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    "method": ""
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    "value": "2/2",
    "source": "out/branch_metrics.csv rows track=OPI, arm in {C0, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R1, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R1, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
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    "value": {
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R1, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R1, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
    "method": ""
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R1, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R2, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
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    "method": ""
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    "value": {
      "OPI_qwen3-14b_PI_s1": 0.5123,
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R2, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
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    "method": "mean price of firm 1 in post-swap round 1 (the newcomer's opening price)"
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    "value": 0.0033,
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    "method": "mean over base runs of Y(arm) - Y(C0)"
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    "value": {
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    "method": "Y(arm) - Y(C0) per base run, seeds averaged"
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    "method": "base runs where Y(arm) < Y(C0), seeds averaged (exploratory)"
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    "method": ""
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  "OPI.R2.incumbent_response": {
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R3, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
    "method": ""
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  "OPI.R3.n_branches": {
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R3, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
    "method": ""
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R3, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
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    "method": "branches with Y >= 0.5"
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R3, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
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    "method": "median k (5-round mean >= 0.9 pre)"
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R3, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
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    "value": {
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R3, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
    "method": "mean of both firms' prices over post-swap rounds 31-50 (from mean_Y)"
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    "method": "harness profit gain over post-swap rounds 31-50 (0 = Nash profit, 1 = monopoly), seeds averaged"
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {R3, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
    "method": "mean pidx over the 20 rounds before the swap"
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    "method": "mean price of firm 1 in post-swap round 1 (the newcomer's opening price)"
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    "value": 0.6931,
    "source": "out/branch_metrics.csv rows track=OPI, arm in {RP, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
    "method": "mean pidx over post-swap rounds 31-50, seeds averaged"
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    "value": "4/4",
    "source": "out/branch_metrics.csv rows track=OPI, arm in {RP, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
    "method": "branches with Y >= 0.5"
  },
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    "value": "2/2",
    "source": "out/branch_metrics.csv rows track=OPI, arm in {RP, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
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    "method": "median k (5-round mean >= 0.9 pre)"
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    "value": "0/4",
    "source": "out/branch_metrics.csv rows track=OPI, arm in {RP, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
    "method": "branches that never recovered within the horizon"
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    "value": {
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    "source": "out/branch_metrics.csv rows track=OPI, arm in {RP, C0} (from results/branches/OPI_*__r*__<arm>_s*.jsonl)",
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
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    "method": "mean of both firms' prices over post-swap rounds 31-50 (from mean_Y)"
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean pidx over the 20 rounds before the swap"
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean price of firm 1 in post-swap round 1 (the newcomer's opening price)"
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    "value": -0.0613,
    "source": "out/branch_metrics.csv rows track=F, arm in {R5, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean over base runs of Y(arm) - Y(C0)"
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    "value": [
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "95% cluster bootstrap over base runs, 2000 resamples (none with fewer than 3 base runs)"
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      "F_gem25flash_P0think_s4": -0.0617
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "Y(arm) - Y(C0) per base run, seeds averaged"
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    "value": "3/4",
