diff --git a/docs/best-practices.rst b/docs/best-practices.rst
index 34ade0cdb3b..205f375b010 100644
--- a/docs/best-practices.rst
+++ b/docs/best-practices.rst
@@ -32,3 +32,29 @@ an ``images`` directory.
 The `Schelling
 <https://github.com/projectmesa/mesa/tree/master/examples/Schelling>`_ model is
 a good example of a small well-packaged model.
+
+Randomization
+-------------
+
+If your model involves some random choice, you can use either ``random``
+(Python's built-in random number generator) or ``numpy.random`` (the generator
+included with Numpy).
+
+The constructor for the ``Model`` class automatically "seeds" these random
+number generators using the current time, so each run will produce different
+random numbers. For testing purposes, it can be helpful to use the same
+random-number seed for multiple runs. To accomplish this, pass a value to the
+Model constructor:
+
+.. code:: python
+
+    class AwesomeModel(Model):
+        def __init__(self, seed=None):
+            super().__init__(seed)
+            # ...
+
+    model = AwesomeModel(seed=1234)
+    # ...
+
+This approach will cause ``RandomActivation`` to activate agents in a
+repeatable fashion.
diff --git a/mesa/model.py b/mesa/model.py
index 6e4d4241157..849caeaf45d 100644
--- a/mesa/model.py
+++ b/mesa/model.py
@@ -7,6 +7,7 @@
 """
 import datetime as dt
 import random
+import numpy
 
 
 class Model:
@@ -23,11 +24,14 @@ def __init__(self, seed=None):
             running: a bool indicating if the model should continue running
 
         """
+        # seed both the numpy and Python random number generators
         if seed is None:
             self.seed = dt.datetime.now()
         else:
             self.seed = seed
         random.seed(seed)
+        numpy.random.seed(seed)
+
         self.running = True
         self.schedule = None