Taxonomies of World Models › Summary

This chapter mapped the world-model design space along four largely independent axes:

• Explicit versus implicit: explicit models expose queryable predictions and enable planning, analysis, and counterfactuals; implicit models embed dynamics in policies, values, or decision-scoped predictors like MuZero, trading inspectability for freedom from irrelevant reconstruction.
• State representation: pixel, token, latent, object, and symbolic formats trade abstraction timing against information preservation. Pixels drown in irrelevant detail, latents risk silently discarding relevant detail, objects and symbols buy compositionality at the price of strong assumptions.
• Deterministic versus stochastic dynamics: deterministic models regress toward conditional means and imagine single confident futures; stochastic models represent spread through distribution heads, latent noise, ensembles, or generative samplers, distinguishing aleatoric from epistemic uncertainty.
• Task-specific versus general-purpose: scope changes action conditioning, evaluation, and failure modes, not merely dataset size.

The pendulum experiment made the stakes concrete: with data, actions, and evaluation held fixed, a dynamics-compatible latent representation reduced sixty-step error by roughly a factor of ten relative to a linear observation-space baseline, and the deterministic/stochastic choice determined whether uncertainty was visible at all.

These categories did not emerge from a vacuum. The ideas behind them, feedback, state, prediction, and the strange loop of an organism modeling its own environment, accumulated over more than a century of cybernetics, control theory, cognitive science, and machine learning. The next chapter traces that intellectual and cognitive history, because knowing where these ideas came from explains a great deal about why today's systems look the way they do.