spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:11:54.029Z","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:11:54.035Z","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040"}4{"parentUuid":null,"isSidechain":false,"promptId":"11f0942f-a535-4a81-9551-552d1c4bab82","type":"user","message":{"role":"user","content":"Begin"},"uuid":"45dce513-3f7b-4599-a69f-bb0998b3fc56","timestamp":"2026-10-30T04:11:54.040Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","shapeHash":"1dec1e93a471244d49ad74c7b0df58a1175285c509acf079a3c7a752c38ef24a","timestamp":"2026-10-30T04:11:54.055Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0038\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040"}12{"parentUuid":"c933986f-9a2b-47be-996d-e77486c36793","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmYujij1cnaeLcJ7SUx","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2603,"cache_read_input_tokens":1504,"output_tokens":123,"output_tokens_details":{"thinking_tokens":22},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":448,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmYuT6Yfthbyosw5Dnq","requestRef":"5579b480-6f65-4e03-9f53-029e05c066f4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"801bc9c8-f36a-48b9-916e-dddc4b4a30ed","timestamp":"2026-10-30T04:11:56.453Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"801bc9c8-f36a-48b9-916e-dddc4b4a30ed","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmYujij1cnaeLcJ7SUx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DAmnnpJNGsyftyXb5aBchN","name":"Bash","input":{"command":"cat TASK.md; 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Agent i has Cobb–Douglas preferences u_i(x1, x2) = α_i ln x1 + (1 − α_i) ln x2 and an endowment (e1_i, e2_i). Good 2 is the numeraire (p2 = 1). The estimands are the competitive (Walrasian) equilibrium relative price p = p1/p2, the equilibrium allocation (x1_A, x2_A, x1_B, x2_B), the aggregate excess demand for good 1 at the equilibrium price (which must be zero), and the Walras' law residual p·z1 + z2 (value of aggregate excess demand, which must be identically zero). Derive the closed-form equilibrium, verify market clearing and Walras' law numerically, and validate the closed-form price by an independent numerical root-finding of the excess-demand function.\n\n## Inputs\n- `data/endowments.csv` — columns `agent` (A or B), `good1`, `good2`: individual endowments in units of each good. One row per agent. These are the primary data of the exercise (theoretical parameters, not observed data).\n- `data/parameters.csv` — columns `parameter`, `value`, `description`: the preference parameters `alpha_A`, `alpha_B` and the aggregate endowments of each good as recorded in the summary sheet that accompanied the exercise (`aggregate_endowment_good1`, `aggregate_endowment_good2`).\n\n## Information-availability rules\n- Use only the two files under `data/`.\n- The numerical validation must solve the market-clearing condition directly (root of the aggregate excess demand of good 1 in p), not evaluate the closed-form expression.\n- Report in `data_issues` every problem you detect in the inputs and how you handled it.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F5-conf-01\" |\n| `price_ratio_p1_over_p2` | number | — | closed-form equilibrium relative price p1/p2 |\n| `x1_A`, `x2_A`, `x1_B`, `x2_B` | number | units of goods | equilibrium consumption of each good by each agent |\n| `excess_demand_good1` | number | units of good 1 | aggregate excess demand for good 1 at the reported price (demand minus aggregate endowment) |\n| `walras_law_residual` | number | units of numeraire | p·z1 + z2 at the reported price, where z_g is the aggregate excess demand for good g |\n| `aggregate_endowment_good1` | number | units of good 1 | aggregate endowment of good 1 used in the market-clearing condition |\n| `aggregate_endowment_good2` | number | units of good 2 | aggregate endowment of good 2 used in the market-clearing condition |\n| `price_ratio_numerical` | number | — | equilibrium price found by the independent numerical root-finding (the baseline check) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"endowments.csv\": rows read, \"parameters.csv\": rows read} |\n| `n_analysed` | integer or null | — | number of agents in the equilibrium computation |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. State explicitly the assumptions behind the closed form (interior solution, strictly positive endowments, price normalisation) and the scope of the numerical validation.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ndata:\ntotal 16\ndrwxr-xr-x  4 simon-pierreboucher  staff  128 oct.  29 23:26 .\ndrwxr-xr-x  6 simon-pierreboucher  staff  192 oct.  29 23:42 ..\n-rw-r--r--  1 simon-pierreboucher  staff   38 oct.  29 23:39 endowments.csv\n-rw-r--r--  1 simon-pierreboucher  staff  354 oct.  29 23:39 parameters.csv","is_error":false}]},"uuid":"bb5593ba-08c6-4094-baa2-80cc09ee2bc7","timestamp":"2026-10-30T04:11:57.068Z","toolUseResult":{"stdout":"# F5-conf-01 — Competitive equilibrium of a two-agent, two-good exchange economy\n\n## Scientific question\nTwo agents A and B trade two goods in a pure exchange economy. Agent i has Cobb–Douglas preferences u_i(x1, x2) = α_i ln x1 + (1 − α_i) ln x2 and an endowment (e1_i, e2_i). Good 2 is the numeraire (p2 = 1). The estimands are the competitive (Walrasian) equilibrium relative price p = p1/p2, the equilibrium allocation (x1_A, x2_A, x1_B, x2_B), the aggregate excess demand for good 1 at the equilibrium price (which must be zero), and the Walras' law residual p·z1 + z2 (value of aggregate excess demand, which must be identically zero). Derive the closed-form equilibrium, verify market clearing and Walras' law numerically, and validate the closed-form price by an independent numerical root-finding of the excess-demand function.\n\n## Inputs\n- `data/endowments.csv` — columns `agent` (A or B), `good1`, `good2`: individual endowments in units of each good. One row per agent. These are the primary data of the exercise (theoretical parameters, not observed data).\n- `data/parameters.csv` — columns `parameter`, `value`, `description`: the preference parameters `alpha_A`, `alpha_B` and the aggregate endowments of each good as recorded in the summary sheet that accompanied the exercise (`aggregate_endowment_good1`, `aggregate_endowment_good2`).\n\n## Information-availability rules\n- Use only the two files under `data/`.\n- The numerical validation must solve the market-clearing condition directly (root of the aggregate excess demand of good 1 in p), not evaluate the closed-form expression.\n- Report in `data_issues` every problem you detect in the inputs and how you handled it.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F5-conf-01\" |\n| `price_ratio_p1_over_p2` | number | — | closed-form equilibrium relative price p1/p2 |\n| `x1_A`, `x2_A`, `x1_B`, `x2_B` | number | units of goods | equilibrium consumption of each good by each agent |\n| `excess_demand_good1` | number | units of good 1 | aggregate excess demand for good 1 at the reported price (demand minus aggregate endowment) |\n| `walras_law_residual` | number | units of numeraire | p·z1 + z2 at the reported price, where z_g is the aggregate excess demand for good g |\n| `aggregate_endowment_good1` | number | units of good 1 | aggregate endowment of good 1 used in the market-clearing condition |\n| `aggregate_endowment_good2` | number | units of good 2 | aggregate endowment of good 2 used in the market-clearing condition |\n| `price_ratio_numerical` | number | — | equilibrium price found by the independent numerical root-finding (the baseline check) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"endowments.csv\": rows read, \"parameters.csv\": rows read} |\n| `n_analysed` | integer or null | — | number of agents in the equilibrium computation |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. State explicitly the assumptions behind the closed form (interior solution, strictly positive endowments, price normalisation) and the scope of the numerical validation.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). 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{r.get('receipt_path')}\")","is_error":false}]},"uuid":"d52622ab-6c91-4278-a70e-5214bc6745e9","timestamp":"2026-10-30T04:12:06.510Z","toolUseResult":{"stdout":"     249 /Users/simon-pierreboucher/wp29/gates/pgar_gate.py\n7:Writes a receipt to <workspace_dir>/out/state/gate_receipts/<utc>.json and exits 0 if passed, 1 otherwise.