@@ -1080,7 +1080,7 @@ axes[1].set(title=r'Follower control variable $x_{t}$', xlabel='t')
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1080 | 1080 | |
1081 | 1081 | axes[2].plot(range(n), vt_leader, 'bo', ms=2) |
1082 | 1082 | axes[2].plot(range(n), vt_reset_leader, 'ro', ms=2) |
1083 | | -axes[2].set(title=r'Leader value function $v(y_{t})$', xlabel='t') |
| 1083 | +axes[2].set(title=r'Leader value $v(y_{t})$', xlabel='t') |
1084 | 1084 | |
1085 | 1085 | plt.tight_layout() |
1086 | 1086 | plt.show() |
@@ -1371,13 +1371,13 @@ v2_direct_alt = - z[:, 0].T @ lq1.P @ z[:, 0] + lq1.d
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1371 | 1371 | |
1372 | 1372 | ## Comparing Markov Perfect Equilibrium and Stackelberg Outcome |
1373 | 1373 | |
1374 | | -It is enlightening to compare equilbrium quantities for firms 1 and 2 under two alternative |
| 1374 | +It is enlightening to compare equilbrium values for firms 1 and 2 under two alternative |
1375 | 1375 | settings: |
1376 | 1376 | |
1377 | 1377 | * A Markov perfect equilibrium like that described in [this lecture](https://python.quantecon.org/markov_perf.html) |
1378 | 1378 | * A Stackelberg equilbrium |
1379 | 1379 | |
1380 | | -The following code performs the required computations. |
| 1380 | +The following code performs the required computations, then plots the continuation values. |
1381 | 1381 | |
1382 | 1382 | |
1383 | 1383 | ```{code-cell} python3 |
@@ -1392,7 +1392,7 @@ fig, ax = plt.subplots()
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1392 | 1392 | ax.plot(vt_MPE, 'b', label='MPE') |
1393 | 1393 | ax.plot(vt_leader, 'r', label='Stackelberg leader') |
1394 | 1394 | ax.plot(vt_follower, 'g', label='Stackelberg follower') |
1395 | | -ax.set_title(r'MPE vs. Stackelberg Value Function') |
| 1395 | +ax.set_title(r'Values for MPE duopolists and Stackelberg firms') |
1396 | 1396 | ax.set_xlabel('t') |
1397 | 1397 | ax.legend(loc=(1.05, 0)) |
1398 | 1398 | plt.show() |
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