I' am reading Introduction to Stochastic Processes by Lawler and am struggling to understand how continuous time, discrete state space processes are defined. Quote from the book,
A (time-homogeneous) continuous-time Markov chain with rates $\alpha$ is a stochastic process $X_t$ taking values in S satisfying
$$P(X_{t+\triangle t}=x|X_t=x)=1-\alpha (x)\triangle t+o(\triangle t)$$ $$P(X_{t+\triangle t}=x|X_t=y)=1-\alpha (y,x)\triangle t+o(\triangle t),y\neq x$$
I really don't understand the usefulness of including the $o(\triangle t)$. I know how $o()$ (little o) is defined, but what is its mathematical usefulness here?