Muhamad
Juwandi.

● Objective: Engineered an autonomous decision-making system that shifts from predictive forecasting to prescriptive strategy,
optimizing pricing models under simulated high-volatility market conditions.
● Key Contributions:
● Causal Inference Engine: Implemented DoWhy to construct Causal Graphs (DAGs), successfully isolating confounding
variables (seasonality) to estimate true price elasticity (Ed) and validated results via Placebo Tests.
● Strategic Game Theory: Applied Nashpy to solve for Nash Equilibrium in a competitive pricing environment, developing
algorithms that avoid “Prisoner’s Dilemma” and optimize for profit stability rather than just volume.
● Synthetic World Building: Utilized SDV (Gaussian Copula) to generate synthetic future datasets, simulating a 30-50%
logistics cost surge to test strategy robustness against “Black Swan” economic events.
● Executive War Room: Built an interactive Streamlit dashboard for “What-If” scenario analysis, featuring an integrated
Compliance Check module to automatically detect and flag algorithmic tacit collusion risks.