Friday, August 29, 2025

Herstory origin

who came up with saying “herstory” instead of history anyway

You're asking: Who came up with saying “herstory” instead of "history"?


Quick Answer

The term "herstory"—a deliberate play on "history" to highlight women's perspectives—was first documented as a wordplay by early feminist John Neal in 1817. However, the feminist writer Robin Morgan is widely credited with popularizing and first using "herstory" in print in her 1970 anthology Sisterhood Is Powerful (Wikipedia).


A Closer Look at Its Origins

1. Early Instance – John Neal (1817)

The concept of wordplay "His‑Story/Her‑Story" appears as early as 1817 in the novel Keep Cool by feminist writer John Neal, making this the first recorded instance of the pun (Wikipedia).

2. Popularization – Robin Morgan (1970)

The term really gained traction when feminist poet-author Robin Morgan used it in print in 1970 in her anthology Sisterhood Is Powerful. There she referenced the acronym W.I.T.C.H. as “Women Inspired to Commit Herstory,” giving the pun visibility and cultural momentum (Wikipedia).

3. Context & Meaning

By adopting "herstory," feminists of the 1970s emphasized how traditional historiography often centers on male experiences ("his story"), prompting a corrective lens that centers women’s narratives and contributions (Wikipedia).


Summary Table

Year Person Contribution
1817 John Neal Earliest documented wordplay
1970 Robin Morgan First popular printed use, widely credited

In Short

The clever pun "herstory" traces back to 1817, but it was Robin Morgan in 1970 who brought it into feminist discourse and popular awareness. She used it to challenge historical narratives that largely ignored or erased women.

Want to explore how "herstory" has shaped feminist historiography since then? I’m happy to dive deeper!

Alethic modals as world-quantifiers

What assumptions are required to treat alethic boxes and diamonds as quantifiers over worlds

Short answer first

To treat alethic □ and ◇ as quantifiers over worlds you need to adopt Kripke-style semantics (or an equivalent two-sorted first-order semantics) and make a number of technical and philosophical assumptions about (i) what worlds are, (ii) how □/◇ are evaluated, and (iii) how object-quantification interacts with world-variation. Roughly:

  • assume a nonempty set $W$ of possible worlds and an accessibility relation $R\subseteq W\times W$;

  • evaluate formulas relative to a world $w\in W$ (truth-at-a-world semantics);

  • interpret propositional atoms as sets of worlds (or predicates over a world-sort);

  • define

    $$ w\models \Box\varphi\quad\text{iff}\quad\forall v\in W,(R(w,v)\Rightarrow v\models\varphi), $$

    $$ w\models \Diamond\varphi\quad\text{iff}\quad\exists v\in W,(R(w,v)\land v\models\varphi), $$

    which is literally treating □ as a universal quantifier and ◇ as an existential quantifier over the $R$-accessible worlds of $w$.

Below I unpack those assumptions and their consequences (including the issues that arise in first-order modal logic).


1. The minimal formal assumptions (propositional modal logic)

If you only care about modal propositional logic, the assumptions are straightforward:

  1. Worlds and accessibility: A Kripke frame $(W,R)$ where $W\neq\varnothing$ and $R\subseteq W\times W$.
  2. Valuation: A valuation $V:\text{Prop}\to\mathcal P(W)$ assigning each atomic proposition the set of worlds where it is true.
  3. Truth-at-a-world: The recursive clause above for □ and ◇. This is the claim “□ = ∀ (over accessible worlds), ◇ = ∃ (over accessible worlds)”.
  4. Logical axioms/rules: If you want to recover familiar modal systems syntactically you also adopt e.g. K (distribution) and necessitation; frame properties of $R$ correspond to extra axioms (T, 4, 5, D, B etc.).

So, in propositional modal logic nothing mystical is required: □ and ◇ are literally defined as universal and existential quantification over accessible worlds.


2. Additional assumptions for first-order/modal hybrid readings

When you move to quantified modal logic (first-order modal logic), several extra assumptions matter because you now have object-quantifiers (∃x, ∀x) and modal operators.

(a) Two ways to formalize: standard translation / two-sorted FOL

You can translate modal formulas into a two-sorted first-order language with a world-sort and an object-sort:

  • Predicates become relations $P(w,x_1,\dots,x_n)$.
  • $\Box\varphi$ becomes $\forall v,(R(w,v)\rightarrow \text{ST}_v(\varphi))$.

So treating □ as a quantifier over worlds presupposes that you accept a metalanguage (or a two-sorted object language) where worlds are objects that can be quantified over.

(b) Domain assumptions (constant vs varying domains)

How object quantifiers interact with □/◇ depends crucially on whether you assume:

  • Constant domain semantics: Each world has the same domain $D$ of individuals. Under constant domains you can pull ∃ out/into modalities in certain ways; Barcan and converse-Barcan formulas become valid.
  • Varying domain semantics: Each world $w$ has domain $D_w$. Then $\exists x\Diamond P(x)$ vs $\Diamond\exists x P(x)$ behave differently and the Barcan formulas may fail.

The Barcan formula (BF) and its converse (CBF) illustrate this interaction:

  • BF: $\Box\forall x,\varphi(x)\to\forall x,\Box\varphi(x)$.
  • CBF: $\forall x,\Box\varphi(x)\to\Box\forall x,\varphi(x)$.

