Sugarscape {G1,{M,T}}: Add tests

examples/sugarscape_g1mt/Readme.md

@@ -3,7 +3,7 @@
 ## Summary
 
 This is Epstein & Axtell's Sugarscape model with Traders, a detailed description is in Chapter four of
-*Growing Artificial Societies: Social Science from the Bottom Up.* (1996)
+*Growing Artificial Societies: Social Science from the Bottom Up.* (1996) The model shows an emergent price equilibrium can happen via a decentralized dynamics.
 
 This code generally matches the code in the Complexity Explorer Tutorial, but in `.py` instead of `.ipynb` format.
 
@@ -83,6 +83,7 @@ Then open your browser to [http://127.0.0.1:8521/](http://127.0.0.1:8521/) and p
 * ``server.py``: Sets up and launches and interactive visualization server.
 * ``run.py``: Runs Server, Single Run or Batch Run  with data collection and basic analysis.
 * `app.py`: Runs a visualization server via Solara (`solara run app.py`).
+* `tests.py`: Has tests to ensure that the model reproduces the results in shown in Growing Artificial Societies.
 
 ## Additional Resources
 

examples/sugarscape_g1mt/sugarscape_g1mt/model.py

@@ -49,6 +49,7 @@ def __init__(
         metabolism_max=5,
         vision_min=1,
         vision_max=5,
+        enable_trade=True,
     ):
         # Initiate width and heigh of sugarscape
         self.width = width
@@ -61,6 +62,7 @@ def __init__(
         self.metabolism_max = metabolism_max
         self.vision_min = vision_min
         self.vision_max = vision_max
+        self.enable_trade = enable_trade
         self.running = True
 
         # initiate activation schedule
@@ -175,6 +177,11 @@ def step(self):
             agent.eat()
             agent.maybe_die()
 
+        if not self.enable_trade:
+            # If trade is not enabled, return early
+            self.datacollector.collect(self)
+            return
+
         trader_shuffle = self.randomize_traders()
 
         for agent in trader_shuffle:

examples/sugarscape_g1mt/tests.py

@@ -0,0 +1,72 @@
+import random
+
+import numpy as np
+from scipy import stats
+from sugarscape_g1mt.model import SugarscapeG1mt, flatten
+from sugarscape_g1mt.trader_agents import Trader
+
+random.seed(1)
+
+
+def check_slope(y, increasing):
+    x = range(len(y))
+    slope, intercept, _, p_value, _ = stats.linregress(x, y)
+    result = (slope > 0) if increasing else (slope < 0)
+    # p_value for significance.
+    assert result and p_value < 0.05, (slope, p_value)
+
+
+def test_decreasing_variance():
+    # The variance of the average trade price should decrease over time (figure IV-3)
+    # See Growing Artificial Societies p. 109.
+    model = SugarscapeG1mt()
+    model.datacollector._new_model_reporter(
+        "price_variance",
+        lambda m: np.var(
+            flatten([a.prices for a in m.schedule.agents_by_type[Trader].values()])
+        ),
+    )
+    model.run_model(step_count=50)
+
+    df_model = model.datacollector.get_model_vars_dataframe()
+
+    check_slope(df_model.price_variance, increasing=False)
+
+
+def test_carrying_capacity():
+    def calculate_carrying_capacities(enable_trade):
+        carrying_capacities = []
+        visions = range(1, 10)
+        for vision_max in visions:
+            model = SugarscapeG1mt(vision_max=vision_max, enable_trade=enable_trade)
+            model.run_model(step_count=50)
+            carrying_capacities.append(len(model.schedule.agents_by_type[Trader]))
+        return carrying_capacities
+
+    # Carrying capacity should increase over mean vision (figure IV-6).
+    # See Growing Artificial Societies p. 112.
+    carrying_capacities_with_trade = calculate_carrying_capacities(True)
+    check_slope(
+        carrying_capacities_with_trade,
+        increasing=True,
+    )
+    # Carrying capacity should be higher when trade is enabled (figure IV-6).
+    carrying_capacities_no_trade = calculate_carrying_capacities(False)
+    check_slope(
+        carrying_capacities_no_trade,
+        increasing=True,
+    )
+
+    t_statistic, p_value = stats.ttest_rel(
+        carrying_capacities_with_trade, carrying_capacities_no_trade
+    )
+    # t_statistic > 0 means carrying_capacities_with_trade has larger values
+    # than carrying_capacities_no_trade.
+    # p_value for significance.
+    assert t_statistic > 0 and p_value < 0.05
+
+
+# TODO:
+# 1. Reproduce figure IV-12 that the log of average price should decrease over average agent age
+# 2. Reproduce figure IV-13 that the gini coefficient on trade should decrease over mean vision, and should be higher with trade
+# 3. a stricter test would be to ensure the amount of variance of the trade price matches figure IV-3