Add BoltzmannWealth model as example for no space model

.github/setup.sh

@@ -20,7 +20,7 @@ julia --project=@. -e 'using Pkg; Pkg.instantiate()'
 
 # install mesa
 sudo apt install python3-pip
-pip install mesa==2.1.4
+pip install mesa==2.4.0
 
 # install netlogo
 sudo wget http://ccl.northwestern.edu/netlogo/6.4.0/NetLogo-6.4.0-64.tgz

BoltzmannWealth/Agents/BolzmannWealth.jl

@@ -0,0 +1,21 @@
+using Agents
+
+
+@agent struct WealthAgent(NoSpaceAgent)
+    wealth::Int
+end
+
+function wealth_model(; numagents = 100, initwealth = 1)
+    model = ABM(WealthAgent; agent_step!, scheduler = Schedulers.Randomly(), 
+                rng = Xoshiro(42), container = Vector)
+    for _ in 1:numagents
+        add_agent!(model, initwealth)
+    end
+    return model
+end
+
+function agent_step!(agent, model)
+    agent.wealth <= 0 && return
+    agent.wealth -= 1
+    random_agent(model).wealth += 1
+end

BoltzmannWealth/Mesa/BoltzmannWealth.py

@@ -0,0 +1,38 @@
+
+
+class MoneyAgent(mesa.Agent):
+    """An agent with fixed initial wealth."""
+
+    def __init__(self, unique_id, model):
+        # Pass the parameters to the parent class.
+        super().__init__(unique_id, model)
+
+        # Create the agent's variable and set the initial values.
+        self.wealth = 1
+
+    def step(self):
+        # Verify agent has some wealth
+        if self.wealth > 0:
+            other_agent = self.random.choice(self.model.agents)
+            other_agent.wealth += 1
+            self.wealth -= 1
+
+
+class MoneyModel(mesa.Model):
+    """A model with some number of agents."""
+
+    def __init__(self, N):
+        super().__init__()
+        self.num_agents = N
+        # Create scheduler and assign it to the model
+        self.agents = [MoneyAgent(i, self) for i in range(self.num_agents)]
+
+    def step(self):
+        """Advance the model by one step."""
+        self.random.shuffle(self.agents)
+        for agent in self.agents:
+            agent.step()
+
+    def run_model(self, n_steps) -> None:
+        for _ in range(n_steps):
+            self.step()