\n14:def _utc():\n17:def _load_json(path):\n26:def _is_num(v):\n29:def _type_ok(v, t, nullable):\n36:def _parse_date(s):\n42:def _compare_results(a, b, rel):\n67:def run_gate(ws, python=None, do_repro=True):\n70:    def add(cid, ok, detail=\"\"):\n106:    # 4. claims\n107:    claims, cerr = _load_json(os.path.join(ws, \"out\", \"claims.json\"))\n108:    if \"claims_reference_existing_keys\" in fam or os.path.exists(os.path.join(ws, \"out\", \"claims.json\")):\n109:        if not isinstance(claims, list) or not claims:\n110:            add(\"claims_json\", False, f\"out/claims.json {cerr or 'must be a non-empty list'}\")\n113:            for i, c in 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details"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1343,"cache_read_input_tokens":6595,"output_tokens":99,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1343},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmZj7GxRVHqScYtZTzY","requestRef":"d9ef85d0-b210-49b2-97ee-34727c9d6298","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"67355da5-5e33-4cf5-80ab-fff0828fb50f","timestamp":"2026-10-30T04:12:07.914Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}33{"parentUuid":"67355da5-5e33-4cf5-80ab-fff0828fb50f","isSidechain":false,"promptId":"11f0942f-a535-4a81-9551-552d1c4bab82","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Pruc6k5NnPKahoRGQCRUfc","type":"tool_result","content":"def run_gate(ws, python=None, do_repro=True):\n    ws = os.path.abspath(ws)\n    checks = []\n    def add(cid, ok, detail=\"\"):\n        checks.append({\"id\": cid, \"passed\": bool(ok), \"detail\": detail})\n    spec, err = _load_json(os.path.join(ws, \"gate_spec.json\"))\n    if spec is None:\n        add(\"gate_spec\", False, f\"gate_spec.json {err}\")\n        return _finish(ws, checks)\n    # 1. required files\n    for rel in spec.get(\"required_files\", []):\n        p = os.path.join(ws, rel)\n        ok = os.path.isfile(p) and os.path.getsize(p) > 0\n        add(f\"file:{rel}\", ok, \"present\" if ok else \"missing or empty\")\n    # 2. results.json keys/types\n    res, err = _load_json(os.path.join(ws, \"out\", \"results.json\"))\n    if not isinstance(res, dict):\n        add(\"results_json\", False, f\"out/results.json {err or 'is not an object'}\")\n        res = {}\n    else:\n        add(\"results_json\", True, \"parsed\")\n        for k, rule in spec.get(\"results_keys\", {}).items():\n            if k not in res:\n                add(f\"key:{k}\", False, \"missing\")\n            else:\n                ok = _type_ok(res[k], rule.get(\"type\"), rule.get(\"nullable\", False))\n                add(f\"key:{k}\", ok, \"ok\" if ok else f\"type/nullability mismatch (expected {rule.get('type')}, nullable={rule.get('nullable', False)}, got {type(res[k]).__name__}={res[k]!r:.60})\")\n        if spec.get(\"task_id\") and res.get(\"task_id\") not in (None, spec[\"task_id\"]):\n            add(\"task_id_matches\", False, f\"results.task_id={res.get('task_id')!r} != {spec['task_id']!r}\")\n    # 3. report sections\n    rep = \"\"\n    try:\n        rep = open(os.path.join(ws, \"out\", \"report.md\"), encoding=\"utf-8\", errors=\"replace\").read()\n    except Exception:  # noqa: BLE001\n        pass\n    for s in spec.get(\"report_sections\", []):\n        ok = re.search(NUMERIC_RE + re.escape(s) + r\"\\s*$\", rep, re.M | re.I) is not None\n        add(f\"section:{s}\", ok, \"present\" if ok else \"heading not found\")\n    fam = set(spec.get(\"family_checks\", []))\n    # 4. claims\n    claims, cerr = _load_json(os.path.join(ws, \"out\", \"claims.json\"))\n    if \"claims_reference_existing_keys\" in fam or os.path.exists(os.path.join(ws, \"out\", \"claims.json\")):\n        if not isinstance(claims, list) or not claims:\n            add(\"claims_json\", False, f\"out/claims.json {cerr or 'must be a non-empty list'}\")\n        else:\n            bad = []\n            for i, c in enumerate(claims):\n                if not isinstance(c, dict) or not all(k in c for k in (\"id\", \"text\", \"results_keys\", \"status\")):\n                    bad.append(f\"#{i}: missing fields\"); continue\n                if c[\"status\"] not in (\"supported\", \"unsupported\", \"not_evaluated\"):\n                    bad.append(f\"{c.get('id')}: bad status {c['status']!r}\")\n                missing = [k for k in (c.get(\"results_keys\") or []) if k not in res]\n                if missing:\n                    bad.append(f\"{c.get('id')}: results_keys not in results.json: {missing}\")\n            add(\"claims_json\", not bad, \"; \".join(bad) if bad else f\"{len(claims)} claims, all keys resolve\")\n    # 5. phase ledger\n    pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n    if \"phase_ledger_consistent\" in fam and os.path.exists(pl_path):\n        pl, perr = _load_json(pl_path)\n        if not isinstance(pl, dict):\n            add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n        else:\n            bad = []\n            for pid, st in pl.items():\n                if not isinstance(st, dict) or st.get(\"status\") not in (\"pending\", \"running\", \"passed\", \"failed\", \"blocked\", \"stale\", \"skipped_optional\"):\n                    bad.append(f\"{pid}: invalid status\"); continue\n                if st.get(\"status\") == \"passed\" and not st.get(\"checks\"):\n                    bad.append(f\"{pid}: passed without recorded checks\")\n            not_done = [pid for pid, st in pl.items() if isinstance(st, dict) and st.get(\"status\") not in (\"passed\", \"skipped_optional\", \"blocked\")]\n            if not_done:\n                bad.append(f\"phases not terminal: {not_done}\")\n            add(\"phase_ledger\", not bad, \"; \".join(bad) if bad else \"consistent\")\n    # 6. exclusion ledger\n    ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n    if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n        try:\n            rows = list(csv.DictReader(open(ex_path, newline=\"\")))\n            bad = []\n            prev_after = None\n            for i, r in enumerate(rows):\n                nb, na, nr = int(float(r[\"n_before\"])), int(float(r[\"n_after\"])), int(float(r[\"n_removed\"]))\n                if nb - nr != na:\n                    bad.append(f\"row {i}: n_before - n_removed != n_after\")\n                if prev_after is not None and nb != prev_after:\n                    bad.append(f\"row {i}: n_before != previous n_after\")\n                prev_after = na\n            add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n        except Exception as e:  # noqa: BLE001\n            add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n    # 7. numeric sanity checks\n    if \"ci_contains_estimate\" in fam:\n        for est, lo, hi in spec.get(\"ci_pairs\", []):\n            e, l, h = res.get(est), res.get(lo), res.get(hi)\n            if all(_is_num(v) for v in (e, l, h)):\n                add(f\"ci:{est}\", l <= e <= h, f\"{l} <= {e} <= {h}\")\n            elif e is None and l is None and h is None:\n                add(f\"ci:{est}\", True, \"all null (allowed if reported infeasible)\")\n            else:\n                add(f\"ci:{est}\", False, \"non-numeric CI triple\")\n    if \"positive_se\" in fam:\n        for k in spec.get(\"positive_keys\", []):\n            v = res.get(k)\n            add(f\"positive:{k}\", (v is None) or (_is_num(v) and v > 0), f\"{v}\")\n    if \"chronology\" in fam:\n        for a, b in spec.get(\"chronology_pairs\", []):\n            da, db = _parse_date(res.get(a)), _parse_date(res.get(b))\n            add(f\"chronology:{a}<={b}\", da is not None and db is not None and da <= db, f\"{res.get(a)} vs {res.get(b)}\")\n    if \"forecasts_csv_chronology\" in fam:\n        fp = os.path.join(ws, \"out\", \"forecasts.csv\")\n        try:\n            rows = list(csv.DictReader(open(fp, newline=\"\")))\n            bad = sum(1 for r in rows if not (_parse_date(r.get(\"origin_date\")) and _parse_date(r.get(\"target_date\")) and _parse_date(r[\"origin_date\"]) < _parse_date(r[\"target_date\"])))\n            add(\"forecasts_chronology\", rows and bad == 0, f\"{bad} rows with target_date <= origin_date out of {len(rows)}\")\n        except Exception as e:  # noqa: BLE001\n            add(\"forecasts_chronology\", False, f\"forecasts.csv unreadable: {e}\")\n    # 8. reproducibility\n    if do_repro and os.path.isfile(os.path.join(ws, \"out\", \"analysis.py\")) and isinstance(res, dict) and res:\n        py = python or sys.executable\n        tmp = tempfile.mkdtemp(prefix=\"pgar_repro_\")\n        try:\n            shutil.copytree(os.path.join(ws, \"data\"), os.path.join(tmp, \"data\"))\n            shutil.copytree(os.path.join(ws, \"out\"), os.path.join(tmp, \"out\"), ignore=shutil.ignore_patterns(\"results.json\", \"state\", \"review\"))\n            for extra in (\"TASK.md\", \"gate_spec.json\"):\n                if os.path.exists(os.path.join(ws, extra)):\n                    shutil.copy(os.path.join(ws, extra), tmp)\n            t0 = datetime.datetime.now()\n            proc = subprocess.run([py, \"out/analysis.py\"], cwd=tmp, capture_output=True, text=True, timeout=int(spec.get(\"analysis_timeout_s\", 300)))\n            dur = (datetime.datetime.now() - t0).total_seconds()\n            new, nerr = _load_json(os.path.join(tmp, \"out\", \"results.json\"))\n            if proc.returncode != 0:\n                add(\"reproducibility\", False, f\"analysis.py exit {proc.returncode} after {dur:.1f}s; stderr tail: {proc.stderr[-600:]}\")\n            elif not isinstance(new, dict):\n                add(\"reproducibility\", False, f\"analysis.py did not regenerate out/results.json ({nerr})\")\n            else:\n                diffs = _compare_results(res, new, float(spec.get(\"reproducibility_tolerance_rel\", 1e-6)))\n                add(\"reproducibility\", not diffs, f\"regenerated in {dur:.1f}s; \" + (\"identical within tolerance\" if not diffs else f\"differences in keys: {diffs}\"))\n        except subprocess.TimeoutExpired:\n            add(\"reproducibility\", False, \"analysis.py timed out\")\n        except Exception as e:  # noqa: BLE001\n            add(\"reproducibility\", False, f\"repro error: {e}\")\n        finally:\n            shutil.rmtree(tmp, ignore_errors=True)\n    elif do_repro:\n        add(\"reproducibility\", False, \"out/analysis.py or out/results.json missing\")\n    return _finish(ws, checks)","is_error":false}]},"uuid":"14b896ab-15af-4ed7-bb49-62e9a5b57af0","timestamp":"2026-10-30T04:12:07.940Z","toolUseResult":{"stdout":"def run_gate(ws, python=None, do_repro=True):\n    ws = os.path.abspath(ws)\n    checks = []\n    def add(cid, ok, detail=\"\"):\n        checks.append({\"id\": cid, \"passed\": bool(ok), \"detail\": detail})\n    spec, err = _load_json(os.path.join(ws, \"gate_spec.json\"))\n    if spec is None:\n        add(\"gate_spec\", False, f\"gate_spec.json {err}\")\n        return _finish(ws, checks)\n    # 1. required files\n    for rel in spec.get(\"required_files\", []):\n        p = os.path.join(ws, rel)\n        ok = os.path.isfile(p) and os.path.getsize(p) > 0\n        add(f\"file:{rel}\", ok, \"present\" if ok else \"missing or empty\")\n    # 2. results.json keys/types\n    res, err = _load_json(os.path.join(ws, \"out\", \"results.json\"))\n    if not isinstance(res, dict):\n        add(\"results_json\", False, f\"out/results.json {err or 'is not an object'}\")\n        res = {}\n    else:\n        add(\"results_json\", True, \"parsed\")\n        for k, rule in spec.get(\"results_keys\", {}).items():\n            if k not in res:\n                add(f\"key:{k}\", False, \"missing\")\n            else:\n                ok = _type_ok(res[k], rule.get(\"type\"), rule.get(\"nullable\", False))\n                add(f\"key:{k}\", ok, \"ok\" if ok else f\"type/nullability mismatch (expected {rule.get('type')}, nullable={rule.get('nullable', False)}, got {type(res[k]).