LLM-Powered Agent Simulation

The Extension

Instead of rule-based agents, we use Claude Opus as the reasoning engine:

Given:
  - Your persona (income, location, preferences, habits)
  - Real location data (competitors, distance, demographics)
  - Mathematical model outputs (Huff probability, catchment zone)
  - Company concept (what they want to open, pricing, target)

Reason as this person: Would you eat here? Why or why not?

Why Opus, Not Sonnet

Factor	Opus	Sonnet
Persona consistency	Excellent	Good
Nuanced tradeoffs	Excellent	Good
Contradictory reasoning	Handles well	Sometimes flattens
Cost per agent	~$0.05	~$0.01

For 10 agents, total cost: ~$0.50. Trivial compared to the analysis value.

How Company Input Changes Agents

This is the critical link. Each agent receives:

1. Open Data — same for all agents (restaurants, demographics, transport)
2. Model outputs — Huff probability, catchment zone
3. Company concept — your business profile, pricing, target customer

A ramen shop gets different agent reactions than a wine bar at the same address. The models calculate different β values. The agents reason about different tradeoffs.

Note

The agents don’t just say yes/no. They explain why, revealing barriers and opportunities that pure math misses. See Simulation Results.

Cost of the Full Pipeline

Component	Method	Cost
Open Data	CKAN API, FEHD XML, MTR CSV	Free
Mathematical models	Huff + Gravity + Catchment	Free
Agent simulation	10 × Claude Opus	~$0.50
Total		~$0.50

A comparable analysis from a retail consultancy: $50,000-100,000.