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+
+run length and adapt vs. average risk adjustment¶
-
+
- how long does hawk/dove multi risk attitude take to converge with the new logic?
-
+
- how many do not converge ? +
+ - what difference does it make if we use adapt or average risk adjustment strategy? +
+
Most recent analysis based on data generated with this command:
+./simulatingrisk/hawkdovemulti/batch_run.py --params risk_adjust
+Ran some tests with more iterations; when 200 iterations were specified, the percent of simulations that converged were closer to 85%, but all batches tested were in the 80% range.
+
+
+
+
+
+
+
+In [218]:
+
+
+
+
+
+import polars as pl
+
+df = pl.read_csv("../../data/hawkdovemulti/riskadjust/dist-uniform/2025-08-05T153548_956082_model.csv")
+
+
+
+
+
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+
+
+In [219]:
+
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+
+
+
+total_runs = len(df)
+
+print(f"Analyzing {total_runs} runs")
+
+
+
+
+
+
+
+
+
+
+
+Analyzing 600 runs ++
+
+
+
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+
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+
+
+simulation run length¶
They either finished very quickly (~50 steps) or never finished)
+
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+In [220]:
+
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+
+
+df["Step"].describe()
+
+
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+
+
+
+Out[220]:
+
+
+
+shape: (9, 2)
+| statistic | value |
|---|---|
| str | f64 |
| "count" | 600.0 |
| "null_count" | 0.0 |
| "mean" | 278.398333 |
| "std" | 363.777872 |
| "min" | 50.0 |
| "25%" | 61.0 |
| "50%" | 101.0 |
| "75%" | 200.0 |
| "max" | 1000.0 |
+
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+In [221]:
+
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+
+
+df["Step"].plot.hist()
+
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+Out[221]:
+
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+In [222]:
+
+
+
+
+
+# what about those that converged?
+
+converged = df.filter(pl.col("status") == "converged")
+
+
+
+
+
+
+
+
+In [223]:
+
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+
+
+converged["Step"].plot.hist()
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+Out[223]:
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+
+what % converged?¶
+
+
+
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+
+In [224]:
+
+
+
+
+
+status_totals = df["status"].value_counts()
+status_totals
+
+
+
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+
+
+
+Out[224]:
+
+
+
+shape: (2, 2)
+| status | count |
|---|---|
| str | u32 |
| "converged" | 481 |
| "running" | 119 |
+
+
+
+
+
+
+
+In [225]:
+
+
+
+
+
+converg_total = status_totals.filter(status_totals["status"] == "converged")["count"][0]
+
+print(f"{converg_total} runs out of {total_runs}; {converg_total/total_runs*100:.2f}% complete")
+
+
+
+
+
+
+
+
+
+
+
+481 runs out of 600; 80.17% complete ++
+
+
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+
+
+
+
+
+risk adjustment (adopt / average)¶
hypothesis: adjustment strategy does not have a significant impact on the final result, only affects how long it takes to get there
+
+
+
+
+
+
+
+In [226]:
+
+
+
+
+
+from scipy import stats
+
+
+df_riskadjust = converged.clone()
+
+# TODO: make reusable functions for annotating data
+
+for i in range(0, 10):
+ # calculate new series based on existing
+ pct_risk_category = df_riskadjust.select(pl.col(f"total_r{i}") / pl.col("total_agents"))
+ # add new column to the dataframe
+ df_riskadjust = df_riskadjust.with_columns(pl.Series(name=f"pct_r{i}", values=pct_risk_category))
+
+df_riskadjust = df_riskadjust.with_columns(
+ pl.Series('pct_risk_inclined', values=df_riskadjust.select((pl.col("total_r0") + pl.col("total_r1") + pl.col("total_r2")) / pl.col("total_agents"))),
+ pl.Series('pct_risk_moderate', values=df_riskadjust.select((pl.col("total_r3") + pl.col("total_r4") + pl.col("total_r5") + pl.col("total_r6")) / pl.col("total_agents"))),
+ pl.Series('pct_risk_avoidant', values=df_riskadjust.select((pl.col("total_r7") + pl.col("total_r8") + pl.col("total_r9")) / pl.col("total_agents")))
+)
+
+df_riskadjust = df_riskadjust.with_columns(pl.Series('risk_attitude_mean', values=df_riskadjust.select(
+ (pl.col("total_r1") + pl.col("total_r2")*2 + pl.col("total_r3")*3 + pl.col("total_r4")*4 + pl.col("total_r5")*5 + pl.col("total_r6")*6 + pl.col("total_r7")*7 + pl.col("total_r8")*8 + pl.col("total_r9")*9)
+ / pl.col("total_agents"))))
+
+
+df_adopt = df_riskadjust.filter((pl.col("risk_adjustment") == "adopt"))
+df_avg = df_riskadjust.filter((pl.col("risk_adjustment") == "average"))
+
+print(f"adopt: {len(df_adopt):,} rows")
+print(f"average: {len(df_avg):,} rows")
+
+
+
+
+
+
+
+
+
+
+
+adopt: 251 rows +average: 230 rows ++
+
+
+
+
+
+
+In [246]:
+
+
+
+
+
+maxlen = min(len(df_adopt), len(df_avg))
+
+stats.ttest_rel(df_adopt.select("pct_risk_inclined")[:maxlen], df_avg.select("pct_risk_inclined")[:maxlen])
+
+
+
+
+
+
+
+
+Out[246]:
+
+
+TtestResult(statistic=array([0.30599364]), pvalue=array([0.75988749]), df=array([229]))+
+
+
+
+
+
