Realization:	There is a dynamical causal network $G_u$ and a transition $v_{t-1}\prec v_t$, such that $p_u\left(v_t\right|v_{t-1})>0$.
Composition:	All $x_{t-1}\subseteq v_{t-1}$ may have actual effects and be actual causes, and all $y_t\subseteq v_t$ may have actual causes and be actual effects.
Information:	Occurrences must increase the probability of their causes or effects ($\rho (x_{t-1},y_t)>0$).
Integration:	Moreover, they must do so above and beyond their parts ($\alpha (x_{t-1},y_t)>0$).
Exclusion:	An occurrence has only one actual cause (or effect), and it is the occurrence that maximizes ${\alpha }_c$ (or ${\alpha }_e$).