Synthetic Populations

In one sentence: 10,000 cognitive agents with realistic personalities, emotions, habits, and social bonds — for testing products, campaigns, and policies before launch, at 1000$\times$ lower cost than real focus groups.

Theory sources: BM (G-profiles as personality, WM competition, cognitive biases), SM (population dynamics, social bonds, stigmergy), NM (trust hysteresis, information cascading), AGI_F (lifecycle, ontogeny)

Implementation: Same bmc-core engine. Environment = product/campaign stimuli. Oracle = engagement/conversion metric.

The Problem

Companies spend **$85B+ per year** on market research. The gold standard --- focus groups --- costs $50–100K per study, takes weeks, and samples 8–12 people. The results are biased by group dynamics (conformity, social desirability) and impossible to reproduce.

LLM-based synthetic users (GPT-4 with a persona prompt) are cheaper but fundamentally flawed: they have no internal state, no memory between sessions, no habits, no real emotional dynamics. Ask the same question twice, get a different answer.

The BMC Solution

BMC agents are cognitive decision-makers, not text generators:

How It Works

1. Calibrate population: Define G-profiles matching target demographics (age $\to$ WM capacity, income $\to$ FEAR sensitivity, brand history $\to$ $\kappa$-levels)
2. Introduce stimulus: Product concept, ad campaign, or policy enters S-layer of each agent
3. Observe cognitive processing: I-filter acceptance/rejection, emotional response (SEEKING = interest, FEAR = price anxiety, DISGUST = rejection), WM competition with existing brand loyalty ($\kappa=2$)
4. Measure outcomes: Adoption rate, segment-level reactions, churn drivers, price sensitivity curves
5. Test interventions: “What if price +15%?” — re-run with changed parameter, compare distributions

Use Cases

Product Testing

Run 10,000 agents through a product launch scenario. Segment by G-profile:

• Early adopters (SEEKING$\uparrow$, FEAR$\downarrow$): fast acceptance via SIT-gap (“novelty!”)
• Conservatives (FEAR$\uparrow$, SEEKING$\downarrow$): rejection via I-filter (high novelty penalty)
• Brand loyalists (CARE$\uparrow$, $\kappa=2$): high switching cost, emotional barrier
• Price-sensitive (FEAR$\uparrow$, loss aversion): FEAR $\times$ price delta $>$ SEEKING $\times$ value delta $\to$ rejection

Campaign Optimization

Test messaging variants against cognitive segments. BMC reveals why a message works or fails: did it trigger SEEKING (curiosity) or FEAR (urgency)? Did the I-filter reject it as incompatible with existing beliefs? Which $\kappa=2$ habits does it need to overcome?

Churn Prediction

Not statistical correlation ($P_{churn} = \sigma(\mathbf{w} \cdot \mathbf{x})$), but a mechanistic model: competitor meme enters WM, competes with brand meme ($\kappa=2$, CARE-bond). Trust hysteresis means a single bad experience can break what took months to build.

Policy Simulation

10,000 citizens with calibrated G-profiles react to a policy change. Model panic (FEAR cascades), resistance (I-filter rejection of new rules), migration (SEEKING activation when local conditions deteriorate), protest (RAGE when perceived fairness $<$ threshold).

Market

What We’re Looking For

• Market research firm interested in cognitive-grade synthetic populations (vs statistical synthetic data)
• Enterprise partner with churn/conversion data for calibration and validation
• Government / think tank for policy simulation pilot

For the cognitive model of personality that underlies agent calibration, see Personality. For the theory of population dynamics, see Swarm Memetics.