<jupyter_start><jupyter_text>Ambiguity level grid analysis---Following ambiguity-level grids are available to simulate the model:- `dense_ambiguity_grid.pkl` includes a simulated model (100 agents) under each of the the ambiguity set sizes   { 0.000, 0.010, 0.020, 0.100, 0.200, 0.500, 1.000, 1.500, 2.000 }.- `spread_ambiguity_grid.pkl` includes a simulated model (1,000 agents) under each of the the ambiguity set sizes { 0.000, 1.000, 2.000, 3.000, 4.000, 5.000 }.<jupyter_code>dense_ambiguity_grid = pkl.load(open("dense_ambiguity_grid.pkl", "rb"))
spread_ambiguity_grid = pkl.load(open("spread_ambiguity_grid.pkl", "rb"))
ambiguity_effect_experiences(dense_ambiguity_grid)
ambiguity_effect_experiences(spread_ambiguity_grid)<jupyter_output><empty_output><jupyter_text>Plotting ---We will select ambiguity levels that will be plotted. In the following we will choose - **dense_ambiguity_levels_selected** includes low ambiguity levels (up to 1.000)- **spread_ambiguity_levels_selected** includes high ambiguity levels that are widely spread (2.000 to 5.000)<jupyter_code>dense_ambiguity_levels_selected = ["0010", "0100", "0500", "1000"]
spread_ambiguity_levels_selected = ["2000", "3000", "4000", "5000"]
plot_ambiguity_effect_choiceshare(
    dense_ambiguity_grid, dense_ambiguity_levels_selected, "dense"
)
plot_ambiguity_effect_choiceshare(
    spread_ambiguity_grid, spread_ambiguity_levels_selected, "spread"
)<jupyter_output><empty_output>
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