Agent-Based Simulator

Overview

The Agent-Based Simulator skill provides capabilities for modeling complex adaptive systems through the simulation of heterogeneous, interacting agents. It enables bottom-up understanding of emergent market behaviors, customer dynamics, and competitive interactions for strategic decision support.

Capabilities

Agent definition and behavior modeling
Environment and spatial modeling
Interaction rules specification
Emergent behavior observation
Parameter sweeping
Ensemble simulation runs
Visualization and animation
Statistical analysis of outcomes

Used By Processes

War Gaming and Competitive Response Modeling
Market Sizing and Opportunity Assessment
Customer Segmentation Analysis

Usage

Agent Definition

# Define customer agent
customer_agent = {
    "type": "Customer",
    "attributes": {
        "budget": {"distribution": "normal", "mean": 1000, "std": 200},
        "brand_loyalty": {"distribution": "uniform", "min": 0, "max": 1},
        "price_sensitivity": {"distribution": "beta", "alpha": 2, "beta": 5},
        "preferred_features": ["list of features"]
    },
    "behaviors": {
        "purchase_decision": {
            "triggers": ["need_arises", "promotion_seen"],
            "evaluation": "weighted_utility",
            "factors": ["price", "quality", "brand_match"]
        },
        "word_of_mouth": {
            "probability": 0.3,
            "reach": {"distribution": "poisson", "lambda": 5},
            "sentiment_spread": True
        },
        "brand_switching": {
            "threshold": 0.7,
            "factors": ["satisfaction", "competitor_promotion"]
        }
    }
}

Environment Definition

# Define market environment
environment = {
    "topology": "network",  # or "grid", "continuous"
    "network_type": "small_world",
    "network_params": {"k": 6, "p": 0.1},
    "global_properties": {
        "economic_condition": {"initial": "normal", "transitions": "markov"},
        "market_size": 10000,
        "growth_rate": 0.02
    }
}

Interaction Rules

# Define interaction rules
interactions = {
    "customer_customer": {
        "information_sharing": {
            "probability": "based_on_relationship",
            "content": ["product_experience", "price_info"]
        },
        "social_influence": {
            "mechanism": "threshold_model",
            "threshold_distribution": "normal"
        }
    },
    "customer_company": {
        "purchase": {
            "frequency": "need_based",
            "channel": ["online", "physical", "hybrid"]
        },
        "complaint": {
            "trigger": "satisfaction < 0.3",
            "resolution_impact": 0.5
        }
    },
    "company_company": {
        "price_competition": "cournot|bertrand|stackelberg",
        "market_signaling": True
    }
}

Simulation Configuration

# Simulation settings
simulation_config = {
    "time_steps": 365,
    "agents": {
        "Customer": 5000,
        "Company": 3
    },
    "ensemble_runs": 100,
    "parameter_sweep": {
        "price_sensitivity_mean": [0.3, 0.5, 0.7],
        "word_of_mouth_probability": [0.1, 0.3, 0.5]
    },
    "data_collection": {
        "agent_level": ["satisfaction", "brand_choice"],
        "model_level": ["market_shares", "total_revenue", "gini_coefficient"]
    }
}

Input Schema

{
  "agents": {
    "agent_type": {
      "count": "number",
      "attributes": "object",
      "behaviors": "object"
    }
  },
  "environment": {
    "topology": "string",
    "properties": "object"
  },
  "interactions": "object",
  "simulation_config": {
    "time_steps": "number",
    "ensemble_runs": "number",
    "parameter_sweep": "object",
    "random_seed": "number"
  }
}

Output Schema

{
  "summary_statistics": {
    "metric_name": {
      "mean": "number",
      "std": "number",
      "percentiles": "object",
      "time_series": ["number"]
    }
  },
  "emergent_patterns": [
    {
      "pattern": "string",
      "frequency": "number",
      "conditions": "object"
    }
  ],
  "parameter_sweep_results": {
    "parameter_combination": {
      "outcomes": "object"
    }
  },
  "agent_trajectories": "object (sample)",
  "network_metrics": {
    "clustering_coefficient": "number",
    "average_path_length": "number",
    "degree_distribution": "object"
  },
  "visualization_paths": ["string"]
}

Best Practices

Start with simple agents and add complexity incrementally
Validate agent behaviors against real-world observations
Use ensemble runs to account for stochastic variability
Perform sensitivity analysis on key parameters
Document all behavioral rules and their justification
Test for emergent behaviors under extreme conditions
Compare results with aggregate-level data when available

Use Cases

| Use Case | Agent Types | Key Behaviors | |----------|------------|---------------| | Market Dynamics | Customers, Firms | Purchasing, Pricing | | Innovation Diffusion | Adopters, Influencers | Adoption, Communication | | Supply Chain | Suppliers, Distributors, Retailers | Ordering, Inventory | | Opinion Formation | Citizens, Media | Influence, Information spread |

Integration Points

Connects with System Dynamics Modeler for hybrid approaches
Feeds into War Game Orchestrator for competitive scenarios
Supports Scenario Narrative Generator for storyline creation
Integrates with Monte Carlo Engine for uncertainty propagation