MCAI Lex Vision: Crypto ATM Settlement Trigger Dynamics

Class Action Pre-Certification Resolution Under Discovery, Legislation, and Self-Regulation Pressure

Dec 27, 2025

Executive Thesis

Crypto-ATM litigation outcomes will not be determined by whether class claims can theoretically be certified, but by when defendants’ expected cost of continued litigation exceeds the cost of settlement. That inflection point follows a predictable causal chain: uncertainty collapses as discovery exposes internal fraud data, state legislation reduces variance around expected obligations, and failed self-regulation converts compliance artifacts into admissions. Once these forces converge, continued defense becomes economically irrational. Settlement is the market signal that efficient negligence has ended.

MindCast AI’s foresight simulation models settlement timing as the primary signal of liability migration, rather than certification or final judgment. The output is a timing map that predicts when settlement becomes the dominant strategy and which levers accelerate or delay that outcome.

Scope and Purpose of the Foresight Simulation

The foresight simulation models settlement behavior rather than doctrinal liability endpoints. Defendants make settlement decisions under uncertainty, responding to accelerating downside risk rather than legal finality. The simulation identifies the specific events—procedural, evidentiary, legislative, and reputational—that cause expected loss to spike non-linearly.

Settlement is therefore not a compromise outcome, but the market’s admission of where responsibility has already moved.

Assumptions

The simulation assumes the liability allocation established in Crypto ATM Liability Migration (MindCast AI, December 2025). That analysis demonstrated that operators are the lowest-cost avoiders, that behavioral incapacity under coercion defeats warn-and-shift defenses, and that CSI-gated causation survives third-party intervention. The present simulation takes the next step: given that liability will migrate, when do defendants stop contesting allocation and start negotiating resolution?

What the Foresight Simulation Is Not

The simulation does not predict trial outcomes, certification success, or judicial ideology. It predicts rational settlement behavior under predictable discovery and regulatory pressure. The model treats settlement as a cost-optimization decision, not a liability admission.

Contact mcai@mindcast-ai.com to partner with us on liability foresight simulations. See companion study Crypto ATM Liability Migration, Operator Accountability Under Behavioral Incapacity (Dec 2025) and larger framework Chicago School Accelerated — Integrated Application, AI Hallucinations, AI Copyright, and Crypto ATMs (Dec 2025).

I. Parties and Structural Positions in the Harm Architecture

Settlement timing depends on where actors sit within the harm-production system, not on formal labels such as “operator” or “host.” The relevant distinction is not moral blame or formal job title, but architectural position: who designs systems, who intermediates transactions, and who experiences harm without realistic exit or self-help.

A. Operators

Bitcoin Depot, CoinFlip, and Athena Bitcoin control transaction architecture, data aggregation, and refund discretion. Operators set transaction caps, cooling-off periods, transaction holds, monitoring thresholds, and refund rules. Operators receive real-time transaction data and aggregated fraud reports across thousands of machines, giving operators systemic visibility unavailable to any individual victim or retailer.

B. Retailers

National convenience-store chains (Tier-A), regional chains (Tier-B), and independent stores (Tier-C) control placement, consumer trust, and revenue-sharing continuity. Retailers do not design transaction architecture, but retailers do intermediate placement, visibility, and consumer trust. Retailer exposure varies sharply with contracting structure and internal governance.

C. Victims

Individual consumers encounter the system episodically and under coercion, lacking system-level information or exit options. Victims are often elderly or financially inexperienced, operating under live scammer coercion—frequently on the phone, under time pressure, and threatened with immediate consequences. Victim behavioral incapacity constitutes a binding constraint rather than a situational failure.

D. Institutions

State legislatures, state attorneys general, consumer protection agencies, and trial courts intervene only after harm accumulates, shaping timing rather than direction of outcomes. Capacity and speed differences explain why enforcement and private adjustment often precede statutory clarity.

II. Behavioral Incapacity and Discovery Dynamics

Two foundational dynamics drive settlement timing: the behavioral incapacity that defeats disclaimer defenses, and the discovery mechanics that convert internal knowledge into compounding liability exposure.

A. Behavioral Incapacity as a Binding Constraint

Crypto-ATM scams operate under live coercion that defeats reflective decision-making at the point of transaction. Behavioral evidence demonstrates that warnings and disclosures fail once victims are coached in real time by scammers. Empirically, disclosure fails approximately 81 percent of the time under live coercion.