__name__}={res[k]!r:.60})\")\n        if spec.get(\"task_id\") and res.get(\"task_id\") not in (None, spec[\"task_id\"]):\n            add(\"task_id_matches\", False, f\"results.task_id={res.get('task_id')!r} != {spec['task_id']!r}\")\n    # 3. report sections\n    rep = \"\"\n    try:\n        rep = open(os.path.join(ws, \"out\", \"report.md\"), encoding=\"utf-8\", errors=\"replace\").read()\n    except Exception:  # noqa: BLE001\n        pass\n    for s in spec.get(\"report_sections\", []):\n        ok = re.search(NUMERIC_RE + re.escape(s) + r\"\\s*$\", rep, re.M | re.I) is not None\n        add(f\"section:{s}\", ok, \"present\" if ok else \"heading not found\")\n    fam = set(spec.get(\"family_checks\", []))\n    # 4. claims\n    claims, cerr = _load_json(os.path.join(ws, \"out\", \"claims.json\"))\n    if \"claims_reference_existing_keys\" in fam or os.path.exists(os.path.join(ws, \"out\", \"claims.json\")):\n        if not isinstance(claims, list) or not claims:\n            add(\"claims_json\", False, f\"out/claims.json {cerr or 'must be a non-empty list'}\")\n        else:\n            bad = []\n            for i, c in enumerate(claims):\n                if not isinstance(c, dict) or not all(k in c for k in (\"id\", \"text\", \"results_keys\", \"status\")):\n                    bad.append(f\"#{i}: missing fields\"); continue\n                if c[\"status\"] not in (\"supported\", \"unsupported\", \"not_evaluated\"):\n                    bad.append(f\"{c.get('id')}: bad status {c['status']!r}\")\n                missing = [k for k in (c.get(\"results_keys\") or []) if k not in res]\n                if missing:\n                    bad.append(f\"{c.get('id')}: results_keys not in results.json: {missing}\")\n            add(\"claims_json\", not bad, \"; \".join(bad) if bad else f\"{len(claims)} claims, all keys resolve\")\n    # 5. phase ledger\n    pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n    if \"phase_ledger_consistent\" in fam and os.path.exists(pl_path):\n        pl, perr = _load_json(pl_path)\n        if not isinstance(pl, dict):\n            add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n        else:\n            bad = []\n            for pid, st in pl.items():\n                if not isinstance(st, dict) or st.get(\"status\") not in (\"pending\", \"running\", \"passed\", \"failed\", \"blocked\", \"stale\", \"skipped_optional\"):\n                    bad.append(f\"{pid}: invalid status\"); continue\n                if st.get(\"status\") == \"passed\" and not st.get(\"checks\"):\n                    bad.append(f\"{pid}: passed without recorded checks\")\n            not_done = [pid for pid, st in pl.items() if isinstance(st, dict) and st.get(\"status\") not in (\"passed\", \"skipped_optional\", \"blocked\")]\n            if not_done:\n                bad.append(f\"phases not terminal: {not_done}\")\n            add(\"phase_ledger\", not bad, \"; \".join(bad) if bad else \"consistent\")\n    # 6. exclusion ledger\n    ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n    if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n        try:\n            rows = list(csv.DictReader(open(ex_path, newline=\"\")))\n            bad = []\n            prev_after = None\n            for i, r in enumerate(rows):\n                nb, na, nr = int(float(r[\"n_before\"])), int(float(r[\"n_after\"])), int(float(r[\"n_removed\"]))\n                if nb - nr != na:\n                    bad.append(f\"row {i}: n_before - n_removed != n_after\")\n                if prev_after is not None and nb != prev_after:\n                    bad.append(f\"row {i}: n_before != previous n_after\")\n                prev_after = na\n            add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n        except Exception as e:  # noqa: BLE001\n            add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n    # 7. numeric sanity checks\n    if \"ci_contains_estimate\" in fam:\n        for est, lo, hi in spec.get(\"ci_pairs\", []):\n            e, l, h = res.get(est), res.get(lo), res.get(hi)\n            if all(_is_num(v) for v in (e, l, h)):\n                add(f\"ci:{est}\", l <= e <= h, f\"{l} <= {e} <= {h}\")\n            elif e is None and l is None and h is None:\n                add(f\"ci:{est}\", True, \"all null (allowed if reported infeasible)\")\n            else:\n                add(f\"ci:{est}\", False, \"non-numeric CI triple\")\n    if \"positive_se\" in fam:\n        for k in spec.get(\"positive_keys\", []):\n            v = res.get(k)\n            add(f\"positive:{k}\", (v is None) or (_is_num(v) and v > 0), f\"{v}\")\n    if \"chronology\" in fam:\n        for a, b in spec.get(\"chronology_pairs\", []):\n            da, db = _parse_date(res.get(a)), _parse_date(res.get(b))\n            add(f\"chronology:{a}<={b}\", da is not None and db is not None and da <= db, f\"{res.get(a)} vs {res.get(b)}\")\n    if \"forecasts_csv_chronology\" in fam:\n        fp = os.path.join(ws, \"out\", \"forecasts.csv\")\n        try:\n            rows = list(csv.DictReader(open(fp, newline=\"\")))\n            bad = sum(1 for r in rows if not (_parse_date(r.get(\"origin_date\")) and _parse_date(r.get(\"target_date\")) and _parse_date(r[\"origin_date\"]) < _parse_date(r[\"target_date\"])))\n            add(\"forecasts_chronology\", rows and bad == 0, f\"{bad} rows with target_date <= origin_date out of {len(rows)}\")\n        except Exception as e:  # noqa: BLE001\n            add(\"forecasts_chronology\", False, f\"forecasts.csv unreadable: {e}\")\n    # 8. reproducibility\n    if do_repro and os.path.isfile(os.path.join(ws, \"out\", \"analysis.py\")) and isinstance(res, dict) and res:\n        py = python or sys.executable\n        tmp = tempfile.mkdtemp(prefix=\"pgar_repro_\")\n        try:\n            shutil.copytree(os.path.join(ws, \"data\"), os.path.join(tmp, \"data\"))\n            shutil.copytree(os.path.join(ws, \"out\"), os.path.join(tmp, \"out\"), ignore=shutil.ignore_patterns(\"results.json\", \"state\", \"review\"))\n            for extra in (\"TASK.md\", \"gate_spec.json\"):\n                if os.path.exists(os.path.join(ws, extra)):\n                    shutil.copy(os.path.join(ws, extra), tmp)\n            t0 = datetime.datetime.now()\n            proc = subprocess.run([py, \"out/analysis.py\"], cwd=tmp, capture_output=True, text=True, timeout=int(spec.get(\"analysis_timeout_s\", 300)))\n            dur = (datetime.datetime.now() - t0).total_seconds()\n            new, nerr = _load_json(os.path.join(tmp, \"out\", \"results.json\"))\n            if proc.returncode != 0:\n                add(\"reproducibility\", False, f\"analysis.py exit {proc.returncode} after {dur:.1f}s; stderr tail: {proc.stderr[-600:]}\")\n            elif not isinstance(new, dict):\n                add(\"reproducibility\", False, f\"analysis.py did not regenerate out/results.json ({nerr})\")\n            else:\n                diffs = _compare_results(res, new, float(spec.get(\"reproducibility_tolerance_rel\", 1e-6)))\n                add(\"reproducibility\", not diffs, f\"regenerated in {dur:.1f}s; \" + (\"identical within tolerance\" if not diffs else f\"differences in keys: {diffs}\"))\n        except subprocess.TimeoutExpired:\n            add(\"reproducibility\", False, \"analysis.py timed out\")\n        except Exception as e:  # noqa: BLE001\n            add(\"reproducibility\", False, f\"repro error: {e}\")\n        finally:\n            shutil.rmtree(tmp, ignore_errors=True)\n    elif do_repro:\n        add(\"reproducibility\", False, \"out/analysis.py or out/results.json missing\")\n    return _finish(ws, 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0)\n**Question.** Find the Walrasian equilibrium of a 2-agent, 2-good pure exchange economy with Cobb–Douglas preferences\nu_i = α_i ln x1 + (1-α_i) ln x2, endowments (e1_i, e2_i), numeraire p2 = 1.\n\n**Estimands.** p = p1/p2 (closed form); allocation x1_A, x2_A, x1_B, x2_B; aggregate excess demand of good 1 at p (should be 0);\nWalras residual p·z1 + z2 (should be 0 identically); numerical p from root-finding z1(p)=0 (not evaluating the closed form).\nAssumptions: α_i in (0,1), strictly positive endowments/wealth → interior demands x1_i = α_i w_i / p, x2_i = (1-α_i) w_i,\nw_i = p e1_i + e2_i; p2 = 1 normalisation; z1 is strictly decreasing in p so the root is unique.\n\n**Outputs.