1{2 "experiment": 5,3 "name": "bargaining_power",4 "hypothesis": "Bargaining power: higher seller share beta raises prices and lowers tightness and hazards; total welfare is maximised near the Hosios value beta = 1-alpha with homogeneous sellers; with heterogeneous sellers the welfare-maximising beta deviates (numerical).",5 "rows": 34,6 "columns": [7 "theta",8 "Lam_s",9 "Lam_b",10 "S",11 "B",12 "Q",13 "sold_share",14 "mean_listing_duration",15 "mean_tom_sold",16 "mean_tom_sold_months",17 "buyer_mean_duration",18 "price_mean",19 "price_var_within",20 "price_var_between",21 "price_sd_total",22 "price_between_share",23 "V_min",24 "V_max",25 "pbar_patient",26 "pbar_urgent",27 "D_urgent",28 "D_mean",29 "eta_patient",30 "eta_urgent",31 "acc_patient",32 "acc_urgent",33 "p_star",34 "Gamma",35 "participation_ok",36 "welfare_total",37 "welfare_sellers",38 "welfare_buyers_net",39 "welfare_holding_costs",40 "free_entry_resid",41 "seller_resid_max",42 "converged",43 "n_theta_roots",44 "beta",45 "sellers",46 "hosios_beta"47 ],48 "env": {49 "python": "3.14.4",50 "numpy": "2.4.4",51 "scipy": "1.17.1",52 "pandas": "3.0.2",53 "config_hash": "0a165f317815d76c",54 "seed": 20261020,55 "timestamp": "2026-10-20T08:02:32"56 }57}