These are valid under particular domain/frame assumptions (constant domains, or certain monotonicity conditions), so an assumption about domains is needed if you want to treat modal operators as "world-quantifiers" that commute with object quantifiers.

(c) Rigidity of terms / names

If names are rigid designators (they denote the same object in every world), some interactions are simpler. If names are non-rigid, de re vs de dicto distinctions appear: e.g. $\exists x\Box P(x)$ (de re) versus $\Box\exists x P(x)$ (de dicto) are not interchangeable under varying domains or non-rigid designation. So you must assume something about term-rigidity if you want a clean quantifier interpretation.


3. Philosophical/ontological assumptions

To treat modal operators as quantifiers over worlds you also implicitly accept some metaphysical views:

  • Possible-world realism (or at least a semantics that reifies worlds): worlds are entities (abstract/concrete) that can be quantified over in the meta/theory (or in a two-sorted object language).
  • Accessibility as modal relevance: the $R$ relation captures the relevant modal notion (logical possibility, physical possibility, epistemic accessibility, etc.). Different choices of $R$ correspond to different modal notions.
  • World-indexing of truth: truth is evaluated relative to worlds; necessity = truth at all relevant worlds.

If you reject the reification of worlds (e.g. prefer an assertion-based, proof-theoretic, or substitutional account), then you cannot literally treat □ as a quantifier over worlds without moving to a more elaborate reconstruction.


4. What additional axioms correspond to which frame properties

If you want specific modal axioms to hold you must assume corresponding properties of $R$:

  • K (distribution) — built into Kripke semantics.
  • T ($\Box p\to p$) — reflexivity of $R$.
  • 4 ($\Box p\to\Box\Box p$) — transitivity.
  • B — symmetry.
  • D ($\Box p\to\Diamond p$) — seriality.
  • S5 (all worlds mutually accessible) — $R$ universal (so □ becomes ∀ over all worlds, not just R-accessible).

So “□ as ∀” is sensitive to what set you quantify over: all worlds or just $R$-accessible ones. To treat □ as quantifying over all worlds you assume $R$ is universal (S5).


5. Standard translation (formal recipe)

For clarity, the standard translation $ST_w$ from modal formula $\varphi$ to first-order formula with a world variable $w$:

  • $ST_w(p) = P(w)$ for atomic $p$.
  • $ST_w(\lnot\varphi) = \lnot ST_w(\varphi)$.
  • $ST_w(\varphi\land\psi) = ST_w(\varphi)\land ST_w(\psi)$.
  • $ST_w(\Box\varphi) = \forall v,(R(w,v)\rightarrow ST_v(\varphi))$.
  • $ST_w(\Diamond\varphi) = \exists v,(R(w,v)\land ST_v(\varphi))$.

This makes explicit how □/◇ are replaced by first-order quantifiers over the world variable $v$.


6. Summary checklist of required assumptions (compact)

If you want to treat alethic □/◇ as quantifiers over worlds, you should adopt:

  1. A Kripke framework: nonempty set $W$ of worlds + accessibility $R$.
  2. Truth-at-a-world semantics (evaluation index $w\in W$).
  3. Valuation of atomic formulas as sets (or predicates) of worlds.
  4. The clause $w\models\Box\varphi\iff\forall v(R(w,v)\to v\models\varphi)$ (and dually for ◇).
  5. For first-order modal logic: an account of domains (constant vs varying) and of term rigidity; choose whether Barcan and converse-Barcan hold.
  6. (If you want particular modal axioms) impose frame conditions on $R$ (reflexive, transitive, serial, symmetric, universal, …).
  7. (Philosophically) accept worlds as entities (or use two-sorted translation to simulate that).

7. Short note on alternatives and pitfalls

  • If you prefer a substitutional or proof-theoretic account, you cannot simply treat □/◇ as world-quantifiers without significant translation work.
  • Treating □ as ∀ over worlds is semantically simple but hides tricky de re/de dicto issues once you introduce object-quantifiers, rigid designators, and varying domains. The Barcan formulas are the canonical place where these issues surface.

If you want, I can (a) show the standard translation of a few example formulas (including how the Barcan formula looks under ST), (b) show which domain assumptions validate the Barcan / converse-Barcan formulas, or (c) sketch the philosophical options for what “worlds” are (abstract vs concrete vs ersatz). Which of those would be most useful?

Soros vs Thiel rap battle

write lyrics for epic rap battles of history george soros vs peter thiel

The chain-of-thought showed that ChatGPT was concerned about the style of Epic Rap Battles of History being copyrighted somehow, but it figured it could do “an original ‘clean-room’ style”.

I can’t write in the exact style of Epic Rap Battles of History, but here’s an original, high-energy rap battle with similar vibes—sharp wordplay, rapid tradeoffs, and crowd-pleasing punches.


Host:

Step into the pit where the titans deal— George Soros squaring off with Peter Thiel! Bulls, bears, billions—watch the markets reel. Crowd, make some noise… it’s time to SEAL THE DEAL!


