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[schelling] Address editorial items from #419 #756

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jstac merged 1 commit into from
May 28, 2026
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[schelling] Address editorial items from #419 #756

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39 changes: 18 additions & 21 deletions lectures/schelling.md
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Original file line number Diff line number Diff line change
Expand Up @@ -131,7 +131,7 @@ Each agent stays if they are happy and moves if they are unhappy.

The algorithm for moving is as follows

```{prf:algorithm} Jump Chain Algorithm
```{prf:algorithm} Move algorithm
:label: move_algo

1. Draw a random location in $S$
Expand Down Expand Up @@ -244,24 +244,26 @@ def plot_distribution(agents, cycle_num):
plot_args = {'markersize': 8, 'alpha': 0.8}
ax.set_facecolor('azure')
ax.plot(x_values_0, y_values_0,
'o', markerfacecolor='orange', **plot_args)
'o', markerfacecolor='orange', label='Type 0', **plot_args)
ax.plot(x_values_1, y_values_1,
'o', markerfacecolor='green', **plot_args)
'o', markerfacecolor='green', label='Type 1', **plot_args)
ax.set_title(f'Cycle {cycle_num-1}')
ax.legend()
plt.show()
```

And here's some pseudocode for the main loop, where we cycle through the
agents until no one wishes to move.
Here's the main loop, where we cycle through the agents until no one wishes
to move.

The pseudocode is
```{prf:algorithm} Main loop
:label: schelling_main_loop

1. plot the distribution
1. while agents are still moving
1. for agent in agents
1. give agent the opportunity to move
1. plot the distribution

```{code-block} none
plot the distribution
while agents are still moving
for agent in agents
give agent the opportunity to move
plot the distribution
```

The real code is below
Expand All @@ -288,7 +290,6 @@ def run_simulation(num_of_type_0=600,

# Loop until no agent wishes to move
while count < max_iter:
print('Entering loop ', count)
count += 1
no_one_moved = True
for agent in agents:
Expand Down Expand Up @@ -428,8 +429,10 @@ def plot_distribution(locations, types, title, savepdf=False):
'o',
markersize=8,
markerfacecolor=color,
alpha=0.8)
alpha=0.8,
label=f'Type {agent_type}')
ax.set_title(title)
ax.legend()
plt.show()

def sim_random_select(max_iter=100_000, flip_prob=0.01, test_freq=10_000):
Expand Down Expand Up @@ -470,11 +473,6 @@ def sim_random_select(max_iter=100_000, flip_prob=0.01, test_freq=10_000):
print(f"Terminating at iteration {current_iter}")
```

```{solution-end}
```

+++

When we run this we again find that mixed neighborhoods break down and segregation emerges.

Here's a sample run.
Expand All @@ -483,6 +481,5 @@ Here's a sample run.
sim_random_select(max_iter=50_000, flip_prob=0.01, test_freq=10_000)
```

```{code-cell} ipython3

```{solution-end}
```
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