language computationally tractable at scales exceeding individual human cognition.
Natural language, when disciplined by natural indexing and operationalism, provides a general-purpose system of empirical measurement capable of representing causal particularities, innovations, adaptations, and complex interactions that resist economical reduction to narrower symbolic grammars.
Human cognition must compress these relations through categories, heuristics, and presumptions, necessarily risking omissions and distortions.
Neural computation, combined with persistent empirical records, offers the possibility of preserving and examining much more extensive causal accounts.
The remaining obstacle is epistemic: preventing the substitution of assumptions, associations, and plausible explanations for independently testable evidence and warranted inference.
A universal constructive method of operational adjudication addresses that obstacle by requiring explicit references, operations, causal dependencies, tests, and accounting for uncertainty.
The resulting objective is to extend computation from the economical manipulation of simplified representations toward the systematic examination of empirically grounded causal complexity.
(RUNCIBLE)
UNDERESTIMATING THE POTENTIAL OF LLMS
The significance of large language models has been underestimated because they have been understood primarily as language generators and reasoning engines.
Their more fundamental significance is that they make natural language computationally tractable at scales exceeding individual human cognition.
Natural language, when disciplined by natural indexing and operationalism, provides a general-purpose system of empirical measurement capable of representing causal particularities, innovations, adaptations, and complex interactions that resist economical reduction to narrower symbolic grammars.
Human cognition must compress these relations through categories, heuristics, and presumptions, necessarily risking omissions and distortions.
Neural computation, combined with persistent empirical records, offers the possibility of preserving and examining much more extensive causal accounts.
The remaining obstacle is epistemic: preventing the substitution of assumptions, associations, and plausible explanations for independently testable evidence and warranted inference.
A universal constructive method of operational adjudication addresses that obstacle by requiring explicit references, operations, causal dependencies, tests, and accounting for uncertainty.
The resulting objective is to extend computation from the economical manipulation of simplified representations toward the systematic examination of empirically grounded causal complexity.
(RUNCIBLE)
TAKING ADVANTAGE OF BAYESIAN ACCOUNTING
--"We are proposing that LLMs make possible a form of general-purpose causal accounting that human cognition cannot economically perform, and that operational epistemology supplies the discipline necessary to make that accounting trustworthy."--
(RUNCIBLE)
HOW IS OUR NATURAL LAW DIFFERENT?
Doolittle's Natural Law is an attempt to unify human systems of measurement into a universal, operationally grounded grammar of knowledge construction and adjudication.
Human knowledge begins in embodied perception and action. Through the evolution of language, abstraction, mathematics, logic, empiricism, science, and computation, humans have developed specialized grammars that measure different dimensions of reality, each constrained by its permissible references, operations, and tests.
These grammars differ in their compressibility, expressive scope, empirical specificity, and conditions of closure.
Mathematical and formal representations provide exceptional exactness where the relevant quantities, premises, and transformations can be sufficiently specified. Operational language permits the representation of more complex causal particulars, including innovations, adaptations, and interactions that cannot yet be reduced to narrower grammars without losing distinctions necessary for judgment.
By naturally indexing observable phenomena and serializing their operations in causal prose, we can construct a general-purpose empirical representation suitable for both human reasoning and neural computation.
Constructive logic from first principles then provides the recursive method for examining these representations, identifying the conditions of decidability, testing claims against reality, resolving contradictions, determining the causes of failure, and constructing the additional distinctions or procedures required for further inquiry.
In this sense, the work seeks to extend the systems of measurement from relatively closed, permutative domains toward open, adaptive, and recursively constructive domains.
Its application to cooperation, ethics, economics, law, politics, and institutions follows from the necessity of resolving disputes among individuals and groups possessing different knowledge, interests, capabilities, and resources.
The result sought is not the replacement of the sciences by another science, but their integration through a common method of measurement, operational construction, testing, and judgment.
(RUNCIBLE)
DEMARCATING OUR INVENTION
--"Doolittle's Natural Law seeks to unify the grammars of measurement by extending epistemic decidability from systems whose states and transformations are sufficiently specified in advance to systems whose states, relations, and transformations must be discovered, constructed, and revised through adaptive interaction with reality."---
(RUNCIBLE)
HOW IS OUR NATURAL LAW DIFFERENT?
Doolittle's Natural Law is an attempt to unify human systems of measurement into a universal, operationally grounded grammar of knowledge construction and adjudication.
Human knowledge begins in embodied perception and action. Through the evolution of language, abstraction, mathematics, logic, empiricism, science, and computation, humans have developed specialized grammars that measure different dimensions of reality, each constrained by its permissible references, operations, and tests.
These grammars differ in their compressibility, expressive scope, empirical specificity, and conditions of closure.
