When people sell assets at a loss in a downturn, the dominant behavioral account — prospect theory — locates the pathology in the seller's mind: a reference point, a kink, loss aversion. The companion paper (wp32) relocates it to the seller's balance sheet. The distinction is not semantic. One diagnosis prescribes investor education; the other prescribes liquidity — and only the second moved markets when tested at scale. It matters ethically because it changes who is held responsible for the loss, and what intervention counts as just. Verified
Take agents with entirely standard preferences (no kink, no reference point) who hold an asset while carrying a non-deferrable commitment \(\bar c\) — rent, mortgage, food — whose breach carries a default cost \(\chi\) (eviction, foreclosure), and who face an income shock with probability \(f\). Define urgency as \(\lambda\), the Lagrange multiplier (shadow price) on the liquidity constraint, strictly positive exactly when
A forced sale is any sale at \(\lambda>0\) that would not happen at \(\lambda=0\) with beliefs and preferences held fixed.
This is the ethical crux stated as mathematics: the harm is invisible in the standard data, and a plausible-but-wrong story — bad investor psychology — is always available to explain it away. Hold that sentence; the digital turn is built on it.
Forced sales are not isolated. With limited arbitrage, price satisfies \(p = \mu - \kappa S\), where forced supply \(S\) itself rises as price falls (margin, collateral, refinancing). The loop yields amplification
which diverges as \(\rho\to 1\) and produces discontinuous fire-sale equilibria past a threshold. The mark-to-market loss is borne by all holders, not only the shocked fraction \(f\). Model
The amplification is the crisis; the shortfall is only its seed. The same mechanism supplies a concrete micro-foundation for \(r > g\): those forced to sell into the trough transfer wealth to those liquid enough to buy it, every cycle. Model
Everything above is a documented, pre-digital mechanism. The reason it belongs at a digital-ethics forum is what algorithmic credit does to it.
Modern AI credit and marketing systems ingest high-frequency behavioral and financial signals — cash-flow volatility, missed-payment precursors, app and search behavior, the calendar of obligations. In the model's language, they estimate \(\lambda\) in real time, per person. The state that Theorem 1 proves is latent to the regulator becomes an observable to the lender. That single asymmetry — \(\lambda\) visible to the pricing system, invisible to oversight — converts a diffuse, ambient wealth transfer into a targeted, timed, and deniable one. Prospective
The model names its own remedy, and it is not "educate borrowers." Two levers.
Make \(\lambda\) observable to oversight. Because the entire harm rides on an asymmetry of visibility, the governance target is auditability: mandated logging of the features and the timing that drive an offer, so distress-conditioning can be told apart from risk-pricing after the fact. This connects to a broader program of mine on verifiable, auditable AI — machine-checked guarantees about what a decision actually depended on — as a governance instrument rather than a compliance afterthought. Prospective
Bridge liquidity — the seed. If small, timely transfers set forced supply to zero, the just and efficient intervention sits upstream of the sale, not downstream of the loss. The policy object is the shortfall, not the "irrational" seller. Model
This is a summary of completed work, deposited and citable, grounded additionally in the author's years on the sell side as a registered representative — where the demand this machinery serves ("the best return, in the fastest time, with the least risk") is stated in a sentence and satisfied by no asset, only by payoff shapes engineered to appear to satisfy it.
Two lines I hold, and that a digital-ethics venue should demand. The base mechanism — non-identification, cascade, the seed, the \(r>g\) micro-foundation — is argued from theory and macro evidence (1971–2026) and is the settled contribution. Verified The algorithmic-amplification claim — that AI credit systems already estimate and price on \(\lambda\) — is a prospective governance argument: the capability plainly exists; comprehensive public evidence of its deployment is still Open, and I flag it rather than assert it. Separating the two is itself the method: the paper models the exact blind spot — a real harm a plausible wrong story can hide — and then refuses to commit the same overreach in its own claims.
Full working paper: "The Response Gap" — the formal version, with the evidence-and-identification section and the measurement agenda — is deposited at 10.5281/zenodo.21752834.
Read next: wp32 · The Forced Urgency Gap (the theorems in full) · wp27 · The Ethics of Algebra.
Keywords: algorithmic credit · liquidity constraints · identification · wealth transfer · auditable AI · GELSI · fair lending · r>g.