meta_agents

- Add create meta-agents to experimental
- Add tests of meta-agents
- Add example with an alliance formation model in basic examples

mesa/examples/basic/alliance_formation_model/Readme.md

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+# Alliance Formation Model
+
+## Summary
+This model demonstrates Mesa's ability to dynamically create new classes of agents that are composed of existing agents. These meta-agents  inherits functions and attributes from their sub-agents and users can specify new functionality or attributes they want the meta agent to have. For example, if a user is doing a factory simulation with autonomous systems, each major component of that system can be a sub-agent of the overall robot agent. Or, if someone is doing a simulation of an organization, individuals can be part of different organizational units that are working for some purpose.
+
+To provide a simple demonstration of this capability is an alliance formation model.
+
+In this simulation n agents are created, who have two attributes (1) power and (2) preference. Each attribute is a number between 0 and 1 over a gaussian distribution. Agents then randomly select other agents and use the [bilateral shapley value](https://en.wikipedia.org/wiki/Shapley_value) to determine if they should form an alliance. If the expected utility support an alliances, the agent creates a meta-agent. Subsequent steps may add agents to the meta-agent, create new instances of similar hierarchy, or create a new hierarchy level where meta-agents form an alliance of meta-agents. In this visualization of this model a new meta-agent hierarchy will be a larger node and a new color.
+
+In its current configuration, agents being part of multiple meta-agents is not supported
+
+## Installation
+
+This model requires Mesa's recommended install and scipy
+```
+    $ pip install mesa[rec] scipy
+```
+
+## How to Run
+
+To run the model interactively, in this directory, run the following command
+
+```
+    $ solara run app.py
+```
+
+## Files
+
+* ``model.py``: Contains creation of agents, the network and management of agent execution.
+* ``agents.py``: Contains logic for forming alliances and creation of new agents
+* ``app.py``: Contains the code for the interactive Solara visualization.
+
+## Further Reading
+
+The full tutorial describing how the model is built can be found at:
+https://mesa.readthedocs.io/en/latest/tutorials/intro_tutorial.html
+
+An example of the bilateral shapley value in another model:
+[Techno-Social Energy Infrastructure Siting: Sustainable Energy Modeling Programming (SEMPro)](https://www.jasss.org/16/3/6.html)
+

mesa/examples/basic/alliance_formation_model/__init__.py

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+import logging
+
+# Configure logging
+logging.basicConfig(
+    level=logging.DEBUG, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+
+# Example usage of logging
+logger = logging.getLogger(__name__)
+logger.info("Logging is configured and ready to use.")

mesa/examples/basic/alliance_formation_model/agents.py

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+import mesa
+from mesa.experimental.meta_agents import create_meta_agent
+
+
+def calculate_shapley_value(calling_agent, other_agent):
+    """
+    Calculate the Shapley value of the two agents
+    """
+    new_position = 1 - abs(calling_agent.position - other_agent.position)
+    potential_utility = (calling_agent.power + other_agent.power) * 1.1 * new_position
+    value_me = 0.5 * calling_agent.power + 0.5 * (potential_utility - other_agent.power)
+    value_other = 0.5 * other_agent.power + 0.5 * (
+        potential_utility - calling_agent.power
+    )
+
+    # Determine if there is value in the alliance
+    if value_me > calling_agent.power and value_other > other_agent.power:
+        if other_agent.hierarchy > calling_agent.hierarchy:
+            hierarchy = other_agent.hierarchy
+        elif other_agent.hierarchy == calling_agent.hierarchy:
+            hierarchy = calling_agent.hierarchy + 1
+        else:
+            hierarchy = calling_agent.hierarchy
+
+        return (potential_utility, new_position, hierarchy)
+    else:
+        return None
+
+
+class AllianceAgent(mesa.Agent):
+    """
+    Agent has three attributes power (float), position (float) and hierarchy (int)
+
+    """
+
+    def __init__(self, model, power, position, hierarchy=0):
+        super().__init__(model)
+        self.power = power
+        self.position = position
+        self.hierarchy = hierarchy
+
+    def form_alliance(self):
+        # Randomly select another agent of the same type
+        other_agents = [
+            agent for agent in self.model.agents_by_type[type(self)] if agent != self
+        ]
+
+        # Determine if there is a beneficial alliance
+        if other_agents:
+            other_agent = self.random.choice(other_agents)
+            shapley_value = calculate_shapley_value(self, other_agent)
+            if shapley_value:
+                class_name = f"MetaAgentHierarchy{shapley_value[2]}"
+                meta = create_meta_agent(
+                    self.model,
+                    class_name,
+                    {other_agent, self},
+                    meta_attributes={
+                        "hierarchy": shapley_value[2],
+                        "power": shapley_value[0],
+                        "position": shapley_value[1],
+                    },
+                )
+
+                # Update the network if a new meta agent instance created
+                if meta:
+                    self.model.network.add_node(
+                        meta.unique_id,
+                        size=(meta.hierarchy + 1) * 300,
+                        hierarchy=meta.hierarchy,
+                    )

