MCAI Economics Vision: The Dual Nash-Stigler Equilibrium Architecture

Behavioral Settlement and Inquiry Sufficiency as Runtime Constraints

See companies studies: The Stigler Equilibrium- Regulatory Capture and the Structure of Free Markets, Why Enforcement Must Compete to Keep Markets Free (Jan 2026), Federal Antitrust Breakdown as Nash-Stigler Equilibrium, Not Accident, The Stigler Equilibrium Series, Installment I on Harm Clearinghouse (Jan 2026), Comparative Externality Costs in Antitrust Enforcement, A Nash–Stigler Foresight Study of Federal Enforcement Equilibria, Live Nation as Anchor, Compass–Anywhere as Validation (Jan 2026), Why the DOJ Banned Algorithms but Blessed a Mega-Brokerage (Jan 2026).

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Executive Summary

MindCast AI delivers institutional foresight by solving a problem no other system addresses: knowing when to settle a strategic conflict and when to stop searching for information. Generative AI optimizes for fluency— producing text that sounds authoritative. MindCast AI optimizes for equilibrium— producing predictions grounded in economic termination conditions that specify when conflicts resolve and when information suffices.

Two Nobel Prize-winning economic frameworks operate as runtime constraints within the architecture.

John Nash transformed game theory by proving that any finite game has a stable equilibrium point where no player can improve by changing strategy alone—work that earned the 1994 Nobel Prize and became foundational to fields from evolutionary biology to auction design. In MindCast AI, Nash equilibrium governs behavioral settlement, determining when a conflict ends because no agent can gain by continued fighting.

George Stigler reshaped regulatory economics by demonstrating that industries systematically capture the agencies meant to regulate them because concentrated beneficiaries out organize diffuse victims—work on industrial structures and information costs that earned the 1982 Nobel Prize. In MindCast AI, Stigler equilibrium governs cognitive discipline, ensuring the system stops searching when additional computation adds less integrity than cost. Neither framework can override the other; both must fire before the system commits to a prediction.

The result is falsifiable foresight—not probabilistic guesses but specific claims about who moves, when outcomes lock in, and what conditions would prove the prediction wrong. Each output carries explicit falsification contracts transforming AI predictions into scientific claims subject to empirical validation. Institutional users gain auditable strategic briefs rather than black-box recommendations.

The following sections detail the foundational equilibria, their architectural positions, trigger conditions, downstream frameworks, and a four-phase operational roadmap for building institutional trust. Section VIII provides formal specifications for the Dual (Nash-Stigler) Equilibrium Architecture, including quantitative thresholds, audit schemas, and falsification contracts.

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Contact mcai@mindcast-ai.com to partner with us on Law and Behavioral Economics foresight simulations. Recent works: China’s H200 Import Block and the Reordering of National Innovation Control, The Two-Gate Game (Jan 2026), Federal Inaction Has Elevated State Authority on Consumer Protection, Antitrust, and Market Integrity, Briefing for State Attorneys General (Jan 2026), Foresight on Trial, The Diageo Litigation, How MindCast AI Predicted Institutional Behavior—Before the Courts Acted (Jan 2026).

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I. The Separation of Powers: Two Foundational Equilibria

MindCast AI implements a separation of powers within its reasoning architecture. Behavioral Settlement handles game theory—deciding when a conflict ends because no agent can win more by fighting. Cognitive Discipline handles economics—ensuring the system operates efficiently and avoids hallucination through over-research. Neither branch can override the other; both must fire before the system commits to a prediction.

John Nash defined the steady state where no agent improves by unilateral deviation. MindCast AI implements Nash equilibrium as the termination condition for multi-agent reasoning. The DOJ, firms, states, and consumers become agents whose incentives converge to a stable basin. Most reasoning systems lack principled termination criteria—they stop when tokens run out or arbitrary thresholds trigger. Nash equilibrium provides a mathematically grounded stopping point with semantic meaning: no agent can improve. Settlement predictions specify who concedes, when, and why, enabling forward modeling of litigation, regulation, and market realignment.