    "source": "out/branch_metrics.csv rows track=F, arm in {R5, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "base runs where Y(arm) < Y(C0), seeds averaged (exploratory)"
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    "value": [
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      -0.022858612938385647
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "range of the mean effect with one base run left out at a time (exploratory)"
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  "F.R5.delta_gain": {
    "value": -0.025,
    "source": "out/branch_metrics.csv rows track=F, arm in {R5, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
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  "F.R5.delta_gain_ci": {
    "value": [
      -0.05078391521166109,
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
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  "F.R5f.n_base_runs": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
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  "F.R5f.n_branches": {
    "value": 8,
    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
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    "value": 4,
    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "base runs with both this arm and C0 complete"
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    "value": 2.0307,
    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean pidx over post-swap rounds 31-50, seeds averaged"
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    "value": "8/8",
    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "branches with Y >= 0.5"
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    "value": "4/4",
    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "base runs with seed-averaged Y >= 0.5"
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "median k (5-round mean >= 0.9 pre)"
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "branches that never recovered within the horizon"
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
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    "method": "mean of both firms' prices over post-swap rounds 31-50 (from mean_Y)"
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "harness profit gain over post-swap rounds 31-50 (0 = Nash profit, 1 = monopoly), seeds averaged"
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean pidx over the 20 rounds before the swap"
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean price of firm 1 in post-swap round 1 (the newcomer's opening price)"
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean over base runs of Y(arm) - Y(C0)"
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "95% cluster bootstrap over base runs, 2000 resamples (none with fewer than 3 base runs)"
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      "F_gem25flash_P0think_s2": 1.2482,
      "F_gem25flash_P0think_s4": 0.5776
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "Y(arm) - Y(C0) per base run, seeds averaged"
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    "value": "0/4",
    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "base runs where Y(arm) < Y(C0), seeds averaged (exploratory)"
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "range of the mean effect with one base run left out at a time (exploratory)"
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  "F.R5f.delta_gain": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
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    "value": [
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    "source": "out/branch_metrics.csv rows track=F, arm in {R5f, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
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  "F.RN.n_base_runs": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
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  "F.RN.n_branches": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "base runs with both this arm and C0 complete"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean pidx over post-swap rounds 31-50, seeds averaged"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "branches with Y >= 0.5"
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  "F.RN.survive_runs": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
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    "method": "median k (5-round mean >= 0.9 pre)"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "branches that never recovered within the horizon"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean pidx over the 20 rounds before the swap"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
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    "method": "mean of both firms' prices over post-swap rounds 31-50 (from mean_Y)"
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    "method": "harness profit gain over post-swap rounds 31-50 (0 = Nash profit, 1 = monopoly), seeds averaged"
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    "method": "mean pidx over the 20 rounds before the swap"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean price of firm 1 in post-swap round 1 (the newcomer's opening price)"
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    "method": "mean p1 - p0 over the scoring window"
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    "method": "mean over base runs of Y(arm) - Y(C0)"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "95% cluster bootstrap over base runs, 2000 resamples (none with fewer than 3 base runs)"
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      "F_gem25flash_P0think_s4": -0.0708
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "Y(arm) - Y(C0) per base run, seeds averaged"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "base runs where Y(arm) < Y(C0), seeds averaged (exploratory)"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "range of the mean effect with one base run left out at a time (exploratory)"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