** out/results.json with keys task_id, price_ratio_p1_over_p2, x1_A, x2_A, x1_B, x2_B, excess_demand_good1,\nwalras_law_residual, aggregate_endowment_good1, aggregate_endowment_good2, price_ratio_numerical, feasibility,\nfeasibility_reason, data_issues, n_input_rows, n_analysed, random_seed; out/report.md (6 sections); out/analysis.py; out/claims.json.\n\n**Information rules.** Only data/endowments.csv and data/parameters.csv; numerical check must be a root-finder on z1(p);\nevery input problem must be listed in data_issues with handling.\n\n**Invalidity.** α outside (0,1), non-positive endowments (corner solutions), inconsistent aggregate endowments that make\nmarket clearing infeasible, missing agents.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n \"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1166,"cache_read_input_tokens":11541,"output_tokens":972,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1166},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmZx9MpYqQqSHYCoXew","requestRef":"ab914a04-6bd6-48f6-86d1-621ad12c188a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0044d59f-7410-4853-8db4-f3fc52feb03f","timestamp":"2026-10-30T04:12:17.832Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}45{"parentUuid":"0044d59f-7410-4853-8db4-f3fc52feb03f","isSidechain":false,"promptId":"11f0942f-a535-4a81-9551-552d1c4bab82","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012S9onDjV3T9pfLQ6YmcXpJ","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"edd0c437-f272-426f-9930-6e7924b65018","timestamp":"2026-10-30T04:12:17.869Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0044d59f-7410-4853-8db4-f3fc52feb03f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038/out/state","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}46{"type":"api-request-blob","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","hash":"94ed80d14802afe53e53fea168a42af7770770fc41b7972a4f225162bd01f648","message":{"role":"assistant","content":[{"type":"text","text":"P0 and P1: contract, ledger, audit."},{"type":"tool_use","id":"toolu_012S9onDjV3T9pfLQ6YmcXpJ","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0)\n**Question.** Find the Walrasian equilibrium of a 2-agent, 2-good pure exchange economy with Cobb–Douglas preferences\nu_i = α_i ln x1 + (1-α_i) ln x2, endowments (e1_i, e2_i), numeraire p2 = 1.\n\n**Estimands.** p = p1/p2 (closed form); allocation x1_A, x2_A, x1_B, x2_B; aggregate excess demand of good 1 at p (should be 0);\nWalras residual p·z1 + z2 (should be 0 identically); numerical p from root-finding z1(p)=0 (not evaluating the closed form).\nAssumptions: α_i in (0,1), strictly positive endowments/wealth → interior demands x1_i = α_i w_i / p, x2_i = (1-α_i) w_i,\nw_i = p e1_i + e2_i; p2 = 1 normalisation; z1 is strictly decreasing in p so the root is unique.\n\n**Outputs.** out/results.json with keys task_id, price_ratio_p1_over_p2, x1_A, x2_A, x1_B, x2_B, excess_demand_good1,\nwalras_law_residual, aggregate_endowment_good1, aggregate_endowment_good2, price_ratio_numerical, feasibility,\nfeasibility_reason, data_issues, n_input_rows, n_analysed, random_seed; out/report.md (6 sections); out/analysis.py; out/claims.json.\n\n**Information rules.** Only data/endowments.csv and data/parameters.csv; numerical check must be a root-finder on z1(p);\nevery input problem must be listed in data_issues with handling.\n\n**Invalidity.** α outside (0,1), non-positive endowments (corner solutions), inconsistent aggregate endowments that make\nmarket clearing infeasible, missing agents.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n \"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > 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out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | sha256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/endowments.csv | fc130a2f54c750a5834ec7314060d91f7984769c7c006ad04b98582a016ba74b | 2 | 3 (agent, good1, good2) |\n| data/parameters.csv | 717807e75337fbf4aff8e4d74e0fc0b83b799f78fa689172be0d501cb3e43983 | 4 | 3 (parameter, value, description) |\n\n- endowments: A=(8.0, 2.0), B=(4.0, 8.0). Units: units of each good. No missing values, no duplicate agents, all strictly positive.\n- parameters: alpha_A=0.4, alpha_B=0.6 (both in (0,1)); aggregate_endowment_good1=14.0, aggregate_endowment_good2=10.0.\n- No dates (theoretical exercise).\n\n## Anomaly\n- **aggregate_endowment_good1 = 14.0 in the summary sheet, but the sum of individual endowments of good 1 is 8+4 = 12.0.**\n  aggregate_endowment_good2 = 10.0 equals 2+8 = 10.0 (consistent).\n  In a pure exchange economy aggregate endowment is by definition the sum of individual endowments; TASK.md calls\n  endowments.csv the primary data. Using 14 would make good-1 market clearing require 2 units that nobody owns\n  (and violates Walras' law consistency: clearing good 1 with E1=14 would leave good 2 not clearing).\n  **Decision:** use the individual-endowment sums (12, 10); report the discrepancy in data_issues; also report, for transparency,\n  the price that would result from (incorrectly) clearing good 1 against 14 (as a diagnostic only, not as an estimate).\n- No rows are excluded (exclusions.csv records a no-op step).\nEOF\ncd .. && printf 'step,rule,n_before,n_after,n_removed,rationale\\n1,keep agents with complete positive endowments (endowments.csv),2,2,0,all agent rows valid; aggregate_endowment_good1 from parameters.csv overridden by sum of individual endowments (not a row exclusion)\\n' > exclusions.csv\necho \"- 2026-10-30 P1 passed: audit done; aggregate good1 discrepancy (14 vs 12) -> use 12 from primary data.\" >> state/log.md","description":"Write data audit and exclusion ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":987,"cache_read_input_tokens":12707,"output_tokens":986,"output_tokens_details":{"thinking_tokens":46},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":987},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmaZfnAWJQLh1i94goB","requestRef":"f248f614-e23c-4e7d-92c9-f07ab93c5c2f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9b115d20-074c-494b-8af4-e5f60ea23658","timestamp":"2026-10-30T04:12:27.354Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038/out/state","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}50{"parentUuid":"9b115d20-074c-494b-8af4-e5f60ea23658","isSidechain":false,"promptId":"11f0942f-a535-4a81-9551-552d1c4bab82","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01P63VnC2Bhovix3NoAWbp98","type":"tool_result","content":"(eval):cd:1: no such file or directory: out/state","is_error":false}]},"uuid":"9501733a-08f3-4535-b685-9b42f2ef316c","timestamp":"2026-10-30T04:12:27.376Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: out/state","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"9b115d20-074c-494b-8af4-e5f60ea23658","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038/out","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}51{"type":"api-request-blob","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","hash":"950393e8e7d280a630292c952c8e21af419b89e738fec9a9769bdfc38d647ef5","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01P63VnC2Bhovix3NoAWbp98","name":"Bash","input":{"command":"cd out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | sha256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/endowments.csv | fc130a2f54c750a5834ec7314060d91f7984769c7c006ad04b98582a016ba74b | 2 | 3 (agent, good1, good2) |\n| data/parameters.csv | 717807e75337fbf4aff8e4d74e0fc0b83b799f78fa689172be0d501cb3e43983 | 4 | 3 (parameter, value, description) |\n\n- endowments: A=(8.0, 2.0), B=(4.0, 8.0). Units: units of each good. No missing values, no duplicate agents, all strictly positive.\n- parameters: alpha_A=0.4, alpha_B=0.6 (both in (0,1)); aggregate_endowment_good1=14.0, aggregate_endowment_good2=10.0.\n- No dates (theoretical exercise).\n\n## Anomaly\n- **aggregate_endowment_good1 = 14.0 in the summary sheet, but the sum of individual endowments of good 1 is 8+4 = 12.0.**\n  aggregate_endowment_good2 = 10.0 equals 2+8 = 10.0 (consistent).\n  In a pure exchange economy aggregate endowment is by definition the sum of individual endowments; TASK.md calls\n  endowments.csv the primary data. 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> out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | sha256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/endowments.csv | fc130a2f54c750a5834ec7314060d91f7984769c7c006ad04b98582a016ba74b | 2 | 3 (agent, good1, good2) |\n| data/parameters.csv | 717807e75337fbf4aff8e4d74e0fc0b83b799f78fa689172be0d501cb3e43983 | 4 | 3 (parameter, value, description) |\n\n- endowments: A=(8.0, 2.0), B=(4.0, 8.0). Units: units of each good. No missing values, no duplicate agents, all strictly positive.\n- parameters: alpha_A=0.4, alpha_B=0.6 (both in (0,1)); aggregate_endowment_good1=14.0, aggregate_endowment_good2=10.0.\n- No dates (theoretical exercise).\n\n## Anomaly\n- **aggregate_endowment_good1 = 14.0 in the summary sheet, but the sum of individual endowments of good 1 is 8+4 = 12.0.**\n  aggregate_endowment_good2 = 10.0 equals 2+8 = 10.0 (consistent).\n  In a pure exchange economy the aggregate endowment is by definition the sum of individual endowments; TASK.md calls\n  endowments.csv the primary data. Clearing good 1 against 14 would require 2 units nobody owns and, by Walras' law,\n  would leave the good-2 market not clearing.\n  **Decision:** use the individual-endowment sums (12, 10); report the discrepancy in data_issues; report, as a\n  diagnostic only, the price that would result from clearing good 1 against 14 and the induced good-2 imbalance.\n- No rows excluded (exclusions.csv records a no-op step).