$4$ Workflow for simulating stochastic populations Once we leave the safe deterministic world for the crazy stochastic one, one population trajectory is not enough for an analysis. What if you happened to generate a weird set of random numbers? Instead, we have to simulate many possible trajectories based on the same underlying properties. The exact number necessary before you are pretty sure adding more simulations won$$t change things varies, but $1000$ is usually a good starting point. However, if you start right off the bat simulating $1000$ populations you will get very overwhelmed very fast. Your code is almost always wrong the first time you try it$.($Mine$,$ too!$)1000$ trajectories of wrong code are hard to sift through, and it can also take time to generate them. Why wait around while your computer generates $1000$ wrong simulations? My general process for simulating stochastic populations $($and I do this a lot$)$ is: $1.$ A single simulation. Of course this isn$$t going to answer my scientific question, but I need to make sure the population model is working. $2.$ A small number of simulations that I can still visualize. Usually around ten. Simulating more than one population is usually done inside some sort of loop because you$$re doing the same thing over and over. This helps me ensure I built my new loop correctly. $43.$ Finally, and only once I have done steps $1$ and $2$ and am confident my code is working, do I try with the full $1000$ trajectories. Oftentimes there is even a step $0$: Simulate a determinstic population based on the same model. And usually there is some back and forth as I slowly build a more and more complicated model. The questions in this assignment will intentionally walk you through these steps.