Agent-Based Modeling Theory

The Concept

Agent-Based Modeling simulates individual agents that interact with their environment. Instead of aggregate statistics, you model each person’s decision.

Source: Wilensky, U. & Rand, W. — An Introduction to Agent-Based Modeling (MIT Press, 2015)

Classical ABM

Traditional agents follow rule-based logic:

IF distance < 400m AND price < budget AND cuisine_matches:
    visit_probability = base_rate * distance_decay * preference_weight
    IF random() < visit_probability: VISIT

Strengths: Fast, reproducible, can simulate millions of agents. Weakness: Rules are brittle. Real humans weigh tradeoffs, mood, weather, social context.

Three Design Principles (Wilensky & Rand, Ch. 3)

1. Heterogeneity — Agents differ in meaningful ways: income, location, preferences, habits.

2. Autonomy — Each agent decides based on its own perception. An office worker sees “289 competitors” as options; a shop owner sees saturation.

3. Interaction — Agents influence each other. Mrs. Cheung only visits if neighbors recommend. Jenny only visits if friends post on Instagram.

What Emerges

ABM reveals emergent patterns that no single formula predicts:

Network effects from word-of-mouth
Tipping points where a restaurant goes from unknown to popular
Conflicting requirements that make serving all segments impossible