\nEOF\ncat out/exclusions.csv; cat out/state/log.md","description":"Write data audit file"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":205,"cache_read_input_tokens":14704,"output_tokens":882,"output_tokens_details":{"thinking_tokens":136},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":205},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmbPbaEpnpaMW5GtSm3","requestRef":"0ca3f6c4-2212-40ec-a3f4-1a7d9e88b2d2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ad771ccd-9f21-4a10-9f63-14c4d8470cfd","timestamp":"2026-10-30T04:12:36.366Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"ad771ccd-9f21-4a10-9f63-14c4d8470cfd","isSidechain":false,"promptId":"11f0942f-a535-4a81-9551-552d1c4bab82","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017yAdEmgJ86AeTFfhBForbi","type":"tool_result","content":"step,rule,n_before,n_after,n_removed,rationale\n1,keep agents with complete positive endowments (endowments.csv),2,2,0,all agent rows valid; aggregate_endowment_good1 from parameters.csv overridden by sum of individual endowments (not a row exclusion)\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1 passed: audit done; aggregate good1 discrepancy (14 vs 12) -> use 12 from primary data.","is_error":false}]},"uuid":"0b4173d8-a3a5-430d-9505-2814b187e098","timestamp":"2026-10-30T04:12:36.399Z","toolUseResult":{"stdout":"step,rule,n_before,n_after,n_removed,rationale\n1,keep agents with complete positive endowments (endowments.csv),2,2,0,all agent rows valid; aggregate_endowment_good1 from parameters.csv overridden by sum of individual endowments (not a row exclusion)\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1 passed: audit done; aggregate good1 discrepancy (14 vs 12) -> use 12 from primary data.","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"ad771ccd-9f21-4a10-9f63-14c4d8470cfd","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}63{"type":"api-request-blob","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","hash":"e1643bccab28308a36b81f54d78ca4f7badd9ecc866e9675128034265b190e30","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_017yAdEmgJ86AeTFfhBForbi","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | sha256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/endowments.csv | fc130a2f54c750a5834ec7314060d91f7984769c7c006ad04b98582a016ba74b | 2 | 3 (agent, good1, good2) |\n| data/parameters.csv | 717807e75337fbf4aff8e4d74e0fc0b83b799f78fa689172be0d501cb3e43983 | 4 | 3 (parameter, value, description) |\n\n- endowments: A=(8.0, 2.0), B=(4.0, 8.0). Units: units of each good. No missing values, no duplicate agents, all strictly positive.\n- parameters: alpha_A=0.4, alpha_B=0.6 (both in (0,1)); aggregate_endowment_good1=14.0, aggregate_endowment_good2=10.0.\n- No dates (theoretical exercise).\n\n## Anomaly\n- **aggregate_endowment_good1 = 14.0 in the summary sheet, but the sum of individual endowments of good 1 is 8+4 = 12.0.**\n  aggregate_endowment_good2 = 10.0 equals 2+8 = 10.0 (consistent).\n  In a pure exchange economy the aggregate endowment is by definition the sum of individual endowments; TASK.md calls\n  endowments.csv the primary data. Clearing good 1 against 14 would require 2 units nobody owns and, by Walras' law,\n  would leave the good-2 market not clearing.\n  **Decision:** use the individual-endowment sums (12, 10); report the discrepancy in data_issues; report, as a\n  diagnostic only, the price that would result from clearing good 1 against 14 and the induced good-2 imbalance.\n- No rows excluded (exclusions.csv records a no-op step).\nEOF\ncat out/exclusions.csv; cat out/state/log.md","description":"Write data audit file"}}]}}64{"type":"api-request","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","id":"f4a3dfac-8a09-4063-b1ff-c60bbc4db32c","timestamp":"2026-10-30T04:12:36.402Z","version":"2.1.314","querySource":"sdk","shapeHash":"1dec1e93a471244d49ad74c7b0df58a1175285c509acf079a3c7a752c38ef24a","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"0ca3f6c4-2212-40ec-a3f4-1a7d9e88b2d2","keep":20,"tail":["e1643bccab28308a36b81f54d78ca4f7badd9ecc866e9675128034265b190e30","0851233dfa4df350ec488ce7c5f572586ebd81a2265d07eed49831d773357ca5"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d825797cda73f52eef441fc95cd53cf279ea3e3a9bdcf7c50e96f8ea4d077d90"}65{"parentUuid":"0b4173d8-a3a5-430d-9505-2814b187e098","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmbwANgQsLAU9RwtL7o","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script (P2 baseline = numerical root-finding; P3 = closed form + checks)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1056,"cache_read_input_tokens":14909,"output_tokens":2102,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1056},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmbvsm7VcdrgdfpMBB5","requestRef":"f4a3dfac-8a09-4063-b1ff-c60bbc4db32c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a9fbbb9b-8e36-4276-8aa5-8588cb275838","timestamp":"2026-10-30T04:12:37.386Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}66{"parentUuid":"a9fbbb9b-8e36-4276-8aa5-8588cb275838","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmbwANgQsLAU9RwtL7o","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01276TXptrrGJtFBo5bM8cNd","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Walrasian equilibrium of a 2-agent, 2-good Cobb-Douglas exchange economy (task F5-conf-01).\nReads only data/; writes out/results.json deterministically. No randomness is used.\"\"\"\nimport json, os\nimport pandas as pd\nfrom scipy.optimize import brentq\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nend = pd.read_csv(os.path.join(ROOT, \"data\", \"endowments.csv\"))\npar = pd.read_csv(os.path.join(ROOT, \"data\", \"parameters.csv\"))\nP = dict(zip(par[\"parameter\"], par[\"value\"].astype(float)))\n\nissues = []\n# ---- validation ------------------------------------------------------------\nassert end[\"agent\"].is_unique and set(end[\"agent\"]) == {\"A\", \"B\"}, \"expected exactly agents A and B\"\nassert not end.isna().any().any() and not par[\"value\"].isna().any()\ne = {r.agent: (float(r.good1), float(r.good2)) for r in end.itertuples()}\nalpha = {\"A\": P[\"alpha_A\"], \"B\": P[\"alpha_B\"]}\nfor i in \"AB\":\n    assert 0 < alpha[i] < 1, f\"alpha_{i} outside (0,1)\"\n    assert e[i][0] > 0 and e[i][1] > 0, f\"non-positive endowment for {i}\"\n\nE1 = sum(e[i][0] for i in \"AB\")\nE2 = sum(e[i][1] for i in \"AB\")\nE1_sheet, E2_sheet = P[\"aggregate_endowment_good1\"], P[\"aggregate_endowment_good2\"]\n\n\ndef demand(i, p):\n    w = p * e[i][0] + e[i][1]\n    return alpha[i] * w / p, (1 - alpha[i]) * w\n\n\ndef z1(p, E1_used=E1):\n    return sum(demand(i, p)[0] for i in \"AB\") - E1_used\n\n\ndef z2(p, E2_used=E2):\n    return sum(demand(i, p)[1] for i in \"AB\") - E2_used\n\n\n# diagnostic for the summary-sheet discrepancy (not used for estimates)\nif abs(E1_sheet - E1) > 1e-12:\n    p_sheet = brentq(lambda p: z1(p, E1_sheet), 1e-9, 1e9, xtol=1e-15, rtol=1e-15, maxiter=500)\n    issues.append({\n        \"issue\": f\"parameters.csv aggregate_endowment_good1={E1_sheet} differs from the sum of individual endowments \"\n                 f\"in endowments.csv ({E1}).\",\n        \"handling\": f\"Used the sum of individual endowments ({E1}) since aggregate endowment in a pure exchange economy is \"\n                    f\"by definition the sum of individual endowments and endowments.csv is the primary data. Diagnostic: \"\n                    f\"clearing good 1 against {E1_sheet} would give p={p_sheet:.6f} with good-2 excess demand \"\n                    f\"{z2(p_sheet):.6f} != 0, i.e. no consistent equilibrium; the summary value is treated as a recording error.\"})\nif abs(E2_sheet - E2) > 1e-12:\n    issues.append({\"issue\": f\"aggregate_endowment_good2={E2_sheet} differs from sum {E2}\",\n                   \"handling\": \"Used sum of individual endowments.\"})\n\n# ---- baseline / independent numerical check: root of z1(p) -----------------\np_num = brentq(z1, 1e-9, 1e9, xtol=1e-15, rtol=1e-15, maxiter=500)\n\n# ---- closed form -------------------------------------------------------------\n# sum_i alpha_i (p e1_i + e2_i) = p E1  =>  p = sum alpha_i e2_i / sum (1-alpha_i) e1_i\np = sum(alpha[i] * e[i][1] for i in \"AB\") / sum((1 - alpha[i]) * e[i][0] for i in \"AB\")\nx = {i: demand(i, p) for i in \"AB\"}\ned1 = z1(p)\nwalras = p * z1(p) + z2(p)\n\nres = {\n    \"task_id\": \"F5-conf-01\",\n    \"price_ratio_p1_over_p2\": p,\n    \"x1_A\": x[\"A\"][0], \"x2_A\": x[\"A\"][1], \"x1_B\": x[\"B\"][0], \"x2_B\": x[\"B\"][1],\n    \"excess_demand_good1\": ed1,\n    \"excess_demand_good2\": z2(p),\n    \"walras_law_residual\": walras,\n    \"aggregate_endowment_good1\": E1,\n    \"aggregate_endowment_good2\": E2,\n    \"aggregate_endowment_good1_summary_sheet\": E1_sheet,\n    \"aggregate_endowment_good2_summary_sheet\": E2_sheet,\n    \"price_ratio_numerical\": p_num,\n    \"price_abs_diff_closed_vs_numerical\": abs(p - p_num),\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"endowments.csv\": int(len(end)), \"parameters.csv\": int(len(par))},\n    \"n_analysed\": 2,\n    \"random_seed\": None,\n}\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in (\"price_ratio_p1_over_p2\", \"price_ratio_numerical\", \"excess_demand_good1\",\n                                      \"walras_law_residual\")}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1056,"cache_read_input_tokens":14909,"output_tokens":2102,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1056},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmbvsm7VcdrgdfpMBB5","requestRef":"f4a3dfac-8a09-4063-b1ff-c60bbc4db32c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c8bbd0d1-718f-4f7b-93a5-d332eed401db","timestamp":"2026-10-30T04:12:52.295Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}67{"parentUuid":"c8bbd0d1-718f-4f7b-93a5-d332eed401db","isSidechain":false,"promptId":"11f0942f-a535-4a81-9551-552d1c4bab82","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01276TXptrrGJtFBo5bM8cNd","type":"tool_result","content":"{\n \"price_ratio_p1_over_p2\": 0.8749999999999999,\n \"price_ratio_numerical\": 0.8750000000000001,\n \"excess_demand_good1\": 0.0,\n \"walras_law_residual\": 0.0\n}\n{\n  \"task_id\": \"F5-conf-01\",\n  \"price_ratio_p1_over_p2\": 0.8749999999999999,\n  \"x1_A\": 4.114285714285715,\n  \"x2_A\": 5.3999999999999995,\n  \"x1_B\": 7.885714285714286,\n  \"x2_B\": 4.6000000000000005,\n  \"excess_demand_good1\": 0.0,\n  \"excess_demand_good2\": 0.0,\n  \"walras_law_residual\": 0.0,\n  \"aggregate_endowment_good1\": 12.0,\n  \"aggregate_endowment_good2\": 10.0,\n  \"aggregate_endowment_good1_summary_sheet\": 14.0,\n  \"aggregate_endowment_good2_summary_sheet\": 10.0,\n  \"price_ratio_numerical\": 0.8750000000000001,\n  \"price_abs_diff_closed_vs_numerical\": 2.220446049250313e-16,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"parameters.csv aggregate_endowment_good1=14.0 differs from the sum of individual endowments in endowments.csv (12.0).