Collective Intelligence: Exploration vs Exploitation

Agent-based simulation studying how spatially segregated agents with limited lifespans balance exploration and exploitation for survival and reproduction.

Research Question

How do internal states (lifespan, energy) influence agents' willingness to leave safe territory in search of food or mates, and how do these decisions affect survival and reproduction?

Model

• Agents: Foxes (predators) and rabbits (prey), each with male/female variants
• Environment: Grid world with four corner nests connected by corridors
• Mechanics:
  • Lifespan decreases each tick; death occurs at zero
  • Movement speed increases as lifespan depletes (urgency-driven exploration)
  • Foxes hunt rabbits to regain lifespan
  • Sexual reproduction requires opposite-sex contact; probability scales with urgency
  • Nests are predator-free safe zones but contain no food

Key Dynamics

Agents face a trade-off: staying in nests is safe but leads to starvation and no reproduction. Leaving exposes them to predation but enables feeding and mating. Urgency emerges naturally — as lifespan drops, agents move faster and mate more readily.

Formulas (Custom)

Speed as a function of remaining lifespan

As agents approach death, movement speed increases quadratically:

$$v(t) = v_{min} + \left(1 - \frac{L_{remaining}}{L_{max}}\right)^2 \cdot (v_{max} - v_{min})$$

Lifespan recovery on feeding

When a fox eats a rabbit, it regains lifespan (capped at maximum):

$$L_{new} = \min(L_{current} + \Delta L_{food}, L_{max})$$

Urgency function

A unified urgency score combining internal state and local opportunity:

$$U = \alpha \cdot \left(1 - \frac{L_{remaining}}{L_{max}}\right) + \beta \cdot (1 - O_{local})$$

Where $O_{local}$ represents nearby opportunities (prey or mates). Urgency scales movement:

$$v = v_{min} + U \cdot (v_{max} - v_{min})$$

Reproduction probability

Probability of mating upon contact scales with urgency (desperation-driven reproduction):

$$P_{reproduce} \propto \left(1 - \frac{L_{remaining}}{L_{max}}\right)^2$$

Repository Structure

├── vi/                      # Simulation framework ("Violet")
│   ├── agent.py             # Base Agent class (extend this for custom behavior)
│   ├── simulation.py        # Simulation engine (headless + GUI modes)
│   ├── config.py            # Configuration dataclasses
│   ├── proximity.py         # Spatial chunking for neighbor detection
│   └── metrics.py           # Data collection utilities
│
├── Assignment 0/            # Flocking (Boids)
│   └── flocking.py          # Reynolds flocking with alignment/cohesion/separation
│
├── Assignment 1/            # Aggregation
│   └── aggregation.py       # Agent clustering behavior
│
├── Assignment 2/            # Predator-Prey (main project)
│   ├── baseline.py          # Fox-rabbit simulation entry point
│   ├── map_design.py        # Grid layout with nests and corridors
│   └── images/              # Agent and environment sprites
│
├── images/                  # Shared sprite assets
├── main.py                  # Alternative entry point
└── snapshots_*.csv          # Collected simulation data

Running

# With GUI
python Assignment\ 2/baseline.py

# Headless (batch experiments)
python Assignment\ 2/baseline.py --headless

Dependencies

• Python 3.10+
• pygame
• polars
• vi (included simulation framework)