Post by MindCast AI

MindCast AI filed a U.S. Provisional Patent Application covering the computational architecture underlying its Cognitive Digital Twin (CDT) Foresight Simulation methodology. System and Method for Multi-Agent Institutional Simulation Using Causal Validation, Adaptive Model Governance, and Dual-Equilibrium Foresight Prediction The application, filed April 18, 2026, discloses a system and method for simulating multi-agent institutional behavior through causal validation, adaptive model governance, and dual-equilibrium foresight prediction. The architecture addresses three technical deficiencies in prior multi-agent simulation systems. First, a Causal Signal Integrity (CSI) module operates as a computational gate upstream of simulation, validating causal relationships represented as directed acyclic graphs before they propagate downstream. Second, a Dual-Equilibrium Termination Architecture (DETA) requires convergence of both Nash behavioral equilibrium and Stigler institutional sufficiency before simulation terminates — producing system-level rather than agent-level outputs. Third, a rule mutability mechanism detects changes in governing rules during execution and dynamically updates CDT parameters, payoff structures, and simulation pathways. The nine-component system pipeline — spanning the Cognitive Model Interface Layer, Multi-Agent CDT Representation, CSI Validation Gate, Game Regime Identification, Vision Function Architecture, Adaptive Strategic Simulation Engine, Cybernetic Feedback Control, Dual-Equilibrium Termination, and Foresight Prediction System — operates in ordered combination to produce falsifiable predictions with pre-defined trigger conditions and scoring criteria. Regulators are modeled as strategic agents with endogenous incentive structures, enforcement discretion parameters, and rent-seeking behavior parameters, enabling direct simulation of regulatory capture dynamics and Signal Suppression Equilibria. Prediction outputs are scored against falsification criteria established prior to observation and used to recursively recalibrate system parameters. The filing formalizes the computational architecture that has produced MindCast’s published analytical corpus across antitrust litigation, prediction markets regulation, real estate market structure, and AI infrastructure competition.