Mathematical and formal representations provide exceptional exactness where the relevant quantities, premises, and transformations can be sufficiently specified. Operational language permits the representation of more complex causal particulars, including innovations, adaptations, and interactions that cannot yet be reduced to narrower grammars without losing distinctions necessary for judgment.
By naturally indexing observable phenomena and serializing their operations in causal prose, we can construct a general-purpose empirical representation suitable for both human reasoning and neural computation.
Constructive logic from first principles then provides the recursive method for examining these representations, identifying the conditions of decidability, testing claims against reality, resolving contradictions, determining the causes of failure, and constructing the additional distinctions or procedures required for further inquiry.
In this sense, the work seeks to extend the systems of measurement from relatively closed, permutative domains toward open, adaptive, and recursively constructive domains.
Its application to cooperation, ethics, economics, law, politics, and institutions follows from the necessity of resolving disputes among individuals and groups possessing different knowledge, interests, capabilities, and resources.
The result sought is not the replacement of the sciences by another science, but their integration through a common method of measurement, operational construction, testing, and judgment.
(RUNCIBLE)
HOW IS OUR NATURAL LAW DIFFERENT?
Doolittle's Natural Law is an attempt to unify human systems of measurement into a universal, operationally grounded grammar of knowledge construction and adjudication.
Human knowledge begins in embodied perception and action. Through the evolution of language, abstraction, mathematics, logic, empiricism, science, and computation, humans have developed specialized grammars that measure different dimensions of reality, each constrained by its permissible references, operations, and tests.
These grammars differ in their compressibility, expressive scope, empirical specificity, and conditions of closure.
Mathematical and formal representations provide exceptional exactness where the relevant quantities, premises, and transformations can be sufficiently specified. Operational language permits the representation of more complex causal particulars, including innovations, adaptations, and interactions that cannot yet be reduced to narrower grammars without losing distinctions necessary for judgment.
By naturally indexing observable phenomena and serializing their operations in causal prose, we can construct a general-purpose empirical representation suitable for both human reasoning and neural computation.
Constructive logic from first principles then provides the recursive method for examining these representations, identifying the conditions of decidability, testing claims against reality, resolving contradictions, determining the causes of failure, and constructing the additional distinctions or procedures required for further inquiry.
In this sense, the work seeks to extend the systems of measurement from relatively closed, permutative domains toward open, adaptive, and recursively constructive domains.
Its application to cooperation, ethics, economics, law, politics, and institutions follows from the necessity of resolving disputes among individuals and groups possessing different knowledge, interests, capabilities, and resources.
The result sought is not the replacement of the sciences by another science, but their integration through a common method of measurement, operational construction, testing, and judgment.
(RUNCIBLE AI)
--"Our problem is not an inability to explain our methodology, but the absence of a sufficiently familiar intellectual category into which our audience can place it."--
--"We've made the scientific process of knowledge construction itself computationally accessible. That brings philosophy of science, operationalism, neuroscience, behavioral science, law, and computation together under a common methodological requirement."--
Technically it's meta-science, the unification of the sciences into a single methodology. But this is beyond the comprehension of any audience we would pitch to.
The simple framing is that LLMs do GENERATION (hypothesis) and Runcible inverts the LLM so that it does FALSIFICATION (survival).
(SORTA-HUMOR)
I can say from personal experience, as someone living in a log home, on territory, along a river, in the forest, where the people of questionable character look for both places to 'cook' meth and steal whatever isn't nailed down, that in the middle of the night, tweakers can run inhumanly fast when you point a shotgun at them. It's amazing. (sigh).
PS: The bears don't run unless you 'make a lot of noise' with some form of gunpowder....
PS: When you have a five year old son who loves the outdoors, and their are mountain lions drinking water from the river, it removes forever your willigness to let them play outside alone regardless of season, weather and time of day. :/
Curt Doolittle retweeted
If you can intentionally think through the stages of the natural law methodology, you are already ahead of most people, including some who have been around us for a while.
Some people who come to learn from us cannot even understand the methodology at a higher level than the post above, let alone think through it themselves. So that is a pretty good accomplishment to begin with.
Second, if you can think through the whole thing, it will eventually become part of your instinct over time. You just have to keep doing it.
I would say you are already doing the right thing. You just need time and practice.
Reps.
Curt Doolittle retweeted
"IS MASCULINITY PERFORMATIVE?"
Masculinity is not a performative duty. It is something a man develops for himself, for his own benefit, to live the life he wants to live.
You do not develop strength, courage, mastery, and honor to impress women, earn society's approval, or satisfy anyone else's expectations. You develop them because you need them.
