The Simulative-Elasticity Model (SEM)

A dynamic systems framework redefining intelligence as regulatory phase-control — the mechanical ability to shift between automated habits and deep reasoning.

A Systems Theory of Intelligence: Navigating the Space Between Habit and Adaptation

A dynamic systems framework that redefines intelligence as regulatory phase-control — the mechanical ability to shift between automated habits and deep reasoning in response to environmental volatility.

• Static measures of intelligence — IQ tests, LLM benchmarks — evaluate how much a system knows, not how well it adapts when knowledge becomes obsolete.
• Dual-process theory provides two gears (fast/slow); SEM provides a full manual transmission with a clutch, five mechanical states, and a calibration theory for when transitions fail.
• AI systems hallucinate because they possess Reconstructive power without Simulative grounding — pattern completion without causal verification.

Abstract

Contemporary measures of intelligence, in both human psychometrics and artificial intelligence benchmarking, remain anchored to static, capacity-based paradigms. Systems are evaluated by how much they know rather than how well they adapt when what they know becomes obsolete. This paper introduces the Simulative-Elasticity Model (SEM), a dynamic systems framework that redefines intelligence as regulatory phase-control: the mechanical ability to shift between automated habit execution and deep, energy-intensive reasoning in response to environmental volatility.

The framework identifies five functional states, Architectural, Reconstructive, Simulative, Liquid, and Elastic Intelligence, and proposes that the critical variable governing adaptive behavior is the Delta (Δ), a continuous divergence signal between internal prediction and external reality. We introduce the Script-to-Capability Pipeline showing how Liquidated scripts become encapsulated tools that compose into observable expertise, extend the model to account for gradient Delta states and calibration errors, introduce compound operations, most notably the Inverse Simulation loop for diagnostic and root cause reasoning, draw a strict boundary between Acquired Capacity and Raw Intelligence with attention to individual differences and neurodivergent profiles, outline preliminary conditioning protocols for improving phase-transition speed, propose a SEM-guided path to artificial general intelligence grounded in regulatory architecture rather than parameter scaling, and decompose Emotional Intelligence into Acquired Capacity (social scripts) and Raw Intelligence (emotional phase-control).

SEM is positioned against existing frameworks including Kahneman’s dual-process theory, Friston’s Free Energy Principle, Anderson’s ACT-R architecture, and Goleman’s Emotional Intelligence model, demonstrating that it offers a more mechanistically complete account of adaptive intelligence across biological and artificial systems.

Keywords: Simulative-Elasticity Model, adaptive intelligence, phase-control, cognitive elasticity, Delta divergence, predictive processing, habit automation, script-to-capability pipeline, toolchain fragility, dependency collapse, calibration error, inverse simulation, abductive reasoning, artificial general intelligence, AI alignment, emotional intelligence, emotional Delta signals, neurodivergence, individual differences, dual-process theory, free energy principle, working memory, cognitive flexibility