diff --git a/aerosandbox/numpy/surrogate_model_tools.py b/aerosandbox/numpy/surrogate_model_tools.py
index fa145cc3..d09e0862 100644
--- a/aerosandbox/numpy/surrogate_model_tools.py
+++ b/aerosandbox/numpy/surrogate_model_tools.py
@@ -4,8 +4,8 @@
 
 def softmax(
         *args: Union[float, _np.ndarray],
+        softness: float = 1.,
         hardness: float = None,
-        softness: float = None,
 ) -> Union[float, _np.ndarray]:
     """
     An element-wise softmax between two or more arrays. Also referred to as the logsumexp() function.
@@ -35,16 +35,12 @@ def softmax(
         Soft maximum of the supplied values.
     """
     ### Set defaults for hardness/softness
+    if not (hardness is None) ^ (softness is None):
+        raise ValueError("You must provide exactly one of `hardness` or `softness`.")
     if hardness is not None:
-        if softness is not None:
-            raise ValueError("You can't specify both `hardness` and `softness`.")
-    else:
-        if softness is not None:
-            hardness = 1 / softness
-        else:
-            hardness = 1.0
+        softness = 1 / hardness
 
-    if _np.any(hardness <= 0):
+    if _np.any(softness <= 0):
         if softness is not None:
             raise ValueError("The value of `softness` must be positive.")
         else:
@@ -54,8 +50,8 @@ def softmax(
         raise ValueError("You must call softmax with the value of two or more arrays that you'd like to take the "
                          "element-wise softmax of.")
 
-    ### Scale the args by hardness
-    args = [arg * hardness for arg in args]
+    ### Scale the args by softness
+    args = [arg / softness for arg in args]
 
     ### Find the element-wise max and min of the arrays:
     min = args[0]
@@ -68,14 +64,14 @@ def softmax(
         [_np.exp(_np.maximum(array - max, -500)) for array in args]
     )
     )
-    out = out / hardness
+    out = out * softness
     return out
 
 
 def softmin(
         *args: Union[float, _np.ndarray],
+        softness: float = 1.,
         hardness: float = None,
-        softness: float = None,
 ) -> Union[float, _np.ndarray]:
     """
     An element-wise softmin between two or more arrays. Related to the logsumexp() function.
@@ -106,8 +102,8 @@ def softmin(
     """
     return -softmax(
         *[-arg for arg in args],
+        softness=softness,
         hardness=hardness,
-        softness=softness
     )