Market learning
Event timing remains uncertain, but conditional probabilities become estimable. As observations accumulate, traders can learn when risk is elevated even when they cannot know the exact event.
Adversarial markets / radical disclosure / institutional funding
RugPull
Ethical crypto through randomized unethical behavior.
A paired-token experiment asking whether a behavior normally hidden from market participants can become a legitimate object of economic and anthropological study when its rules, probability, institutional beneficiary, and consequences are visible in advance.
Algorithm disclosedFunding fee disclosedTestnet executionHigh-volatility research
A
B
R
sell
rotate
Nothing hidden.The treasury allocation, hazard equation, random draw, event size, routing fee, price impact, and beneficiary are public.
Nothing promised.RugPull is not presented as a stable investment or guaranteed appreciation mechanism.
Everything measured.The experiment records learning, anticipation, drawdown, extraction, institutional revenue, and participant asymmetry.
01 / Research proposition
Can disclosure change the moral character of a market shock?
RugPull does not claim that disclosure automatically makes manipulation lawful or harmless. It creates an observable laboratory for testing whether informed participation, public rules, verifiable randomness, and a socially beneficial institutional purpose alter behavior, legitimacy, and wealth distribution.
Moral economy
The project measures whether participants tolerate losses differently when the beneficiary is an educational and research institution rather than an anonymous promoter.
Regulatory evidence
Every event becomes a documented case of disclosed algorithmic destabilization, producing evidence relevant to market integrity, investor comprehension, and possible regulatory design.
Primary questionCan radical transparency convert a concealed exploit into informed participation without merely giving sophisticated actors a better mechanism for extracting value from less sophisticated participants?
02 / Economic architecture
Separate the experiment from Stella Nova's income
The treasury rotation remains inside the paired-token ecosystem. Stella Nova receives a fixed, disclosed protocol fee from each completed event rather than silently removing the full proceeds.
RugPullA or RugPullB
A public hazard score identifies elevated event risk.
Virtual USD route
The source treasury sale crosses a modeled constant-product pool.
Stella Nova fee
A published percentage is separated and recorded.
Opposing token
Remaining proceeds purchase the paired token.
Protocol revenue
Rt = f Et
f is the disclosed Stella Nova funding rate and E is gross event value.
Treasury continuity
The initial 50% allocations are reported honestly after every swap. The model does not falsely force either treasury position back to 50%.
Depth-aware limits
Standard events are capped by available liquidity and trailing trading volume. The catastrophic full-pull condition remains available as a simulation variable.
03 / Public algorithm
Predictable risk, unpredictable realization
The hazard is deterministic and visible. A separately generated random draw determines whether the known probability becomes an event.
Initial price
P0 = Y0 / X0
Initial price is established by the USD-to-token reserve ratio, not an arbitrary displayed number.
Market curve
x y = k
Large trades experience nonlinear slippage, making liquidity depth more consequential than nominal market capitalization.
Hazard score
H = .35T + .25F + .25V + .15I
Elapsed time, trading frequency, valuation, and A/B imbalance determine observable event risk.
Random event
u < pt
A uniform random value u triggers the event when it falls below the published daily probability.
04 / Interactive research model
One-year market-learning simulator
Compare transparent capped events with an uncapped catastrophic scenario. The model also follows three participant archetypes to expose how public probability signals can create unequal outcomes.
Ready.
Prices and event days
RugPullARugPullBEvent
Published daily event probabilities
A probabilityB probability
Participant archetypes
Event ledger
| Day | Source | Published p | Executed treasury | Gross USD | SN funding | Source impact | Cap |
|---|---|---|---|---|---|---|---|
| Run the model to populate event data. | |||||||
05 / Research outputs
What the experiment can actually measure
Probability learning
Measure calibration, signal lift, pre-event positioning, and the rate at which human and automated participants infer the hazard function.
Distributional effects
Compare outcomes among passive holders, hazard-aware participants, post-event buyers, liquidity providers, arbitrageurs, and the institutional treasury.
Legitimacy and consent
Test whether radical disclosure and public-benefit funding produce informed consent or merely normalize loss transfer under an ethical narrative.
Critical falsification condition: if sophisticated actors systematically capture value from participants who understand the branding but not the mathematics, transparency may function as legalistic disclosure rather than meaningful informed consent.
06 / Documentation
RugPull White Paper v2.0
Updated thesis, public-benefit funding design, hazard model, participant adaptation, one-year reference results, and technical architecture.
07 / Open methodological review
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