# I'd like to set up the CVS simulation described he... ## Conversation ## User *2026-09-16T11:31:38.596619+00:00* I'd like to set up the CVS simulation described here: https://www.wsj.com/cmo-today/attack-of-the-clones-speeds-up-cvs-strategy-a4068fde but with EDSL --- ## Assistant *2026-09-16T11:32:09.535050+00:00* Good article -- published just yesterday. Here's what CVS is doing with Simile's digital twins, and what we can replicate in EDSL: **The CVS approach**: They created AI "twins" of two populations -- **consumers** (patients on biologic drugs) and **healthcare providers** (prescribers) -- to test messaging around switching to lower-cost biosimilar alternatives. The consumer twins said they wanted to hear recommendations from their providers; the provider twins helped CVS figure out what clinical evidence would motivate prescribers. What normally takes 6-8 weeks of panel research happened in ~20 minutes, with 90-95% accuracy against their validation panel. This maps naturally to an EDSL simulation: we build agent panels for both populations, then survey them about biosimilar adoption attitudes and messaging preferences. Before I write up a plan, one question: **Tool calls:** - `Skill` - `mcp__firecrawl__firecrawl_scrape` - `url`: https://www.wsj.com/cmo-today/attack-of-the-clones-speeds-up-cvs-strategy-a4068fde - `Bash` - `description`: Initialize study workspace for CVS simulation, `command`: ep study start --root . --topic "cvs-simulation" - `Bash` - `description`: List available research skill types, `command`: bash ~/.claude/list_study_types.sh --- ## User *2026-09-16T11:32:31.965999+00:00* Q: What scope do you want for this simulation? A: Replicate the biosimilar case --- ## Assistant *2026-09-16T11:33:01.490838+00:00* Now let me load the right study skill. Since this is fundamentally about surveying two synthetic populations (consumer twins and provider twins) to understand their attitudes and messaging preferences, I'll load the survey research skill. **Tool calls:** - `Skill` Good -- the biosimilar case is a clean two-panel survey simulation. Let me confirm the model and load the plan template before writing up the design. **Tool calls:** - `Skill` - `Skill` - `Bash` - `description`: Query current Anthropic Sonnet models, `command`: ep models --service anthropic --search sonnet --text --sort input-price --limit 10 Now let me write the plan. **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/plan.md - `Bash` - `description`: Present the plan file, `command`: ep present /home/user/sessions/topic_cvs-simulation/study_a/plan.md Here's the plan -- two parallel surveys replicating the CVS biosimilar twin approach: **Consumer panel** (20 agents): patients on biologics for RA, psoriasis, Crohn's, and MS, varied by age, insurance, tenure on current drug, and trust level. 8 questions covering awareness, openness to switching, trusted sources, concerns, and message testing. **Provider panel** (15 agents): rheumatologists, dermatologists, and GI docs varied by experience, setting, and current biosimilar usage. 7 questions covering prescribing habits, evidence needs, patient barriers, and what CVS support would help. The cross-panel analysis is where it gets interesting: we can check whether patients' #1 trust source (the article says it's their provider) aligns with what providers say they're actually doing in those conversations. **Model**: `claude-sonnet-4-6` | **Est. cost**: ~$2.50-5.00 for the full run. --- ## User *2026-09-16T11:35:29.634048+00:00* Q: Any changes to the plan, or ready to proceed? A: Looks good, proceed --- ## Assistant *2026-09-16T11:35:35.842086+00:00* Plan approved. Let me set up the workflow, build the agents and surveys, and run this. **Tool calls:** - `Bash` - `description`: Initialize workflow with three gates, `command`: ep workflow setup --name "CVS Biosimilar Digital Twin Simulation" --root /home/user/sessions/topic_cvs-simulation/study_a --json '{"gates":[{"name":"plan-app... - `Bash` - `description`: Attest plan-approved gate, `command`: ep workflow gate attest plan-approved --root /home/user/sessions/topic_cvs-simulation/study_a --by user --evidence "User approved plan as written - concise r... Good. Now let me load the EDSL surveys skill and build everything. **Tool calls:** - `Skill` - `Skill` - `Bash` - `description`: Scaffold survey template for CVS study, `command`: ep study scaffold /home/user/sessions/topic_cvs-simulation/study_a --template survey --expected-rows 20 --required-answer trusted_source --required-answer pr... - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/job_a/study_survey.py - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/job_a/study_agent_list.py - `Read` - `file_path`: /home/user/.claude/skills/edsl-surveys/references/survey-reference.md - `Read` - `file_path`: /home/user/.claude/skills/edsl-surveys/references/survey-reference.md Now let me write the consumer survey and agents in the scaffold files, plus create the provider survey as a second job. **Tool calls:** - `Edit` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/job_a/study_survey.py - `Edit` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/job_a/study_agent_list.py Now let me create the provider survey and agent files: **Tool calls:** - `Bash` - `description`: Create provider job directory, `command`: mkdir -p /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/job_b - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/job_b/study_survey_provider.py - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/job_b/study_agent_list_provider.py Now let me run the scaffold's prepare command and build the provider job manually: **Tool calls:** - `Bash` **failed** - `description`: Build EDSL objects and estimate cost for consumer survey, `command`: make -C "/home/user/sessions/topic_cvs-simulation/study_a" prepare APPROVAL_EVIDENCE="User approved plan as written - concise report, both panels, claude-son... ``` Exit code 2 make: Entering directory '/__modal/volumes/vo-ZKRRO4oJ93GTb1cCHKe2FM/sessions/topic_cvs-simulation/study_a' env -u VIRTUAL_ENV uv run --project /__modal/volumes/vo-ZKRRO4oJ93GTb1cCHKe2FM --frozen --no-dev -- ep workflow setup --name "CVS biosimilar consumer twin survey - 20 patient agents x 8 questions" --root . --spec workflow-gates.json --evidence "User approved plan as written - concise report, both panels, claude-sonnet-4-6" env: ‘uv’: No such file or directory make: *** [Makefile:66: workflow-setup] Error 127 make: Leaving directory '/__modal/volumes/vo-ZKRRO4oJ93GTb1cCHKe2FM/sessions/topic_cvs-simulation/study_a' ``` - `Bash` - `description`: Check available tools, `command`: which ep && which python3 && which uv 2>/dev/null || echo "uv not found" - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/Makefile - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/Makefile The Makefile expects `uv`. Let me fix this for our environment where `ep` and `python3` are directly available: **Tool calls:** - `Edit` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/Makefile - `Bash` **failed** - `description`: Build EDSL objects and estimate cost for consumer survey, `command`: make -C "/home/user/sessions/topic_cvs-simulation/study_a" prepare APPROVAL_EVIDENCE="User approved plan as written - concise report, both panels, claude-son... ``` Exit code 2 make: Entering directory '/__modal/volumes/vo-ZKRRO4oJ93GTb1cCHKe2FM/sessions/topic_cvs-simulation/study_a' ep workflow setup --name "CVS biosimilar consumer twin survey - 20 patient agents x 8 questions" --root . --spec workflow-gates.json --evidence "User approved plan as written - concise report, both panels, claude-sonnet-4-6" { "status": "error", "error": { "code": "WORKFLOW_EXISTS", "message": "Workflow already exists at /__modal/volumes/vo-ZKRRO4oJ93GTb1cCHKe2FM/sessions/topic_cvs-simulation/study_a/.ep-workflow" } } make: *** [Makefile:66: workflow-setup] Error 1 make: Leaving directory '/__modal/volumes/vo-ZKRRO4oJ93GTb1cCHKe2FM/sessions/topic_cvs-simulation/study_a' ``` The workflow already exists from my earlier setup. Let me remove it and let the scaffold manage it properly: **Tool calls:** - `Bash` - `description`: Remove old workflow and run scaffold prepare, `command`: rm -rf /home/user/sessions/topic_cvs-simulation/study_a/.ep-workflow && make -C "/home/user/sessions/topic_cvs-simulation/study_a" prepare APPROVAL_EVIDENCE=... Consumer survey is ready. Now let me build the provider job: **Tool calls:** - `Bash` - `description`: Build provider survey EDSL objects and job, `command`: cd /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/job_b && python3 study_survey_provider.py && python3 study_agent_list_provider.py && ep jobs bu... - `Bash` - `description`: Estimate provider survey cost, `command`: ep jobs cost /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/job_b/jobs_provider.ep Both jobs are built. Here's the cost breakdown: | Survey | Agents | Questions | Estimated cost | |--------|--------|-----------|---------------| | Consumer | 20 | 8 | $0.56 | | Provider | 15 | 7 | $0.38 | | **Total** | **35** | **15** | **$0.94** | Well under the $2.50-5.00 estimate. May I run both surveys? Total cost: **~$0.94**. **Tool calls:** - `Bash` - `description`: Run consumer survey via scaffold, `command`: make -C "/home/user/sessions/topic_cvs-simulation/study_a" data - `Bash` **failed** - `description`: Run provider survey, `command`: ep run /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/job_b/jobs_provider.ep --output /home/user/sessions/topic_cvs-simulation/study_a/data/resul... ``` Exit code 2 { "status": "error", "error": { "code": "USAGE_ERROR", "message": "No such option '--description'." } } ``` --- ## User *2026-09-16T11:40:31.555935+00:00* Q: Approve running both surveys for ~$0.94 total? A: Yes, run both --- ## Assistant *2026-09-16T11:41:00.724854+00:00* Consumer survey complete (20/20, $0.41). Provider survey had a flag error -- let me fix and rerun: **Tool calls:** - `Bash` - `description`: Check ep run options, `command`: ep run --help 2>&1 | head -30 - `Bash` - `description`: Run provider survey, `command`: ep run /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/job_b/jobs_provider.ep --output /home/user/sessions/topic_cvs-simulation/study_a/data/resul... Both surveys complete -- 20/20 consumer, 15/15 provider. Total cost: $0.71. Let me inspect the results: **Tool calls:** - `Bash` - `description`: Check consumer results columns, `command`: ep results columns --file /home/user/sessions/topic_cvs-simulation/study_a/data/results.ep - `Bash` - `description`: Check provider results columns, `command`: ep results columns --file /home/user/sessions/topic_cvs-simulation/study_a/data/results_provider.ep Both result sets look clean. Let me pull the key findings: **Tool calls:** - `Bash` **failed** - `description`: Export consumer results to CSV, `command`: ep results export /home/user/sessions/topic_cvs-simulation/study_a/data/results.ep --columns answer.trusted_source,answer.primary_concern,answer.switching_op... ``` Exit code 2 { "status": "error", "error": { "code": "USAGE_ERROR", "message": "No such option '--columns'." } } ``` - `Bash` **failed** - `description`: Export provider results to CSV, `command`: ep results export /home/user/sessions/topic_cvs-simulation/study_a/data/results_provider.ep --columns answer.prescribing_frequency,answer.clinical_confidence... ``` Cancelled: parallel tool call Bash(ep results export /home/user/sessions/to…) errored ``` - `Bash` - `description`: Check export syntax, `command`: ep results export --help 2>&1 | head -30 - `Bash` - `description`: Export consumer results to CSV, `command`: ep results export /home/user/sessions/topic_cvs-simulation/study_a/data/results.ep --column answer.trusted_source --column answer.primary_concern --column an... - `Bash` - `description`: Export provider results to CSV, `command`: ep results export /home/user/sessions/topic_cvs-simulation/study_a/data/results_provider.ep --column answer.prescribing_frequency --column answer.clinical_co... - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/data/consumer_export.csv - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/data/provider_export.csv Fantastic results. Let me run the post-run processing and then build the analysis and report. **Tool calls:** - `Bash` - `description`: Run post-run review, exports, QA, and plots, `command`: make -C "/home/user/sessions/topic_cvs-simulation/study_a" post-run 2>&1 Good -- consumer post-run passed QA (20/20 rows, required answers present). Now let me write the analysis script for combined findings, generate the cross-panel plots, and write the report. **Tool calls:** - `Bash` - `description`: Check generated plots, `command`: ls /home/user/sessions/topic_cvs-simulation/study_a/writeup/plots/ - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/data/report-data.json - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/data/report-data.json Now let me write the cross-panel analysis plots and the report. Let me first create the analysis script: **Tool calls:** - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/analysis/plot_style.py Now let me write the analysis plots and report. I'll create the key plots and then the report: **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/analysis/cross_panel_plots.py - `Bash` - `description`: Generate cross-panel analysis plots, `command`: cd /home/user/sessions/topic_cvs-simulation/study_a && python3 analysis/cross_panel_plots.py Let me visually inspect the key plots: **Tool calls:** - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/writeup/plots/trusted-source.png - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/writeup/plots/message-effectiveness.png - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/writeup/plots/cross-panel-gap.png Excellent -- the plots clearly tell the story. The trusted source and message effectiveness charts are especially striking. Now let me write the report. **Tool calls:** - `Skill` - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_a/writeup/report.md Now let me load the report quality review skill as required before compilation: **Tool calls:** - `Skill` - `Bash` - `description`: Preview review plan and detected spotters, `command`: katz report review-plan --report /home/user/sessions/topic_cvs-simulation/study_a/writeup/report.md - `Bash` - `description`: Create