mesa/examples/basic/alliance_formation_model/app.py

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+import matplotlib.pyplot as plt
+import networkx as nx
+import solara
+from matplotlib.figure import Figure
+from model import AllianceModel
+
+from mesa.mesa_logging import DEBUG, log_to_stderr
+from mesa.visualization import SolaraViz
+from mesa.visualization.utils import update_counter
+
+log_to_stderr(DEBUG)
+
+model_params = {
+    "seed": {
+        "type": "InputText",
+        "value": 42,
+        "label": "Random Seed",
+    },
+    "n": {
+        "type": "SliderInt",
+        "value": 50,
+        "label": "Number of agents:",
+        "min": 10,
+        "max": 100,
+        "step": 1,
+    },
+}
+
+# Create visualization elements. The visualization elements are solara components
+# that receive the model instance as a "prop" and display it in a certain way.
+# Under the hood these are just classes that receive the model instance.
+# You can also author your own visualization elements, which can also be functions
+# that receive the model instance and return a valid solara component.
+
+
+@solara.component
+def plot_network(model):
+    update_counter.get()
+    g = model.network
+    pos = nx.kamada_kawai_layout(g)
+    fig = Figure()
+    ax = fig.subplots()
+    labels = {agent.unique_id: agent.unique_id for agent in model.agents}
+    node_sizes = [g.nodes[node]["size"] for node in g.nodes]
+    node_colors = [g.nodes[node]["size"] for node in g.nodes()]
+
+    nx.draw(
+        g,
+        pos,
+        node_size=node_sizes,
+        node_color=node_colors,
+        cmap=plt.cm.coolwarm,
+        labels=labels,
+        ax=ax,
+    )
+
+    solara.FigureMatplotlib(fig)
+
+
+# Create initial model instance
+model = AllianceModel(50)
+
+# Create the SolaraViz page. This will automatically create a server and display the
+# visualization elements in a web browser.
+# Display it using the following command in the example directory:
+# solara run app.py
+# It will automatically update and display any changes made to this file
+page = SolaraViz(
+    model,
+    components=[plot_network],
+    model_params=model_params,
+    name="Alliance Formation Model",
+)
+page  # noqa

mesa/examples/basic/alliance_formation_model/model.py

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+import networkx as nx
+import numpy as np
+from agents import AllianceAgent
+
+import mesa
+
+
+class AllianceModel(mesa.Model):
+    def __init__(self, n=50, mean=0.5, std_dev=0.1, seed=42):
+        super().__init__(seed=seed)
+        self.population = n
+        self.network = nx.Graph()  # Initialize the network
+        self.datacollector = mesa.DataCollector(model_reporters={"Network": "network"})
+
+        # Create Agents
+        power = np.random.normal(mean, std_dev, n)
+        power = np.clip(power, 0, 1)
+        position = np.random.normal(mean, std_dev, n)
+        position = np.clip(position, 0, 1)
+        AllianceAgent.create_agents(self, n, power, position)
+        agent_ids = [
+            (agent.unique_id, {"size": 300, "hierarchy": 0}) for agent in self.agents
+        ]
+        self.network.add_nodes_from(agent_ids)
+
+    def add_link(self, meta_agent, agents):
+        for agent in agents:
+            self.network.add_edge(meta_agent.unique_id, agent.unique_id)
+
+    def step(self):
+        for agent_class in list(
+            self.agent_types
+        ):  # Convert to list to avoid modification during iteration
+            self.agents_by_type[agent_class].shuffle_do("form_alliance")
+
+            # Update graph
+            if agent_class is not AllianceAgent:
+                for meta_agent in self.agents_by_type[agent_class]:
+                    self.add_link(meta_agent, meta_agent.agents)

mesa/experimental/meta_agents/__init__.py

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+"""This method is for dynamically creating new agents (meta-agents).
+
+Meta-agents are defined as agents composed of existing agents.
+
+Meta-agents are created dynamically with a pointer to the model, name of the meta-agent,,
+iterable of agents to belong to the new meta-agents, any new functions for the meta-agent,
+any new attributes for the meta-agent, whether to retain sub-agent functions,
+whether to retain sub-agent attributes.
+
+Examples of meta-agents:
+- An autonomous car where the subagents are the wheels, sensors,
+battery, computer etc. and the meta-agent is the car itself.
+- A company where the subagents are employees, departments, buildings, etc.
+- A city where the subagents are people, buildings, streets, etc.
+
+Currently meta-agents are restricted to one parent agent for each subagent/
+one meta-agent per subagent.
+
+Goal is to assess usage and expand functionality.
+
+"""
+
+from .meta_agents import create_meta_agent
+
+__all__ = ["create_meta_agent"]