adding ai_economist for modding

2023-01-12 16:41:38 +01:00
parent 0479a4f6a4
commit f177f8f0ba
85 changed files with 19373 additions and 2 deletions
@@ -0,0 +1,75 @@
+# Copyright (c) 2020, salesforce.com, inc.
+# All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+# For full license text, see the LICENSE file in the repo root
+# or https://opensource.org/licenses/BSD-3-Clause
+
+import numpy as np
+
+
+def get_gini(endowments):
+    """Returns the normalized Gini index describing the distribution of endowments.
+
+    https://en.wikipedia.org/wiki/Gini_coefficient
+
+    Args:
+        endowments (ndarray): The array of endowments for each of the agents in the
+            simulated economy.
+
+    Returns:
+        Normalized Gini index for the distribution of endowments (float). A value of 1
+            indicates everything belongs to 1 agent (perfect inequality), whereas a
+            value of 0 indicates all agents have equal endowments (perfect equality).
+
+    Note:
+        Uses a slightly different method depending on the number of agents. For fewer
+        agents (<30), uses an exact but slow method. Switches to using a much faster
+        method for more agents, where both methods produce approximately equivalent
+        results.
+    """
+    n_agents = len(endowments)
+
+    if n_agents < 30:  # Slower. Accurate for all n.
+        diff_ij = np.abs(
+            endowments.reshape((n_agents, 1)) - endowments.reshape((1, n_agents))
+        )
+        diff = np.sum(diff_ij)
+        norm = 2 * n_agents * endowments.sum(axis=0)
+        unscaled_gini = diff / (norm + 1e-10)
+        gini = unscaled_gini / ((n_agents - 1) / n_agents)
+        return gini
+
+    # Much faster. Slightly overestimated for low n.
+    s_endows = np.sort(endowments)
+    return 1 - (2 / (n_agents + 1)) * np.sum(
+        np.cumsum(s_endows) / (np.sum(s_endows) + 1e-10)
+    )
+
+
+def get_equality(endowments):
+    """Returns the complement of the normalized Gini index (equality = 1 - Gini).
+
+    Args:
+        endowments (ndarray): The array of endowments for each of the agents in the
+            simulated economy.
+
+    Returns:
+        Normalized equality index for the distribution of endowments (float). A value
+            of 0 indicates everything belongs to 1 agent (perfect inequality),
+            whereas a value of 1 indicates all agents have equal endowments (perfect
+            equality).
+    """
+    return 1 - get_gini(endowments)
+
+
+def get_productivity(coin_endowments):
+    """Returns the total coin inside the simulated economy.
+
+    Args:
+        coin_endowments (ndarray): The array of coin endowments for each of the
+            agents in the simulated economy.
+
+    Returns:
+        Total coin endowment (float).
+    """
+    return np.sum(coin_endowments)