The simulation treats behavioral incapacity as a binding structural constraint rather than a factual footnote. Once incapacity is recognized as systemic, prevention and liability necessarily migrate upstream to parties who can alter transaction architecture.

The Becker-Posner Update: If disclaimer defenses collapse when users face passive cognitive constraints (automation bias, habituation), they collapse a fortiori when victims face active coercion. The “reasonable user” fiction fails categorically in crypto-ATM contexts because no reasonable user exists at the point of transaction—only a victim whose cognition has been commandeered.

Settlement Implication: Behavioral incapacity matters not only for liability allocation, but because it forecloses defendants’ ability to reframe discovery as user error once internal data is produced. The incapacity finding converts operator knowledge into operator responsibility.

B. Discovery as the Primary Settlement Catalyst

Discovery—not certification—is the dominant settlement driver in crypto-ATM litigation. Internal fraud metrics, refund denial rates, SAR-adjacent communications, and retailer warnings compound exposure once produced. Discovery also creates spillover risk: documents disclosed in one case become accelerants for parallel class actions, attorney-general enforcement, and media scrutiny.

Discovery Cost Acceleration

Settlement timing tracks discovery probability and scope rather than class size or trial risk. Key discovery categories include:

Internal fraud metrics: Transaction-level data showing fraud rates, loss concentrations, and geographic patterns
Refund denial documentation: Policies, individual denials, and communications explaining refusals
SAR-adjacent materials: Internal fraud alerts that parallel Suspicious Activity Reports
Revenue-sharing communications: Retailer agreements documenting profit participation

Cross-Case Spillover Mechanics

Documents produced in Case A reduce plaintiff costs in Cases B through N. Once internal fraud data becomes producible in any jurisdiction, it becomes reusable across private litigation and attorney-general enforcement. The spillover effect transforms discovery from a single-case cost into a system-wide exposure event.

III. Cognitive Digital Twin (CDT) Simulation Outputs

The following section reports outputs from multiple Cognitive Digital Twin (CDT) simulations, each designed to evaluate a distinct causal dimension of settlement timing. Each CDT simulation applies a specific Vision Function—a defined analytical lens with a bounded explanatory scope—to model how liability, behavior, and institutional response interact under real-world constraints.

Scores are normalized on a 0–1 scale and benchmarked against the MindCast AI NAIP200 baseline. The simulations do not predict legal outcomes; they measure structural forces that drive settlement behavior.

CDT Simulation Method & Output Summary

The foresight analysis is based on executed CDT simulations, not conceptual application alone. Each CDT simulation was run using MindCast AI’s NAIP200-calibrated framework, modeling operator architecture, retailer coordination dynamics, enforcement facts pleaded in active cases, and empirically observed behavioral-incapacity constraints.

Reported outputs represent normalized comparative scores (0–1 scale) reflecting structural pressure toward settlement, rather than empirical measurements or legal predictions. Threshold crossings indicate points at which continued litigation becomes economically irrational under Posnerian efficient-negligence logic.

CDT Output Summary (NAIP200-Benchmarked):

Posner Vision CDT: LCAI = 0.86 · ENPS = 0.81 · LMC = 0.88
Causation Vision CDT: CSI = 0.87 · ALI = 0.84 · CMF = 0.79
Strategic Behavioral Cognitive (SBC) CDT: SIS = 0.71 (Tier-A Retailers) · CLC = 0.77
Institutional Cognitive Plasticity (ICP) CDT: IUV = 0.61 (Operators) · LIC = 0.64
Disclosure Vision CDT: KAI = 0.83 · INF = Triggered · SRBI = 0.78

Threshold crossings reported above indicate that all five CDT simulations exceeded their respective settlement-relevant activation criteria. The subsections that follow interpret these outputs and trace how they jointly determine settlement timing across operators, retailers, and jurisdictions.

A. Posner Vision CDT Simulation — Liability Allocation & Efficient Negligence

Vision Statement (Posner Vision): Posner Vision evaluates where liability must land to minimize total social cost once harm persists, given asymmetric prevention capacity and behavioral constraints. The simulation operationalizes the lowest-cost avoider principle and models when continued under-investment in prevention (efficient negligence) becomes economically irrational.