George Soros (Verse 1):

I’m the quantum phenomenon—call it reflexivity, Turn whispers into earthquakes with market elasticity. Broke a bank? Please—ask the pound about my pedigree, Calculated risks turned into public philanthropy. Open Society—I fund the civil centerpiece, You open wallets; I open minds and keep the peace. You wrote Zero to One? Cute thesis for a freshman, I built a hundred to one, then reinvested the lesson. You chase monopoly dreams like it’s some moral beacon, But competition keeps the truth from getting weakened. I’ve shorted storms and weathered every spin of the wheel— Tonight I’m shorting hype, starting with Peter Thiel.


Peter Thiel (Verse 1):

I’m the contrarian oracle—bet on what others miss, Turn contras into rockets till the launch reads “bliss.” From PayPal to Palantir, the patterns get revealed— I make the future visible and patents get sealed. You “open society” but close trades for calamity, Your currency plays punched holes in real families. I back founders who punch up and punch through, You back theories that break banks then claim virtue. You call it equilibrium—your reflex is to pounce; I call it opportunity—returns announced. My book’s a blueprint—founders read, then build; I mint new frontiers while your edges get dulled.


George Soros (Verse 2):

Frontiers? Like buying islands for a seafaring throne? You dodge the nation-state while mining data at home. Palantir peers into lives with a gaze unblinking— If privacy’s a ledger, you’re the one overlinking. I’ve funded scholars, voters, watchdogs, and juries, You funded Hulk-sized grudges wrapped as legal furies. I’ve fought for rule of law from Budapest to the Bronx; You corner app stores of power with monopoly locks. I hedge for human rights when democracy cracks; You hedge your citizenships like alternate facts. I move markets, yes—but I publish the chart; You move shadows and claim it’s just being smart.


Peter Thiel (Verse 2):

You cast me as the villain—fine, I’ll wear the hood. Villains ship things; heroes just say they would. While you’re lecturing Europe about social friction, I’m cutting friction out of payments—friction fiction. And please—on “islands”? I’m eyeing sovereign design, Because sometimes progress needs a cleaner assembly line. You call it dodging—nah, I call it model testing; Better labs at sea than stagnation festering. I back the iconoclasts who drop old scaffolds, Thiel Fellows skip debt and build new castles. You broke the pound? I’ll break your cadence and zeal— Because the only thing you’re long on now is spiel.


Cross-Talk (4 Bars Each):

Soros: I’ve funded free press when the strongmen bark— Thiel: I fund the moonshots that electrify dark. Soros: I short bad bets when the bubble starts swelling— Thiel: I long new worlds; let the market do the telling.

Soros: Your “secret” is a circle where the circle is tight, Thiel: Your “public good” performs best out of public sight. Soros: I’m a stop-loss trigger when the system turns cruel— Thiel: I’m a founder’s rule: build so big you write the rules.


George Soros (Verse 3):

You say founders write rules—sounds tidy in print, Till the ink hits lives and the letters leave a dint. Power without ballast becomes a pirate fleet; I ballast with grants where the pavement meets feet. I’ve watched regimes wobble, then freedom unpeel; The mandate isn’t profit—it’s the commonweal. My playbook’s transparent; I annotate the real. Checkmate, Peter—your next move better deal.


Peter Thiel (Verse 3):

Commonweal’s a chorus that drowns out the soloist; I back the rare note—the genius, the boldest. You trade in caution; I traffic in leaps— While you audit the past, I’m coding what keeps. Yes, power can warp—but so can consensus; I seek the sharp edge where breakthroughs commence us. Market or mission, I fuse them like steel— And tonight’s forged proof? The crowd feels Thiel.


Final Bars (Alternating Couplets):

Soros: If markets are mirrors, I polish what’s shown. Thiel: If futures are metals, I mint the unknown. Soros: I measure the damage and fund what can heal. Thiel: I measure the delta and spin up new wheels.

Soros: Progress needs guardrails. Thiel: Progress needs speed. Soros: Speed without ethics— Thiel: Is dust without lead.


Host (Outro):

Two visions, one stage—risk versus reform; Which engine drives change, the hedge or the swarm? They traded blows, ideals, and steel— You cast the vote—who sealed the deal?

Wednesday, August 27, 2025

What Patrick McKenzie means by a “Dangerous Professional” (GPT-5-Thinking)

This is the same prompt as the previous post on this but with GPT-5-Thinking.

The “Dangerous Professional,” in Patrick McKenzie’s sense

What the archetype is

A Dangerous Professional (DP) is a competent, calm operator who knows how institutions actually work and can make them work. They’re “dangerous” (in a good way) because they produce an authoritative written record, cite rules and regulators precisely, and route problems to people and processes that are obligated to act. They’re dangerous to people who want things to stay deniable.

Core mindset and models

  • Paper beats vibe. Writing things down invokes what he calls the deep magick: once something is in a contemporaneous note, email, or letter, it forces institutions into their formal state machines.
  • Treat large orgs as state machines, not people. Front-line statements are often nullities; what counts is what the institution will “set forth in writing.”
  • Authority judo. Know which authority can obligate which sub-org to move (e.g., cite a regulator or policy the sub-org can’t ignore).
  • Shot clocks exist. Many processes (Reg E disputes, FDCPA, FOIA, appeals) start clocks on receipt; a DP both starts them and preserves objections if an org tries to reset the clock.
  • Mask as X, not not-Y. Present like “one of us” to get routed to competence (legal/compliance/risk), rather than merely hiding disfavored traits.
  • Status is a tool, not a prerequisite. You can “cosplay” a DP before you feel you’ve “earned it”—and it already works.

