For my capstone I am making a cybernetic control system using input/output with the use of leontief inverse and using Bayesian with algedonic alerts to refine the model. I can summarize its work as cybernetic control architecture based off the cybersyn project made in Chile using Stafford Beers cybernetic planning to calculate output within a sector.
What I've built (working prototype):
• Leontief input-output model with 3x3 technical matrix, solved in real-time
• Bayesian Kalman filter achieving >90% confidence within 20 observations/ticks
• Algedonic alert escalation (Factory → Branch → Sector )
• Petkovich capacity constraint scenarios (Cases 1-6)
• Opsroom dashboard with live matrix visualization and Chart.js telemetry
Where I'm stuck:
1. My Kalman filter's process noise parameter is arbitrary what's a principled way to tune it for economic time series that have structural breaks? (1 tick a second)
2. Petkovich Case 6 (total destruction, limited imports) requires linear programming is an iterative approximation acceptable for a capstone, or should I implement the full simplex solution?
3. I'm extending my Bayesian updating from just tracking factory output to also updating the A-matrix coefficients as new production data arrives. Is applying a Kalman filter to each matrix cell individually a reasonable approach, or is there a simpler method I should consider?
My goal is to make a modern inspired version of project cybersyn.