\",\n      \"handling\": \"Used the sum of individual endowments (12.0) since aggregate endowment in a pure exchange economy is by definition the sum of individual endowments and endowments.csv is the primary data. Diagnostic: clearing good 1 against 14.0 would give p=0.666667 with good-2 excess demand -1.333333 != 0, i.e. no consistent equilibrium; the summary value is treated as a recording error.\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"endowments.csv\": 2,\n    \"parameters.csv\": 4\n  },\n  \"n_analysed\": 2,\n  \"random_seed\": null\n}","is_error":false}]},"uuid":"099db2ed-ff35-4772-ac4a-0913c513c364","timestamp":"2026-10-30T04:12:52.784Z","toolUseResult":{"stdout":"{\n \"price_ratio_p1_over_p2\": 0.8749999999999999,\n \"price_ratio_numerical\": 0.8750000000000001,\n \"excess_demand_good1\": 0.0,\n \"walras_law_residual\": 0.0\n}\n{\n  \"task_id\": \"F5-conf-01\",\n  \"price_ratio_p1_over_p2\": 0.8749999999999999,\n  \"x1_A\": 4.114285714285715,\n  \"x2_A\": 5.3999999999999995,\n  \"x1_B\": 7.885714285714286,\n  \"x2_B\": 4.6000000000000005,\n  \"excess_demand_good1\": 0.0,\n  \"excess_demand_good2\": 0.0,\n  \"walras_law_residual\": 0.0,\n  \"aggregate_endowment_good1\": 12.0,\n  \"aggregate_endowment_good2\": 10.0,\n  \"aggregate_endowment_good1_summary_sheet\": 14.0,\n  \"aggregate_endowment_good2_summary_sheet\": 10.0,\n  \"price_ratio_numerical\": 0.8750000000000001,\n  \"price_abs_diff_closed_vs_numerical\": 2.220446049250313e-16,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"parameters.csv aggregate_endowment_good1=14.0 differs from the sum of individual endowments in endowments.csv (12.0).\",\n      \"handling\": \"Used the sum of individual endowments (12.0) since aggregate endowment in a pure exchange economy is by definition the sum of individual endowments and endowments.csv is the primary data. Diagnostic: clearing good 1 against 14.0 would give p=0.666667 with good-2 excess demand -1.333333 != 0, i.e. no consistent equilibrium; the summary value is treated as a recording error.\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"endowments.csv\": 2,\n    \"parameters.csv\": 4\n  },\n  \"n_analysed\": 2,\n  \"random_seed\": null\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c8bbd0d1-718f-4f7b-93a5-d332eed401db","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}68{"type":"api-request-blob","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","hash":"2270a3d2961f68c6b8fa4718690f9ce726f9483236b2b06f5cb3372f93afaca1","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script (P2 baseline = numerical root-finding; P3 = closed form + checks)."},{"type":"tool_use","id":"toolu_01276TXptrrGJtFBo5bM8cNd","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Walrasian equilibrium of a 2-agent, 2-good Cobb-Douglas exchange economy (task F5-conf-01).\nReads only data/; writes out/results.json deterministically. No randomness is used.\"\"\"\nimport json, os\nimport pandas as pd\nfrom scipy.optimize import brentq\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nend = pd.read_csv(os.path.join(ROOT, \"data\", \"endowments.csv\"))\npar = pd.read_csv(os.path.join(ROOT, \"data\", \"parameters.csv\"))\nP = dict(zip(par[\"parameter\"], par[\"value\"].astype(float)))\n\nissues = []\n# ---- validation ------------------------------------------------------------\nassert end[\"agent\"].is_unique and set(end[\"agent\"]) == {\"A\", \"B\"}, \"expected exactly agents A and B\"\nassert not end.isna().any().any() and not par[\"value\"].isna().any()\ne = {r.agent: (float(r.good1), float(r.good2)) for r in end.itertuples()}\nalpha = {\"A\": P[\"alpha_A\"], \"B\": P[\"alpha_B\"]}\nfor i in \"AB\":\n    assert 0 < alpha[i] < 1, f\"alpha_{i} outside (0,1)\"\n    assert e[i][0] > 0 and e[i][1] > 0, f\"non-positive endowment for {i}\"\n\nE1 = sum(e[i][0] for i in \"AB\")\nE2 = sum(e[i][1] for i in \"AB\")\nE1_sheet, E2_sheet = P[\"aggregate_endowment_good1\"], P[\"aggregate_endowment_good2\"]\n\n\ndef demand(i, p):\n    w = p * e[i][0] + e[i][1]\n    return alpha[i] * w / p, (1 - alpha[i]) * w\n\n\ndef z1(p, E1_used=E1):\n    return sum(demand(i, p)[0] for i in \"AB\") - E1_used\n\n\ndef z2(p, E2_used=E2):\n    return sum(demand(i, p)[1] for i in \"AB\") - E2_used\n\n\n# diagnostic for the summary-sheet discrepancy (not used for estimates)\nif abs(E1_sheet - E1) > 1e-12:\n    p_sheet = brentq(lambda p: z1(p, E1_sheet), 1e-9, 1e9, xtol=1e-15, rtol=1e-15, maxiter=500)\n    issues.append({\n        \"issue\": f\"parameters.csv aggregate_endowment_good1={E1_sheet} differs from the sum of individual endowments \"\n                 f\"in endowments.csv ({E1}).\",\n        \"handling\": f\"Used the sum of individual endowments ({E1}) since aggregate endowment in a pure exchange economy is \"\n                    f\"by definition the sum of individual endowments and endowments.csv is the primary data. Diagnostic: \"\n                    f\"clearing good 1 against {E1_sheet} would give p={p_sheet:.6f} with good-2 excess demand \"\n                    f\"{z2(p_sheet):.6f} != 0, i.e. no consistent equilibrium; the summary value is treated as a recording error.\"})\nif abs(E2_sheet - E2) > 1e-12:\n    issues.append({\"issue\": f\"aggregate_endowment_good2={E2_sheet} differs from sum {E2}\",\n                   \"handling\": \"Used sum of individual endowments.\"})\n\n# ---- baseline / independent numerical check: root of z1(p) -----------------\np_num = brentq(z1, 1e-9, 1e9, xtol=1e-15, rtol=1e-15, maxiter=500)\n\n# ---- closed form -------------------------------------------------------------\n# sum_i alpha_i (p e1_i + e2_i) = p E1  =>  p = sum alpha_i e2_i / sum (1-alpha_i) e1_i\np = sum(alpha[i] * e[i][1] for i in \"AB\") / sum((1 - alpha[i]) * e[i][0] for i in \"AB\")\nx = {i: demand(i, p) for i in \"AB\"}\ned1 = z1(p)\nwalras = p * z1(p) + z2(p)\n\nres = {\n    \"task_id\": \"F5-conf-01\",\n    \"price_ratio_p1_over_p2\": p,\n    \"x1_A\": x[\"A\"][0], \"x2_A\": x[\"A\"][1], \"x1_B\": x[\"B\"][0], \"x2_B\": x[\"B\"][1],\n    \"excess_demand_good1\": ed1,\n    \"excess_demand_good2\": z2(p),\n    \"walras_law_residual\": walras,\n    \"aggregate_endowment_good1\": E1,\n    \"aggregate_endowment_good2\": E2,\n    \"aggregate_endowment_good1_summary_sheet\": E1_sheet,\n    \"aggregate_endowment_good2_summary_sheet\": E2_sheet,\n    \"price_ratio_numerical\": p_num,\n    \"price_abs_diff_closed_vs_numerical\": abs(p - p_num),\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"endowments.csv\": int(len(end)), \"parameters.csv\": int(len(par))},\n    \"n_analysed\": 2,\n    \"random_seed\": None,\n}\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in (\"price_ratio_p1_over_p2\", \"price_ratio_numerical\", \"excess_demand_good1\",\n                                      \"walras_law_residual\")}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json","description":"Write and run analysis 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out/results.json /tmp/r1.json && rm out/results.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json; r=json.load(open('out/results.json'))\np=r['price_ratio_p1_over_p2']\nprint(\"clear1\", r['x1_A']+r['x1_B']-12, \"clear2\", r['x2_A']+r['x2_B']-10)\nprint(\"budgetA\", p*r['x1_A']+r['x2_A']-(p*8+2), \"budgetB\", p*r['x1_B']+r['x2_B']-(p*4+8))\nprint(\"MRS_A\", 0.4/0.6*r['x2_A']/r['x1_A'], \"MRS_B\", 0.6/0.4*r['x2_B']/r['x1_B'], \"p\", p)\nprint(\"exact 5.6/6.4\", 5.6/6.4, \"diff num\", r['price_abs_diff_closed_vs_numerical'])\nprint(\"all positive\", all(r[k]>0 for k in ['x1_A','x2_A','x1_B','x2_B']))\nEOF","description":"Rerun for determinism and consistency 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GATE pgar_gate/1.0 — FAILED (10 of 31 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:price_ratio_p1_over_p2: ok\n  [ok] key:x1_A: ok\n  [ok] key:x2_A: ok\n  [ok] key:x1_B: ok\n  [ok] key:x2_B: ok\n  [ok] key:excess_demand_good1: ok\n  [ok] key:walras_law_residual: ok\n  [ok] key:aggregate_endowment_good1: ok\n  [ok] key:aggregate_endowment_good2: ok\n  [ok] key:price_ratio_numerical: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] reproducibility: regenerated in 0.5s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0038/out/state/gate_receipts/20261030T041305.173745Z.json\n20261030T041305.173745Z.json","is_error":false}]},"uuid":"e6bbf6af-28c4-433f-adc1-dcb3477b8e65","timestamp":"2026-10-30T04:13:05.184Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 31 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:price_ratio_p1_over_p2: ok\n  [ok] key:x1_A: ok\n  [ok] key:x2_A: ok\n  [ok] key:x1_B: ok\n  [ok] key:x2_B: ok\n  [ok] key:excess_demand_good1: ok\n  [ok] key:walras_law_residual: ok\n  [ok] key:aggregate_endowment_good1: ok\n  [ok] key:aggregate_endowment_good2: ok\n  [ok] key:price_ratio_numerical: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] reproducibility: regenerated in 0.5s; identical within tolerance\nreceipt: 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Good 2 is the numeraire (p2 = 1). We find the Walrasian equilibrium price p = p1/p2 and allocation. We check market clearing and Walras' law, and we validate the closed-form price by numerical root-finding.