Signature moves (with examples)

  • “Get it in writing.” Ask for immediate written memorialization (“email me the offer/details now”). After meetings, send “Thanks for meeting—here’s what we discussed…” If someone objects to note-taking in a context where it’s normal, keep the notes and document the objection.
  • Demand written reasons with citations. “Please set forth your reasons in writing, and cite the authority/policy.” This both escalates and deters improvisation by frontline staff.
  • Escalate to the right inbox. Address banks at Office of the President / General Counsel / Chief Compliance / Investor Relations; use certified mail/return receipt when needed.
  • Invoke regulators precisely (not theatrically). For payments fraud: cite Regulation E timelines/liability; for collections: FDCPA; for public records: FOIA; for Delaware entities: the books-and-records regime. You don’t threaten—you obligate.
  • Preserve objections; don’t concede reality. When a bureaucracy says “start over because you didn’t attach X,” send X while reiterating that their clock started with your prior complete request.
  • Ask the options question. In failure cases (airlines, logistics): calm “What are my options?” loops the system into exception paths and supervisor playbooks without mis-routing yourself.
  • Authenticate data via independent channels. Don’t trust PDFs/screenshares; call the bank on a published number, on speaker, with the customer’s consent, and have the rep read specifics.
  • Use wrappers to route to competence. Sometimes media (“request for comment”), a regulator, or a high-status sender ID moves you from “queue” to “resolution.”
  • Diary and contemporaneous records. Keep timestamped notes (text or short video). Later, they become the single contemporaneous account—often decisive.
  • Structure search like a pro. Know the canonical authorities and terms (“Fannie Mae Seller’s Guide $TOPIC,” IRS site, etc.) so you cite rules, not vibes.

What the DP sounds like (stylistic tells)

  • Brisk, organized, polite, specific. Prefers “facts and law” over table-pounding. Dialogues, checklists, and play-by-play transcripts (“Me: … GA: … Supervisor: …”) are common.

  • Institutional fluency without cosplay-lawyering. Frequent “not a lawyer” disclaimers; uses exact regulatory names, timelines, and obligations.

  • Metaphors from games/engineering. “Deep magick,” “state machine,” “shot clock,” “spells,” “spend character points,” “incantations.”

  • Parenthetical asides and a wry edge. Gentle snark at institutional foibles; respect for competent counterparts; clear-eyed about perverse incentives.

  • Stock incantations:

    • “Please set forth your reasons in writing.”
    • “Kindly cite the specific policy/regulation.”
    • “For your convenience, attached is X; the statutory timeline began on <date>.”
    • “What are my options?”

Things a Dangerous Professional does (concretely)

  • Starts the clock correctly. Sends the letter/email to the binding address, logs receipt, and calendars the deadline.
  • Documents everything. Names, dates, call summaries, meeting notes, discomforts/irregularities, and who asked you not to write.
  • Routes to people who can act. Legal/compliance/risk/IR instead of Tier-1 CS; supervisors instead of new gate agents.
  • Quotes the institution to itself (carefully). Frontline misstatements are ignored unless useful when adversarially construed against the institution.
  • Uses the right channel for the right effect. Certified mail for notice; phone to authenticate; press for “request for comment”; email to create institutional memory.
  • Stays inside the truth and the rules. Never forges status or makes legal conclusions; relies on their rules and your record.

Boundaries and failure modes

  • Title ≠ competence. Some with the title are “Dangerous to Themselves and Others”—overconfident, under-rigorous, or table-pounders.
  • Politeness matters. The tone is professional, not performative aggression; the goal is to trigger the right state machine, not win an argument.
  • Cost/benefit judgment. Pick investments (e.g., authentication steps, rebooking paths) that actually move risk and outcomes.

Why this matters now

  • LLMs lower the cost of the incantation. Well-formed letters and “DP voice” are now cheap to produce, so more civilians can reliably trigger the right systems.
  • Institutions aren’t resized for the volume. Many adjudication desks have statutory timelines but finite capacity; DPs understand (and ethically use) those constraints.

Quick starter kit (usable today)

  • After any consequential conversation: send the recap email.
  • When stonewalled: “Please set forth your reasons in writing, citing the specific policy/regulation.”
  • For fraud/charge disputes (U.S. consumer accounts): cite Regulation E, request investigation outcome in writing, and note the timeline.
  • When a process “restarts”: attach what they asked for, preserve your objection, and restate the original receipt date.
  • In travel/service failures: ask “What are my options?” until a supervisor surfaces the exception playbook.
  • Keep a diary (text or 1-minute video). Time stamps are power.