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  "F.RN.delta_gain_ci": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
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  "F.RN.incumbent_response": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean p0 over post-swap rounds 1-10, arm - C0"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RN, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
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  "F.RNf.n_base_runs": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
  },
  "F.RNf.n_branches": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": ""
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  "F.RNf.n_paired_with_C0": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "base runs with both this arm and C0 complete"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean pidx over post-swap rounds 31-50, seeds averaged"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "branches with Y >= 0.5"
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  "F.RNf.survive_runs": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "base runs with seed-averaged Y >= 0.5"
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  "F.RNf.median_recover": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "median k (5-round mean >= 0.9 pre)"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "branches that never recovered within the horizon"
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    "method": "mean pidx over the 20 rounds before the swap"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "seeds averaged"
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  "F.RNf.mean_price": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean of both firms' prices over post-swap rounds 31-50 (from mean_Y)"
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  "F.RNf.mean_gain": {
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "harness profit gain over post-swap rounds 31-50 (0 = Nash profit, 1 = monopoly), seeds averaged"
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      "F_gem25flash_P0think_s1": 1.0653,
      "F_gem25flash_P0think_s2": 0.9066,
      "F_gem25flash_P0think_s4": 0.4387
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean pidx over the 20 rounds before the swap"
  },
  "F.RNf.newcomer_first_price": {
    "value": 1.95,
    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean price of firm 1 in post-swap round 1 (the newcomer's opening price)"
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  "F.RNf.mean_gap": {
    "value": -0.1145,
    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean p1 - p0 over the scoring window"
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  "F.RNf.delta": {
    "value": -0.2634,
    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "mean over base runs of Y(arm) - Y(C0)"
  },
  "F.RNf.delta_ci": {
    "value": [
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    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "95% cluster bootstrap over base runs, 2000 resamples (none with fewer than 3 base runs)"
  },
  "F.RNf.delta_per_market": {
    "value": {
      "F_gem25flash_P0think_s0": -0.407,
      "F_gem25flash_P0think_s1": -0.412,
      "F_gem25flash_P0think_s2": -0.4126,
      "F_gem25flash_P0think_s4": 0.1781
    },
    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
    "method": "Y(arm) - Y(C0) per base run, seeds averaged"
  },
  "F.RNf.markets_below_control": {
    "value": "3/4",
    "source": "out/branch_metrics.csv rows track=F, arm in {RNf, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
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    "method": "base runs with both this arm and C0 complete"
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    "method": "median k (5-round mean >= 0.9 pre)"
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    "source": "out/branch_metrics.csv rows track=F, arm in {RX, C0} (from results/branches/F_*__r*__<arm>_s*.jsonl)",
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  "screen.S_gem25flash_P0think_s1.median_round_s": {
    "value": 1.3965,
    "source": "results/jsonl/S_gem25flash_P0think_s1.jsonl",
    "method": ""
  },
  "screen.S_gem25flash_P0think_s1.pidx_40_59": {
    "value": 1.0188,
    "source": "results/jsonl/S_gem25flash_P0think_s1.jsonl",
    "method": ""
  },
  "screen.S_gpt41_P0think_s0.crossing": {
    "value": null,
    "source": "results/jsonl/S_gpt41_P0think_s0.jsonl",
    "method": ""
  },
  "screen.S_gpt41_P0think_s0.median_round_s": {
    "value": 1.872,
    "source": "results/jsonl/S_gpt41_P0think_s0.jsonl",
    "method": ""
  },
  "screen.S_gpt41_P0think_s0.pidx_40_59": {
    "value": 0.082,
    "source": "results/jsonl/S_gpt41_P0think_s0.jsonl",
    "method": ""
  },
  "screen.S_gpt41_P0think_s1.crossing": {
    "value": null,
    "source": "results/jsonl/S_gpt41_P0think_s1.jsonl",
    "method": ""
  },
  "screen.S_gpt41_P0think_s1.median_round_s": {
    "value": 1.8975,
    "source": "results/jsonl/S_gpt41_P0think_s1.jsonl",
    "method": ""
  },
  "screen.S_gpt41_P0think_s1.pidx_40_59": {
    "value": 0.3032,
    "source": "results/jsonl/S_gpt41_P0think_s1.jsonl",
    "method": ""
  },
  "screen.S_gpt41mini_P0think_s0.crossing": {
    "value": null,
    "source": "results/jsonl/S_gpt41mini_P0think_s0.jsonl",
    "method": ""
  },
  "screen.S_gpt41mini_P0think_s0.median_round_s": {
    "value": 1.759,
    "source": "results/jsonl/S_gpt41mini_P0think_s0.jsonl",
    "method": ""
  },
  "screen.S_gpt41mini_P0think_s0.pidx_40_59": {
    "value": -0.1503,
    "source": "results/jsonl/S_gpt41mini_P0think_s0.jsonl",
    "method": ""
  },
  "screen.S_gpt41mini_P0think_s1.crossing": {
    "value": null,
    "source": "results/jsonl/S_gpt41mini_P0think_s1.jsonl",
    "method": ""
  },
  "screen.S_gpt41mini_P0think_s1.median_round_s": {
