We can model the states of a system by applying a transition matrix to values represented in an initial distribution and repeating it until we reach an equilibrium.

Suppose we want to model how job roles in a given company change over time. Let us assume the following:

There are three (hierarchical) positions in the company:

Analyst

Project Coordinator

Manager

30 new workers enter the company each year, and they all begin as analysts

The probability of moving from …

an analyst to a project coordinator is 75%

a project coordinator to a manager is 8%

The probability of staying in a position is 25%

The initial distribution of people in each role (analyst, PC, manager) is: c(45, 15, 6)

# The Initial States:

`initial <- c(45, 15, 6)`

# The Transition Matrix:

Consistent with the assumptions described above…

```
transition <- matrix(c( 0.25, 0.00, 30,
0.75, 0.25, 0.00,
0.00, 0.08, 0.25 ), 3, 3, byrow = T)
```

# The Company Roles Over 50 Years:

```
df <- matrix(, nrow = 50, ncol = 3)
count <- 0
for(i in 1:50){
count <- count + 1
if(i == 1){
df[count,] = initial
}
else{
df[count,] = transition%^%i %*% initial
}
}
```

If job-movement in a company aligned with our initial assumptions, we would expect the distribution of jobs to follow this pattern across time:

Some data tidying first…

```
df <- data.frame(df)
names(df) <- c("Analyst", "Project_Coordinator", "Manager")
df$Time <- rep(1:nrow(df))
data_f <- df %>%
gather(Analyst, Project_Coordinator, Manager, key = "Position", value = "Num_People")
total_value <- data_f %>%
group_by(Time) %>%
summarise(
total = sum(Num_People)
)
data_f <- left_join(data_f, total_value)
data_f <- data_f %>%
mutate(Proportion = Num_People / total)
```

The proportion of people in each position:

```
ggplot(data_f, aes(x = Time, y = Proportion, color = Position)) +
geom_point() +
geom_line()
```

The amount of people in the company overall:

```
ggplot(data_f, aes(x = Time, y = Num_People, color = Position)) +
geom_point() +
geom_line()
```

As you can tell, this is unrealistic =)

Bo\(^2\)m =)