MCAI Economics Vision: Runtime Geometry, A Framework for Predictive Institutional Economics
Field-Geometry, Nash-Stigler, Tirole Arbitrage, Externalities
Runtime Geometry establishes the canonical analytical framework for MindCast AI. Individual publications operationalize specific pillars; the vision statement defines the complete architecture and its pertinence across domains. See companion study: MindCast AI Economics Frameworks (Jan 2026).
I. The Crisis of Reactive Oversight
Consider what happens when a homeowner tries to sell their house. The listing data—the price, the photos, the history—feels like public information. It should be. But increasingly, that data flows through proprietary gatekeepers who treat it as private inventory. By the time anyone notices the distortion, the market has already been reshaped around it. The homeowner pays fees they don’t understand. The buyer sees only what the platform chooses to show. And the $100 trillion residential real estate market drifts further from the competitive ideal that justifies its existence.
The pattern repeats everywhere. Concert tickets. Cloud computing. Healthcare data. AI infrastructure. In each case, the sequence is the same: a platform positions itself as the necessary intermediary, information asymmetry compounds, and by the time the structural damage becomes visible—illiquid markets, eroded consumer equity, locked-in dependencies—reversal has become nearly impossible.
Our institutions remain tethered to a linear, post-mortem model of oversight. We investigate fraud after the victims have lost their savings. We identify monopolies after they’ve captured their markets. We study institutional failures after they’ve already cascaded. The lag between structural change and institutional response has become existential.
MindCast AI was founded on a different premise: that institutional failure is predictable. Not in the sense of prophecy, but in the sense of physics. When you understand the forces acting on a system, you can model where it will break. The solution lies in what we call Predictive Institutional Economics—a framework that moves beyond autopsy and toward diagnosis, from post-mortem analysis to runtime simulation. The full theoretical architecture is developed in Predictive Institutional Economics Architecture.
The framework rests on four interconnected pillars: Field-Geometry, the Nash-Stigler Equilibrium, Tirole Advocacy Arbitrage, and Systemic Externality Analysis. Together, they constitute a diagnostic architecture for institutional integrity—a way of seeing decay while the system is still running.
Contact mcai@mindcast-ai.com to partner with us on Law and Behavioral Economics foresight simulations. See recent publications: Structural Intergenerational Behavioral Economics (Jan 2026), Music as Installed Cognitive Grammar (Jan 2026), A Cognitive Digital Twin Simulation of Shakespeare, Dostoyevsky, Kafka on Federalism as an Enforcement Market (Jan 2026).
II. Field-Geometry: The Topology of Power
Before any transaction occurs, a market possesses a shape. Economists have long spoken of market “structure,” but we mean something more literal: a topology. Picture a healthy system as a symmetrical field where information flows frictionlessly to all participants. Buyers know what sellers are offering. Sellers know what buyers will pay. Prices emerge from the genuine intersection of supply and demand.
Now picture what happens when a single actor positions itself at the center of that field and begins to gate the flow. Information no longer moves freely; it channels through the gatekeeper. The field warps. What was once a flat plane becomes a gravity well, with the dominant node at its center, pulling all interactions toward itself.
The distortion isn’t metaphor—it’s measurable. When a firm alters the geometry of a market by gating data or controlling inventory, it creates a structural distortion that prevents the field from reaching equilibrium. The distortion compounds. Network effects accelerate it. And at a certain threshold, the original rules of the system—supply, demand, price discovery—are effectively suspended. The gatekeeper’s rules replace them. We develop the diagnostic methodology in Field-Geometry Reasoning.
Field-Geometry gives us a way to visualize and measure this distortion before it becomes irreversible. It transforms analysis from subjective debate into objective assessment: when the geometry crosses certain thresholds, intervention isn’t a preference—it’s a structural necessity. The application to regulatory dynamics is explored in Antitrust & Regulatory Capture Geometry.
III. The Nash-Stigler Equilibrium: Why Institutions Stop
If Field-Geometry explains how systems become captured, the Nash-Stigler Equilibrium explains why correction fails.
We’ve all watched the pattern. A dominant actor engages in obvious extraction. Public awareness builds. Investigations are announced. And then... accommodation. A settlement that changes nothing structural. A reform that addresses symptoms while preserving the underlying distortion. The cycle continues.
The traditional explanation is that oversight lacks will or competence. But the structural reality runs deeper. Using John Nash’s game theory, we can model the strategic payoffs facing any institutional actor: a visible accommodation is often more attractive than genuine correction. The accommodation generates positive signals. Genuine correction consumes resources, creates enemies, and produces uncertain outcomes. From a game-theoretic perspective, the rational move is to perform reform while preserving equilibrium. The dynamic is modeled in Federal Antitrust Breakdown as Nash-Stigler Equilibrium.
George Stigler identified this dynamic decades ago. What we add is the recognition that capture isn’t corruption—it’s equilibrium. When oversight is concentrated, it naturally gravitates toward accommodation with the systems it oversees. The referee and the player reach a strategic stalemate that benefits both at the expense of everyone else. The theoretical foundations are developed in The Stigler Equilibrium: Regulatory Capture and Free Markets.