\n\n## Data and exclusions\n- `data/endowments.csv` (2 rows; `n_input_rows`): A = (8, 2), B = (4, 8). `data/parameters.csv` (4 rows): α_A = 0.4, α_B = 0.6, and summary-sheet aggregates 14 (good 1) and 10 (good 2).\n- **Data issue (`data_issues`):** the summary sheet gives aggregate good-1 endowment 14, but the individual endowments add up to 8 + 4 = 12. The good-2 figure is consistent (10).\n- In an exchange economy the aggregate endowment is, by definition, the sum of the individual endowments, and the task names endowments.csv as the primary data. I therefore used 12 and 10 (`aggregate_endowment_good1`, `aggregate_endowment_good2`).\n- As a diagnostic only, I cleared good 1 against 14 instead. That gives p ≈ 0.6667 with good-2 excess demand ≈ −1.333 ≠ 0, so the economy has no consistent equilibrium. I treat the value 14 as a recording error. Both summary-sheet values are kept in `aggregate_endowment_good1_summary_sheet` and `aggregate_endowment_good2_summary_sheet`.\n- No rows were excluded (`out/exclusions.csv`). Both agents were analysed (`n_analysed` = 2).\n\n## Method\n- **Assumptions of the closed form:**\n  - α_i ∈ (0,1) and strictly positive endowments, so each agent's wealth w_i = p e1_i + e2_i is positive and demands are interior: x1_i = α_i w_i / p and x2_i = (1−α_i) w_i.\n  - Price normalisation p2 = 1.\n- **Closed form:** market clearing for good 1, Σ α_i(p e1_i + e2_i) = p E1, gives p = Σ α_i e2_i / Σ (1−α_i) e1_i = (0.4·2 + 0.6·8)/(0.6·8 + 0.4·4) = 5.6/6.4. The good-2 market then clears by Walras' law.\n- **Independent numerical validation:** Brent's method (`scipy.optimize.brentq`) finds the root of the aggregate excess-demand function z1(p) = Σ x1_i(p) − E1 on the bracket [1e-9, 1e9], with tolerance 1e-15. It never evaluates the closed-form expression.\n  - z1 is strictly decreasing in p here, so the root is unique.\n  - Scope: this check validates the price for these parameters only. It is not a general proof.\n- No randomness is involved (`random_seed` = null).\n\n## Results\n| quantity | value | key |\n|---|---|---|\n| p1/p2 (closed form) | 0.875 | `price_ratio_p1_over_p2` |\n| p1/p2 (numerical root) | 0.875 (abs. difference 2.2e-16) | `price_ratio_numerical`, `price_abs_diff_closed_vs_numerical` |\n| x1_A, x2_A | 4.1143, 5.4 | `x1_A`, `x2_A` |\n| x1_B, x2_B | 7.8857, 4.6 | `x1_B`, `x2_B` |\n| excess demand good 1 | 0.0 | `excess_demand_good1` |\n| excess demand good 2 | 0.0 | `excess_demand_good2` |\n| Walras residual p·z1 + z2 | 0.0 | `walras_law_residual` |\n\nThe allocation adds up to the aggregate endowment (12, 10). Each agent's budget constraint binds. Both agents' marginal rates of substitution equal p = 0.875 (`out/state/checks.md`), so the allocation is Pareto efficient. `feasibility` = \"complete\".\n\n## Uncertainty\nThis is a deterministic theoretical calculation, so there is no sampling uncertainty. The only numerical error is floating-point error: the closed-form and numerical prices differ by about 2.2e-16 (`price_abs_diff_closed_vs_numerical`), and the residuals are 0 at double precision.\n\nThe main source of uncertainty in the result comes from the data, not the computation: which aggregate good-1 endowment is correct. If the individual endowments were wrong and 14 were the right total, the equilibrium would be different. That case cannot be resolved from the inputs.\n\n## Limitations and open issues\n- The summary-sheet aggregate for good 1 (14) contradicts the individual endowments (12). I resolved this in favour of the primary individual data. If the true error is instead in an individual endowment (for example, if A or B actually holds 2 more units of good 1), the price and allocation would change. The inputs cannot tell these cases apart.\n- The closed form relies on interior Cobb–Douglas demands. It does not cover corner solutions, zero endowments or α ∈ {0, 1}.\n- The numerical validation covers this parameter set only.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The closed-form Walrasian equilibrium price is p1/p2 = 0.875 (=5.6/6.4), using aggregate endowments (12, 10) from the individual endowments.\",\"results_keys\":[\"price_ratio_p1_over_p2\",\"aggregate_endowment_good1\",\"aggregate_endowment_good2\"],\"status\":\"supported\",\"caveat\":\"Relies on resolving the summary-sheet discrepancy (14 vs 12) in favour of endowments.csv.\"},\n {\"id\":\"C2\",\"text\":\"Independent root-finding of the aggregate excess demand for good 1 reproduces the closed-form price to within 2.2e-16.\",\"results_keys\":[\"price_ratio_numerical\",\"price_abs_diff_closed_vs_numerical\"],\"status\":\"supported\",\"caveat\":\"Validation for this parameter set only.\"},\n {\"id\":\"C3\",\"text\":\"Equilibrium allocation: A consumes (4.1143, 5.4), B consumes (7.8857, 4.6).\",\"results_keys\":[\"x1_A\",\"x2_A\",\"x1_B\",\"x2_B\"],\"status\":\"supported\",\"caveat\":\"Interior Cobb-Douglas demands assumed.\"},\n {\"id\":\"C4\",\"text\":\"Both markets clear and the Walras' law residual is zero at the reported price.\",\"results_keys\":[\"excess_demand_good1\",\"excess_demand_good2\",\"walras_law_residual\"],\"status\":\"supported\",\"caveat\":\"Zero to double precision.\"},\n {\"id\":\"C5\",\"text\":\"The summary-sheet aggregate endowment of good 1 (14) is inconsistent with the individual endowments (sum 12); clearing against 14 yields no consistent equilibrium.\",\"results_keys\":[\"data_issues\",\"aggregate_endowment_good1_summary_sheet\",\"aggregate_endowment_good1\"],\"status\":\"supported\",\"caveat\":\"Which source is wrong cannot be determined from the inputs.\"}\n]\nEOF\nR1=$(ls out/state/gate_receipts | head -1)\ncat > out/state/phases.json <<EOF\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"sha256/rows/cols recorded in data_audit.md\",\"missing/duplicates: none\",\"aggregate endowment consistency: good1 14 vs 12 mismatch detected\",\"exclusions.csv sums: 2-0=2\"],\"notes\":\"Use individual endowment sums.\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline = numerical root of z1(p) via brentq: 0.875 (price_ratio_numerical)\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, no randomness\",\"closed form 0.875 matches hand computation\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"market clearing, budgets, MRS, Walras residual all pass (checks.md)\",\"gate run 1 receipt out/state/gate_receipts/$R1: all P4 checks (keys, types, reproducibility) ok; failures only for P5 files not yet written\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 sections written\",\"claims.json 5 claims\",\"gate run 2 (see log.md for receipt)\"],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2-P4 passed: p=0.875 closed form & numerical; gate run 1 receipt $R1 (only P5 files missing).\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"; ls out/state/gate_receipts","description":"Write report, claims, phases; run final gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1237,"cache_read_input_tokens":19626,"output_tokens":3365,"output_tokens_details":{"thinking_tokens":127},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1237},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXme3yCYuqHpTvdrNPhW","requestRef":"2c166b33-c527-40f0-a05f-fe74302719a3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ad5bce7c-c314-424b-adc9-b9b7083c53d5","timestamp":"2026-10-30T04:13:32.619Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}84{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"ad5bce7c-c314-424b-adc9-b9b7083c53d5","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040"}85{"type":"atis-latch","atis":"","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040"}86{"parentUuid":"ad5bce7c-c314-424b-adc9-b9b7083c53d5","isSidechain":false,"promptId":"11f0942f-a535-4a81-9551-552d1c4bab82","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JKPTnq7na6Zc4KHBDcQTjo","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 31 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0038/out/state/gate_receipts/20261030T041333.229194Z.json\n20261030T041305.173745Z.json\n20261030T041333.229194Z.json","is_error":false}]},"uuid":"a85cf35f-4161-42e5-b34a-2e8966278f11","timestamp":"2026-10-30T04:13:33.240Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 31 