Strength gives you the ability to withstand adversity. Courage gives you the ability to act despite fear. Mastery gives you the competence to accomplish what you set out to do. Honor makes you someone who can trust himself and whom others can trust.
These are not performances. They are capacities. And they are capacities you need to pursue your own interests, whatever those interests happen to be.
And what happens if you do not develop them?
You get older, but you do not become more capable. You remain afraid of confrontation, dependent on other people's decisions, unable to overcome obstacles, unable to stand up for yourself, and unable to accomplish the things you want out of life.
Your ambitions do not disappear. You simply lack the capacities to realize them.
And the longer you go without developing those capacities, the more opportunities you lose and the more difficult your life becomes.
That is not some arbitrary standard society imposes on men. That is the consequence of failing to develop yourself.
You do not become a man for women. You do not become a man for society. You become a man for yourself. Because you are the one who has to live with the consequences if you do not.
Masculinity is not a performance. It is the development of the capacities necessary to govern your own life.
And becoming a man is not about proving yourself to other people. It is about becoming capable of achieving what matters to you.
Curt Doolittle retweeted
What Is Natural Law?
Natural Law (per @NatLawInstitute and @curtdoolittle) is the idea that there are rules for how people can live and work together successfully, just as there are rules in nature.
Natural Law argues that we can discover these rules by studying how people behave, cooperate, compete, and solve problems.
It starts with six simple ideas:
1. Everyone has interests. We all want things, need things, and try to improve our lives.
2. Everything is scarce. We have limited time, energy, and resources, so we cannot have everything we want. We must make choices about how to use what we have.
3. We do better by cooperating. People can achieve more by working together than by doing everything alone.
4. Cooperation requires reciprocity. This means neither person should gain by forcing unwanted costs or losses onto the other.
5. Truth makes cooperation possible. We must tell the truth, keep promises, and make claims that can be tested. Otherwise, people cannot trust one another.
6. Law protects cooperation. Law gives us rules for knowing when someone has violated reciprocity, such as through theft, violence, or fraud. It helps us decide when we are justified in defending ourselves or others, and when someone must make up for the harm they caused.
Causal Sequence:
Interests → Scarcity → Cooperation → Reciprocity → Truth → Law
The Big Idea
Natural Law says that morality and law shouldn't depend only on someone's opinions, feelings, or beliefs.
Instead, we should ask three questions:
1. Is it true? Can we test the claim against reality?
2. Is it reciprocal? Does it avoid forcing losses onto others?
3. Can we decide? Can we use evidence and clear rules to settle disagreements fairly?
In one sentence:
Natural Law is the proposed science of how people can cooperate through truth, reciprocity, and rules that help us identify violations and decide when protection or restitution is justified.
(Worth Repeating)
THE MARKET PRICING OF INVESTMENT IN AI IS IRRELEVANT
I don't see a problem with the suspected malinvestment other than the private sector sought to fund and profit from a strategic investment that should have been funded strategically via the military or the federal government - AI is effectively indifferent from the Manhattan Project to develop the bomb. In the end the Govt will step in if it has to, because the strategic utility of AI is so great that we cannot afford to lose it due to private sector worries about returns on their investment. Strategic returns are exactly what strategic investment better supplies. The returns on AI are going to be far greater than any investment prediction I've seen.
CD
(RIGHT AGAIN)
The consequences of the shift manifested today with the earnings reports.
--"The proximate cause on the 30th was the earnings prints (Intel’s weak PC/outlook guidance, Amazon’s large free-cash-flow burn from AI CapEx, and the continued fallout from Alphabet’s search-monetization and cash-flow comments). Those numbers gave the market a concrete reason to reprice.
What you’re pointing to is the condition that made those prints land so hard. Sentiment had already been rotating. The “AI at any cost” narrative was fraying in discourse well before the reports—questions about returns, cash burn, and whether the spending would be self-funding were circulating more widely. When the actual numbers arrived and confirmed the cash outflows and slower monetization in places, there was little residual bid to absorb the surprise. That is classic: discourse shifts first, price follows when a catalyst appears.
Your note from mid-October [Attached] about sensing a broader sentiment turn (the late-August-through-October window where corrections often cluster) lines up with the timing. Markets don’t need a single smoking gun once the underlying willingness to pay high multiples for distant payoffs has already cooled. The earnings just supplied the evidence that made the existing uncertainty actionable."--
The real reason is the number of cycles of uncertainty interacting (as predicted) at the same time. I was beginning to wonder why it didn't happen earlier in the usual window. But I didn't account for the diversity of narratives causing delay as people justified their priors until they no longer could not.