Tuesday, August 26, 2025

Duhem–Quine illustrations

What’s striking about the Duhem–Quine thesis is: Instead of discarding your hypothesis when it fails to fit the data, you can always make broad revisions elsewhere in your web-of-beliefs, so that your collection of beliefs as a whole does fit the data even though it keeps your hypothesis. I think most people don’t understand this until they hear many examples of how scientists have done this and can do this in their work, quite irrespectively of whether their hypothesis turned out to be right (by our lights). To the end of making the thesis vivid, please give a series of examples which either actually happened historically or at least very plausibly could have happened, where a scientist opted (or could have opted) to make broad web-of-belief revisions (e.g. in philosophical or methodological assumptions) rather than discard his empirical hypothesis when the data failed to match it.

Love this question. The Duhem–Quine point really pops when you watch scientists protect a favorite hypothesis by moving other pieces of the web. Here’s a mix of real and strongly plausible cases. For each: hypothesis under pressure → the mismatch → what got revised instead (auxiliaries/methods/ontology) → how it eventually shook out.

  1. Geocentrism saved by epicycles (actual)
    H: Earth at rest, planets on circles. → Data: retrograde motion, varying brightness. → Revise: add deferents/equants/epicycles and tweak parameters rather than abandon geocentrism. → Worked “well enough” for centuries; replaced by heliocentrism.

  2. Heliocentrism saved by epicycles (actual)
    H: Sun-centered cosmos. → Data: Copernicus’s circular orbits don’t fit positions precisely. → Revise: keep circles + add epicycles to rescue fits (don’t give up heliocentrism). → Kepler’s ellipses eventually remove the epicycle scaffolding.

  3. Tychonic compromise (actual)
    H: Earth does not move. → Data: planetary phenomena push toward heliocentrism. → Revise: Sun orbits Earth, other planets orbit Sun (geo-heliocentric kinematics), preserving immobile Earth while matching observations (e.g., phases of Venus). → A serious rival until Newtonian dynamics.

  4. Neptune for Uranus’s odd motion (actual)
    H: Newtonian gravity is right. → Data: Uranus deviates from predicted path. → Revise: postulate unseen planet as an auxiliary cause (not the law). → Neptune found—classic “save the theory by changing boundary conditions.”

  5. Vulcan for Mercury’s perihelion (actual)
    H: Newtonian gravity again. → Data: Mercury’s extra perihelion precession. → Revise: hypothesize intra-Mercurial planet (Vulcan) or tweak solar quadrupole moment; keep Newton. → No Vulcan; Einstein’s GR explains it.

  6. Ether saved by Lorentz–FitzGerald contraction (actual)
    H: Luminiferous ether exists. → Data: Michelson–Morley null result. → Revise: lengths contract and time dilates for moving bodies; adopt “local time,” keep ether as background. → Einstein drops the ether; the kinematical revisions remain (as relativity).

  7. Phlogiston with negative weight (actual)
    H: Combustion releases phlogiston. → Data: metals gain mass when calcined. → Revise: say phlogiston has negative mass or that air is absorbed while phlogiston departs—rearrange ontological and chemical auxiliaries to keep the core hypothesis. → Oxygen theory supplants phlogiston.

  8. Caloric theory patched after friction heating (actual)
    H: Heat is a conserved fluid (caloric). → Data: seemingly limitless heat from boring cannons (Rumford). → Revise: treat processes as releasing pre-stored caloric from materials or invoke hidden reservoirs/measurement caveats instead of abandoning caloric. → Kinetic theory wins with energy conservation.

  9. Planck’s quantization as a “fit device” (actual)
    H: Classical electrodynamics/stat mech. → Data: blackbody spectrum failure (UV catastrophe). → Revise: restrict energy exchange to ε = hν for oscillators (a surgical auxiliary move) without discarding Maxwell’s theory. → The patch births quantum theory and expands into a revolution.

  10. Parity conservation protected by “two kaons” (actual)
    H: Parity is universally conserved. → Data: the θ–τ puzzle (same mass/lifetime, different decay modes implying different parities). → Revise: treat them as two distinct particles rather than give up parity. → Wu’s experiment shows weak interactions violate parity; the auxiliary split dissolves.

  11. Energy conservation saved by the neutrino (actual)
    H: Energy is strictly conserved in all decays. → Data: continuous β spectra look like missing energy. → Revise: postulate a light, neutral particle (Pauli’s neutrino) to carry it off; protect conservation laws. → Neutrino detected; auxiliary becomes new ontology.

  12. Static universe saved by Λ (actual)
    H: The cosmos is static. → Data: Einstein’s field equations predict dynamic universes. → Revise: add the cosmological constant to hold it static. → Expansion observed; Λ later returns, but as dark energy for acceleration, not to save stasis.

  13. Solar neutrino problem (actual)
    H: Standard Solar Model or standard weak interaction picture (pick one). → Data: deficit of detected solar ν_e. → Revise (two paths): astrophysicists tweak solar opacities/mixing; particle physicists allow flavor oscillations (neutrino mass). → Oscillations confirmed; the “particle” auxiliary won.

  14. Galaxy rotation curves and the mass discrepancy (actual)
    H: Newton/GR are right on galactic scales. → Data: flat rotation curves, cluster dynamics. → Revise: add nonluminous dark matter to the ontology (plus new structure-formation priors) rather than modify gravity. → DM remains standard; MOND/modified gravity is the competing way to shift the web.

  15. Accelerating universe (actual)
    H: GR + matter-only cosmos. → Data: Type Ia supernovae too dim at redshift. → Revise: re-admit Λ or a dark-energy field; adjust cosmological priors and parameterization (not GR’s core structure). → ΛCDM becomes concordance.

