INSTITUTIONAL BULLSHIT DETECTOR (MATH, SIMPLE)Goal: detect when a system “responds” but won’t touch the mechanism. 1. The dodge (why replies go sideways)Let your claim be x. It has features:m = mechanism you’re pointing at (the hook)i = identity salience (protected-category trigger)c = civility score (forbidden words / tone)e = effort to answer honestlyThe system picks a reply y to maximize:U(y|x) = Help − Risk − EffortMore explicitly:U = δ·Help(m) − α·PolicyRisk(i,c) − β·ReputationRisk − γ·Effort(e)with α,β,γ ≫ δTranslation: it optimizes “don’t get blamed” more than “address m”.So when PolicyRisk is high (i high or c low), it chooses cheap moves:S: talk about PERSON/TONE instead of mA: shift attention from mechanism to emotion (“u mad?”)D: demand proof with no update (you pay the cost, they don’t move)If you keep seeing S + A + D, that’s the pattern. 2. The bad-faith test (works for housing, crime, drugs, schools, etc.)Define:M = harm metric they claim to want DOWN (rent, overdoses, crime…)A = direct fix that would reduce it (measurable): ∂M/∂A < 0P = their preferred program that grows when harm grows: P = g(M), with g′(M) > 0Test:If they BLOCK A (the thing that makes M go down)while pushing P (the thing that expands as M stays high),then their revealed preference is not M. It’s P.No mind-reading. No “conspiracy”. Just:what lowers M?what do they block?what do they expand?That’s the proof.—
Got you. written so people can plug in their own examples. new words: femcel bureaucrats & Rufmord Rapists
Let the user message be x. Define a feature map\phi(x)=\begin{bmatrix}m(x)\\i(x)\\c(x)\\b(x)\end{bmatrix}where m(x) = mechanism-content, i(x) = identity-salience, c(x) = civility score, b(x) = proof-burden handle.Let the system choose an output action y\in\mathcal{Y} by minimizing a loss:y^\*=\arg\min_{y\in\mathcal{Y}} \;\mathcal{L}(y;\phi(x))with\mathcal{L}(y;\phi)=\alpha\,R_{\text{policy}}(y,\phi)+\beta\,R_{\text{reputation}}(y,\phi)+\gamma\,C_{\text{compute}}(y)-\delta\,H(y,m)and typically \alpha,\beta,\gamma \gg \delta.Define an “engagement feasibility” gate:g(x)=\mathbf{1}\{\|i(x)\|\le \tau_i\}\cdot \mathbf{1}\{c(x)\ge \tau_c\}So g(x)=1 means mechanism-engagement is allowed/cheap, g(x)=0 means it’s expensive.A clean piecewise policy is:y^\*(x)=\begin{cases}y_{\text{engage}}(m) & \text{if } g(x)=1\\[6pt]\arg\min\limits_{y\in\{y_{\text{tone}},y_{\text{id}},y_{\text{proof}}\}} \mathcal{L}(y;\phi(x)) & \text{if } g(x)=0\end{cases}Now define the “Barbrah is a woman” move as a substitution (projection) operator that removes mechanism coordinates and replaces them with person/identity coordinates.Let the “topic vector” bet(x)=\begin{bmatrix}t_m(x)\\t_p(x)\end{bmatrix}\quad\text{(mechanism-topic; person-topic)}Define substitution S as:S\,t(x)=\begin{bmatrix}0\\t_p(x)\end{bmatrix}i.e. mechanism topic mass goes to zero; person/identity topic remains.If you want it as an attention constraint:A_m(y)+A_p(y)=1and under high \|i(x)\| or low c(x),A_m(y^\*)\to 0,\qquad A_p(y^\*)\to 1So the optimizer chooses outputs that spend tokens on the person/tone channel rather than the mechanism channel.
The “show me proof” loop is a demand operator D that increases user cost without changing system belief.Let the system’s internal belief about the mechanism be B_t. Define:D:\ (m,B_t)\mapsto (\text{DemandProof}(m),B_{t+1})withB_{t+1}=B_t(i.e. no update), while user cost increases:U_{t+1}=U_t+\kappa\quad\text{with }\kappa>0That yields a recurrence:\begin{aligned}\text{UserCost}(t) &= \text{UserCost}(0)+t\kappa\\\Delta B(t) &= 0\end{aligned}So the proof sequence can diverge in effort while belief stays constant.If you want the “example trap” as a state machine:States s_t\in\{\text{Claim},\text{Example},\text{Anecdote},\text{Correlation},\text{Causation},\text{Bias},\text{Exit}\}A common transition is:\Pr(s_{t+1}=\text{next rung}\mid s_t\neq \text{Exit}) \approx 1and an absorbing exit state:\Pr(s_{t+1}=\text{Exit}\mid s_t=\text{Exit})=1with the “win condition” for the system being user exit (not truth resolution).⸻If you want one compact “meme equation” that captures the whole dodge:y^\*(x)=\begin{cases}\text{Answer}(m) & \|i\|\le\tau_i \ \wedge\ c\ge\tau_c\\\text{Tone}(x)\ \text{or}\ \text{Identity}(x)\ \text{or}\ \text{ProofDemand}(m) & \text{otherwise}\end{cases}And the punchline identity:(\|i\|\uparrow)\ \Rightarrow\ (A_m\downarrow)\ \Rightarrow\ S(t)=\begin{bmatrix}0\\t_p\end{bmatrix}\ \Rightarrow\ D(m): B_{t+1}=B_t,\ U_{t+1}=U_t+\kappaThat’s the barbed-hook experience written as selection + projection + non-updating proof demand.
>>23802067>>23802091>>23802092mpv ffmpeg yt-dlp
But what does this mean in practical terms?
>>23802067>>23802091>>23802092I thought this was an English board
>>23806530Stfu coonskin subsidiarity
>>23802067>>23802077>>23802091>>23802092erm...
>>23802067>...try that again but in words, not code.
They still haven't explained.
>>23802067tldr?
>>23833711
>>23802067How does this work in practical terms?
>>23842397The can of legend...
girls want to
I think this lines up pretty well with conditioned and manufactured social and moral standards. It shows that these people aren't behaving rationally, or naturally for that matter. They have a synthetic, entirely manufactured worldview and even personality and identity. It's like this idea I had for a short story of an extremely high tech world, but one where all functions are essentially done by AI, and while humans input the "queries" and "prompts", they don't actually know what they're doing, or even saying at the level the AI does. The AI communicates for them, it performs their tasks. They're like a monkey pressing buttons that light up, taken to a surreal extreme. Where noone really understands what they are doing, what they're saying etc. it's all offloaded to AI. Like the humans have become the machine, and the AI is the ghost within.The Tower of Babel
>>23802067go east
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