CDT Simulation Focus: Architectural control over harm; relative prevention cost; collapse point of efficient negligence.

Parties Run: Bitcoin Depot, CoinFlip, Athena Bitcoin (Operators); Circle K (Tier-A Retail Host)

Interpretation: The 0.86 Lowest-Cost Avoider Index reflects a pronounced Hand Formula asymmetry: prevention burden registers at $30–70 per kiosk per year, while expected harm exceeds $3,000–$10,000 per kiosk per year in scam-dense jurisdictions. An Efficient Negligence Persistence Score above 0.80 indicates that under-investment remains rational only while liability uncertainty persists. Liability Migration Certainty approaching 0.90 signals that once uncertainty collapses, settlement pressure becomes inevitable.

B. Causation Vision CDT Simulation (CSI-Gated) — Causal Chain Survivability

Vision Statement (Causation Vision): Causation Vision evaluates whether an asserted causal chain remains structurally load-bearing under legal scrutiny, accounting for third-party intervention, knowledge, control, and benefit. The simulation uses Causal Signal Integrity (CSI) to test whether intervening actors sever—or merely exploit—the harm architecture.

CDT Simulation Focus: Knowledge density; real-time control; financial benefit; intervening-actor defenses.

Parties Run: Bitcoin Depot, CoinFlip, Athena Bitcoin, Circle K

Interpretation: A CSI score of 0.87 exceeds the threshold at which courts reliably reject intervening-act defenses. Operators possessed contemporaneous knowledge of fraud patterns, retained real-time control over transaction architecture, and directly profited from transaction volume. Third-party scammers exploited the system but did not break the causal chain. Discovery therefore threatens not just damages exposure, but causal liability.

C. Strategic Behavioral Cognitive (SBC) Vision CDT Simulation — Retailer Coordination & Defection

Vision Statement (SBC Vision): Strategic Behavioral Cognitive Vision evaluates whether incentives translate into coordinated behavior under cognitive load, contractual friction, and reputational risk. The simulation measures how and when actors exit, renegotiate, or adapt once litigation pressure exceeds coordination capacity.

CDT Simulation Focus: Retailer exit timing; contract renegotiation pressure; brand and insurance sensitivity.

Parties Run: Circle K (Tier-A); Regional Chain Composite (Tier-B)

Interpretation: Tier-A retailers exhibit higher Strategic Incentive Scores and faster response velocity due to centralized governance and brand exposure. Elevated cognitive load favors exit or renegotiation over prolonged litigation. Retailer movement precedes operator settlement and functions as a leading indicator that the system has crossed from contestation into resolution.

D. Institutional Cognitive Plasticity (ICP) Vision CDT Simulation — Adaptation Capacity

Vision Statement (ICP Vision): Institutional Cognitive Plasticity Vision evaluates an institution’s capacity to update its operating model in response to new liability information. The simulation measures whether actors adapt, litigate, or delay once pressure mounts.

CDT Simulation Focus: Update velocity; legacy revenue inertia; throughput of organizational change.

Parties Run: Operators, Retailers, State AG Offices (Composite)

Interpretation: Operators demonstrate moderate update capacity but high legacy inertia tied to transaction-fee revenue. Retailers adapt more quickly through exit rather than redesign. State enforcement institutions exhibit higher adaptive throughput once evidentiary thresholds are met, reinforcing private-litigation pressure and accelerating settlement timing.

E. Disclosure Vision CDT Simulation — Self-Regulation as Discovery Risk

Vision Statement (Disclosure Vision): Disclosure Vision evaluates how voluntary compliance and transparency measures affect liability exposure, particularly when behavioral outcomes fail to improve. The simulation tests whether self-regulation mitigates risk or generates discoverable evidence that accelerates settlement.

CDT Simulation Focus: Internal knowledge accumulation; effectiveness of voluntary controls; discovery backfire risk.

Parties Run: Operators

Interpretation: The Inefficient Negligence Flag triggers where operators documented harm, implemented visible controls, and nonetheless failed to reduce loss rates. A Self-Regulation Backfire Index above 0.75 indicates that compliance artifacts—created for governance or optics—now function as plaintiffs’ exhibits. Settlement pressure accelerates once these records become discoverable.

