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
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
env is a reference to the environment, which is a
Sugarscape object. It provides methods
look_and_movetakes 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.
harvesttakes 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
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 self.occupied.add(agent.loc) # 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
make_capacity, which initializes the capacity of the cells using NumPy functions
look_and_move, which uses