MCAI Economics Vision: MindCast AI Constraint Geometry and Institutional Field Dynamics
Beyond Incentives: How Institutional Geometry Selects Outcomes
Companion studies:
MindCast AI Field-Geometry Reasoning, A Unifying Framework for Structural Explanation in Law, Economics and Artificial Intelligence,
The Runtime Causation Arbitration Directive, Operationalizing Structural Foresight, Across Domains a
Runtime Geometry, A Framework for Predictive Institutional Economics, Field-Geometry, Nash-Stigler, Tirole Arbitrage, Externalities
The Class Your Physician Should’ve Taken in Medical School, The Critical Role of 4th-Degree Causation Analysis in Redesigning Modern Health Care
In Economic Analysis of Law, first published in 1973, Richard Posner argued that legal rules behave like economic prices. Comply with the law and you pay nothing. Violate it and the penalty functions as a cost. Rational actors weigh expected costs against expected benefits, and behavior follows. Working alongside William Landes through the 1970s, Posner extended that framework into a more ambitious claim: institutional structures do not merely price behavior — they exert something closer to gravitational force. Legal and economic structures concentrate authority and capital, and that concentration bends behavior the way a massive object bends the trajectory of smaller ones. Not by persuading anyone. Not by raising prices. By warping the space of available options. Actors do not choose to orbit the constraint. The constraint selects their path.
Einstein showed why that analogy, taken seriously, required a complete reframing. Gravity is not a force acting between objects at all. Gravity is the shape of the space objects move through. Mass curves spacetime, and objects follow the resulting geometry not because anything pushes them but because curved space offers fewer paths than flat space. The photon bending around a star is not being deflected — it is following the only geometry available. Measuring push produces different questions, different instruments, and different predictions than measuring curvature. Einstein’s contribution was not a better theory of force. It was a fundamental reframing of what requires explanation.
Institutional analysis has been waiting for the same reframing. Posner and Landes established that structure shapes behavior. What they left unfinished was the instrumentation that Einstein’s reframing made possible — the shift from detecting force to measuring curvature. Field-Geometry Reasoning established when geometry dominates: constraint topology governs outcomes when it explains behavior better than incentives or actor intent. That contribution defined the causal hierarchy. The companion study builds what follows — a metric architecture capable of measuring not just whether geometry dominates, but how curvature forms, how fast it moves, where it stabilizes, and what force would be required to escape it.
I. From Dominance Detection to Institutional Field Theory
Field-Geometry Reasoning asked a binary question: does geometry dominate in this situation? Answering it required only a dominance test — a method for determining whether constraint topology explained behavior better than incentive narratives. That test was sufficient for causal attribution. Prediction requires more.
Prediction requires a theory of how geometry forms, how it changes over time, what determines where trajectories settle, and whether the resulting equilibria are reversible. Einstein did not just claim that spacetime curves — he specified what curves it, how curvature propagates, and what equations govern its evolution. Institutional field theory demands the same discipline.
Five structural claims carry the extension. Each carries a falsification condition — not rhetorical hedging, but empirical tests that could, in principle, defeat the claim.
Institutional geometry is endogenously generated. Curvature does not arrive from outside the system. Concentrated authority, capital dependence, infrastructural lock-in, doctrinal rigidity, legitimacy exposure, and time compression are the mass variables that produce it. Substantial shifts in those variables must produce corresponding movement in constraint density or curvature steepness within the assessment window — if they do not, the endogenous generation claim fails.
Institutional geometry is a time-indexed state variable. Constraint fields thicken, relax, and redistribute. Geometry possesses measurable velocity and directional change. Topology redistribution or enforcement reallocation must produce measurable change in density, steepness, or evolution velocity — absent that movement, the dynamic claim fails.
Structural irreversibility is a mass-persistence function. Equilibria persist when counter-mass fails to exceed structural thresholds. An escape velocity threshold defines the minimum force required to alter topology. Actors exiting predicted geometry-trapped equilibria without surpassing that threshold would falsify the irreversibility claim.