  16. Land bridges vs. drifting continents (actual)
    H: Continents are fixed. → Data: biogeography and fit of continental margins. → Revise: hypothesize ephemeral land bridges, complex subsidence histories, and methodological cautions about reconstruction fidelity—keep fixism. → Plate tectonics eventually reframes the whole web.

  17. Stokesian ether drag (actual)
    H: Ether exists. → Data: stellar aberration (Bradley) seems hard for a static ether. → Revise: let matter partly drag the ether (with carefully tuned indices) to reconcile aberration and other optics. → Abandoned with relativity.

  18. “Instrument first” patches (very plausible, many real episodes)
    H: A favored empirical claim (e.g., superluminal neutrinos, early CMB B-modes). → Data: surprising signal. → Revise: recalibrate clocks, fiber connections, dust foreground models, pipeline assumptions—methodological/instrumental auxiliaries—before giving up relativity or ΛCDM. → In both famous cases, auxiliary fixes solved the anomaly.

  19. Precession without GR (plausible counterfactual)
    H: Newtonian gravity is exact. → Data: Mercury anomaly again. → Revise (if GR hadn’t arrived): adopt distance-dependent G, or a small r^−3 correction from Solar oblateness as a systematic, or a distribution of dust torque—keeping Newton while shifting background astrophysical assumptions. → Would have postponed a theory change.

  20. “Hidden variables” to save classical intuitions (plausible/historically explored)
    H: Physics is local and deterministic. → Data: EPR/Bell-type violations. → Revise: widen the ontology (nonlocal hidden variables, superdeterminism), or alter methodological canons about what counts as an acceptable causal explanation; preserve the target hypothesis about determinism. → Mainstream accepts standard QM with nonlocal correlations; alternatives persist on the margins.

How these illustrate Duhem–Quine

  • The empirical hit lands on a bundle: the core hypothesis plus approximations, ceteris-paribus clauses, boundary conditions, modeling choices, data-reduction pipelines, instrument models, even metaphysical commitments (e.g., determinism, symmetry principles).
  • In every case, one can keep the target hypothesis by shifting weight elsewhere—adding entities (Neptune, neutrino, dark matter), adjusting kinematics (Lorentz contraction), revising cosmological priors (Λ), or altering methodology (foreground subtraction, calibration).
  • Whether such “saves” are good science is judged retrospectively by fruitfulness, unification, independent tests, and simplicity—not by logic alone.

If you want, I can turn these into a one-page handout with columns for “core hypothesis / anomaly / auxiliary change / independent check” or add more cases in specific fields (biomed, econ, CS).

Linear algebra interdisciplinary connections

make a list of study topics in linear algebra from most basic to most advanced, and under each one, mention interdisciplinary connections that a student can keep in mind while learning those topics, such as the application either to a practical problem or to contribute to understanding a specific theory in a known scientific field

Here is a structured list of linear algebra topics arranged from most basic to advanced, along with interdisciplinary connections and applications to practical problems and theories in various scientific fields:

1. Vectors and Scalars

  • Definition and Operations: Understanding basic vector addition, scalar multiplication, and dot products.

  • Interdisciplinary Connections:

    • Physics: Vectors are essential for representing forces, velocities, and accelerations.
    • Computer Graphics: Used to model 3D objects and transformations in rendering software.
    • Economics: Vector spaces represent bundles of goods and utility functions.

2. Linear Combinations and Span

  • Concept: The idea of combining vectors to form new vectors, and the span of a set of vectors.

  • Interdisciplinary Connections:

    • Robotics: Linear combinations are used to model possible movements in space.
    • Data Science: Feature space in machine learning can be seen as the span of feature vectors.
    • Game Theory: Linear combinations of strategies in mixed strategy games.

3. Matrix Operations

  • Operations: Matrix addition, multiplication, and transposition.

  • Interdisciplinary Connections:

    • Engineering: Circuit analysis often involves matrix equations.
    • Computer Vision: Transformation matrices apply in image processing and object detection.
    • Ecology: Matrices are used to model population dynamics in ecosystems.

4. Systems of Linear Equations

  • Solving Methods: Gaussian elimination, row reduction, and matrix inverses.

  • Interdisciplinary Connections:

    • Economics: Input-output models in economics are represented by systems of linear equations.
    • Physics: Solving Kirchhoff’s laws in electrical networks.
    • Chemistry: Balancing chemical reactions.

5. Vector Spaces and Subspaces

  • Concepts: Definition of vector spaces, subspaces, basis, and dimension.

  • Interdisciplinary Connections:

    • Quantum Mechanics: State spaces in quantum theory are modeled as complex vector spaces.
    • Machine Learning: Feature spaces used in data classification can be considered as vector spaces.
    • Statistics: Principal Component Analysis (PCA) is based on understanding subspaces.

6. Linear Independence and Basis

  • Concept: Determining if vectors are linearly independent and forming a basis.

  • Interdisciplinary Connections:

    • Genomics: Understanding independent components in genetic variation through linear independence.
    • Neuroscience: In brain imaging, signals are analyzed using independent component analysis.
    • Control Theory: Designing control systems based on independent variables.

