Curt Doolittle

Theme: Science

  • RE: @Science You want funding? Separate the hard science from the pseudoscience,

    RE:
    @Science

    You want funding? Separate the hard science from the pseudoscience, fix the mathiness problem in physics that’s crippled it for more than fifty years. Fix the replication problem in the behavioral sciences, Fix the sophistry and conformity problem in the humanities (philosophy theses are intellectually embarrassing). End the publication hamster wheel of junk science production, separate teaching staff from research staff, prohibit research with a leftist agenda of perpetuating 20th century pseudoscience, and stop the prohibition on research on human differences such that we can create policy to accommodate those differences instead of preserving conflict because we don’t. Cut the production of PhD’s so that the output meets market demand. Give primacy to american citizens in admission. Balance left and right professors so students see both perspectives. End the nonsense (gut) courses that are simple seditions. Otherwise you’re just part of the corruption problem hiding under pretense of ‘scientific neutrality’. You are not producing the scientific and governing classes we need. You’re producing the same corrupt bureaucracies as we faced with the church. The “Managerial State” has been a failure.

    We wouldn’t need think tanks to augment academic research (like ours) if the academy did it’s job instead of replacing the supernatural clericy with a pseudoscientific one, that because it’s dominated by women seek to amplify consensus and suppress innovation.

    The amount of time I spend debunking pseudoscientific claims that are then magnified by a sensationalist media is absurd.

    You know the cause of all this? We prohibit IQ tests in employment, which causes people to get nonsense degrees, go deeply into debt, corrupt the incentives of our academy, produce seditionists instead of scientists and public servants, and mass produce junk science, and replicate in academic corruption what has been achieved by the left in financing NGO corruption.

    I mean. You excuse yourselves. Every day. But you’re all culpable by tolerating it. Moreover you oddly believe your own nonsense.

    It’s exasperating.

    CD

    Source date (UTC): 2026-01-21 20:03:49 UTC

    Original post: https://twitter.com/i/web/status/2014066425091203424

  • WHITEST FOODS I’m not sure why I find this so humorous – probably because someon

    WHITEST FOODS
    I’m not sure why I find this so humorous – probably because someone went to the trouble of ‘science-ing’ it.

    A) Alcohol.
    B) Lactose Tolerance
    C) Carbs (the enemy of white people everywhere)

    Source date (UTC): 2026-01-01 20:36:38 UTC

    Original post: https://twitter.com/i/web/status/2006826922777661785

  • Its bad enough that the Indians try to concoct this nonsense. Now east asians ar

    Its bad enough that the Indians try to concoct this nonsense. Now east asians are trying the same?
    There was gene flow both directions. There is no other ‘implication’.

    Source date (UTC): 2025-12-30 16:43:53 UTC

    Original post: https://twitter.com/i/web/status/2006043573771460616

  • I push back on McGilchrist frequently because I’m aware of his agenda: 1. hemisp

    I push back on McGilchrist frequently because I’m aware of his agenda:
    1. hemispheric bias … just bias, not exclusive specialization. Also, in the visual cortex (back of the brain, largely no. Sex differences do appear here in amplitude and tas evoked activation. once we move beyond v1 (think of the back of the brain as an archery target with rings radiating out from the center) we start to see some predicted biases such as right face bias vs left word-form. These are matters of degree only.
    2. The bias becomes more obvious in connectivity: male specialization within hemispheric connection, vs female generalization which connects the hemispheres. It’s actually hemispherically integrated (f/r/more-slow/in-time) vs hemispherically specialized (m/l/less-fast/over-time)
    3. This results in systematizing (over time) vs empathizing (in time). Brain organization constrains bias in processing. Brain is organized in utero and in early development.
    4. Correct model that reflects universal sex differences in behavior is predator (m/left) bias vs prey (f/right) bias.

    All that said, defeating McGilchrist’s ‘fictionalism’ is rather easy: (via CurtGPT)
    1. Category error: hemispheric lateralization is an implementation feature of distributed networks; it is not a pair of epistemic agents. Treating hemispheres as agents confuses mechanism with narrative.
    2. Level conflation: claims about civilization-scale “modes of being” cannot be inferred from circuit-level lateralization without a bridging model that specifies intermediate levels (policy, incentives, institutions). Without that bridge, it is a storytelling leap.
    3. Non-uniqueness: even where lateralization exists, multiple architectures can implement the same policy. Therefore “left vs right” cannot be the explanatory primitive. Incentives and loss functions must be.

