# 11.7. Implementing Sugarscape¶

Sugarscape is more complicated than the previous models, so we won’t present the entire implementation here. we will outline the structure of the code and you can see the details in the Jupyter notebook for this chapter, chap11.ipynb, which is in the repository for this book. If you are not interested in the details, you can skip this section.

During each step, the agent moves, harvests sugar, and ages. Here is the Agent class and its step method:

class Agent:

def step(self, env):
self.loc = env.look_and_move(self.loc, self.vision)
self.sugar += env.harvest(self.loc) - self.metabolism
self.age += 1


The parameter env is a reference to the environment, which is a Sugarscape object. It provides methods look_and_move and harvest:

• look_and_move takes the location of the agent, which is a tuple of coordinates, and the range of the agent’s vision, which is an integer. It returns the agent’s new location, which is the visible cell with the most sugar.

• harvest takes the (new) location of the agent, and removes and returns the sugar at that location.

Sugarscape inherits from Cell2D, so it is similar to the other grid-based models we’ve seen.

The attributes include agents, which is a list of Agent objects, and occupied, which is a set of tuples, where each tuple contains the coordinates of a cell occupied by an agent.

Here is the Sugarscape class and its step method:

class Sugarscape(Cell2D):

def step(self):

# loop through the agents in random order
random_order = np.random.permutation(self.agents)
for agent in random_order:

# mark the current cell unoccupied
self.occupied.remove(agent.loc)

# execute one step
agent.step(self)

# if the agent is dead, remove from the list
if agent.is_starving():
self.agents.remove(agent)
else:
# otherwise mark its cell occupied

# grow back some sugar
self.grow()
return len(self.agents)


During each step, the Sugarscape uses the NumPy function permutation so it loops through the agents in random order. It invokes step on each agent and then checks whether it is dead. After all agents have moved, some of the sugar grows back. The return value is the number of agents still alive.

I won’t show more details here; you can see them in the notebook for this chapter. If you want to learn more about NumPy, you might want to look at these functions in particular:

• make_visible_locs, which builds the array of locations an agent can see, depending on its vision. The locations are sorted by distance, with locations at the same distance appearing in random order. This function uses np.random.shuffle and np.vstack.

• make_capacity, which initializes the capacity of the cells using NumPy functions indices, hypot, minimum, and digitize.

• look_and_move, which uses argmax.