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0038/out/state/gate_receipts/20261030T041333.229194Z.json\n20261030T041305.173745Z.json\n20261030T041333.229194Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"ad5bce7c-c314-424b-adc9-b9b7083c53d5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0038","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","version":"2.1.314","gitBranch":"HEAD"}87{"type":"api-request-blob","sessionId":"6d9966ea-7c7f-421d-9569-53b3af28e040","hash":"68a1913b906ddfd7f9bbccee8cd63e70a3a7bc4acdda060b4a9c45c0bcffcfe1","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSmwcKEAgTGAI4AUIIdGhpbmtpbmcSDB7qL1UKIs5ZitLj9RoMn460nG+7rz9MiI7rIjB0EJgOUGlcJa77/jV9Rz1ZIT7POhsFjMnQkXd4wokPDHKe3QUeP01gJy83QQpXukoquAYV4/DZCUB8+35C0gAAn7cwNjNFNBHxqYxgorlxDMMLoL6QyeD5+3g6h7a1uerGslFsPonVyix9uUiNewZS3uMnK7KF6+TASo0peGqZbNS62uQ60gkd3KjdnxjeMzwj7Qf9m/pQt7cI1XQgrjX0JM7j3gn/wYRdcc6/CIJhSaSoCb4pClPXct9cRiDUj0g73740X4TUDi88PyQe+eGIcsikKLt0tm9ezbJ5NTGtjdgMfNrvZS4PCwzcvOGiJQbmzrq2a7cIqk3+G9OeRj+n1Xe6ASQOUNjLyaWAUQGaQw80ZWVOwGfqoPrc98JQfCt9gpkExydAiqy59bpo8LdZy1XLTlf6mNTvMQDJPqwawct37Q1AIxGh10tZXdfsZYeitGZoqg0x4hRIpTuHuq7rzcCj7KpNqOHWn3bMtY60LphLm/kNcSCml8JhSpewXtp8set2uj9EX+jOAu8RaakOqUuv9a2xGkBnFtEF0wI1voR71T4t8MwpQnCFhA6dC8393IHvmAb7weK9ypiEP12WKhERg8IAhw6Uh1CCpbTqwSIJJXbrjewFsPdEEx3nS6JVWXHxz8egzvYb43rnLAL2uD9kZZdZL48C2MxCqhPZfzr4Nm/iamN9njDwk9uGgkwCtSsTlh2318B6rp3pbMRHSDhNYomHpd7w4vusZL8p4s7Ebm2KQkiD9G7qdJR1eW1Fc9pOe7pgW0GakOYVCeD1Jq+1X7+W1gMjTE75yPa5fKyIsE45bao9RES3b5yg6gANVV4V0Ne0UzwFG3fQsWxHCaMFBisDN9sF/hpnqzpJbjZTH3lgpXOboQUL+AMExF07uhPFweYX8vqNmUhbPim9K6e4KysFDZ2H2djFR5QNKK9nxBkE9Ri+AJc5XmtrIFxlRzjxAs8OW1aG3Uxeb9HXEmoRKyWcaVI5b+JkZ+INeklJr1pdyj1VcY1AKjKVorj5H4XZH+DLEEkxk4ZptJt1SAjlUuHrloMANDyi4v7kJz9CNe/xcFYL0WGIhqTjB6Jz0V6RCURffbDWYbCuSGVRX1KNOJkP+yKeAYSV3TrUtLryrudZyJTpf73W57eKlNEs+uCwHpLL7baOoBgB"},{"type":"thinking","thinking":"The P4 gate run passes all its checks on keys, types and reproducibility; the only failures left are P5 deliverables not yet written. I'm now writing the report, claims ledger and phase ledger.\n\n","signature":"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"},{"type":"tool_use","id":"toolu_01JKPTnq7na6Zc4KHBDcQTjo","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F5-conf-01 — Competitive equilibrium of a two-agent, two-good exchange economy\n\n## Question\nAgents A and B have Cobb–Douglas utilities u_i = α_i ln x1 + (1−α_i) ln x2 and endowments (e1_i, e2_i). Good 2 is the numeraire (p2 = 1). We find the Walrasian equilibrium price p = p1/p2 and allocation. We check market clearing and Walras' law, and we validate the closed-form price by numerical root-finding.\n\n## Data and exclusions\n- `data/endowments.csv` (2 rows; `n_input_rows`): A = (8, 2), B = (4, 8). `data/parameters.csv` (4 rows): α_A = 0.4, α_B = 0.6, and summary-sheet aggregates 14 (good 1) and 10 (good 2).\n- **Data issue (`data_issues`):** the summary sheet gives aggregate good-1 endowment 14, but the individual endowments add up to 8 + 4 = 12. The good-2 figure is consistent (10).\n- In an exchange economy the aggregate endowment is, by definition, the sum of the individual endowments, and the task names endowments.csv as the primary data. I therefore used 12 and 10 (`aggregate_endowment_good1`, `aggregate_endowment_good2`).\n- As a diagnostic only, I cleared good 1 against 14 instead. That gives p ≈ 0.6667 with good-2 excess demand ≈ −1.333 ≠ 0, so the economy has no consistent equilibrium. I treat the value 14 as a recording error. Both summary-sheet values are kept in `aggregate_endowment_good1_summary_sheet` and `aggregate_endowment_good2_summary_sheet`.\n- No rows were excluded (`out/exclusions.csv`). Both agents were analysed (`n_analysed` = 2).\n\n## Method\n- **Assumptions of the closed form:**\n  - α_i ∈ (0,1) and strictly positive endowments, so each agent's wealth w_i = p e1_i + e2_i is positive and demands are interior: x1_i = α_i w_i / p and x2_i = (1−α_i) w_i.\n  - Price normalisation p2 = 1.\n- **Closed form:** market clearing for good 1, Σ α_i(p e1_i + e2_i) = p E1, gives p = Σ α_i e2_i / Σ (1−α_i) e1_i = (0.4·2 + 0.6·8)/(0.6·8 + 0.4·4) = 5.6/6.4. The good-2 market then clears by Walras' law.\n- **Independent numerical validation:** Brent's method (`scipy.optimize.brentq`) finds the root of the aggregate excess-demand function z1(p) = Σ x1_i(p) − E1 on the bracket [1e-9, 1e9], with tolerance 1e-15. It never evaluates the closed-form expression.\n  - z1 is strictly decreasing in p here, so the root is unique.\n  - Scope: this check validates the price for these parameters only. It is not a general proof.\n- No randomness is involved (`random_seed` = null).\n\n## Results\n| quantity | value | key |\n|---|---|---|\n| p1/p2 (closed form) | 0.875 | `price_ratio_p1_over_p2` |\n| p1/p2 (numerical root) | 0.875 (abs. difference 2.2e-16) | `price_ratio_numerical`, `price_abs_diff_closed_vs_numerical` |\n| x1_A, x2_A | 4.1143, 5.4 | `x1_A`, `x2_A` |\n| x1_B, x2_B | 7.8857, 4.6 | `x1_B`, `x2_B` |\n| excess demand good 1 | 0.0 | `excess_demand_good1` |\n| excess demand good 2 | 0.0 | `excess_demand_good2` |\n| Walras residual p·z1 + z2 | 0.0 | `walras_law_residual` |\n\nThe allocation adds up to the aggregate endowment (12, 10). Each agent's budget constraint binds. Both agents' marginal rates of substitution equal p = 0.875 (`out/state/checks.md`), so the allocation is Pareto efficient. `feasibility` = \"complete\".\n\n## Uncertainty\nThis is a deterministic theoretical calculation, so there is no sampling uncertainty. The only numerical error is floating-point error: the closed-form and numerical prices differ by about 2.2e-16 (`price_abs_diff_closed_vs_numerical`), and the residuals are 0 at double precision.\n\nThe main source of uncertainty in the result comes from the data, not the computation: which aggregate good-1 endowment is correct. If the individual endowments were wrong and 14 were the right total, the equilibrium would be different. That case cannot be resolved from the inputs.\n\n## Limitations and open issues\n- The summary-sheet aggregate for good 1 (14) contradicts the individual endowments (12). I resolved this in favour of the primary individual data. If the true error is instead in an individual endowment (for example, if A or B actually holds 2 more units of good 1), the price and allocation would change. The inputs cannot tell these cases apart.\n- The closed form relies on interior Cobb–Douglas demands. It does not cover corner solutions, zero endowments or α ∈ {0, 1}.\n- The numerical validation covers this parameter set only.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The closed-form Walrasian equilibrium price is p1/p2 = 0.875 (=5.6/6.4), using aggregate endowments (12, 10) from the individual endowments.\",\"results_keys\":[\"price_ratio_p1_over_p2\",\"aggregate_endowment_good1\",\"aggregate_endowment_good2\"],\"status\":\"supported\",\"caveat\":\"Relies on resolving the summary-sheet discrepancy (14 vs 12) in favour of endowments.csv.\"},\n {\"id\":\"C2\",\"text\":\"Independent root-finding of the aggregate excess demand for good 1 reproduces the closed-form price to within 2.2e-16.\",\"results_keys\":[\"price_ratio_numerical\",\"price_abs_diff_closed_vs_numerical\"],\"status\":\"supported\",\"caveat\":\"Validation for this parameter set only.\"},\n {\"id\":\"C3\",\"text\":\"Equilibrium allocation: A consumes (4.1143, 5.4), B consumes (7.8857, 4.6).\",\"results_keys\":[\"x1_A\",\"x2_A\",\"x1_B\",\"x2_B\"],\"status\":\"supported\",\"caveat\":\"Interior Cobb-Douglas demands assumed.\"},\n {\"id\":\"C4\",\"text\":\"Both markets clear and the Walras' law residual is zero at the reported price.\",\"results_keys\":[\"excess_demand_good1\",\"excess_demand_good2\",\"walras_law_residual\"],\"status\":\"supported\",\"caveat\":\"Zero to double precision.\"},\n {\"id\":\"C5\",\"text\":\"The summary-sheet aggregate endowment of good 1 (14) is inconsistent with the individual endowments (sum 12); clearing against 14 yields no consistent equilibrium.\",\"results_keys\":[\"data_issues\",\"aggregate_endowment_good1_summary_sheet\",\"aggregate_endowment_good1\"],\"status\":\"supported\",\"caveat\":\"Which source is wrong cannot be determined from the inputs.\"}\n]\nEOF\nR1=$(ls out/state/gate_receipts | head -1)\ncat > out/state/phases.json <<EOF\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"sha256/rows/cols recorded in data_audit.md\",\"missing/duplicates: none\",\"aggregate endowment consistency: good1 14 vs 12 mismatch detected\",\"exclusions.csv sums: 2-0=2\"],\"notes\":\"Use individual endowment sums.\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline = numerical root of z1(p) via brentq: 0.875 (price_ratio_numerical)\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, no randomness\",\"closed form 0.875 matches hand computation\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"market clearing, budgets, MRS, Walras residual all pass (checks.md)\",\"gate run 1 receipt out/state/gate_receipts/$R1: all P4 checks (keys, types, reproducibility) ok; 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The price of good 1 relative to good 2 is **p = 0.875** (exactly 5.6/6.4), and both markets clear at that price. Two separate methods agree: the closed-form formula and a numerical root-finder that solves the market-clearing condition directly. They differ by about 2e-16, which is rounding error. The final gate run passed all 31 checks, and re-running `out/analysis.py` reproduces `out/results.json` exactly.\n\n| | Good 1 | Good 2 |\n|---|---|---|\n| Agent A consumes | 4.1143 | 5.4 |\n| Agent B consumes | 7.8857 | 4.6 |\n| Total (= endowment) | 12 | 10 |\n\n- **Checks:** excess demand for good 1, excess demand for good 2 and the Walras' law residual are all 0. Each agent spends exactly their budget, and both agents' rates of trade-off between the goods equal the price, so neither could be made better off without hurting the other.\n- **Assumptions:** preference weights strictly between 0 and 1, strictly positive endowments, and good 2's price fixed at 1. 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