Prebiotic Functional Programs: Endogenous Selection in an Artificial Chemistry

2025-08-26

AI Summary

This work addresses the challenge of guiding dynamic evolution in artificial chemical systems lacking an external fitness function. Methodologically, it employs the type-free lambda-calculus-driven AlChemy model, eschewing explicit learning rules or external interventions; instead, evolution is regulated solely through intrinsic autocatalytic structures and reaction selectivity. The core contribution lies in uncovering and exploiting the structural isomorphism between chemical kinetics and computational processes, enabling the system to spontaneously synthesize nontrivial Church-encoded functions—such as addition and the successor function—through endogenous dynamics. Experimental results demonstrate that functional programs emerge via internal selection pressures, even without predefined target functions. This establishes a novel paradigm for self-organized computation, basal programming, and endogenous adaptation in artificial life, advancing the theoretical foundations of chemically instantiated computation.

Technology Category

• Machine Learning: Evolutionary Learning
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Search and Optimization: Evolutionary Computation

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Artificial chemistry simulations produce many intriguing emergent behaviors, but they are often difficult to steer or control. This paper proposes a method for steering the dynamics of a classic artificial chemistry model, known as AlChemy (Algorithmic Chemistry), which is based on untyped lambda calculus. Our approach leverages features that are endogenous to AlChemy without constructing an explicit external fitness function or building learning into the dynamics. We demonstrate the approach by synthesizing non-trivial lambda functions, such as Church addition and succession, from simple primitives. The results provide insight into the possibility of endogenous selection in diverse systems such as autocatalytic chemical networks and software systems.

Problem

• Steering dynamics in artificial chemistry without external control
• Synthesizing complex lambda functions from simple primitives
• Exploring endogenous selection in autocatalytic networks and software

Innovation

• Steering AlChemy dynamics without external fitness
• Using endogenous features for selection mechanism
• Synthesizing complex lambda functions from primitives