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Causal states are minimal sufficient statistics of prediction of a stochasticprocess, their coding cost is called statistical complexity, and the impliedcausal structure yields a sense of the process' "intrinsic computation". Wediscuss how statistical complexity changes with slight variations on abiologically-motivated dynamical model, that of a Monod-Wyman-Changeuxmolecule. Perturbations to nonexistent transitions cause statistical complexityto jump from finite to infinite, while perturbations to existent transitionscause relatively slight variations in the statistical complexity. The same isnot true for excess entropy, the mutual information between past and future. Wediscuss the implications of this for the relationship between intrinsic anduseful computation of biological sensory systems.

Intrinsic computation of a Monod-Wyman-Changeux molecule