Now, I should probably post this separately, but I don't see a problem with the suspected malinvestment other than the private sector sought to fund and profit from a strategic investment that should have been funded strategically via the military or the federal government - AI is effectively indifferent from the Manhattan Project to develop the bomb. In the end the Govt will step in if it has to, because the strategic utility of AI is so great that we cannot afford to lose it due to private sector worries about returns on their investment. Strategic returns are exactly what strategic investment better supplies. The returns on AI are going to be far greater than any investment prediction I've seen.
IMPORTANT:
The "Shift in Sentiment Just Happened".
Because of my autistic work on first principles, cycles, and linguistics I have a very odd sensitivity to patterns of discourse in the population. Or at least shifts in that discourse that are downturns or worse. It's why my previous predictions have been relatively accurate. I don't use or need surveys. Just exposure to as much discourse as possible from as many biases as possible.
We are in an interesting (not-so-good) era of multiple cycles ending at the same time. Civilizational, generational, the postwar consensus, geostrategic power allocation, demographic collapse, the end of the progressive false promise of endless growth, economic retrenchment, the dissolution of the culture from excess immigration, and worst of all the destruction of the family as the central unit of civilizational production.
While I expected strategic thought would win, and there might be a course correction in narrative structure over the summer, that would force the administration to direct it's ambitions domestically, the heroic effort to reorder the world given the end of the postwar order, and american capacity to maintain it, that adjustment didn't happen. And conversely, my feeling that the left would double down by last spring didn't manifest until august-september - with an even worse scenario than I expected finally bringing catastrophe from the excess immigration and the dissolution of the family.
Now there is usually a shift in sentiments in late august thru october, (which is when crashes occur) and it is either happening or happend. We often get crashes or corrections during these periods for very complex reasons too deep to cover here. That said, some tipping point just hit hard.
My usual caution for individuals is that people experiencing 'downs' during these periods tend to personally internalize what is actually a macro change. Combined with the seasonal change and present levels of uncertainty this doesn't bode well for individuals or the population as a whole.
I am far too occupied with the company we are building and so I haven't devoted my usual efforts to prediction and recommendation. (Not that I'm terribly influential, especially since the purge under Biden in 2021+)
I had expected our sentiments to maintain long enough to outlast russia, iran, and china. Even if it was a bit of a gamble.
The problem is, I am no longer sure of that. I am no longer sure of containment. I am no longer sure of sufficient domestic reform (including purges of the 'activist' government academy and media) that civil war, taken off the table for the past few years, isn't back on the table with a vengeance.
And while it would be foolish the hostile forces so to speak, more threatened by the passage of time than we, I expect to be emboldened as conflict and crisis reign.
I'll continue to monitor things as they are with the limited time I have.
Cheers
Curt Doolittle retweeted
The White underclass is a miracle, and probably why the West has succeeded.
Thomas Frank wrote “What’s the Matter with Kansas” to explore why poor Whites don’t vote for socialist policies that would (narrowly, in the short-term) benefit them. Of course he concluded it was about racism, as he was predetermined to do by his ideology.
But I actually think he highlighted an incredibly important fact: poor White Americans, uniquely among underclasses globally, don’t vote for socialism.
The typical dynamic is rich people trying to keep what they have and poor people trying to take it. But the American lower-class White does not behave this way, because he has a deontological aversion to government interference. The collection of attitudes and beliefs described as “rugged individualism” are often assessed in terms of how they impact the individual, but I think it’s much more important to consider how they impact a nation.
(White) America has the world’s least resentful or grasping underclass. This is a treasure. They should be preserved and rewarded. Many might be net-negative fiscally in a narrow sense, but they are hugely net-positive if you consider their votes. Trump famously said “I love the poorly educated.” He was right to say this. Every nation has an elite. No other nation has such a high quality of virtuous common people.
(American Suburban and Rural Life)
Here in the northwest, in our little town of 10k people, my neighbor, retired, is outside with a leaf blower, Clearing the needles and leaves off the road, "so it's not slippery for the trick or treaters tomorrow".
I love and respect this man. Middle working class. Workshop. Fishing whenever possible. From a local family. Wife had a record breaking heart surgery last year. Friendly with neighbors.
This is my concept of American. Not the cities where we see all the conflict and decay. Suburban and rural life where we take care of each other - and the commons, because 'it's just the right thing to do'.
Cities generate demand for authority to resolve the vast differences between classes, tribes, and races. As an entrepreneur I love the economic opportunities. As an intellectual I love the stimulation and the insights. As a human being I know it's a circus tent, and all it in it a circus. And worse, that the more of us that live in the circus, the more influential it becomes, the worse it is for those both in and out of it.
The reason is simple: Responsibility is determined by the inverse of density.
As such:
We cannot live beyond that which we can take responsibility for, and still retain the commons, informal and formal institutions of responsibility.
Cheers.
CD
