Expert Modeling & AI-Augmented Analytics
Mathematical Modeling • AI-Accelerated Simulation • Policy Architecture
About Our Approach
CausalPaths Analytics provides rigorous problem framing and decision support for complex policy challenges characterized by multi-level and deep uncertainties—including both parametric variation and structural ambiguity about behavioral mechanisms, feedback dynamics, and system interactions. We specialize in problems where conventional modeling fails: systems with adaptive behavior, feedback loops, tipping points, and policies that alter the very dynamics they aim to control.
We integrate advanced computational methods—agent-based modeling, network science, machine learning, and exploratory analysis frameworks—to support evidence-based policy in health, climate, security, and economic domains. Our focus is understanding what is structurally possible: identifying regimes, mapping critical transitions, and quantifying relative performance across plausible futures.
We build models from the ground up when problems require bespoke solutions, and we leverage AI to accelerate development when appropriate—always applying rigorous domain expertise to validate outputs and ensure structural soundness. Whether building from scratch or augmenting with AI, our focus remains on delivering trustworthy insights for high-stakes policy decisions.
While we generate forecasts as part of our analysis, our emphasis is on enabling well-informed choices through structured comparison of policy alternatives across uncertainties. We trust relative differences between strategies more than absolute predictions—policy margins matter more than point estimates.
Led by Dr. Raffaele Vardavas (PhD Physics, Imperial College London), we bring rigorous mathematical and computational expertise to real-world decision challenges.
What Makes Us Different
- Expert modeling with AI acceleration: We build custom models from scratch when problems demand it, and leverage AI to accelerate development when appropriate. Whether hand-crafted or AI-augmented, all work is validated through 20+ years of domain expertise in behavioral science, epidemiology, and policy analysis.
- Problem framing over optimization: Models that aim to evaluate the performance of policy interventions are more vulnerable to a failure in specifying the right mechanisms in the model structure than to failure in specifying wrong parameter values. We emphasize a deep understanding of system dynamics and structural relationships before proposing interventions.
- Relative robustness: In complex systems, absolute forecasts are often fragile. We prioritize comparative performance across strategies, trusting policy margins and the rank-ordering of outcomes over point predictions.
- Criticality and regime shifts: We identify tipping points, phase transitions, and transformative societal changes across plausible futures. By focusing on structural uncertainty, we help clients navigate environments that move between fundamentally different states, rather than just varying around an equilibrium.
- Behavioral realism: We model how people and societies actually adapt to policy and the environments they help shape—incorporating learning, memory, attention, and misperception. Rather than relying on static statistical estimates, we build mechanistic models of human adaptation under changing conditions.
- Cognitive architectures: Instead of assuming "rational" behavior, we use agents grounded in established cognitive science. These models simulate the actual constraints of human thought—how people filter information, forget details, and develop habits—allowing us to map the possibilities of how they will respond to policy pressure.
Core Research Methods
1. Computational Modeling & Simulation
Agent-Based Models (ABM) — AI-Enhanced Behavioral Realism
- Traditional rule-based agents OR AI-powered behavioral personas
- Survey-calibrated psychographic profiles using LLM techniques
- Coupled behavioral and epidemiological dynamics
- Emergent collective outcomes with unprecedented realism
Compartmental Models
- Coupled differential equation systems
- Stochastic extensions and mean-field approximations
- Numerical stability and sensitivity analysis
- Inference of governing equations from simulation outputs
Policy Microsimulation
- Individual life-course dynamics
- Health, labor, and income processes
- High-resolution policy counterfactuals
Network Science
- City-scale contact network synthesis
- Contagion and percolation analysis
- System vulnerability and resilience mapping
2. Machine Learning & Data Analytics
Scenario Discovery
- Rule-based identification of vulnerable futures
- Classification for regime differentiation
- Interpretability-focused analytics
Generative Modeling
- Synthetic populations and networks
- Interpretable, reduced-form dynamical structure extraction
- Simulation-based optimization (e.g., stochastic annealing)
AI-Augmented Model Development
- LLM-powered behavioral personas for agent-based models
- AI-accelerated code generation with expert validation
- Rapid prototyping and sensitivity exploration
Longitudinal & Survey Analysis
- Mixed-effects and multilevel modeling
- Behavioral stability and social influence
- Empirical calibration of agent parameters
3. Decision Analysis Under Uncertainty
Decision Analysis Under Deep Uncertainty
- Exploratory simulation across thousands of futures
- Structural and path-dependent uncertainty
- Adaptive policy pathway design
Multi-Objective Trade-Off Analysis
- Pareto frontier exploration
- Health–economic trade-offs
- Competing stakeholder objectives
Economic Evaluation
- Cost–benefit and distributional analysis
- Long-term horizons and discounting
- Monetization of health and social outcomes
Ready to Tackle Complex Decisions?
We help decision-makers navigate uncertainty, trade-offs, and adaptive behavior using rigorous, transparent modeling—combining expert-built solutions with AI acceleration.
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