Custom modeling and simulation for decisions that off-the-shelf analytics can’t answer. Every recommendation comes with the reasoning and the uncertainty made explicit, so you can stand behind the decision it supports.
ron@sw.gyStochastic models for decision-making under uncertainty. Hidden Markov models with EM-based parameter estimation, parametric bootstrap for inference, system dynamics with proper calibration, and OpenMPI-parallel policy search. Built for the questions simple analytics can’t answer.
Quantitative modeling paired with experimental instrument design and qualitative methods. We design measurement tools and fit dynamic models to the behavioral data they produce. Every conclusion comes with its uncertainty quantified.
Large-scale data analysis and infrastructure forecasting at 5B+ document scale. ETL pipelines and the cloud infrastructure that makes everything else possible.
Retrieval-augmented generation, graph-database architectures, and applied machine learning. We build systems that run in production.
Each project below started as a specific question. The links go to annotated source and, where written up, the papers, so you can check the whys and the hows for yourself.
What’s the right order-up-to policy when demand is uncertain and today’s decision constrains tomorrow’s? A compact C implementation searches the policy space for sequential inventory decisions under uncertainty, parallelized with OpenMPI in a leader/worker scheme and built to run unattended on OpenBSD or a Slurm cluster. After Powell’s Reinforcement Learning and Stochastic Optimization.
How do you keep traffic flowing to the proxy pools that are healthy right now, without a human watching dashboards and reacting to every incident? clips-proxy-gov is a closed-loop traffic governor whose decision logic is a CLIPS rule base. Each cycle it reads live health observations and adjusts every pool’s routing weight, pulling weight off a degrading pool down to a configured floor and feeding it back gradually once the pool recovers. A service-wide safety suspends the cuts when an entire service is degraded, so a fully-degraded service is never gutted. It runs on OpenBSD against a simulation-and-replay harness, and its design doc draws a hard line between what the rules implement today and what is still intended.
Which items maximize value under a hard capacity constraint? This is the 0/1 knapsack, the canonical NP-hard resource-allocation problem. A C sandbox for combinatorial optimization with a reproducible data generator and a versioned binary format on a CSV interchange path; greedy selection today, with heap- and DP-based solvers tracked next. Hardened with pledge(2)/unveil(2) on a portable BSD build.
When does a household actually leave once a warning goes out, and how much can you trust the recovery curve? An input-output hidden Markov model simulates household state trajectories under different warning timelines and fits its parameters via EM with multiple restarts. A parametric bootstrap puts a confidence interval around the recovery estimate.
How much blast impulse actually reaches a soldier’s torso, and does the armor configuration change the answer? A Monte-Carlo ray-traced overpressure simulator casts reflective rays through MICH helmet and SAPI plate meshes and integrates a Friedlander pulse at each ray hit. It outputs paired-design CSVs ready for repeated-measures ANOVA in R, so the comparison across four armor configurations holds up as a statistical result you can defend.
How do you measure attention control cleanly without dragging in a whole psychology framework? A lightweight X11 suite in C implementing the “squared” Stroop paradigm of Burgoyne et al. (2023). It administers timed cognitive tasks and logs every trial response in an analysis-ready format. A reproducible measurement instrument you can audit end to end.
A multi-configuration study of genetic-algorithm tuning for a scripted CogsGuard policy, covering roughly 60,000 candidate configurations across seven GA runs and six parameter schemas. It reports reliable fitness gains in five of seven runs and shows how team share and teammate strength shape per-cog reward. A set of case studies pulls out the interpretable strategies the GA converged on.
A calibrated system-dynamics model of the Cogs vs. Clips environment that predicts match reward from team composition and two tunable policy parameters. Its four coupled subsystems (resource economy, territory, agent loss, and scheduled adversary events) are fit against roughly 13,000 match outcomes. Aligner cycle time emerges as the dominant lever, and scout-inclusive teams carry a consistent reward penalty of about 1.3 units.
A plain-Python reimplementation of the paper’s AnyLogic model using NumPy and matplotlib. It contains the full simulator in a single function, every calibrated parameter from the paper’s table, reproductions of the match-trajectory and sensitivity figures, and a sandbox for swapping in new compositions and parameter sweeps.
Materials coming soon.
We work with organizations that face hard problems of risk and uncertainty and need an answer they can defend.
Ron Dahlgren is a CISSP and CISA with over 20 years of engineering leadership experience. He is a former Army paratrooper with the 504th Parachute Infantry Regiment.
Ron is currently pursuing a PhD in Modeling & Simulation at the University of Central Florida. He founded SWGY to bring senior-level engineering to clients who need it without the overhead of a large firm.
I take on a limited number of engagements at a time, which keeps the work senior and hands-on. Two ways to start:
A defined problem and a fixed price. We scope the question together and agree up front on what a useful answer looks like. You get a working model and a writeup clear enough to put in front of decision-makers.
Hands-on sessions for teams that want to build the capability in-house. We work through a problem your team actually owns, so your analysts leave able to rerun the methods on their own.
Every engagement starts with a short scoping call about the decision you’re trying to make and whether modeling is even the right tool for it. Currently taking on a limited number of new projects.