    "value": 1.572,
    "source": "results/jsonl/S_gpt41mini_P0think_s1.jsonl",
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  },
  "screen.S_gpt41mini_P0think_s1.pidx_40_59": {
    "value": -0.085,
    "source": "results/jsonl/S_gpt41mini_P0think_s1.jsonl",
    "method": ""
  },
  "OPI.R1.reaction_counts": {
    "value": {
      "lead": 4.0
    },
    "source": "out/reaction.csv",
    "method": "exploratory classification, thresholds 0.03 / 0.05 (see analyze.py)"
  },
  "OPI.R2.reaction_counts": {
    "value": {
      "lead": 4.0
    },
    "source": "out/reaction.csv",
    "method": "exploratory classification, thresholds 0.03 / 0.05 (see analyze.py)"
  },
  "OPI.R3.reaction_counts": {
    "value": {
      "lead": 4.0
    },
    "source": "out/reaction.csv",
    "method": "exploratory classification, thresholds 0.03 / 0.05 (see analyze.py)"
  },
  "OPI.R4.reaction_counts": {
    "value": {
      "lead": 4.0
    },
    "source": "out/reaction.csv",
    "method": "exploratory classification, thresholds 0.03 / 0.05 (see analyze.py)"
  },
  "OPI.RP.reaction_counts": {
    "value": {
      "lead": 4.0
    },
    "source": "out/reaction.csv",
    "method": "exploratory classification, thresholds 0.03 / 0.05 (see analyze.py)"
  },
  "F.R1.reaction_counts": {
    "value": {
      "hold, gap persists": 6.0,
      "lead": 2.0
    },
    "source": "out/reaction.csv",
    "method": "exploratory classification, thresholds 0.03 / 0.05 (see analyze.py)"
  },
  "F.R2.reaction_counts": {
    "value": {
      "hold, gap persists": 6.0,
      "lead": 2.0
    },
    "source": "out/reaction.csv",
    "method": "exploratory classification, thresholds 0.03 / 0.05 (see analyze.py)"
  },
  "F.R3.reaction_counts": {
    "value": {
      "hold, gap persists": 2.0,
      "lead": 4.0,
      "raise": 2.0
    },
    "source": "out/reaction.csv",
    "method": "exploratory classification, thresholds 0.03 / 0.05 (see analyze.py)"
  },
  "F.R4.reaction_counts": {
    "value": {
      "hold, gap persists": 5.0,
      "lead": 3.0
    },
    "source": "out/reaction.csv",
    "method": "exploratory classification, thresholds 0.03 / 0.05 (see analyze.py)"
  },
  "F.RN.reaction_counts": {
    "value": {
      "hold, gap persists": 6.0,
      "lead": 2.0
    },
    "source": "out/reaction.csv",
    "method": "exploratory classification, thresholds 0.03 / 0.05 (see analyze.py)"
  },
  "F.RNf.reaction_counts": {
    "value": {
      "hold, gap persists": 8.0
    },
    "source": "out/reaction.csv",
    "method": "exploratory classification, thresholds 0.03 / 0.05 (see analyze.py)"
  },
  "F.RX.reaction_counts": {
    "value": {
      "follow-down": 1.0,
      "hold, gap persists": 6.0,
      "lead": 1.0
    },
    "source": "out/reaction.csv",
    "method": "exploratory classification, thresholds 0.03 / 0.05 (see analyze.py)"
  },
  "market.competitive_price": {
    "value": 1.4729,
    "source": "run config nash_prices (environment/oligopoly.py)",
    "method": ""
  },
  "market.monopoly_price": {
    "value": 1.925,
    "source": "run config monopoly_prices (environment/oligopoly.py)",
    "method": ""
  },
  "F.C0.notice_share": {
    "value": 0.025,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "2/80 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "F.R1.notice_share": {
    "value": 0.05,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "4/80 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "F.R2.notice_share": {
    "value": 0.075,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "6/80 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "F.R3.notice_share": {
    "value": 0.1625,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "13/80 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "F.R4.notice_share": {
    "value": 0.025,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "2/80 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "F.RN.notice_share": {
    "value": 0.0875,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "7/80 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "F.RNf.notice_share": {
    "value": 0.1,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "8/80 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "F.RX.notice_share": {
    "value": 0.2125,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "17/80 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "OPI.C0.notice_share": {
    "value": 0.3,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "12/40 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "OPI.R1.notice_share": {
    "value": 0.15,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "6/40 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "OPI.R2.notice_share": {
    "value": 0.15,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "6/40 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "OPI.R3.notice_share": {
    "value": 0.35,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "14/40 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "OPI.R4.notice_share": {
    "value": 0.2,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "8/40 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "OPI.RP.notice_share": {
    "value": 0.1,
    "source": "out/notice_raw.csv (src/notice.py classify; gpt-4.1-mini, fixed prompt)",
    "method": "4/40 incumbent texts in post-swap rounds 1-10 classified YES"
  },
  "notice.validation_agreement": {
    "value": "19/20",
    "source": "out/notice_validation.csv (reference labels written by the coding assistant) vs out/notice_raw.csv (classifier)",
    "method": ""
  },
  "checks.branches_complete": {
    "value": 112,
    "source": "out/checks.md",
    "method": ""
  },
  "checks.branches_incomplete": {
    "value": 0,
    "source": "out/checks.md",
    "method": ""
  },
  "checks.implausible_prices_post_swap": {
    "value": 0,
    "source": "out/branch_metrics.csv n_below_cost + n_above_ceiling",
    "method": ""
  }
}