+
+In [243]:
+
+
+
+
+
+import altair as alt
+
+alt.Chart(df_riskadjust).mark_boxplot().encode(
+ x=alt.X('pct_risk_inclined', title='% risk inclined'), y=alt.Y('risk_adjustment', title="Adjustment"))
+
+
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+Out[243]:
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+In [229]:
+
+
+
+
+
+from simulatingrisk.hawkdovemulti import analysis_utils
+
+# df_adopt, df_average
+
+adopt_chart = analysis_utils.graph_population_risk_category(
+ analysis_utils.groupby_population_risk_category(df_adopt)
+).properties(title="risk adjust: adopt")
+
+average_chart = analysis_utils.graph_population_risk_category(
+ analysis_utils.groupby_population_risk_category(df_avg)
+).properties(title="risk adjust: average")
+
+(adopt_chart | average_chart).properties(title="distribution of population category by run").resolve_scale(y='shared')
+
+
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+
+Out[229]:
+
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+
+Simulations that converged¶
+
+
+
+
+
+
+
+In [230]:
+
+
+
+
+
+# filter to status = running
+converged = converged.with_columns(
+ pct_agents_risk_changed=pl.col("num_agents_risk_changed").truediv(pl.col("total_agents")),
+ seven_pct_pop=pl.col("total_agents").mul(0.07)
+)
+
+alt.Chart(converged).mark_boxplot().encode(
+ x=alt.X('num_agents_risk_changed', title='# Agents that adjusted risk attitude'),
+ y=alt.Y('risk_adjustment', title='Adjustment')).facet('grid_size')
+
+
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+
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+
+Out[230]:
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+In [231]:
+
+
+
+
+
+alt.Chart(converged).mark_boxplot().encode(
+ x=alt.X('pct_agents_risk_changed', title='% of Agents that adjusted risk attitude'),
+ y=alt.Y('risk_adjustment', title='Adjustment')).facet('grid_size')
+
+
+
+
+
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+
+Out[231]:
+
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+
+In [232]:
+
+
+
+
+
+converg_pop_boxplot = alt.Chart(converged).mark_boxplot().encode(
+ x=alt.X('sum_risk_level_changes', title='Total risk attitude changes'),
+ y=alt.Y('risk_adjustment', title='Adjustment'))
+
+converg_pop_boxplot.facet('grid_size')
+
+
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+Out[232]:
+
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+
+In [233]:
+
+
+
+
+
+converg_threshold = alt.Chart(converged).mark_point(color="orange").encode(
+ x="seven_pct_pop",
+ y=alt.Y('risk_adjustment', title='Adjustment')
+)
+(converg_pop_boxplot + converg_threshold).facet('grid_size')
+
+
+
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+
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+
+
+Out[233]:
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+
+
+Simulations that did not converge¶
+
+
+
+
+
+
+
+
+In [234]:
+
+
+
+
+
+# filter to status = running
+not_converged = df.filter(pl.col("status") == "running").with_columns(
+ pct_agents_risk_changed=pl.col("num_agents_risk_changed").truediv(pl.col("total_agents")),
+ seven_pct_pop=pl.col("total_agents").mul(0.07)
+)
+
+
+
+
+
+
+
+
+
+How many simulations with each adjustment type did not converge?
+
+
+
+
+
+
+
+
+In [235]:
+
+
+
+
+
+alt.Chart(not_converged).mark_bar().encode(
+ y=alt.Y('risk_adjustment', title="Adjustment"), x='count(RunId)')
+
+
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+
+
+
+
+Out[235]:
+
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+
+How many agents adjusted on the last adjustment round?
+
+
+
+
+
+
+
+In [236]:
+
+
+
+
+
+alt.Chart(not_converged).mark_boxplot().encode(
+ x=alt.X('num_agents_risk_changed', title='# Agents that adjusted risk attitude'),
+ y=alt.Y('risk_adjustment', title='Adjustment'))
+
+
+
+
+
+
+
+
+Out[236]:
+
+
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+
+In [237]:
+
+
+
+
+
+alt.Chart(not_converged).mark_boxplot().encode(
+ x=alt.X('pct_agents_risk_changed', title='% of Agents that adjusted risk attitude'),
+ y=alt.Y('risk_adjustment', title='Adjustment'))
+
+
+
+
+
+
+
+
+Out[237]:
+
+
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+
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+
+
+
+
+
+
+What about total population adjustments?
+
+
+
+
+
+
+
+
+In [238]:
+
+
+
+
+
+nonconverg_pop_boxplot = alt.Chart(not_converged).mark_boxplot().encode(
+ x=alt.X('sum_risk_level_changes', title='Total risk attitude changes'),
+ y=alt.Y('risk_adjustment', title='Adjustment'))
+
+nonconverg_pop_boxplot
+
+
+
+
+
+
+
+
+Out[238]:
+
+
+
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+
+
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+
+
+
+
+
+
+Facet by grid size, and add a marker (orange circle) to indicate the threshold (based on 7% of total population size).
+
+
+
+
+
+
+
+
+In [239]:
+
+
+
+
+
+converg_threshold = alt.Chart(not_converged).mark_point(color="orange").encode(
+ x="seven_pct_pop",
+ y=alt.Y('risk_adjustment', title='Adjustment')
+)
+(nonconverg_pop_boxplot + converg_threshold).facet('grid_size')
+
+
+
+
+
+
+
+
+Out[239]:
+
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+