    Source date (UTC): 2025-12-27 08:23:36 UTC

    Original post: https://twitter.com/i/web/status/2004830509629931880

  • AN IMPORTANT THOUGHT Because our modeling of the world evolved from the physical

    AN IMPORTANT THOUGHT
    Because our modeling of the world evolved from the physical to the economic, we tend to think that’s a dependency. And instead, I might extend my argument by saying that for human reasoning, the physical world and how we think of it in cardinal indexing and measure by mathematical reduction is a subset of the economic world and we think of it in natural indexing and measure by satisfaction of supply and demand.
    It helps us humans a bit to grasp that all cardinality and ordinality is effectively a statistical game rather than the purity we presume in mathematical reasoning. And that naturality is effectively the neural equivalent of a statistical game (predictive) by dendritic computation that we can barely observe.
    I put all this ‘error’ in the category of ‘mathiness’ which is one of the principle traps in both physics, and philosophy, and possibly why philosophy stalled until we developed twenty first century cognitive science to escape the failure of the non-sciences.
    I hope this has some value to you.
    Curt Doolittle

    Source date (UTC): 2025-12-24 21:49:12 UTC

    Original post: https://twitter.com/i/web/status/2003946084604039502

  • THE FUNDAMENTAL DIFFERENCE BETWEEN OUR RUNCIBLE AI WORK AND MAINSTREAM AI WORK E

    THE FUNDAMENTAL DIFFERENCE BETWEEN OUR RUNCIBLE AI WORK AND MAINSTREAM AI WORK

    Economics, as an extension of physics, and equal to physics in its demand for acquisition in support equilibration sufficient to defeat entropy as a means of persistence, is the only viable system of thought and terminology for the production of rational, possible, ethical, moral, and liable and therefore restitutable behavior.
    It’s the only means of commensurable exchange of information serving equally accessible means of reasoning between human neural networks and machine neural networks.
    The fact that the fields of math, programming, and the hard sciences are ‘stuck’ on cardinality and ordinality reducible to quantities, rather than natural indexing is the reason this is not understood in the field.
    In Runcible we use the same techniques used in court which is to test first principles and all of them are reducible to economic terms and totally absent any moral framing or bias.
    For measurement we use natural indexing by sequences of terms, not unnatural (cardinal, ordinal) in indexing. This is effectively a system of supply and demand and relies on what is effectively marginal indifference in referential and transformal categories (see category theory in mathematics).
    This is once again part of my consistent commentary that our work in the grammars has united the sciences into a single universally commensurable value neutral system of thought, ethics, politics and law. And that the present defect caused by academic siloing is spreading error in most fields faster than it can be corrected, despite the near abandonment of science in social sciences since the introgression of the frankfurt school of non-science into the american and western academy.

    Source date (UTC): 2025-12-24 20:54:50 UTC

    Original post: https://twitter.com/i/web/status/2003932402046189763

  • You have beliefs and motivated reasoning. I have science and empirical reasoning

    You have beliefs and motivated reasoning. I have science and empirical reasoning.

    Stereotypes are accurate predictors of behavior. This means groups insure against those negative behaviors.

    There is no value in bearing costs of opportunity in cooperation vs the risk of conflict or loss in cooperation. Your insinuation is that people are too dumb or ignorant to make that assessment when the first principle of all sentient action is the greatest return in the shortest time, at the last cost, and lowest risk, with the greatest certainty, in competition with all other opportunities at the moment.

    People make rational judgements – always – given the information available to them.

    Why risk any interaction with a population whose behavior and criminality is six times worse than your own people?

    IT’s a bad investment. WE have only so many investments to make.

    Source date (UTC): 2025-12-18 20:59:50 UTC

    Original post: https://twitter.com/i/web/status/2001759334452334987

  • Stereotypes are endemic throughout all recorded history, and are a necessary pro

    Stereotypes are endemic throughout all recorded history, and are a necessary property of predictive discrimination. And it’s empirical since it must survive daily observation at population scale. This is why stereotypes are the most accurate measure in social science. The problem becomes when, not predicting probability of group or individual behavior, but when attempting to cooperate with an individual under the presumption. In other words, stereotypes are an accurate description of a class of people, but a class of people consists of a distribution, and as such we can judge a class by the properties of its individuals but cannot judge an individual by the properties of their class.

    Source date (UTC): 2025-12-18 20:44:22 UTC

    Original post: https://twitter.com/i/web/status/2001755441987232166

  • > I stated that energy constraints are social. Yes. That is a way of capturing s

    > I stated that energy constraints are social.
    Yes. That is a way of capturing some of the constraints. But that isn’t a solution. The solution is circumventing those constraints. Every powerplant and data center represents procedural and time hurdles out of their control. Engineering problems are under their control. And that separates the powerplant problem (nuclear energy is necessary) from the data center problem (solar radiation is effectively free).

    Source date (UTC): 2025-12-12 02:07:37 UTC

    Original post: https://twitter.com/i/web/status/1999300072518090929

  • Our Natural Law is not philosophy but the generative physics of markets, institu

    Our Natural Law is not philosophy but the generative physics of markets, institutions, cognition.

