A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is…

Question

Options

• AThe data scientist should obtain a correlated equilibrium policy by formulating this problem
• BThe data scientist should obtain the optimal equilibrium policy by formulating this problem
• CRather than finding an equilibrium policy, the data scientist should obtain accurate
• DRather than finding an equilibrium policy, the data scientist should obtain accurate

• A
  80% (12)
• B
  7% (1)
• D
  13% (2)

Explanation

This is a multi-agent reinforcement learning (MARL) problem. Each traffic light is an agent, and their behaviors are correlated - meaning agents can benefit from coordinating decisions based on shared signals. A correlated equilibrium (Option A) is the appropriate game-theoretic solution concept here: it allows agents to act on a shared correlation device (e.g., a central signal), achieving better joint outcomes than independently computed Nash equilibria. A standard optimal Nash equilibrium (Option B) assumes agents act independently, ignoring the stated correlation - it would underperform. Options C and D suggest building accurate forecasting models rather than an equilibrium policy, which misses the coordination and congestion-reduction goal: forecasting traffic does not by itself produce optimal control signals for the lights.

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