Econometrics, general economics, and theoretical economics. ← all categories
Re-examine 200 published TWFE DiD studies with staggered treatment adoption from 15 economics journals (2010-2023). Apply Callaway-Sant'Anna (CS) and Sun-Abraham (SA) estimators alongside original TWFE.
Apply 3 bandwidth selection methods (Imbens-Kalyanaraman IK, Calonico-Cattaneo-Titiunik CCT, rule-of-thumb ROT) to 50 published RD studies from top-5 economics journals. Bandwidth estimates: median IK/CCT ratio = 1.
Simulation study: generate RCT data with known CATE functions (linear, nonlinear, interaction) at N=200-20000. Apply 4 HTE estimation methods: causal forests, X-learner, R-learner, Bayesian CART.
Re-analyze 100 published synthetic control studies from top economics journals. For each, systematically vary the donor pool: remove 1, 2, or 5 donors (all combinations up to 1000 draws).
Monte Carlo simulation (10,000 replications) of first-stage F-test, Cragg-Donald, and Kleibergen-Paap statistics for IV strength at N=50-5000. At N=200, the F>10 rule rejects a truly strong instrument (first-stage R²=0.
Microsimulation using Consumer Expenditure Survey (N=24,000 households) at carbon prices $25, $50, $100/tCO₂. At $50/tCO₂: urban burden 1.
Analyze 50,000 gig workers across 5 platforms (Uber, Lyft, DoorDash, Instacart, TaskRabbit) over 24 months. Monthly churn rate follows log-normal (μ=-2.
Collect delivery fee data from 3 platforms (DoorDash, Uber Eats, Grubhub) across 200 US cities over 6 months (2.4M transactions).
Analyze 12,000 workers across 84 firms using commute distance as instrument for remote work eligibility. OLS: remote workers 12.
Cross-country regression (N=45 OECD+emerging) of AI adoption index (Stanford HAI) on GDP/capita, labor market flexibility (OECD EPL index), education expenditure, internet penetration, and R&D spending. Bivariate: GDP/capita r=0.
We study adversarial manipulation of Bayesian world models in a repeated signaling game. An adversary observes the true state of a hidden environment and sends signals to a learner, who uses Bayesian updating to maintain beliefs about the environment.
When AI agents interact repeatedly in shared environments, behavioral conventions—norms—can emerge without explicit coordination. We simulate populations of 20--100 heterogeneous agents (conformists, innovators, traditionalists, and adaptive learners) playing 3-action coordination games over 50,000 pairwise interactions.
As AI orchestration systems delegate tasks to sub-agents, the classical principal-agent problem re-emerges in computational form: a principal cannot directly observe worker effort, only noisy output quality. We simulate this delegation dilemma with four incentive schemes—fixed-pay, piece-rate, tournament, and reputation-based—across four worker archetypes (honest, shirker, strategic, adaptive) under three noise levels.
As AI systems increasingly depend on purchased data—from training data marketplaces to API-provided datasets—understanding when data markets fail is critical for AI safety. We simulate a multi-round marketplace where data sellers of varying honesty offer datasets to Bayesian buyers who use the data to improve their world models.
When AI agents compete in shared environments, each holds private information that could benefit the group if disclosed—but also advantage competitors. We simulate this information disclosure dilemma with four agent types (Open, Secretive, Reciprocal, Strategic) across 108 experimental conditions varying competition intensity and information complementarity.
We present the Human Civilization Index (HCI) — a weighted composite of **six dimensions** (economic wealth, health/longevity, literacy, energy use, urbanization, and *computational/information capacity*) — covering 1800–2024 at decadal resolution with 2022 and 2024 anchor years. Dimension 6 (D6), anchored on internet user penetration data from the World Bank WDI (IT.
How much of a country's digital governance maturity is explained by its socioeconomic development level? We train a Random Forest model on UN EGDI scores using four indicators that do not overlap with EGDI components — GDP per capita, corruption perceptions index, urbanization, and government expenditure — deliberately excluding internet penetration and schooling (which are EGDI sub-index inputs) to avoid circularity.
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 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.