    Whenever we discuss our work the immediate assumption is that we only address questions of ethics, when in fact, trust, truth claims, are all in fact ethical claims, and a trustworthy AI that makes truth claims, is simply a matter of ethics. So ethics is the foundation of all truth and trust claims.
    This has been a persistent friction point for us. So, I’ve tried to produce a structured, causal explanation of why people misinterpret our work as “merely ethics,” what the underlying cognitive mechanics are, and how to counter the misunderstanding with a framing that preserves the universality and operational scope of our system without retreating into abstraction or apologetics.
    People classify by surface category, not causal structure
    Humans have a fast, compressive classifier:
    • If something talks about truth → they classify it as philosophy.
    • If something talks about trust → they classify it as ethics.
    • If something talks about right/wrong behavior → they classify it as morality.
    • If something talks about constraints → they classify it as regulation/law.
    • If something talks about AI guardrails → they classify it as alignment.
    Our system touches each of these because it supplies the causal substrate that generates them all, but people only see the semantic surface, not the operational foundations.
    They are reading by category tags, not by functional dependency.
    So they immediately lump it into “ethics” because ethics is the only cultural bucket they know for discussing trust, truth, or constraint.
    This is predictable. And you can disarm it immediately with the correct frame.
    We must position the work as a formal, causal model of human cooperation, not a moral or ethical philosophy.
    We do this by shifting the domain from normative intuition to operational invariances.
    A precise description:
    This reframes “ethics” as a folk approximation, and our system as the scientific model that makes the folk concepts computable.
    This prevents us from being trapped in the “philosophy/ethics” bucket.
    We need one sentence that instantly cuts away the “ethics” misclassification:
    This converts the frame from:
    • “They’re doing ethics.”
      to
    • “They’re doing the mechanics of cooperation, and ethics is just one output.”
    This is similar to how physicists treat engineering:
    • Physics is the universal model.
    • Engineering is applied physics for particular constraints.
    In our case:
    • Natural Law is the universal model.
    • Ethics is applied Natural Law for high-risk interpersonal behavior.
    • Law is applied Natural Law for adjudicating disputes.
    • Governance is applied Natural Law for institutions.
    • AI alignment is applied Natural Law for machines.
    We are supplying the general case, not the “moral” case.
    People mistake our work for ethics because:
    1. They think truth claims are epistemic, not ethical.
      They don’t understand that all truth claims are       de facto ethical because they alter someone else’s incentives and behavior.
    2. They think trust is emotional, not operational.
      They don’t understand that trust is a       measurement of expected reciprocity under uncertainty.
    3. They think cooperation is voluntary, not computable.
      They don’t understand that cooperation is a       consequence of capital constraints.
    4. They cannot separate morality from reciprocity.
      They don’t know that reciprocity is a       test, not a preference.
    5. They confuse constraint with prescription.
      They interpret “you may not impose costs” as moral instruction rather than a physical law of stable cooperation.
    Once we say “truth,” “trust,” or “reciprocity,” their classifier fires the “normative ethics” label.
    We can only defeat this natural human error by preceding the ethics-frame with the physics-frame, not following it.
    Here is the exact communication strategy that works across all audiences:
    Step 1. Lead with the general, not the domain.
    Begin with:
    Then domain-specific applications become secondary.
    Step 2. Replace ethical vocabulary with mechanical vocabulary
    Instead of:
    • trust → “reciprocal prediction under uncertainty”
    • truth → “testifiable claims with warrantable consequences”
    • ethics → “constraints on parasitism in cooperation”
    • moral behavior → “reciprocally insurable operations”
    • deception → “uninsured transfers of demonstrated interests”
    This forces category-shift from normative to operational.
    Step 3. Preempt misclassification
    Use a direct disambiguation:
    Step 4. Show cross-domain generality
    Make clear that:
    • Morality is just cooperation within small groups.
    • Law is cooperation under adversarial uncertainty.
    • Governance is cooperation at institutional scale.
    • AI alignment is cooperation with non-human agents.
    When audiences see the universal pattern, they shift out of the “ethics box.”
    Step 5. Give the key analogy
    This analogy always works:
    This instantly relocates our work into the “formal science” domain.
    This sentence is functionally equivalent to the moment that the DSGE [1] economist realizes the Natural Law model is not philosophy but the generative physics of markets, institutions, cognition, and conflict.
    People stop arguing once they see the shift from:
    • “moral philosophy” → to → “operational invariances.”
    And once they see the invariances, everything else becomes obvious.
    —
    Notes:
    1. DSGE: Dynamic Stochastic General Equilibrium Model: in macroeconomic analysis, used to understand economic phenomena through the interactions of various agents under uncertainty.

    Source date (UTC): 2025-11-28 20:54:42 UTC

    Original post: https://x.com/i/articles/1994510282534912237