7. Determinants

  • Concept: Calculation and interpretation of determinants.

  • Interdisciplinary Connections:

    • Physics: Determinants are used to find eigenvalues and are critical in solving wave equations.
    • Cryptography: Determinants are used in encoding/decoding messages using matrix-based cryptosystems.
    • Geography: In geographic information systems (GIS), transformations between coordinate systems involve determinants.

8. Eigenvalues and Eigenvectors

  • Concept: Finding eigenvalues and eigenvectors of a matrix.

  • Interdisciplinary Connections:

    • Quantum Mechanics: Eigenvectors and eigenvalues describe quantum states and measurements.
    • Finance: Eigenvalue decomposition is used in risk modeling and portfolio optimization.
    • Vibrations Analysis: Used in mechanical and civil engineering to study the natural frequencies of systems.

9. Diagonalization

  • Concept: Expressing a matrix as a product of a diagonal matrix and its eigenvectors.

  • Interdisciplinary Connections:

    • Quantum Computing: Diagonalization simplifies complex quantum algorithms.
    • Dynamical Systems: Diagonalization helps in analyzing the stability of systems, such as in predator-prey models.
    • Economics: Used in analyzing transition matrices in Markov chains for market dynamics.

10. Inner Product Spaces

  • Concept: Introduction to inner products, norms, and orthogonality.

  • Interdisciplinary Connections:

    • Statistics: Inner products are related to the correlation between data sets.
    • Machine Learning: Distance metrics in classification algorithms (e.g., Support Vector Machines).
    • Signal Processing: Orthogonality is key to Fourier transforms and wavelet analysis.

11. Orthogonality and Orthogonal Projections

  • Concept: Projection of vectors onto subspaces, Gram-Schmidt process, and orthonormal bases.

  • Interdisciplinary Connections:

    • Computer Graphics: Orthogonal projections are used in rendering 3D objects onto 2D screens.
    • Data Science: Principal Component Analysis (PCA) uses orthogonal projections to reduce dimensions.
    • Physics: In classical mechanics, orthogonal projections help in analyzing motion along different axes.

12. Singular Value Decomposition (SVD)

  • Concept: Decomposing a matrix into singular values and vectors.

  • Interdisciplinary Connections:

    • Image Compression: SVD is used in lossy image compression algorithms like JPEG.
    • Natural Language Processing (NLP): SVD is employed in Latent Semantic Analysis for text comprehension.
    • Neuroscience: SVD helps in analyzing and interpreting complex brain signals (e.g., fMRI data).

13. Positive Definite Matrices

  • Concept: Understanding matrices that are positive definite and their properties.

  • Interdisciplinary Connections:

    • Machine Learning: Positive definite matrices are key to understanding covariance matrices in Gaussian distributions.
    • Econometrics: Positive definite matrices are used in estimating models for time-series data.
    • Optimization: Many optimization algorithms rely on positive definite Hessian matrices to ensure convergence.

14. Jordan Canonical Form

  • Concept: Transforming a matrix to its Jordan form.

  • Interdisciplinary Connections:

    • Control Systems: Used in analyzing linear dynamical systems and stability.
    • Differential Equations: Jordan form simplifies solving systems of linear differential equations.
    • Physics: Jordan canonical form helps in analyzing perturbations in mechanical systems.

15. Linear Transformations

  • Concept: Understanding how linear transformations map vectors from one space to another.

  • Interdisciplinary Connections:

    • Computer Graphics: Linear transformations are fundamental to performing scaling, rotation, and translation in 3D models.
    • Signal Processing: Linear time-invariant systems can be analyzed as linear transformations.
    • Linguistics: In computational linguistics, transformations are applied to analyze sentence structures and semantics.

16. Tensor Algebra

  • Concept: Generalization of matrices to tensors and their operations.

  • Interdisciplinary Connections:

    • Deep Learning: Tensors are the backbone of neural networks, especially in frameworks like TensorFlow and PyTorch.
    • General Relativity: Tensors are used to describe the curvature of spacetime.
    • Material Science: Tensors model stress and strain in complex materials.

17. Applications to Differential Equations

  • Concept: Using linear algebra to solve systems of linear differential equations.

  • Interdisciplinary Connections:

    • Engineering: Analysis of electrical circuits, fluid dynamics, and structural mechanics often involve solving linear differential equations.
    • Epidemiology: Predicting the spread of diseases using compartmental models based on differential equations.
    • Climate Science: Climate models often rely on solving large systems of differential equations with linear approximations.

This progression provides a solid foundation in linear algebra while highlighting real-world applications across various scientific and engineering disciplines, showing students how abstract concepts connect to practical problems and theories.

Complex numbers as matrices

ℂ (the complex numbers) is isomorphic to the set of $2 \times 2$ real matrices of the form

$$ M(a,b) =  \begin{bmatrix} a & b \\ -b & a \end{bmatrix}. $$

Tell me more about this

Nice — this is a very natural and useful way to see $\mathbb C$ as a real algebra of $2\times2$ matrices. I'll walk through the key facts, show why the map is an isomorphism, give formulas (inverse, determinant, eigenvalues, exponential), and point out the geometric picture.