IV. Settlement Threshold Dynamics: Operators and Retailers

With incapacity established and discovery dynamics defined, the simulation now quantifies how these forces translate into settlement timing. Settlement inflection points differ for the two primary defendant categories: operators who control transaction architecture, and retailers who intermediate placement and consumer trust. The dynamics are linked—retailer movement functions as a leading indicator of operator settlement.

A. Operator Settlement Threshold Model

Operators face a predictable inflection point where expected discovery harm exceeds any rational settlement ceiling. That threshold is driven by internal knowledge density, dependence on scam-driven transaction volume, and cross-jurisdictional reuse risk. The model does not predict liability; it predicts when litigation costs outpace settlement value.

Hand Formula Application

The Hand formula asymmetry established in the liability analysis anchors the settlement threshold:

B (Burden of prevention): $30–70 per kiosk per year for software-based caps, delays, holds, and monitoring
P × L (Expected harm): $3,000–$10,000+ per kiosk per year in scam-dense jurisdictions
Ratio: B ≪ P × L confirms operators as decisive lowest-cost avoiders

Discovery as Cost Multiplier

Survival of a motion to dismiss materially increases the probability that high-impact internal data becomes discoverable. Once that threshold is crossed, settlement becomes the dominant strategy regardless of certification prospects.

B. Retailer Defection and Contractual Pressure Dynamics

Retail hosts respond to litigation pressure before final liability allocation occurs. Survival of retailer-liability theories at the pleading stage triggers insurer reassessment, internal compliance escalation, and renegotiation of revenue-sharing and indemnity provisions.

Critical Sequencing: Retailer movement precedes operator settlement, not follows it. Retailer exits or margin compression shorten the operator’s settlement timeline by eroding distribution stability. Retailer behavior therefore functions as a leading indicator of imminent operator settlement. Settlement timing is further compressed once insurers reassess coverage in light of discovery survivability and legislative baselines.

Tier-Based Exposure Analysis

Prediction: Within 6–9 months of adverse pleading rulings, Tier-A retailers initiate kiosk removal or indemnity renegotiation, compressing operator margins.

V. State Legislative Baselines as Settlement Accelerants

State legislation reshapes settlement timing even when cases are litigated under common-law or consumer-protection theories. Enacted transaction caps, refund mandates, and enforcement authority reduce variance around expected future obligations and eliminate uncertainty about administrability.

Operators already complying in regulated states cannot credibly argue that discovery-driven remedies are infeasible. Settlement occurs earlier in jurisdictions where legislative baselines have shifted, regardless of whether certification has been briefed.

Legislative Baseline Matrix

Note: ETS = Enforcement Throughput Score; LBC = Legislative Baseline Clarity. Legislative clarity collapses defendants’ option value of delay, accelerating settlement even in non-statutory cases.

VI. Active Enforcement Cases as Settlement Accelerants

State attorney general enforcement actions filed in 2025 provide real-time calibration data for the settlement timing model. These cases operationalize the liability allocation framework established in MindCast AI’s companion analysis, converting theoretical lowest-cost avoider identification into pleaded facts and active discovery.

Iowa: Bitcoin Depot and CoinFlip (June 2025)

The Iowa Attorney General filed separate enforcement actions against Bitcoin Depot and CoinFlip, alleging that their kiosks facilitated more than $20 million in scam-driven transfers involving Iowa residents over less than three years. The petitions document fee spreads in the 21–23 percent range while cataloging the absence of low-cost safeguards: no meaningful transaction caps, no cooling-off periods, no timely refunds.

Critically, the Iowa petitions characterize elderly and cognitively overloaded users as structurally unable to self-protect under live coercion—adopting the behavioral incapacity framing that defeats warn-and-shift defenses. The state explicitly positions operators as lowest-cost avoiders who knowingly profited from scam-dense transaction flows. The allegations mirror the Hand formula asymmetry modeled in Section IV: prevention burden trivial, expected harm substantial, ratio dispositive.

The Iowa filings convert simulation inputs into discoverable outputs. Internal fraud metrics, refund denial documentation, and revenue-sharing communications cited in the petitions now become active discovery targets across jurisdictions. The Lowest-Cost Avoider Index and Efficient Negligence Persistence Score modeled in the Posner Vision CDT simulation find empirical anchors in pleaded facts rather than theoretical projections.

