Research
Evaluating how decision frameworks perform on identical history.
My research asks one question: when different decision frameworks are evaluated on identical historical information, how do they actually perform? Answering it rigorously means building the simulation and evaluation infrastructure to replay decades of history under each framework and score them the same way. This is active Honors work at Oregon State University.
Honors thesis
Building infrastructure capable of evaluating decades of market behavior under multiple decision frameworks.
The same historical information, replayed under every framework, scored on equal footing. The contribution is the methodology and the infrastructure — a controlled environment where human, quantitative, and AI-driven decision systems can be compared rigorously rather than anecdotally.
Decades of history, replayed at scale.
Each framework is evaluated against decades of historical market behavior, replayed repeatedly under identical conditions. The research workflows are actively being designed and built around Oregon State University's expanding AI computing infrastructure — including NVIDIA-backed research resources — to run large-scale simulation and evaluation workloads.
The experiment
Decision frameworks under evaluation
The same market history, replayed under four distinct decision frameworks — evaluated on equal footing.
Human decision making
Discretionary judgment as a baseline
Traditional quantitative models
Rules-based, statistical frameworks
Human + AI systems
AI augments, humans decide
AI-first systems
Model-driven decision processes
Where the rigor lives
A methodological program
The contribution is in how the comparison is designed, validated, and evaluated — built to be reproducible.
The engine
Simulation infrastructure
At the core is a simulation engine: a controlled, reproducible environment that replays historical information and lets each decision framework act on exactly the same data. It's built framework-agnostic, so a new decision system can be dropped in and evaluated under identical conditions.
Historical replay engine
Decades of data, deterministic playback
Controlled environment
Identical information for every framework
Reproducible runs
Same inputs, same evaluation, every time
Framework-agnostic harness
Drop in a new decision system and score it
Research areas
Applied mathematics & machine learning systems
The work sits at the intersection of quantitative methods, machine learning, simulation, and evaluation.
Quantitative finance
Rules-based and statistical frameworks as a rigorous comparison baseline.
Machine learning
Applied ML systems for decision modeling and evaluation.
Large language models
LLM-driven reasoning and decision workflows under structured evaluation.
Historical simulation
Replaying decades of market behavior as a controlled evaluation environment.
Human + AI decision systems
How augmentation changes outcomes versus humans or models alone.
Statistical evaluation
Methodology for comparing frameworks fairly and reproducibly.
Market behavior analysis
Characterizing regimes and dynamics that frameworks must hold up against.
Applied research
Research that ships into real software.
Many of the ideas explored in the research program are already reflected in real software through Agentic CRM, where AI recommendations remain subject to human approval and measurable outcomes. Research supports the engineering; the engineering validates the research.
Let's compare notes.
If you work on AI-assisted decision systems, simulation infrastructure, or applied ML evaluation, I'd be glad to talk.