Equilibria are field-embedded attractors. Settlement occurs inside constraint topology where curvature, strategic stability, and cognitive sufficiency converge. Equilibrium is geometry-conditioned, not preference-conditioned. Outcome patterns diverging from predicted attractor basins while curvature metrics remain stable would indicate incomplete field embedding.
Topology redistribution dominates marginal incentives. Reallocation of nodes and edges alters the feasible solution space. Incentive shifts inside an unchanged topology rarely produce structural transformation. Marginal incentive changes generating durable structural outcomes absent measurable topology redistribution would defeat the primacy claim.
Taken together, the five claims convert the companion study into a declarative institutional field theory — testable, falsifiable, and capable of generating forward predictions rather than retrospective descriptions.
Contact mcai@mindcast-ai.com to partner with us on Predictive Cognitive AI in Law and Behavioral Economics. To deep dive on MindCast work in Cognitive AI upload the URL of this publication into any LLM and prompt ‘reconstruct MindCast framework with three degrees of cited sub links.’ See Live-Fire Game Theory Simulators, Runtime Predictive Infrastructure.
II. Measuring the Field: Metrics for the Original Study
Operationalizing the Geometry Dominance Test requires four metrics that can be scored, tracked, and updated as conditions change.
Constraint Density (CD) measures how saturated the decision space is with binding constraints — the ratio of independent binding constraints to plausible decision paths available to primary actors. High CD means multiple structural forces simultaneously limit behavior. Geometry cannot dominate where constraint saturation is thin.
Geodesic Availability Ratio (GAR) measures path feasibility — the ratio of viable trajectories to plausible ones. Low GAR exposes the essential deception of high-constraint environments: actors appear free but move through a corridor so narrow that freedom is nominal. A photon does not choose to bend around a star. Curvature selects the path. GAR makes that dynamic measurable at the institutional level.
Curvature Steepness Index (CSI-G) measures how rapidly penalties escalate when actors deviate from dominant paths. Legal risk magnitude, capital loss sensitivity, reputational escalation velocity, and time compression each contribute to steepness. High CSI-G means small deviations produce large consequences — the difference between a constraint environment an actor can navigate and one that encloses them.
Structural Persistence Threshold (SPT) measures how durable the constraint topology is over the forecast window. Doctrinal rigidity, institutional inertia, capital lock-in, and political stability all feed into persistence. High SPT means curvature will endure absent structural shock. CD, GAR, CSI-G, and SPT together produce a geometry dominance assessment: density is high, availability is narrow, steepness is severe, and the topology will not dissolve on its own.
III. Curvature Formation and Evolution: The Companion Metrics
The original study’s metrics measure current geometry. The companion study requires a second layer — metrics that capture where curvature comes from, how fast it moves, and what it would take to displace it. Consider the Einstein extension again: not just detecting that spacetime curves, but specifying the field equations that govern its curvature.
Institutional Mass Index (IMI) aggregates the mass variables that generate curvature: authority mass, capital mass, infrastructure lock-in, doctrine rigidity, narrative coupling, and timing compression. IMI functions as the institutional analog of the stress-energy tensor — the quantity whose distribution determines how the field curves. Geometry cannot thicken without mass. IMI measures the mass.
Geometry Evolution Velocity (GEV) measures how quickly curvature changes over time — the rate of change in the product of Constraint Density and Curvature Steepness across the assessment window. High GEV signals rapid structural transformation and potential equilibrium transition. Low GEV signals stability. Static measurement cannot detect impending phase transition; GEV provides the early warning.
Topology Redistribution Delta (TRΔ) detects changes in constraint nodes and edges across time — jurisdictional reallocation, regulatory restructuring, doctrinal shifts. TRΔ captures structural movement that incentive analysis misses entirely. When state attorneys general assume enforcement functions that federal agencies have abandoned, the topology redistributes even though no substantive law changes. A New Era of Federalism demonstrates that dynamic in detail. TRΔ measures it.
Escape Velocity Threshold (EVT) formalizes irreversibility. Defined as the product of IMI and SPT, EVT estimates the counter-mass required to flatten curvature. Actors lacking resources or authority sufficient to exceed EVT remain in geometry-trapped equilibrium — not because they prefer it, but because the field holds them. EVT converts irreversibility from a qualitative observation into a measurable threshold.