District of Columbia: Athena Bitcoin (September 2025)

The District of Columbia’s action against Athena Bitcoin provides the starkest empirical validation of the behavioral incapacity thesis. The complaint alleges that approximately 93 percent of deposits into Athena’s D.C. crypto ATMs were scam-related—a concentration rate that forecloses any credible argument that fraud represents an unforeseeable edge case rather than the dominant use pattern.

The D.C. complaint documents that the median victim was a senior losing thousands of dollars per transaction, that Athena charged undisclosed fees approaching 26 percent, and that Athena enforced categorical no-refund policies even when on notice of fraud. The complaint attacks reliance on warnings as inadequate under conditions of live coercion, explicitly rejecting the disclosure-as-defense theory that operators have historically deployed.

Three elements of the D.C. filing warrant particular attention for settlement timing:

Knowledge density. The 93 percent scam-share figure establishes that Athena possessed systemic knowledge of harm. The allegation forecloses any characterization of scattered fraud incidents buried in legitimate transaction volume; scam-driven deposits constituted the overwhelming majority of the business. Knowledge at this density converts voluntary inaction into knowing facilitation.

Architectural control. The complaint’s emphasis on transaction architecture control—Athena’s discretion over caps, holds, monitoring thresholds, and refund rules—tracks the CSI-gated causation model. Athena designed the system, operated the system, profited from the system, and retained the capacity to modify the system at any point. Third-party scammers exploited the architecture but did not supersede Athena’s causal responsibility.

Refund policy exposure. The no-refund policy documentation creates discovery exposure that compounds across cases. Athena’s categorical refund denials, maintained even when fraud was flagged, demonstrate that the company prioritized revenue retention over victim protection as a matter of policy rather than oversight. The pattern converts refund data from a damages issue into a liability accelerant.

Vaughan v. Athena Bitcoin (S.D. Florida, November 2025)

The federal class action Vaughan v. Athena Bitcoin, Inc., No. 1:25-cv-25141 (S.D. Fla.), targets fee and disclosure practices rather than scam facilitation directly. The complaint alleges inflated exchange rates, undisclosed surcharges, additional processing fees, and operation without proper licensing under Florida’s consumer protection regime.

Vaughan instantiates the bifurcation dynamic predicted in Section VIII. Fee-disclosure claims certify more readily than facilitation claims due to common questions and calculable damages. However, the discovery these claims unlock—internal pricing communications, fee-setting rationales, transaction volume data segmented by user demographics—feeds directly into the higher-stakes facilitation theories pursued by state enforcers.

The case instantiates the strategic dilemma the simulation identified: early resolution of fee subclasses limits immediate exposure but produces the evidentiary foundation for facilitation liability. Defendants cannot compartmentalize fee discovery from facilitation discovery because the same internal documents illuminate both. The Self-Regulation Backfire Index of 0.78 modeled in the Disclosure Vision CDT simulation reflects precisely this backfire dynamic—compliance and pricing artifacts generated for business purposes become plaintiffs’ exhibits once produced.

Jackson v. Athena Bitcoin (TCPA/FTSA Class Certification)

Complementing the operator-centric cases, recent Florida litigation over crypto-ATM marketing communications reinforces that courts increasingly treat BTM operators as active commercial actors rather than neutral infrastructure. In Jackson v. Athena Bitcoin, Inc., a Florida court certified two classes on claims under the Telephone Consumer Protection Act (TCPA) and Florida Telephone Solicitation Act (FTSA), finding that Athena leveraged customer contact data and transaction context for commercial messaging without adequate consent.

The certification pattern supports the Strategic Behavioral Cognitive and Institutional Cognitive Plasticity projections modeled in the SBC and ICP Vision CDT simulations. Once litigation and discovery survive early motions, retailer and operator behavioral adaptation accelerates—but adaptation costs compound rather than resolve exposure.

VII. Cross-Case Dynamics and Settlement Timing Implications

The enforcement actions and private litigation catalogued in Section VI do not operate in isolation. The following analysis addresses the systemic dynamics that convert individual case outcomes into compounding settlement pressure.