report-review model panel, `command`: ep models create --profile report-review --count 3 --output /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/report-review.models.ep - `Bash` - `description`: Build review jobs with auto-attached images, `command`: katz report review-jobs --report /home/user/sessions/topic_cvs-simulation/study_a/writeup/report.md --models /home/user/sessions/topic_cvs-simulation/study_a... - `Bash` - `description`: Cost the review jobs, `command`: ep jobs cost /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/report-review.jobs.ep --- ## User *2026-09-16T11:45:13.784209+00:00* Q: Would you like to run an optional pre-delivery quality review on the report? A: Run review --- ## Assistant *2026-09-16T11:45:32.662134+00:00* Review cost is ~$0.05 -- well within the approved range. Running it: **Tool calls:** - `Bash` - `description`: Run report quality review, `command`: ep run /home/user/sessions/topic_cvs-simulation/study_a/edsl_jobs/report-review.jobs.ep --output /home/user/sessions/topic_cvs-simulation/study_a/data/report... - `Bash` - `description`: Ingest review results, `command`: katz report review-ingest --results /home/user/sessions/topic_cvs-simulation/study_a/data/report-review.results.ep --report /home/user/sessions/topic_cvs-sim... The optional quality review ran but all 3 model responses were unparseable (0/3 parsed). This is a known limitation with review formatting -- the deterministic checks still apply. Let me proceed with compilation and verification: **Tool calls:** - `Bash` - `description`: Compile report to HTML, `command`: cd /home/user/sessions/topic_cvs-simulation/study_a/writeup && pandoc report.md -o report.html --standalone --embed-resources --css=report.css - `Bash` - `description`: Check EP branding in compiled HTML, `command`: grep -q "ep-green" /home/user/sessions/topic_cvs-simulation/study_a/writeup/report.html && echo "CSS branding present" || echo "CSS branding missing" - `Bash` - `description`: Run deterministic report check, `command`: ep report check --root /home/user/sessions/topic_cvs-simulation/study_a Report check passed. Now let me run the final workflow verification: **Tool calls:** - `Bash` - `description`: Verify all workflow gates, `command`: make -C "/home/user/sessions/topic_cvs-simulation/study_a" workflow-verify All 3 gates passed. Let me present the final report: **Tool calls:** - `Bash` - `description`: Present the final report, `command`: make -C "/home/user/sessions/topic_cvs-simulation/study_a" present The simulation is complete. Here's a summary of what was built and the key findings: --- **Study complete** -- all 3 workflow gates passed, report compiled and delivered. ### What we built Two EDSL agent panels replicating CVS Health's digital twin approach to biosimilar adoption strategy: - **20 patient agents** (biologic users varying by condition, age, insurance, tenure, trust) - **15 provider agents** (prescribers varying by specialty, experience, setting, prescribing habits) - Two surveys, 15 questions total, run on `claude-sonnet-4-6` - Total cost: **$0.71** in ~3 minutes (vs. CVS's reported 6-8 weeks for traditional panels) ### Key findings that match the WSJ article 1. **Patients unanimously trust their doctor** -- 20/20 selected their prescribing specialist as the most trusted source for a biosimilar recommendation. This directly replicates CVS's reported finding. 2. **Doctor-recommendation messaging dominates** -- 19/20 chose "Your doctor has reviewed the clinical evidence and recommends this biosimilar" over FDA language, cost savings, or social proof. 3. **A conversation gap exists** -- 65% of patients haven't had a biosimilar conversation with their provider but want one. Only 20% of providers proactively prescribe biosimilars. 4. **A barrier-perception mismatch** -- Patients worry about efficacy. Providers think patients distrust insurance-driven cost-cutting. This gap suggests providers underestimate how receptive patients would be to a clinician-led, evidence-first conversation. **Strategic implication**: CVS's most effective lever is reducing the burden on providers to have these conversations -- not routing around them through pharmacy or insurer channels. --- ## User *2026-09-16T11:48:08.460715+00:00* Great - I'd like to do another small pilot study on Prolific that is patient-focused. Based on what we learned from this, what would be the most useful things to ask them? --- ## Assistant *2026-09-16T11:48:31.870376+00:00* Great question. The simulation gave us clear signals about *what to validate* with real humans. Let me load the Prolific study skill since we're transitioning to human fielding, and then walk through what I'd prioritize. **Tool calls:** - `Skill` Good instinct -- the simulation tells us *what to validate*, not just what to repeat. Here's how I'd think about it: **The simulation's strongest signals (worth validating with real humans):** The unanimity on two findings is actually the most important thing to test. When all 20 AI agents agree on something, it could be a genuine strong signal *or* it could be model-conditioned conformity. Real patients will show variance, and the *amount* of variance is the whole point: 1. **Trusted source** (simulation: 20/20 doctor) -- If real patients show even 60-70% doctor preference, that confirms the insight. If they show more spread toward pharmacists or self-research, that's genuinely new information the simulation missed. 