Behavioral Adoption Simulation • Synthetic Market Modeling ← back to home
Most validation tools ask people what they think. This engine simulates what they do — by modelling stakeholder decisions as a behavioral physics problem across 10,000 heterogeneous decision-makers over 12 market periods.
Prospect Theory (Kahneman & Tversky, 1979) — Friction variables are weighted asymmetrically. A blocker resists harder than an equivalent enthusiast pushes.
Bass Diffusion Model (Bass, 1969) — Separates innovator from imitator adoption across 12 sequential market periods.
Watts-Strogatz Network — Local adoption cascades through agent clusters, producing tipping points rather than smooth curves.
Monte-Carlo Simulation — Gaussian variation across 10,000 agents captures the resistant tail that single-point estimates miss.
This engine does not forecast revenue or market size. It estimates whether an idea can overcome adoption friction — which is an earlier question than revenue.
The engine models adoption decisions as a behavioral physics problem. Each stakeholder evaluates a concept by weighing perceived value against structural friction — grounded in four research traditions with empirical foundations in real market behavior.
Each of the 10,000 synthetic agents computes a utility score separating value drivers from friction drivers. This asymmetric weighting reflects loss aversion documented in Prospect Theory — friction costs more than equivalent utility gains.
| Driver | Variable | What it measures |
|---|---|---|
| VALUE | U | Functional utility delivered by the product |
| VALUE | E | Emotional motivation and psychological reward |
| VALUE | S | Social signaling and network effects |
| VALUE | T | Market timing and trend alignment |
| VALUE | M | Infrastructure and ecosystem readiness |
| FRICTION | C | Cognitive cost — learning curve and switching effort |
| FRICTION | R | Perceived financial, regulatory, or technical risk |
| FRICTION | G | Regulatory friction and compliance burden |
| Model | Role in the simulation |
|---|---|
| Prospect Theory Kahneman & Tversky, 1979 | Friction variables weighted asymmetrically — a blocker's resistance costs more adoption than equivalent enthusiasm generates |
| Bass Diffusion Model Bass, 1969 | Separates innovator adoption from imitator adoption across 12 sequential market periods |
| Watts-Strogatz Network Small-world topology | Local adoption cascades through agent neighborhoods — produces tipping points rather than smooth growth curves |
| Monte-Carlo Simulation Gaussian variation σ=1 | 10,000 agents with heterogeneous behavioral vectors capture the resistant tail that single-point estimates miss |
The engine does not forecast revenue or market size. It estimates the behavioral probability that an idea overcomes adoption friction — which is a different, and earlier, question than revenue.
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