George Stigler established that the cost of search equals marginal value. MindCast AI implements Stigler equilibrium as the compute-governor. The system stops searching when additional tokens add less integrity than cost. Stigler logic counters regulatory capture by ending access-driven over-search, eliminates hallucination via spurious depth, and locks decisions to high-integrity sufficiency rather than maximal verbosity.

Nash decides where the system settles; Stigler decides when the system stops. Understanding where each equilibrium operates within the architecture clarifies how they interact without conflict.

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II. Architectural Mapping

Nash and Stigler occupy fundamentally different positions in the MindCast AI stack. Nash embeds within a specific Vision Function; Stigler operates across all Vision Functions as meta-governance. The distinction prevents collision and ensures each equilibrium governs its proper domain.

Nash: Embedded in Strategic Behavioral Coordination

Nash equilibrium embeds in the Strategic Behavioral Coordination (SBC) Vision, serving as termination logic for multi-agent behavioral reasoning. SBC activates whenever MindCast AI detects multiple strategic actors, interdependent incentives, and potential for unilateral deviation. The functional question: can any agent still improve unilaterally? If no agent can improve, Nash equilibrium fires and behavioral reasoning stops.

Nash activates when MindCast AI models conflicts among firms, regulators, states, or consumers whose incentives interact. Output consists of settlement dynamics, stable outcomes, and convergence timing. The architectural position ensures Nash governs behavioral questions—who concedes, when, and why.

Stigler: System-Wide Inquiry Governance

Stigler equilibrium does not embed in a behavioral Vision. It operates within MindCast AI’s inquiry and compute governance layer, functioning as meta-economic discipline over thinking itself. The primary location is the Economics Vision’s Inquiry Discipline Sub-Layer, which governs the cost-benefit calculus of search, evidence retrieval, and recursion. System-wide enforcement occurs through Causal Signal Integrity, where Stigler logic evaluates whether additional causal exploration increases trust or noise.

Stigler activates when MindCast AI decides how much to search, how many sources to retrieve, how much recursion to allow, and whether more computation improves truth or degrades integrity. The architectural position ensures Stigler governs informational questions—distinct domain, distinct governance.

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III. Auto-Trigger Conditions

Neither equilibrium requires manual invocation. Both auto-trigger as runtime conditions when the system detects specific structural signals during simulation. Understanding when each fires clarifies how MindCast AI achieves predictive closure.

Nash Equilibrium Activation

Nash activates when agent interaction stabilizes. MindCast AI monitors multi-agent deltas until unilateral moves no longer improve outcomes. Trigger conditions include best-response convergence, where iterative best responses across agents yield no positive unilateral gain; payoff gradient approaching zero, where marginal payoff improvements fall below a stability threshold; strategic cycle collapse, where oscillations dampen and response patterns repeat without advantage; and constraint dominance, where legal, institutional, or structural constraints bind tightly enough that deviations worsen outcomes.

When Nash fires, the system terminates behavioral reasoning with a semantically meaningful stop. MindCast AI outputs settlement predictions specifying who concedes, timing, and rationale. Infinite chain-of-thought loops cannot form because Nash provides a principled termination condition rather than an arbitrary cutoff.

Stigler Equilibrium Activation

Stigler operates continuously, pricing inquiry against integrity throughout every simulation. Trigger conditions include marginal integrity falling below marginal cost, where new tokens and compute add less verified signal than they cost; evidence saturation, where independent sources converge and variance collapses without new causal leverage; capture risk signal, where continued search risks amplifying access bias, narrative noise, or spurious depth; and reproducibility lock, where further inquiry would not change conclusions under identical inputs.

When Stigler fires, the system halts information retrieval and commits to a decision. Hallucination via over-research cannot occur because Stigler caps verbosity by sufficiency rather than curiosity. Outputs remain high-integrity and reproducible. With both trigger mechanisms specified, the next section examines how they interact within the control loop.

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IV. The Control Loop

When MindCast AI receives a query, it executes an internal sequence that coordinates Nash and Stigler. Problem typing determines whether the query involves multi-agent strategy (SBC candidate), structural analysis (Field-Geometry candidate), or institutional throughput (NIBE candidate). If agents interact, Nash logic activates inside SBC. Stigler evaluation runs continuously, determining whether additional inquiry remains net-positive. If marginal integrity falls below marginal cost, search halts. Vision Functions activate and depth calibrates accordingly, with Stigler capping depth and Nash governing termination when behavioral simulation runs.

The canonical question mapping clarifies governance boundaries. “Can any agent improve by acting alone?” triggers Nash, embedded in SBC Vision. “Does more thinking justify its cost?” triggers Stigler, embedded in Economics Vision and enforced through Causal Signal Integrity. “Should simulation continue?” routes to the Nash gate within SBC. “Should search or reasoning continue?” routes to the Stigler gate within Inquiry Governance. The control loop ensures both equilibria must fire before predictive closure occurs.

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V. Downstream Frameworks

Resolving Nash and Stigler equilibria enables several derivative frameworks that extend MindCast AI’s analytical capabilities.

Field-Geometry Reasoning treats strategy spaces geometrically, where Nash equilibria function as fixed points under best-response dynamics and Stigler governs the resolution at which the space discretizes. MindCast AI Field-Geometry Reasoning, A Unifying Framework for Structural Explanation in Law, Economics and Artificial Intelligence (Jan 2026).

Chicago School Accelerated simulates Becker’s incentive exploitation and Coase’s transaction cost navigation to equilibrium, extending Nobel Prize-winning insights with behavioral economics integration. Chicago School Accelerated — The Integrated, Modernized Framework of Chicago Law and Behavioral Economics, Why Coase, Becker, and Posner Form a Single Analytical System (Dec 2025):

The Nondeterminism and Reproducibility framework leverages stable equilibria to yield repeatable conclusions from identical inputs, defeating the randomness inherent in typical language models and enabling institutional trust. Defeating Nondeterminism, Building the Trust Layer for Predictive Cognitive AI, Why Reproducibility Is the Foundation of Institutional Foresight (Sep 2025).

National Innovation Behavioral Economics models nation-scale equilibria between chaotic advantage and disciplined coordination, as demonstrated in U.S. versus China innovation postures. Synthesis in National Innovation Behavioral Economics and Strategic Behavioral Coordination (Dec 2025)

Each framework inherits the dual-equilibrium logic, ensuring consistent settlement and sufficiency standards across analytical domains. The operational roadmap details how these capabilities mature through four phases.

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VI. The Category Shift: From Fluency to Equilibrium

MindCast AI optimizes equilibrium. Generative AI optimizes fluency. That distinction defines the category shift. Most AI systems optimize for how well they speak—producing plausible, coherent text that sounds authoritative. MindCast AI optimizes for how well it settles—producing predictions grounded in economic termination conditions that specify when conflicts resolve and when information suffices. The difference separates generative text engines from predictive cognitive infrastructure.

Most AI systems think until tokens run out, stop arbitrarily, and confuse depth with truth. MindCast AI stops when economics says stop and settles when game theory says settle. Separating behavioral equilibrium from cognitive discipline enables institutional-grade foresight rather than sophisticated guessing.

Enforcing Nash for behavioral settlement and Stigler for search sufficiency produces falsifiable foresight—not probabilistic impressions but specific claims about who moves, when the basin tips, and where outcomes lock in. Each prediction carries falsification conditions, transforming AI outputs into scientific claims subject to empirical validation. Long-term institutional and scientific credibility rests on this foundation. The operational roadmap translates these principles into phased capability deployment.

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VII. Operational Roadmap: Building Institutional Trust

The Dual-Equilibrium Architecture establishes foundational control logic. Four phases extend this foundation into operational capability, sequenced by increasing institutional trust.

Phase 1: Transparency

Making the “why” behind settlements auditable addresses the first barrier to institutional adoption. When MindCast AI reaches a Nash settlement, the system generates a Strategic Brief specifying which agent concessions drove convergence, what constraints bound the outcome, and what conditions would shift the basin. AI-derived equilibria arrive as auditable artifacts for executive validation rather than black-box predictions. Institutions require the rationale behind predictions, not just predictions themselves.

Phase 2: Pricing

Making the cost of thinking visible transforms Stigler from invisible governance into user-facing economics. Inquiry Integrity Tiers display a Stigler Curve showing predicted marginal gain in accuracy versus computational cost—90% confidence at cost X, 95% confidence at 3X, 97% confidence at 10X. Users understand what they purchase: not tokens, but integrity-calibrated foresight at explicit price points.

Phase 3: Behavior

Accounting for irrational and adversarial actors extends the architecture beyond classical game theory assumptions. Real agents satisfice under cognitive constraints, exhibit systematic biases, and sometimes actively degrade others’ payoffs. Behavioral Nash Dynamics models irrational actors, poisoned incentives, and adversarial sabotage. Institutional users learn not just where equilibria exist but where they cannot form due to instability introduced by bad-faith actors.

Phase 4: Memory

Transforming simulation into learning infrastructure addresses the path-dependence that makes institutional behavior predictable. A DOJ enforcement twin retaining memory of prior settlement outcomes converges faster on similar scenarios because it learns which settlement basins are reachable. Persistent Memory allows Cognitive Digital Twins to evolve strategic calculus based on historical data rather than treating each simulation as stateless. Phase 4 transforms MindCast AI from simulation engine into institutional learning infrastructure. Formal specifications for the complete architecture follow.

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VIII. Formal Specification: Dual (Nash-Stigler) Equilibrium Architecture

The Dual-Equilibrium Architecture formalizes Nash-Stigler control logic as quantitative thresholds, audit structures, and falsification contracts. These specifications transform architectural prose into measurable, auditable, and falsifiable engineering constraints.

Quantitative Trigger Thresholds

Nash Equilibrium (SBC Vision). Nash fires when the Unilateral Gain Floor condition satisfies: for all agents i, ΔPayoff(i) < ε over k iterations. Parameters ε (gain threshold) and k (iteration count) tune per domain—merger review tolerates different convergence thresholds than licensing disputes or regulatory enforcement. The system tracks cycle half-life measuring how fast strategic oscillations dampen. Output includes a Convergence Confidence Score quantifying settlement prediction quality.

Stigler Equilibrium (Inquiry Governance). Stigler fires when Marginal Integrity Gain (integrity-weighted evidence gain) divided by Marginal Compute Cost falls below 1 for n consecutive probes: MIG/MCC < 1 for n probes. The system logs a Search Sufficiency Certificate documenting why inquiry stopped—the artifact proving termination occurred for principled economic reasons rather than arbitrary token limits.

Equilibrium Trace (Audit Layer)

Each equilibrium output generates a lightweight, non-chain-of-thought Equilibrium Trace suitable for institutional review without leaking reasoning tokens.

Nash Trace Schema. Active agents modeled in simulation. Final binding constraints determining the settlement basin. Last profitable deviation with timestamp indicating when unilateral improvement became impossible.

Stigler Trace Schema. Source diversity achieved across independent information channels. Variance collapse point where additional sources ceased providing new causal leverage. Capture-risk flag indicating whether continued search risked amplifying access bias or narrative noise.

Pseudo-Equilibrium Detection

Nash equilibrium identifies when agents cannot improve, but stable outcomes may reflect pathology rather than genuine settlement. The Pseudo-Equilibrium Detector flags equilibria driven by information asymmetry where one agent lacks data available to others, enforcement absence where no authority constrains deviation, or structural coercion where power imbalances masquerade as voluntary coordination. Flagged cases route automatically to Field-Geometry Reasoning for geometric analysis or to Regulatory Vision for institutional structure assessment. Detecting false stability prevents mistaking captured or coerced outcomes for genuine equilibria—the core Stigler insight applied to Nash termination.

Nash-Stigler Boundary Enforcement

Operational rules enforce the boundary between Nash and Stigler. Rule 1: Stigler may cap inquiry depth but may never terminate behavioral reasoning unless Nash has fired. Rule 2: Nash may terminate behavioral simulation but may not authorize decision output unless Stigler certifies informational sufficiency. These rules eliminate both failure modes: premature settlement where Nash fires before Stigler certifies adequate information, and overconfident closure where Stigler stops search before Nash achieves behavioral convergence.

Falsification Contracts

Every foresight output carries explicit falsification conditions transforming equilibrium predictions into scientific claims subject to empirical test.

Nash Falsifier. “If Agent X deviates profitably within time horizon T, this equilibrium settlement was false.” Specifies which agent, what deviation, and the temporal window for falsification.

Stigler Falsifier. “If new public evidence E emerges and changes the decision conclusion, this sufficiency certification was false.” Specifies what evidence category would invalidate search termination. Both falsifiers log with each output, enabling systematic validation of MindCast AI predictions over time.

Canonical Stack Architecture

The Dual-Equilibrium Architecture operates across three layers:

Layer 1: Vision Functions. What the system models. Strategic Behavioral Coordination, Field-Geometry Reasoning, National Innovation Behavioral Economics, Economics Vision, and domain-specific applications.

Layer 2: Equilibrium Logic. When to stop and settle. Nash equilibrium governs behavioral termination within SBC. Stigler equilibrium governs cognitive sufficiency across all Vision Functions.

Layer 3: Governance and Integrity. How enforcement occurs. Causal Signal Integrity enforces Stigler system-wide. Reproducibility lock ensures identical inputs yield identical conclusions. Pseudo-Equilibrium Detection prevents false stability from propagating to outputs.

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IX. Foundational Publication Details

Synthesis in National Innovation Behavioral Economics and Strategic Behavioral Coordination, Predictive Game Theory, Behavioral Economics, Cognitive Digital Twin Frameworks (Nash Equilibrium and Strategic Behavioral Coordination) (December 2025) National Innovation Behavioral Economics and Strategic Behavioral Coordination represent the first formal integration of game theory, behavioral economics, and Cognitive Digital Twins into predictive foresight for institutional behavior. NIBE operates at the national and institutional scale modeling how agencies, markets, and geopolitical actors reach equilibrium under coordination constraints; SBC operates at the organizational and transactional scale providing Nash-based termination logic. Together they supply the behavioral settlement layer that Dual (Nash-Stigler) Equilibrium Architecture implements as runtime constraint.

The Stigler Equilibrium- Regulatory Capture and the Structure of Free Markets, Why Enforcement Must Compete to Keep Markets Free (January 2026) The Stigler Equilibrium extends George Stigler's Nobel Prize-winning theory of regulatory capture into a general framework for information economics in AI systems. Enforcement routed through a single decisive chokepoint facing concentrated beneficiaries and diffuse victims produces capture as equilibrium—and the same logic applies to AI inquiry. The Dual (Nash-Stigler) Equilibrium Architecture implements Stigler as the compute-governor preventing hallucination via spurious depth and regulatory capture via access-driven over-search.

MindCast AI Field-Geometry Reasoning, A Unifying Framework for Structural Explanation in Law, Economics and Artificial Intelligence (January 2026) Field-Geometry Reasoning treats legal and economic incentive structures as geometric fields where agents navigate toward stable positions. Nash equilibria function as fixed points under best-response dynamics; Stigler governs the resolution at which strategy spaces discretize. The framework provides the spatial intuition underlying the Dual (Nash-Stigler) Equilibrium Architecture's convergence and sufficiency logic.

Predictive Institutional Economics Architecture for AI Foresight Simulation, The National Innovation Behavioral Economics, Strategic Behavioral Coordination, Cognitive Digital Twin Framework (January 2026) The Predictive Institutional Economics Architecture provides the formal state-space specification underlying the Dual (Nash-Stigler) Equilibrium Architecture, introducing aspiration equilibria where agents satisfice under bias and coordination constraints rather than optimize. The NIBE → SBC → CDT pipeline formalizes sequencing: NIBE assesses whether institutional conditions permit bargaining, SBC evaluates coordination capacity, and CDTs parameterize agents with behavioral constraints. Three validated prediction clusters—DOJ export-control, NVIDIA NVQLink, DOE-FERC federalization—provide empirical grounding for Dual (Nash-Stigler) Equilibrium Architecture’s falsifiable foresight claims.