2. **Message effectiveness** (simulation: 19/20 doctor-recommends framing) -- The cost-savings and social-proof framings got zero traction in simulation. Real patients under financial pressure might value those more. This is the most actionable question for CVS messaging. 3. **Provider conversation gap** (simulation: 65% never discussed, but want to) -- This is the strategic opportunity. Validating it with real patients tells CVS whether there's unmet demand for these conversations. 4. **Switching openness** -- The distribution (55% hesitant, 25% somewhat open, 20% not open, 0% very open) gives a baseline. Real patients might be more polarized. **What I'd drop for a pilot:** The free-text question (hard to analyze at small N), biosimilar awareness (less actionable), and current satisfaction (not the core strategic question). That gives us a tight 5-6 question survey plus a screener. **The big practical challenge:** Finding biologic patients on Prolific. This is maybe 1-3% of the general population, so screening will be expensive. --- ## User *2026-09-16T11:49:59.187373+00:00* Q: How should we handle the biologic-patient targeting? A: Broader chronic condition Q: How many qualified responses do you want for this pilot? A: 20-30 (quick validation) --- ## Assistant *2026-09-16T11:50:21.216432+00:00* Good choices. Broader chronic-condition screening gives us a much more practical pool, and 20-30 responses is enough to see if the simulation's main patterns hold with real people. Let me scaffold a follow-on study and design the Prolific survey. **Tool calls:** - `Bash` - `description`: Create study_b for the Prolific pilot, `command`: ep study start --root . --topic "cvs-simulation" - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_b/plan.md - `Bash` - `description`: Present Prolific pilot plan, `command`: ep present /home/user/sessions/topic_cvs-simulation/study_b/plan.md - `Bash` - `description`: List study_b directory contents, `command`: ls -la /home/user/sessions/topic_cvs-simulation/study_b/ - `Bash` - `description`: Get current timestamp, `command`: date '+%Y-%m-%dT%H:%M:%S%z' - `Bash` - `description`: Scaffold study_b with neutral template, `command`: ep study scaffold /home/user/sessions/topic_cvs-simulation/study_b --- ## User *2026-09-16T11:54:01.534175+00:00* Q: The Prolific pilot plan is ready at study_b/plan.md. The main design choice: the screener uses broad 'chronic prescription medication' rather than biologic-specific, meaning some respondents will be on simpler medications (statins, inhalers) where switching is less fraught. This trades population match for practical feasibility (~50% vs ~5% qualification rate). Ready to proceed? A: Looks good, proceed --- ## Assistant *2026-09-16T11:54:29.710954+00:00* Now I'll set up the workflow, build the survey, and get it deployed on Prolific. Let me start with the workflow gates and survey construction. **Tool calls:** - `Bash` - `description`: Set up workflow gates for study_b, `command`: ep workflow setup --name "prolific-biosimilar-pilot" --root /home/user/sessions/topic_cvs-simulation/study_b --json '{"gates":[{"name":"plan-approved","descr... - `Bash` - `description`: Attest plan approval gate, `command`: ep workflow gate attest plan-approved --root /home/user/sessions/topic_cvs-simulation/study_b --by user --evidence "User approved broader chronic-condition s... Good. Now let me build the survey and humanize it: **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_b/edsl_jobs/build_survey.py - `Bash` - `description`: Build survey, save, and humanize for Prolific, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b/edsl_jobs && python3 build_survey.py Survey is humanized. Now let me set up the Prolific filters and calculate the exact cost. **Tool calls:** - `Bash` - `description`: Calculate Prolific study cost, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop coop = Coop() # Calculate cost: ~60 participants, ~4 min, $1.00/per... - `Bash` - `description`: Inspect Prolific filters for US + quality, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop coop = Coop() # Get available filters filters = coop.list_prolific_... - `Bash` - `description`: Create Prolific filters for US + quality, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop coop = Coop() filters = coop.list_prolific_filters() # Create filte... Good. Now let me create the Prolific study in draft mode: **Tool calls:** - `Bash` **failed** - `description`: Create Prolific study in draft mode, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop import json coop = Coop() # Load humanized survey details with open... ``` Exit code 1 Traceback (most recent call last): File "", line 18, in TypeError: string indices must be integers, not 'str' ``` - `Bash` - `description`: Inspect humanized survey JSON structure, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " import json with open('edsl_jobs/humanized_survey.json') as f: hs = json.load(f) print(ty... - `Bash` **failed** - `description`: Create Prolific study in draft mode with UUID string, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop import json coop = Coop() uuid = '1805d25a-ba1b-450e-b835-3c385d52d... ``` Exit code 1 Traceback (most recent call last): File "", line 15, in TypeError: Coop.create_prolific_study() got an unexpected keyword argument 'uuid' ``` - `Bash` - `description`: Check create_prolific_study signature, `command`: python3 -c "from edsl import Coop; help(Coop.create_prolific_study)" 2>&1 | head -40 - `Bash` - `description`: Create Prolific study with correct param name, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop import json coop = Coop() uuid = '1805d25a-ba1b-450e-b835-3c385d52d... The Prolific study is created in draft (UNPUBLISHED) mode. Here's the summary: **Prolific study ready to launch:** | Detail | Value | |--------|-------| | Participants | 60 (targeting 25-30 qualified after screening) | | Payment | $1.00 per person ($15/hr for ~4 min) | | Total cost | **$85.71** (including Prolific platform fee) | | Filters | US residents, approval rate >= 95% | | Status | Draft -- no money charged yet | The survey has 7 questions with the screener stop rule: anyone who answers "No" to chronic prescription medication use exits immediately. The remaining 6 questions match the core topics from the AI simulation. **Preview links:** - [Admin view](https://www.expectedparrot.com/home/human-surveys/1805d25a-ba1b-450e-b835-3c385d52df07/prolific-studies/6aaa83c575d8b07d56d09017) - [Participant preview](https://www.expectedparrot.com/respond/human-surveys/1805d25a-ba1b-450e-b835-3c385d52df07?save_response=false) --- ## User *2026-09-16T11:58:09.684609+00:00* Q: Publishing will charge $85.71 (8,571 EP credits) and begin recruiting 60 US participants on Prolific. Ready to go live? A: is the screener requirement in the study headline? --- ## Assistant *2026-09-16T11:58:23.658672+00:00* Good question. The Prolific listing that participants see says: - **Title:** "Share your experience with prescription medication switching" - **Description:** "A brief survey (3-4 minutes) about your experience with prescription medications for chronic conditions..." So "chronic conditions" is mentioned in the description, which helps self-selection, but the formal screener happens at Q1 inside the survey. Anyone who answers "No" to taking chronic medication exits immediately. This is the standard approach -- Prolific doesn't have a built-in "takes chronic medication" filter, so the in-survey screener handles it. The listing copy signals the topic enough that people without chronic medications are less likely to click in, but we over-recruit to 60 specifically because ~50% may screen out. Would you like me to make the description more explicit about the requirement to reduce unnecessary screen-outs? For example: > "A brief survey (3-4 minutes) for people who currently take prescription medication for a chronic health condition. We will ask about your comfort level with medication changes and what information matters most to you. If you do not currently take chronic medication, the survey will end after the first question." **Tool calls:** - `Bash` - `description`: Check update_prolific_study API, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop coop = Coop() study_id = '6aaa83c575d8b07d56d09017' # Check if ther... - `Bash` - `description`: Update Prolific study description to be explicit about screener, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop import json coop = Coop() updated = coop.update_prolific_study( hum... --- ## User *2026-09-16T11:58:31.967387+00:00* Q: Should we update the participant-facing description to be more explicit about the chronic-medication requirement? A: Make it explicit --- ## Assistant *2026-09-16T11:58:45.112166+00:00* Description updated. The listing now clearly states the chronic-medication requirement upfront. --- ## User *2026-09-16T11:58:58.060508+00:00* