Arturo Rodrigues

I studied Mathematics at UCLA, where I focused on real analysis, optimization, linear algebra, and machine learning within the mathematics computing specialization. My academic training shaped how I systematically approach problem solving. Currently, I am gaining real practitioner experience within finance as I am working out how to make time-sensitive decisions under uncertainty. As such, I have been working in finance investing roles in New York City following my graduation from UCLA.

I was born and raised in the northern part of Belgium. I currently work and live in New York City.

You can reach me at arturorodrigues2000 [at] gmail [dot] com

Research interests

  • Causal inference and the causal hierarchy— Pearl's Ladder of Causation (association, intervention, counterfactuals) and the do-calculus as the formal engine for moving from correlation to genuine causal claims about decision quality
  • Transportability theory— Bareinboim's framework for determining when causal knowledge learned in one population generalizes to another, and the conditions under which findings transport across domains, industries, and cultural contexts
  • Decision calibration and forecasting— the empirical evidence that epistemic hygiene (not intelligence) drives forecasting accuracy, drawing on Tetlock's superforecasting research and calibration measurement methods
  • Structural causal models for individual decision-makers — formalizing how individuals reason and decide using structural equations and exogenous noise terms, enabling counterfactual analysis of personal decision histories
  • Pre-outcome decision capture as measurement theory — the epistemological case that valid causal inference about decision quality requires timestamped, pre-outcome data rather than retrospective self-report
  • Cognitive biases in institutional settings — how biases compound, cancel, or transform within organizations, and the feasibility of personalized bias fingerprinting from longitudinal decision data
  • Naturalistic decision-making— Klein's Recognition-Primed Decision model and how experts actually decide under time pressure and uncertainty, as opposed to normative models
  • Focus allocation and attention as capital allocation — formal models of how builders allocate limited attention across competing priorities, and whether calibration gaps between perceived and actual ROI of focus can be measured and reduced