Consider a virtual beacker constitued of \(500\times 500\) loci, all initally at the stage \(0\).
Each of these loci is subject to a discrete in time random walk through \(n\) ordered other stages, with forward and backward evolutions, and with an absorbing last stage.

\[ \begin{matrix} 0 & \rightleftharpoons[d]{u} & 1 & \rightleftharpoons[d]{u} & 2 & \dots & \rightarrow[]{u} & n\\ \end{matrix} \] More precisely, being given a locus at the stage \(i\), (\(1\leq i\leq n-1\)),

*Try yourself !*

\[ \begin{matrix} 0 & \rightleftharpoons[d]{u} & 1 & \rightleftharpoons[d]{u} & 2 & \dots & \rightarrow[]{u} & n\\ \end{matrix} \] More precisely, being given a locus at the stage \(i\), (\(1\leq i\leq n-1\)),

- with probability \(u\), it evolves forward to the stage \(i+1\),
- with probability \(d\), it evolves backward to the stage \(i-1\),
- with probability \(1-(u+d)\), it remains at stage \(i\).

- with probability \(u\) to the stage \(1\),
- with probability \(1-u\), it remains at stage \(0\).

- Choose a set of parameters (with \(u+d<1\)).
- Click 'Complex formation' to initiate the evolution of the beacker.
- Click 'Degree of occupancy' to count the amount of loci in each stages.
- Click 'Number of rejections' to follow the evolution of the average number of times each loci reaches the initial stage 0.