selfsimulation

Self-simulation is the process by which a system models or simulates its own dynamics, structure, or state. It can be used to predict behavior, guide control decisions, test internal assumptions, or support self-understanding. The term appears across computing, cognitive science, philosophy, and related fields with distinct emphases.

In computing, self-simulation may refer to programs that simulate their own execution or the environments they operate in. Reflective or introspective systems can query and modify their own code, state, or configuration. Bootstrapping, self-hosting compilers, and quine-like programs are related concepts that involve creating or validating a representation of oneself within a system.

In artificial intelligence and cognitive science, agents may maintain internal models of their own cognitive processes or bodily states to improve planning, learning, and adaptability. Self-modeling can support meta-learning, error diagnosis, and robust interaction with uncertain environments.

In philosophy and theoretical biology, notions of self-models and agency are used to explain how beings perceive themselves as continuous actors. The brain is often described as maintaining a predictive model of the body and its actions to generate a sense of agency and coordinated action.

In discussions about simulation theory or digital physics, self-simulation raises questions about recursion, measurement, and epistemic limits when a system contains representations of itself. These debates touch on issues of self-reference and the potential for infinite regress in modeling.

Practical challenges include computational cost and the risk of circular reasoning, as well as difficulties in validating self-models. Despite these, self-simulation concepts inform software testing, autonomous systems, meta-cognitive research, and philosophical inquiry into the nature of mind and reality.