Why publish research
Behavioral simulation must be transparent to be trusted. We publish methodology, benchmarks, and replication packs so operators, regulators, and academics can audit our work and extend it in their domains.
Research collaborators
Our studies are co-authored with universities, public policy labs, and enterprise research teams. Together we design experiments that answer revenue-critical questions while advancing the science of human decision modeling.
Contributors gain early access to new agent models, instrumentation guides, and private workshops that accelerate internal adoption.
What our lab delivers
01 :: Replicated studies
We recreate peer-reviewed experiments with agent simulations, documenting accuracy, variance, and failure modes.
02 :: Methodology kits
Reusable experiment templates, instrumentation checklists, and model evaluation rubrics for your internal teams.
03 :: Causal benchmark data
Anonymized, regulation-friendly datasets that let you validate models without exposing sensitive customer data.
04 :: Knowledge transfer
Workshops and office hours with the researchers who built the simulations, so you can adapt them to your roadmap.
Where researchers apply us
01 :: Pricing elasticity studies
Model willingness-to-pay shifts across segments before running costly market trials.
02 :: Narrative framing tests
Understand how language, sequencing, and channel selection influence adoption and trust.
03 :: Policy scenario planning
Simulate regulatory outcomes and equity impacts before piloting large-scale programs.
04 :: Product adoption modeling
Predict feature uptake under different incentive structures and user journeys.
05 :: Support automation research
Evaluate agent-driven support strategies with human-readable explanations and risk safeguards.
06 :: Behavioral segmentation
Discover latent motivational clusters that traditional demographic segmentation misses.
How we validate
01 :: Ground truth pairing
Every simulation is matched with observed human outcomes to quantify causal fidelity.
02 :: Confidence scoring
We publish interval estimates and variance explanations alongside point predictions.
03 :: Bias audits
Automated fairness diagnostics flag demographic drift and recommend mitigations.
04 :: Explainability layers
Agent reasoning is translated into human-readable narratives and decision trees.
05 :: Data minimization
Privacy-preserving techniques ensure sensitive variables never leave secure enclaves.
06 :: Versioned protocols
Every experiment ships with a versioned protocol so teams can reproduce and peer review the results.
Frequently
asked questions
01 :: Can we audit your research?
Yes. Replication packs include datasets, model configs, and evaluation scripts so your team can reproduce every figure.
02 :: Do you publish negative results?
Absolutely. Understanding where simulations underperform is critical for safe deployment and model improvement.
03 :: How do you handle sensitive data?
Partners contribute anonymized or synthetic datasets. Sensitive attributes stay within secure enclaves managed by the partner.
04 :: Can we co-author papers?
Yes. We frequently co-author whitepapers and academic publications with research partners and share attribution.