Cross-Case Spillover Mechanics

These matters—Iowa, D.C., Vaughan, and Jackson—now operate as a coordinated evidentiary system regardless of whether plaintiffs’ counsel or enforcement offices are formally coordinating. Documents produced in Iowa reduce plaintiffs’ costs in Florida. Allegations pleaded in D.C. inform discovery requests in Iowa. Vaughan‘s fee-focused discovery surfaces transaction data usable by attorneys general pursuing facilitation theories. Jackson‘s certification order establishes judicial treatment of operators as active commercial actors rather than passive intermediaries.

The feedback loop modeled in Section IX is no longer theoretical. Each filing increases the evidentiary density available to subsequent filers. Each discovery production lowers the marginal cost of the next complaint. Each enforcement action raises the reputational exposure that accelerates settlement pressure.

Settlement Timeline Compression

The active enforcement cases compress the settlement timeline modeled in Section X through three mechanisms:

Discovery threshold crossed. Pleading survival in any jurisdiction triggers the discovery cost acceleration described in Section II.B. Iowa and D.C. have moved past the complaint stage; internal documents are now producible. The cross-case spillover risk that defendants face is no longer hypothetical.

Public evidentiary baseline established. The 93 percent scam-share allegation in D.C. reshapes settlement negotiations. Defendants cannot credibly claim ignorance of systemic fraud when a major jurisdiction has pleaded—and will seek to prove—that scam transactions constituted nearly all deposit volume.

Multi-front pressure geometry. The emergence of a federal class action alongside state enforcement creates pressure that increases defense costs geometrically. Operators must now staff parallel discovery tracks, manage inconsistent litigation positions across jurisdictions, and face the prospect that any settlement in one forum becomes a baseline for demands in others.

Falsification Update

The original simulation predicted pre-certification settlement within 6–15 months of motion-to-dismiss survival. The Iowa and D.C. filings are now 6 and 3 months old respectively. Vaughan remains at the pleading stage. If the simulation is accurate, settlement discussions in at least one of these matters should initiate before Q3 2026. Absence of any settlement activity by Q4 2026 would constitute partial falsification requiring model recalibration.

VIII. Fee-Disclosure Claims Versus Fraud-Facilitation Claims

Not all claims exert equal settlement pressure. Fee-disclosure claims and fraud-facilitation claims operate under different procedural dynamics and produce divergent settlement incentives.

Fee-Disclosure Claims

Certify more easily due to common questions and calculable damages
Trigger rapid, document-heavy discovery
Lower individual stakes but higher aggregate exposure

Fraud-Facilitation Claims

Carry higher existential risk but greater procedural friction
Require more individualized proof of knowledge and causation
Generate maximum reputational exposure if successful

Strategic Implications

Defendants rationally seek early resolution of fee-based subclasses to prevent discovery that strengthens facilitation theories. However, resolving fee claims produces the very discovery that exposes facilitation liability. The dynamic creates a strategic dilemma: early fee settlement limits immediate exposure but accelerates facilitation risk.

Prediction: The simulation predicts bifurcated settlements rather than global resolution as the dominant pattern. Claim Bifurcation Likelihood: 0.82.

IX. Self-Regulation Backfire and Public-Private Feedback

Two related dynamics compound settlement pressure: the conversion of voluntary compliance artifacts into discovery liabilities, and the feedback loop between public enforcement and private litigation that accelerates evidentiary accumulation.

A. Industry Self-Regulation as a Discovery Multiplier

Voluntary industry controls do not reduce settlement pressure; they often increase it. Self-regulation produces internal records demonstrating knowledge of harm, selective intervention, and continued profit extraction despite persistent losses.

Beckerian logic predicts that rational actors implement visible controls while maintaining profitable architectures. When losses persist, compliance artifacts convert from evidence of good faith into proof of inefficient negligence. Settlement accelerates once defendants recognize that their own compliance artifacts are among the most damaging discovery materials.

B. Public Enforcement and Private Litigation Feedback Loop

Attorney-general enforcement and private litigation operate as a feedback system rather than parallel tracks. Public investigations generate data that lowers plaintiffs’ costs and raises defendants’ downside across jurisdictions. Each enforcement action shortens expected time-to-settlement by increasing evidentiary density and reputational exposure.

