Model Components:
1. Agents: Each agent has:
• Attributes (e.g., perception, behaviour, memory).
• Decision-making rules (e.g., follow majority, imitate successful agents).
• Perception of stimuli (e.g., based on neighbours' actions or external information).
2. Environment:
• Space or network where agents interact.
• Stimuli or signals that influence agents' perceptions.
• Rules governing interaction and communication among agents.
Simulation Steps:
1. Initialization:
• Create a population of agents with initial attributes and perceptions.
• Define the environment and initial stimuli.
2. Interaction:
• Agents perceive stimuli from their environment or neighbours.
• Agents update their attributes or behaviours based on perceived stimuli and predefined decision rules.
• Agents interact with neighbours, influencing or being influenced by their actions.
3. Iteration:
• Repeat the interaction steps for multiple time steps or iterations.
• Observe how individual actions aggregate into collective behaviours.
• Analyse emergent patterns, consensus, or divergence among agents.