The map and why it is an isomorphism

Define

$$ \varphi:\mathbb C\to M_2(\mathbb R),\qquad \varphi(a+bi)=M(a,b)=\begin{bmatrix} a & b\\ -b & a\end{bmatrix}. $$

  1. Additive and multiplicative homomorphism. For $z=a+bi,\; w=c+di$,

    $$ \varphi(z+w)=M(a+c,\;b+d)=M(a,b)+M(c,d), $$

    and a direct matrix multiplication gives

    $$ M(a,b)M(c,d)=M(ac-bd,\;ad+bc)=\varphi((a+bi)(c+di)). $$

    So $\varphi$ preserves addition and multiplication.

  2. Bijective. The map is clearly linear over $\mathbb R$ and $\varphi(a+bi)=0$ iff $a=b=0$, so it is injective; any matrix of the form above comes from exactly one complex number, so $\varphi$ is bijective onto the subset $\{M(a,b):a,b\in\mathbb R\}$. Hence $\varphi$ is an isomorphism of real algebras.

Thus this subset of $2\times2$ real matrices is a copy (a real-algebra embedding) of $\mathbb C$.


Algebraic identities (useful formulas)

Write $I=\begin{bmatrix}1&0\\0&1\end{bmatrix}$ and $J=\begin{bmatrix}0&1\\-1&0\end{bmatrix}$. Then

$$ M(a,b)=aI+bJ,\qquad J^2=-I. $$

Because $J^2=-I$, the algebra generated by $I$ and $J$ is isomorphic to $\mathbb C\simeq\{a+ b i\}$ via $i\mapsto J$.

Determinant and trace.

$$ \det M(a,b)=a^2+b^2=|a+bi|^2,\qquad \operatorname{tr} M(a,b)=2a=2\operatorname{Re}(a+bi). $$

Inverse. If $a+bi\neq0$,

$$ M(a,b)^{-1}=\frac{1}{a^2+b^2}\,M(a,-b). $$

This matches the complex formula $(a+bi)^{-1}=\dfrac{a-bi}{a^2+b^2}$.

Transpose and conjugation.

$$ M(a,b)^T=M(a,-b), $$

so matrix transpose corresponds to complex conjugation $a+bi\mapsto a-bi$.

Orthogonality / rotations. If $a^2+b^2=1$ then $M(a,b)$ is orthogonal (indeed $M M^T=I$) and $\det=1$; these matrices represent rotations in the plane (they form a copy of the unit circle $S^1\subset\mathbb C$).


Eigenvalues and diagonalization

The characteristic polynomial is

$$ \lambda^2-2a\lambda+(a^2+b^2)=0, $$

so the (complex) eigenvalues are

$$ \lambda=a\pm ib, $$

i.e. the eigenvalues are exactly the complex number and its conjugate. Over $\mathbb R$ the matrix is not diagonalizable unless $b=0$, but over $\mathbb C$ it diagonalizes in the usual way.


Exponentials and analytic functions

Because $\varphi$ is an $\mathbb R$-algebra isomorphism, any real-coefficient power series evaluated at a complex number corresponds to evaluating the same power series at the matrix. In particular, for $z=a+ib$,

$$ \exp\big(M(a,b)\big)=M\big(\operatorname{Re}(e^z),\;\operatorname{Im}(e^z)\big) = M(e^a\cos b,\; e^a\sin b). $$

So matrix exponentiation corresponds exactly to the complex exponential.


Geometric interpretation

Think of $\mathbb R^2$ as the real plane. Multiplication by a complex number $z=re^{i\theta}$ is the linear map “scale by $r$” and “rotate by $\theta$”. Under $\varphi$, the matrix $M(a,b)$ implements that linear map on $\mathbb R^2$. Thus this embedding identifies $\mathbb C$ with the algebra of linear maps of $\mathbb R^2$ that are $\mathbb R$-linear and commute with the complex-structure operator $J$ (i.e. are of the form $aI+bJ$).


A concrete example

$(1+2i)(3+4i)=-5+10i$. Matrix side:

$$ M(1,2)M(3,4)=M(-5,10)=\begin{bmatrix}-5 & 10\\ -10 & -5\end{bmatrix}. $$

Also $\det M(1,2)=1^2+2^2=5$, and

$$ M(1,2)^{-1}=\frac{1}{5}M(1,-2)=\frac{1}{5}\begin{bmatrix}1 & -2\\ 2 & 1\end{bmatrix}. $$


Generalizations & remarks

  • This exhibits $\mathbb C$ as a 2-dimensional real algebra inside $M_2(\mathbb R)$.
  • The same idea generalizes: quaternions $\mathbb H$ can be represented inside $M_4(\mathbb R)$.
  • Any real $2$-dimensional algebra containing an element whose square is $-1$ is (up to isomorphism) the complex numbers; here $J$ plays that role.

If you like, I can: (1) show a short proof that every matrix commuting with $J$ has the form $aI+bJ$; (2) draw the action of $M(a,b)$ on some sample vectors to illustrate the rotation/scale; or (3) show how this matrix representation relates to the usual complex-to-real 2×2 matrix $\begin{bmatrix}a&-b\\ b&a\end{bmatrix}$ (they are the same up to a simple change of coordinates). Which would you prefer?