updated hotelling

Author: quaquel

examples/hotelling_law/hotelling_law/agents.py

@@ -2,10 +2,11 @@
 import random
 
 import numpy as np
-from mesa import Agent
 
+from mesa.experimental.cell_space import CellAgent
 
-class StoreAgent(Agent):
+
+class StoreAgent(CellAgent):
     """An agent representing a store with a price and ability to move
     and adjust prices."""
 
@@ -22,10 +23,8 @@ def __init__(self, model, price=10, can_move=True, strategy="Budget"):
         # / differential (Premium)
 
     def estimate_market_share(self, new_position=None):
-        position = new_position if new_position else self.pos
-        nearby_consumers = self.model.grid.get_neighborhood(
-            position, moore=True, include_center=False, radius=8
-        )
+        position = new_position if new_position else self.cell
+        nearby_consumers = position.get_neighborhood(radius=8).agents
 
         # Filter nearby agents to include only ConsumerAgents.
         nearby_consumers = [
@@ -45,24 +44,23 @@ def move(self):
         if not self.can_move:
             return
 
-        best_position = self.pos
+        best_position = self.cell
         best_market_share = self.estimate_market_share()
 
-        for neighbor in self.model.grid.get_neighborhood(
-            self.pos, moore=True, include_center=False
-        ):
+        for neighbor in self.cell.neighborhood:
             potential_market_share = self.estimate_market_share(new_position=neighbor)
             if potential_market_share >= best_market_share:
                 best_market_share = potential_market_share
                 best_position = neighbor
-        self.model.grid.move_agent(self, best_position)
+
+        self.cell = best_position
 
     def adjust_price(self):
         # Calculate competitor prices and the average competitor price
         competitor_prices = [
             store.price
-            for store in self.model.get_store_agents()
-            if store.unique_id != self.unique_id
+            for store in self.model.agents_by_type[StoreAgent]
+            if store is not self
         ]
         average_competitor_price = (
             np.mean(competitor_prices) if competitor_prices else self.price
@@ -71,7 +69,7 @@ def adjust_price(self):
         # Calculate the current and average market share
         current_market_share = self.market_share
         all_market_shares = [
-            store.market_share for store in self.model.get_store_agents()
+            store.market_share for store in self.model.agents_by_type[StoreAgent]
         ]
         average_market_share = np.mean(all_market_shares) if all_market_shares else 0
 
@@ -119,8 +117,8 @@ def adjust_price(self):
 
     def identify_competitors(self):
         competitors = []
-        for agent in self.model.agents:
-            if isinstance(agent, StoreAgent) and agent.unique_id != self.unique_id:
+        for agent in self.model.agents_by_type[StoreAgent]:
+            if agent is not self:
                 # Estimate market overlap as a measure of competition
                 overlap = self.estimate_market_overlap(agent)
                 if overlap > 0:  # If there's any market overlap,
@@ -133,7 +131,7 @@ def estimate_market_overlap(self, other_store):
         This could be based on shared consumer base or other factors."""
         overlap = 0
 
-        for consumer in self.model.get_consumer_agents():
+        for consumer in self.model.agents_by_type[ConsumerAgent]:
             preferred_store = consumer.determine_preferred_store()
             if preferred_store in (self, other_store):
                 overlap += 1
@@ -156,7 +154,7 @@ def step(self):
             self.adjust_price()
 
 
-class ConsumerAgent(Agent):
+class ConsumerAgent(CellAgent):
     """A consumer agent that chooses a store
     based on price and distance."""
 
@@ -165,8 +163,8 @@ def __init__(self, model):
         self.preferred_store = None
 
     def determine_preferred_store(self):
-        consumer_preference = self.model.consumer_preferences
-        stores = self.model.get_store_agents()
+        consumer_preference = self.model.agents_by_type[ConsumerAgent]
+        stores = self.model.agents_by_type[StoreAgent]
 
         if len(stores) == 0:  # Check if the stores AgentSet is empty
             return None
@@ -177,11 +175,11 @@ def determine_preferred_store(self):
         for store in stores:
             # Calculate score based on consumer preference
             if consumer_preference == "proximity":
-                score = self.euclidean_distance(self.pos, store.pos)
+                score = self.euclidean_distance(self.cell.coordinate, store.cell.coordinate)
             elif consumer_preference == "price":
                 score = store.price
             else:  # Default case includes both proximity and price
-                score = store.price + self.euclidean_distance(self.pos, store.pos)
+                score = store.price + self.euclidean_distance(self.cell.coordinate, store.cell.coordinate)
 
             # Update the list of best stores if a new minimum score is found
             if score < min_score:

examples/hotelling_law/hotelling_law/model.py

@@ -4,7 +4,7 @@
 from mesa import Model
 from mesa.agent import AgentSet
 from mesa.datacollection import DataCollector
-from mesa.space import MultiGrid
+from mesa.experimental.cell_space import OrthogonalMooreGrid
 
 from .agents import ConsumerAgent, StoreAgent
 
@@ -99,12 +99,12 @@ def __init__(
 
         # Initialize the spatial grid based on the specified environment type.
         if environment_type == "grid":
-            self.grid = MultiGrid(
-                width, height, True
+            self.grid = OrthogonalMooreGrid(
+                (width, height), True
             )  # A grid where multiple agents can occupy the same cell.
         elif environment_type == "line":
-            self.grid = MultiGrid(
-                1, height, True
+            self.grid = OrthogonalMooreGrid(
+                (1, height), True
             )  # A grid representing a line (single occupancy per cell).
 
         self._initialize_agents()
@@ -186,29 +186,20 @@ def _initialize_agents(self):
 
             agent = StoreAgent(self, can_move=can_move, strategy=strategy)
 
-            self.store_agents.add(agent)
-
             # Randomly place agents on the grid for a grid environment.
-            x = self.random.randrange(self.grid.width)
-            y = self.random.randrange(self.grid.height)
-            self.grid.place_agent(agent, (x, y))
+            x = self.random.randrange(self.grid.dimensions[0])
+            y = self.random.randrange(self.grid.dimensions[1])
+            agent.cell = self.grid[(x,y)]
 
         # Place consumer agents
         for _ in range(self.num_consumers):
             # Ensure unique ID across all agents
             consumer = ConsumerAgent(self)
 
-            self.consumer_agents.add(consumer)
             # Place consumer randomly on the grid
-            x = self.random.randrange(self.grid.width)
-            y = self.random.randrange(self.grid.height)
-            self.grid.place_agent(consumer, (x, y))
-
-    def get_store_agents(self):
-        return self.store_agents
-
-    def get_consumer_agents(self):
-        return self.consumer_agents
+            x = self.random.randrange(self.grid.dimensions[0])
+            y = self.random.randrange(self.grid.dimensions[1])
+            consumer.cell = self.grid[(x,y)]
 
     # Method to advance the simulation by one step.
     def step(self):