docs: Rewrite howto.md using ChatGPT for clarity and conciseness (#2037)

docs/howto.md

@@ -1,65 +1,83 @@
 # How-to Guide
 
-Here you can find code that allows you to get to get started on common tasks in Mesa.
+This guide provides concise instructions and examples to help you start with common tasks in Mesa.
 
 ## Models with Discrete Time
 
-If you have `Multiple` type agents and one of them has time attribute you can still build a model that is run by discrete time. In this example, each step of the model, and the agents have a time attribute that is equal to the discrete time to run its own step.
+For models involving agents of multiple types, including those with a time attribute, you can construct a discrete-time model. This setup allows each agent to perform actions in steps that correspond to the model's discrete time.
 
+Example:
 ```python
 if self.model.schedule.time in self.discrete_time:
     self.model.space.move_agent(self, new_pos)
 ```
 
-## Implementing Model Level Functions in Staged Activation
+## Implementing Model-Level Functions with Staged Activation
 
-In staged activation, if you may want a function to be implemented only on the model level and not at the level of agents.
-For such functions, include the prefix "model." before the model function name, when defining the function list.
-For example, consider a central employment exchange which adjust the wage rate common to all laborers
-in the direction of excess demand.
+In staged activation scenarios, to designate functions that should only operate
+at the model level (and not at the agent level), prepend the function name with
+"model." in the function list definition. This approach is useful for
+model-wide operations, like adjusting a wage rate based on demand.
 
+Example:
 ```python
-stage_list=[Send_Labour_Supply, Send_Labour_Demand, model.Adjust_Wage_Rate] self.schedule = StagedActivation(self,stage_list,shuffle=True)
+stage_list = [Send_Labour_Supply, Send_Labour_Demand, model.Adjust_Wage_Rate]
+self.schedule = StagedActivation(self, stage_list, shuffle=True)
 ```
 
 ## Using `numpy.random`
 
-Sometimes you need to use `numpy`'s `random` library, for example to get a Poisson distribution.
+To incorporate `numpy`'s random functions, such as for generating a Poisson
+distribution, initialize a random number generator in your model's constructor.
 
+Example:
 ```python
-class MyModel(Model):
-    def __init__(self, ...):
+import mesa
+import numpy as np
+
+class MyModel(mesa.Model):
+    def __init__(self, seed=None):
         super().__init__()
         self.random = np.random.default_rng(seed)
 ```
 
-And just use `numpy`'s random as usual, e.g. `self.random.poisson()`.
+Usage example:
+```python
+lambda_value = 5
+sample = self.random.poisson(lambda_value)
+```
 
-## Using multi-process `batch_run` on Windows
+## Multi-process `batch_run` on Windows
 
-You will have an issue with `batch_run` and `number_processes = None`. Your cell will
-show no progress, and in your terminal you will receive *AttributeError: Can't get attribute 'MoneyModel' on
-\<module '\_\_main\_\_' (built-in)>*. One way to overcome this is to take your code outside of Jupyter and adjust the above
-code as follows.
+When using `batch_run` with `number_processes = None` on Windows, you might
+encounter progress display issues or `AttributeError: Can't get attribute
+'MoneyModel' on <module '__main__' (built-in)>`. To resolve this, run
+your code outside of Jupyter notebooks and use the following pattern,
+incorporating `freeze_support()` for multiprocessing support.
 
+Example:
 ```python
+from mesa.batchrunner import batch_run
 from multiprocessing import freeze_support
 
 params = {"width": 10, "height": 10, "N": range(10, 500, 10)}
 
 if __name__ == '__main__':
     freeze_support()
     results = batch_run(
-            MoneyModel,
-            parameters=params,
-            iterations=5,
-            max_steps=100,
-            number_processes=None,
-            data_collection_period=1,
-            display_progress=True,
+        MoneyModel,
+        parameters=params,
+        iterations=5,
+        max_steps=100,
+        number_processes=None,
+        data_collection_period=1,
+        display_progress=True,
     )
 ```
 
-If you would still like to run your code in Jupyter you will need to adjust the cell as noted above. Then you can
-you can add the [nbmultitask library](https://nbviewer.org/github/micahscopes/nbmultitask/blob/39b6f31b047e8a51a0fcb5c93ae4572684f877ce/examples.ipynb)
-or look at this [stackoverflow](https://stackoverflow.com/questions/50937362/multiprocessing-on-python-3-jupyter).
+If you would still like to run your code in Jupyter, adjust your code as
+described and consider integrating external libraries like
+[nbmultitask](https://nbviewer.org/github/micahscopes/nbmultitask/blob/39b6f31b047e8a51a0fcb5c93ae4572684f877ce/examples.ipynb)
+or refer to [Stack
+Overflow](https://stackoverflow.com/questions/50937362/multiprocessing-on-python-3-jupyter)
+for multiprocessing tips.