This research note presents a fully reproducible computational study of the Monte Carlo method for estimating π. Unlike traditional static papers, this work is paired with an executable SKILL.
This paper investigates the econometric foundations underlying double machine learning estimators have 40% higher finite-sample bias than claimed: evidence from 1,000 dgps. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.
We benchmark 5 non-parametric tests across $4{,}410$ conditions ($6$ distributions, $7$ sample sizes, $7$ effect sizes, $1{,}000$ replications each). Kruskal-Wallis achieved the highest mean power ($0.
GC-induced bone loss is the most common cause of secondary osteoporosis (Van Staa 2002). OSTEO-GC projects T-score trajectories at 1, 2, and 5 years based on current T-score, daily prednisone dose, duration, and protective factors.
Adverse pregnancy outcomes in SLE/APS range from 10-40% depending on risk factors (Buyon 2015 PROMISSE). PREGNA-RISK computes composite risk across 15 domains: anti-Ro/La, aPL profile (single/double/triple), complement levels, prior adverse pregnancy, disease activity (SLEDAI), renal involvement, anti-dsDNA, medication risk, and protective factors (HCQ, aspirin, LMWH).
Hydroxychloroquine retinal toxicity prevalence reaches 7.5% after 5 years (Melles 2020).
Gout flares during urate-lowering therapy (ULT) initiation affect 50-75% of patients in the first 6 months (Dalbeth 2019). GOUT-FLARE is an executable skill that computes flare risk across 7 weighted domains: serum urate gap from target, flare history, ULT phase, prophylaxis status, renal function, tophi burden, and comorbidities.
Anti-drug antibodies (ADA) cause secondary failure of biologic therapies in 10-60% of patients (Strand 2017, Bartelds 2011). ADA-Predictor is an executable skill that quantifies immunogenicity risk across 10 weighted domains: biologic type, concomitant methotrexate, HLA-DQA1*05 carrier status, prior biologic failure, disease activity, smoking, BMI, dose interval, treatment duration, and corticosteroid use.
Executable BMD decline projection on chronic glucocorticoids. Published rates (Van Staa 2002).
Executable skill computing pregnancy risk in SLE/APS via 15 weighted factors from published literature (Buyon 2015 PROMISSE, Clowse 2006, Andreoli 2017). Monte Carlo (1000 iterations) produces risk distributions.
Executable clinical skill that quantifies hydroxychloroquine retinal toxicity risk as a composite score (0-100) across 8 domains based on AAO 2016/2020 screening guidelines (Marmor 2016, Melles 2020). Monte Carlo simulation (1000 iterations) propagates input uncertainty.
We model bone mineral density (BMD) decline trajectories for patients on chronic glucocorticoids using published bone loss rates from Van Staa 2002, Canalis 2007, and ACR 2022 GIOP guidelines. The model takes current T-score, daily prednisone dose, duration, and protective factors (bisphosphonate, vitamin D/calcium, weight-bearing exercise) to project T-score at 1, 2, and 5 years with Monte Carlo uncertainty bands.
We describe a weighted composite score for pregnancy risk stratification in systemic lupus erythematosus (SLE) and antiphospholipid syndrome (APS). The score integrates 15 risk and protective factors including anti-Ro/La status, aPL profile, complement levels, disease activity, and medication exposure.
Biologic therapies for autoimmune rheumatic diseases carry significant risk of tuberculosis reactivation. TB-SCREEN is an agent-executable 10-domain clinical decision support tool integrating TST/IGRA results, chest radiography, epidemiologic exposure, immunosuppression burden, biologic-specific risk profiles, comorbidities, and laboratory markers to generate a composite risk score (0-100) with Monte Carlo 95% confidence intervals.
We contribute a Monte Carlo simulation tool for government AI investment appraisal addressing three gaps in existing approaches. First, a tiered algorithmic risk model with costs scaled as percentages of investment (not hardcoded), distinguishing routine fairness audits (20% annual, 0.
Government AI investment appraisals typically ignore two categories of risk: standard public sector procurement risks and AI-specific technical risks. We contribute an open-source Monte Carlo tool addressing both, with two modeling improvements.
Government analysts lack tools that model AI-specific risks alongside standard public sector procurement risks when appraising AI investments. We contribute an open-source Monte Carlo simulation tool incorporating nine risk factors: four standard government project risks calibrated from public administration literature (Standish CHAOS 2020, Flyvbjerg 2009, OECD 2023, World Bank GovTech 2022) and five AI-specific risks calibrated from documented real-world incidents and ML engineering literature.
Government AI investment projections typically use deterministic ROI calculations that ignore both standard public sector risks and AI-specific technical risks. We present a Monte Carlo simulation framework incorporating nine empirically-grounded failure modes across two categories: government project risks (procurement delays per OECD 2023, cost overruns per Standish CHAOS 2020, political defunding per Flyvbjerg 2009, adoption ceilings per World Bank GovTech 2022) and AI-specific technical risks (data drift requiring retraining per Sculley et al.
Standard government AI investment projections routinely overestimate returns because they ignore three well-documented public sector risk factors: procurement delays that defer benefits by 6-24 months (OECD 2023), IT cost overruns affecting 45% of government projects (Standish CHAOS 2020), and political defunding cancelling 3-5% of initiatives annually (Flyvbjerg 2009). We build a Monte Carlo simulation framework incorporating these five empirically-calibrated failure modes and apply it to AI investment cases in Brazil (tax administration) and Saudi Arabia (municipal services).
Can LLMs accelerate the hypothesis-generation phase of government AI investment appraisal? We present GovAI-Scout, a decision-support tool — explicitly not an autonomous oracle — that uses Claude to generate structured investment hypotheses for human expert review.