endogents

Endogents are a theoretical construct in complexity science and artificial life describing self-generated, endogenous agents that arise from a system’s internal dynamics and can influence its evolution. Unlike exogenous agents introduced from outside, endogents originate from feedback loops and local interactions embedded in the system’s rules. The term merges endo- (within) with gent (agent) and was proposed to distinguish internal agency from externally supplied control.

Endogents typically exhibit autonomy, adaptability, and the ability to affect future states through internal feedback. They are not assumed to be conscious; rather, their behavior emerges from simple local rules that scale into system-wide effects.

Most evidence for endogents comes from computational models, including agent-based simulations and cellular automata, where internally generated agents spontaneously appear and steer dynamics. Researchers identify endogents by tracing causal influence, assessing endogenous feedback strength, and using perturbation experiments to separate internal drivers from external ones.

Applications are explored in economics, ecology, and artificial life. Endogents can model internally produced market actors, coevolving species, or self-maintaining structures in digital organisms. Studying them helps illuminate resilience, adaptability, and the emergence of collective behavior within closed systems.

Critics warn that asserting genuine internal agency is difficult and risks conflating complex patterns with agent-like behavior. Because demonstrations are model-dependent and often context-specific, endogents remain a contested concept. As a topic, they function as a framework for analyzing how internal dynamics can generate causal influence in complex systems.