We call it the Nash-Stigler Equilibrium: the predictable endpoint of concentrated institutional authority. Recognizing it allows us to identify “stalemate windows”—the moments when a system has locked into captured stability and genuine correction requires external force.
IV. Tirole Advocacy Arbitrage: The Information Moat
If the Nash-Stigler Equilibrium is the trap, Advocacy Arbitrage is the mechanism that sets it.
Jean Tirole’s Nobel work on information asymmetry revealed that when one party knows far more than the other, this imbalance shapes every interaction. MindCast AI applies this insight to institutional dynamics.
Consider the position of any dominant actor in a complex system. It possesses data that outside observers cannot match—real-time flows, behavioral patterns, internal models, operational knowledge. When engaging with oversight, it doesn’t simply advocate in the traditional sense. It performs arbitrage on the truth—using informational advantage to construct a narrative that obscures extraction while highlighting efficiency. The mechanism is analyzed in Tirole & Advocacy Arbitrage.
Over time, this narrative becomes installed in the institutional framework itself. The dominant actor’s logic becomes the system’s logic. The metrics that matter become the metrics that can be manipulated. The questions that get asked become the questions with prepared answers. A cognitive barrier forms—what we call the Information Moat—that makes it nearly impossible for outside observers to perceive dysfunction through the insider’s lens. We explore how this installation occurs in Installed Cognitive Grammar.
Breaking the moat requires independent access to system geometry—data and analysis that doesn’t flow through the gatekeeper. This is the analytical infrastructure MindCast AI builds.
V. Externalities: The Price of Structural Decay
Traditional institutional analysis focuses on direct costs. Did prices rise? Did efficiency fall? These questions matter, but they miss the deeper harm.
When a system is captured, the true cost is systemic leakage—a negative externality that drains value from the broader environment. The homeowner who pays excess fees doesn’t just lose money; they lose equity that would have compounded over decades. The concert-goer who pays a 30% ticketing fee doesn’t just overpay; they fund a system that locks out independent venues and emerging artists. The startup that can’t access compute at competitive rates doesn’t just struggle; it never exists, and neither does whatever it would have created.
We call this Leakage: the transfer of value from the public domain into captured structures. Measuring it provides the justification for correction. When leakage exceeds the efficiency gains a gatekeeper claims to provide, the case for structural intervention becomes irrefutable.
Comparing externalities across domains—housing markets against entertainment, AI infrastructure against healthcare data—allows construction of a Priority Matrix. Not all capture is equal. Some systems leak more than others. The framework identifies where intervention creates the greatest recovery of public value. A cross-sector comparison demonstrating this methodology appears in Nash-Stigler: LiveNation & Compass.
VI. Where the Framework Applies
The four pillars constitute a universal grammar for institutional analysis. Any domain where information asymmetry meets structural concentration is amenable to this framework. MindCast AI currently recognizes the following verticals:
Markets and Antitrust. The original laboratory. Real estate, ticketing, and platform economics demonstrate Field-Geometry distortion in its purest form. The $100 trillion housing market serves as the lead case study.
Complex Litigation. Sophisticated actors increasingly use coordinated legal strategies across multiple forums to maintain equilibrium. The framework models litigation as a contest of truth-seeking systems, detecting procedural gaming that spans jurisdictions.
Legacy and Intergenerational Coordination. Family enterprises and legacy institutions face coordination problems that prevent value from scaling across generations. Game theory reveals behavioral bottlenecks; simulation models solutions.
Cultural Systems. Music, narrative, and media shape the cognitive architecture of decision-makers. Understanding how cultural artifacts install logic allows forecasting of fracture points in institutional trust.
Performance Under Pressure. High-stakes decision-making under extreme conditions—athletics, medicine, crisis response—provides stress tests for behavioral models. If the framework can predict pivots when milliseconds matter, it can predict them anywhere.
National Innovation Systems. Intellectual property and compute infrastructure form the bedrock of technological sovereignty. As AI reshapes the global economy, the framework identifies where capture threatens innovation capacity at the national level.
One structural response to federal-level capture deserves particular attention: the role of distributed enforcement authority. When concentrated oversight reaches Nash-Stigler equilibrium, alternative institutional actors—state authorities, independent agencies, cross-jurisdictional coalitions—can function as competitive substitutes. This principle is developed in Competitive Federalism as Market Infrastructure.
VII. The Vision
Institutional failure is not random. It follows patterns that can be modeled, measured, and anticipated. The lag between structural change and corrective response—the gap that allows capture to compound until reversal becomes impossible—is not inevitable. It’s a function of analytical infrastructure.
MindCast AI exists to close that gap. By fusing the rigor of Chicago School economics with computational Cognitive Digital Twin methodology, we build the foresight capacity that institutions currently lack. We see the geometry distort before it locks. We identify the equilibrium forming before it stabilizes. We measure the leakage accumulating before it becomes catastrophic.
Prediction here is not mystical—it’s engineering. When you understand the forces acting on a system, you can model its trajectory. When you can model the trajectory, you can intervene before terminal states are reached. The architecture of institutional integrity is knowable. MindCast AI is building the instruments to know it.
Every publication we release operationalizes some component of this vision. Every analysis demonstrates the framework in application. The library accumulates. The grammar refines. And the gap between structural change and institutional response—the space where capture grows—narrows.
The work continues. The vision holds.