[Paper] Evolutionary Systems Thinking -- From Equilibrium Models to Open-Ended Adaptive Dynamics
Source: arXiv - 2602.15957v1
Overview
Dan Adler’s paper challenges the way we model change in economics, policy, and technology. Instead of relying on traditional equilibrium‑oriented system‑dynamics, it proposes a minimal, non‑equilibrium framework—Stability‑Driven Assembly (SDA)—that can generate open‑ended, evolutionary behavior without any built‑in notion of “fitness” or genetic replication.
Key Contributions
- Conceptual shift: Positions evolutionary dynamics as a core systems‑thinking problem rather than a biological metaphor.
- Stability‑Driven Assembly (SDA): Introduces a simple stochastic‑interaction model where persistence (longevity) of patterns drives endogenous selection.
- Emergent fitness‑proportional sampling: Shows how a natural genetic‑algorithm‑like process arises purely from stability dynamics.
- Proof of limitation: Demonstrates analytically and via simulation why equilibrium‑constrained models cannot produce open‑ended evolution.
- Roadmap for practice: Outlines how agent‑based and AI‑augmented simulations can be built to support truly adaptive, novelty‑generating systems.
Methodology
- Population of abstract entities: The model starts with a large set of “patterns” (think of them as lightweight agents or code snippets) that interact randomly.
- Stochastic interactions: Each interaction may create, modify, or destroy a pattern. The outcome is probabilistic, not deterministic.
- Differential persistence: Patterns that survive longer are more likely to be present in future interactions. Persistence is the only “selection pressure.”
- Feedback loop: As long‑lived patterns accumulate, they bias the pool of possible interactions, effectively shaping the system’s own dynamics.
- Emergence of fitness‑proportional sampling: Over time, the probability of a pattern being chosen for interaction becomes proportional to its lifespan, mimicking a genetic algorithm’s fitness weighting—without any explicit fitness function.
The author validates the approach through both analytical derivations (showing non‑stationarity of the transition matrix) and numerical experiments that compare SDA against classic equilibrium models.
Results & Findings
| What was measured | SDA outcome | Equilibrium model outcome |
|---|---|---|
| Diversity over time | Continues to grow; new structures appear indefinitely | Quickly plateaus; system settles into a static attractor |
| Adaptation to changing environment | Rapid re‑configuration as persistence landscape shifts | Slow or no response; stuck in previous equilibrium |
| Emergent “fitness” distribution | Heavy‑tailed, self‑reinforcing distribution | Uniform or artificially imposed fitness landscape |
| Computational cost | Linear in number of interactions; scalable with parallel agents | Similar, but no added expressive power |
The key takeaway: Only when the system’s transition dynamics depend on its own evolving composition can open‑ended novelty arise. Fixed‑state, equilibrium‑seeking models, even when run on powerful hardware, are fundamentally unable to exhibit this behavior.
Practical Implications
- Economic modeling: Policy simulations can incorporate SDA‑style agents that “learn” to persist based on market stability, yielding more realistic forecasts of innovation cycles, market entry/exit, and systemic risk.
- Tech product road‑mapping: Engineers can use SDA to model feature ecosystems where stable components attract more integration, helping prioritize refactoring or deprecation decisions.
- AI‑driven design: Generative design tools can replace hand‑crafted fitness functions with stability‑based selection, allowing the system to discover novel architectures autonomously.
- Infrastructure resilience: SDA can simulate how stable network topologies emerge under random failures, informing robust architecture choices for cloud and edge deployments.
- Policy & governance: By treating regulations as “interactions” that affect persistence, policymakers can explore how certain rules encourage or suppress long‑term innovation without needing to pre‑define desirable outcomes.
Limitations & Future Work
- Abstraction level: SDA operates on highly abstract patterns; mapping these to concrete economic agents or software components requires domain‑specific translation layers.
- Parameter sensitivity: The model’s behavior hinges on interaction rates and persistence decay functions; calibrating these for real‑world data remains an open challenge.
- Scalability of analysis: While simulations scale, rigorous analytical proofs of long‑term behavior become intractable for richer interaction rules.
- Integration with existing tools: The paper sketches how agent‑based platforms (e.g., NetLogo, Mesa) and AI‑enabled simulators could host SDA, but concrete implementations and benchmark suites are still needed.
Future research directions include: extending SDA to multi‑level hierarchies (e.g., firms within sectors), coupling it with reinforcement‑learning agents to explore hybrid fitness‑stability dynamics, and building open‑source libraries that let developers plug SDA into their own system‑dynamics pipelines.
Authors
- Dan Adler
Paper Information
- arXiv ID: 2602.15957v1
- Categories: q-bio.PE, cs.NE, econ.TH
- Published: February 17, 2026
- PDF: Download PDF