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Incentive Optimization and Budget Management

Incentive Optimization and Budget Management

1
posts
2026

Uber has developed a sophisticated platform called Tarot (Targeting Orchestrator) to address the complex challenge of optimizing incentive allocation at scale. This system tackles the Multiple Knapsack Problem (MKP) by integrating uplift models with budget constraints, moving beyond heuristic-based approaches. Tarot enables dynamic ROI calculation by considering multi-objective optimization and strategic weighting, and employs a Budget Pacer to manage spend velocity and ensure quarterly budget adherence. The system has evolved to use a multi-lever optimizer with Google OR-Tools CP-SAT solver and incorporates a two-layer ROI calculation (prediction and valuation) to handle cross-vertical cannibalization and strategic weighting. The Budget Pacer acts as a control loop to reconcile configured budgets with observed and predicted spend, adjusting future allocations to maximize utilization and adhere to quarterly limits.

2026

Beyond Prediction: Solving the Multiple Knapsack Problem at Scale: How Uber Optimizes Incentives

5/14/2026

This post details the evolution of Uber's incentive optimization platform, Tarot, by framing it as a large-scale Multiple Knapsack Problem (MKP). It introduces the concept of a multi-lever optimizer, utilizing Google OR-Tools CP-SAT solver, and a two-layer ROI calculation that incorporates strategic weighting (w) and predicted metric uplifts (x) to handle complex trade-offs like cross-vertical cannibalization and long-term strategic bets. The Budget Pacer's role as a control loop for managing spend velocity against deterministic financial budgets is further elaborated.