Quantum Advantage Partners

Overview

In real organizations, each role (CEO, CFO, VP Sales…) sees different information and applies different decision criteria — yet every multi-agent simulation framework today gives all agents the same shared context. We want to mathematically test whether modeling information compartmentalization and role-specific decision processes produces measurably better collective outcomes than the standard shared-context approach.

Problem Statement

Multi-agent simulation frameworks (CrewAI, TinyTroupe, Concordia) model organizations by assigning role labels to agents that all share the same information. This ignores two fundamental features of real organizations:

1. information is compartmentalized — a CFO doesn’t know everything the VP Sales knows, and vice versa;
2. each role processes information differently — applying distinct criteria, thresholds, and filters when making decisions.

We propose a rule-based (no LLM, no API costs) agent-based simulation of a simplified company with 5-7 roles operating in a stochastic market environment.

Three configurations are tested against identical market scenarios:

1. Baseline: shared context, simple role labels only.
2. Decision process modeling: shared context, but each role applies role-specific decision functions (different weights, thresholds, filters).
3. Full compartmentalization: role-specific information subsets AND role-specific decision functions. Each configuration is run M times across N simulated quarters using Monte Carlo methods.

Primary metric: cumulative simulated revenue.

Secondary metrics:

• decision speed (cycles to consensus)
• decision reversal rate (coherence proxy), and information request patterns between agents.

The mathematical work involves:

• formalizing agent decision functions and information partition matrices,
• designing the stochastic market environment, specifying the experimental design (factorial or fractional factorial),
• running simulations in Python, and performing statistical hypothesis testing to compare outcome distributions across configurations A/B/C.

A sensitivity analysis identifies which compartmentalization parameters have the largest effect on outcomes, including boundary conditions where compartmentalization may hurt rather than help performance.

Expected background

• probability
• stochastic processes
• statistical inference
• Python (NumPy/SciPy).

Game theory or mechanism design is a plus but not required. The industry mentor (Mehdi Bozzo-Rey, QAP) will provide will provide the initial conceptual model of role-specific decision processes, informed by applied work in deeptech commercialization advisory.

The team will collaboratively formalize this model into mathematical specifications during week 1, then implement and test it in weeks 2-3.