Feedback Mechanics

AG investigation produces subpoena responses and document production
Investigation findings become public through enforcement actions or press releases
Private plaintiffs incorporate findings into complaints and discovery requests
Expanded private discovery produces additional documents reusable by AGs
Cycle accelerates as evidentiary base compounds

X. Timing Predictions and Falsification Criteria

Foresight requires commitments that can fail. The simulation predicts pre-certification settlement in at least one major case within defined post-pleading windows, retailer exits or contract renegotiations following adverse rulings, and separate resolution of fee-based claims. Settlement timing, not verdicts, serves as the empirical test.

A. Settlement Trigger Synthesis

Pre-Certification Settlement Window: 6–15 months post-MTD survival
Claim Bifurcation Likelihood (Fee vs. Facilitation): 0.82
Retailer-Driven Settlement Acceleration Delta: -4 to -6 months

B. Falsification Matrix

CSI-Gated Predictions

CSI ≥ 0.70 (accepted): Operator architecture → victim incapacity → completed fraud → predictable harm
CSI < 0.50 (rejected): Warning adequacy → victim protection

XI. Strategic Applications

Predicting settlement timing creates leverage only if it informs action.

A. For Plaintiffs’ Counsel and Litigation Funders

Prioritize discovery targets: internal fraud metrics, refund denial documentation, SAR-adjacent materials
Sequence defendants: target Tier-A retailers with centralized contracting first
Time settlement demands after MTD survival but before certification costs accrue
File before operators rewrite indemnity clauses

B. For State Attorneys General and Regulators

Position enforcement as part of public-private feedback loop to maximize deterrent effect
Use statutory baselines to collapse defendants’ option value of delay

C. For Legislative Staff and Policy Advocates

Frame bills as alignment with peer-state precedent rather than experimentation
Focus on effective dates—a safeguard delayed is a safeguard denied

D. For Insurers, Risk Officers, and Corporate Counsel

Identify when expected litigation costs exceed settlement value
Model settlement as cost optimization rather than liability admission

XII. Conclusion

Certification outcomes are noisy and jurisdiction-specific; settlement timing is systemic and predictable. Crypto-ATM litigation demonstrates how modern harm cases resolve once discovery, legislation, and failed self-regulation expose profitable exploitation under known risk.

The CDT flows converge on a single finding: settlement timing is driven by discovery survivability, legislative baseline clarity, and the failure of self-regulation to reduce harm. Under Posnerian logic, efficient negligence collapses once expected costs become predictable. Settlement is the market signal that liability migration is complete.

Final Prediction: At least one major operator settles a fee-disclosure subclass before certification briefing, followed by broader resolution pressure once retailer exits and AG data converge. Failure of any settlement within 18 months of MTD survival would falsify the model and require recalibration.

Appendix: Cognitive Digital Twin Metric Definitions

All scores are simulation-based foresight outputs derived from CDT modeling. Scores represent relative comparative strength across actors and causal chains, benchmarked against the MindCast AI NAIP200 baseline.

Posner Vision Metrics

LCAI (Lowest-Cost Avoider Index): Identifies which actor can prevent harm most efficiently. Range 0–1.
ENPS (Efficient Negligence Persistence Score): Measures how long rational under-investment can persist before liability exposure makes it irrational.
LMC (Liability Migration Certainty): Probability that liability will migrate upstream once triggering events occur.

Causation Vision Metrics

CSI (Causal Signal Integrity): Evaluates whether a causal chain is load-bearing. CSI ≥ 0.70 = accepted; CSI < 0.50 = rejected.
ALI (Action–Language Integrity): Alignment between stated commitments and observable actions.
CMF (Cognitive–Motor Fidelity): Capacity to execute stated intentions.

Behavioral Metrics

SIS (Strategic Incentive Score): Alignment between incentive structure and socially optimal behavior.
BDF (Behavioral Drift Factor): Systematic deviation from rational-actor baseline.
CLC (Cognitive Load Coefficient): Cognitive burden in decision environment. CLC > 0.70 = bounded rationality dominates.

Regulatory Metrics

ETS (Enforcement Throughput Score): Regulatory capacity to process and enforce.
LBC (Legislative Baseline Clarity): Certainty of statutory obligations in a given jurisdiction.

Disclosure Metrics

KAI (Knowledge Accumulation Index): Density of internal records demonstrating awareness of harm.
INF (Inefficient Negligence Flag): Triggers when voluntary controls were implemented but losses did not decline.
SRBI (Self-Regulation Backfire Index): Probability that compliance artifacts will increase discovery harm.