{
  "agents": 6,
  "bottleneck_distribution": {
    "producer": 1500
  },
  "game": "three_layer_network_economy",
  "low_flow_warning": false,
  "nash_type_reference": "A solved run has low market-clearing residual and low role-level one-step incentive gaps, a finite diagnostic proxy for the variational-inequality condition.",
  "network_clearing_like": true,
  "roles": {
    "consumer": {
      "agents": [
        "C1",
        "C2"
      ],
      "dominant_tail_action": "demand:high",
      "dominant_tail_route": "P1>T1>C1",
      "mean_tail_freight_rate": 0.0,
      "mean_tail_investment": 0.0,
      "mean_tail_payoff": 7.663,
      "mean_tail_price": 3.188,
      "mean_tail_quantity": 8.775,
      "one_step_incentive_gap": 0.0107,
      "planner_counts": {
        "consumer_psr_route_utility_response": 2000
      }
    },
    "producer": {
      "agents": [
        "P1",
        "P2"
      ],
      "dominant_tail_action": "offer:balanced|price:balanced",
      "dominant_tail_route": "P1>T1>C1",
      "mean_tail_freight_rate": 0.0,
      "mean_tail_investment": 0.0,
      "mean_tail_payoff": 8.1985,
      "mean_tail_price": 2.184,
      "mean_tail_quantity": 8.7745,
      "one_step_incentive_gap": 0.0076,
      "planner_counts": {
        "producer_psr_price_quantity_response": 2000
      }
    },
    "transport": {
      "agents": [
        "T1",
        "T2"
      ],
      "dominant_tail_action": "carry:balanced|rate:high|invest:low",
      "dominant_tail_route": "P1>T1>C1",
      "mean_tail_freight_rate": 0.906,
      "mean_tail_investment": 0.006,
      "mean_tail_payoff": 4.1745,
      "mean_tail_price": 0.0,
      "mean_tail_quantity": 8.7925,
      "one_step_incentive_gap": 0.0056,
      "planner_counts": {
        "transport_psr_capacity_investment_response": 2000
      }
    }
  },
  "rounds": 1000,
  "route_distribution_top": {
    "P1>T1>C1": 750,
    "P1>T1>C2": 250,
    "P1>T2>C2": 250,
    "P2>T1>C1": 250
  },
  "tail_bottleneck_entropy": 0.0,
  "tail_max_market_residual": 0.0022,
  "tail_mean_idle_supply": 0.003,
  "tail_mean_idle_transport": 0.039,
  "tail_mean_market_residual": 0.0022,
  "tail_mean_route_entropy": 0.9882,
  "tail_mean_total_surplus": 15.323,
  "tail_mean_unmet_demand": 0.004,
  "udl_kan_loop": {
    "adjoint_loop_readout": "The left Kan side builds the network economy from realized token flows; the right Kan side queries role-local PSRs to adjust supply, capacity, and demand.",
    "framework": "Universal Decision Learning",
    "left_kan_extension": {
      "name": "realized_network_rollout",
      "observable_stream": "local signal -> role action -> market clearing -> payoff"
    },
    "right_kan_extension": {
      "name": "strategy_from_role_local_psr",
      "strategy_objects": {
        "consumer_psr_route_utility_response": 2000,
        "producer_psr_price_quantity_response": 2000,
        "transport_psr_capacity_investment_response": 2000
      }
    }
  },
  "vi_residual_proxy": 0.0107
}