Field Stability Coefficient (FSC) measures attractor durability — the product of attractor dominance score and structural persistence. High FSC indicates a stable equilibrium basin. Low FSC indicates potential transition. FSC connects equilibrium logic to field persistence, enabling prediction of whether a current settlement will hold or whether the geometry is already shifting beneath it.
IV. Equilibria as Measurable Attractor States
Geometry shapes trajectories. Equilibria determine where trajectories settle. Nash-Stigler Equilibria supplies the settlement logic: Nash captures strategic stability under mutual best responses; Stigler adds the cognitive dimension — the point at which further inquiry does not alter expectations. Both conditions must be satisfied for equilibrium to hold. Geometry without attractor classification remains descriptive. Attractor measurement converts field structure into predictive outcome classes.
Four equilibrium classes emerge from metric combinations. Resolving equilibria occur where geometry is present but not dominant — actors retain viable paths and deploy them toward settlement. Frozen equilibria occur where geometry traps actors in mutual constraint with no exit path available to either side. Delay-dominant equilibria emerge where time compression is high and deferral is the lowest-cost move. Geometry-trapped equilibria are the most consequential: curvature is severe, persistence is high, and EVT exceeds available counter-mass. Actors inside a geometry-trapped equilibrium do not exit absent structural shock from outside the field.
Classification is not taxonomy for its own sake. Knowing which equilibrium class governs a situation determines which interventions are viable. Resolving equilibria respond to negotiation. Frozen equilibria require topology redistribution. Geometry-trapped equilibria require force sufficient to exceed EVT — and where that force does not exist, honest prediction requires acknowledging irreversibility rather than forecasting change that cannot come.
V. Prior Work as Empirical Grounding
The metric architecture defined here formalizes patterns that appeared repeatedly across MindCast’s prior analytical work before the metrics existed to name them.
Chicago School Accelerated established historical curvature vectors — the accumulated doctrinal and institutional mass that shapes enforcement trajectories before any individual case reaches a decision-maker. DOJ Cross-Domain Geometry showed curvature thickening through overlapping enforcement axes: when antitrust, criminal, and regulatory authority converge on a single actor, constraint density escalates nonlinearly. Viable paths collapse not because any single enforcement axis is prohibitive, but because the overlapping geometry forecloses the spaces between them.
Tirole Advocacy Arbitrage demonstrated actor-driven constraint reshaping — the mechanism by which sophisticated actors exploit doctrinal inconsistency to alter topology in their favor. Where Tirole Arbitrage succeeds, TRΔ registers a measurable redistribution. Where it fails, EVT explains why: the counter-mass required to flatten entrenched curvature exceeded the resources deployed.
Metrics without cases are definitions. Cases without metrics are stories. Predictive authority requires both — and the prior work supplies the empirical grounding that keeps the metric architecture from floating free of institutional reality.
VI. Runtime Execution
Metric discipline makes runtime execution coherent. Both publications embedded in an LLM create an analytical environment where scoring is prior to prediction — structural variables must be assessed before forward claims are permitted. Required output sequence follows directly from the theory: identify the dominant causal layer, score CD, GAR, CSI-G, and SPT, score IMI, GEV, TRΔ, EVT, and FSC, classify the equilibrium, generate forward predictions with time windows, and specify falsification conditions for each prediction.
A prediction generated before structural variables are scored is an intuition dressed as analysis. The metric architecture exists to enforce the distinction.
Conclusion
Posner and Landes showed that mass bends behavior. Einstein showed that mass bends space itself — and that bending space is what mass bending behavior actually means. Field-Geometry Reasoning established when institutional geometry dominates. The companion study establishes how it forms, how it evolves, where it stabilizes, and what escaping it requires.
Constraint density, curvature steepness, institutional mass, topology redistribution, escape thresholds, and attractor stability are now measurable quantities attached to defined structural primitives — not analogies, not metaphors, not intuitions. The framework moves from dominance detection to disciplined institutional field mechanics. Predictive authority follows from structural clarity. That clarity now exists.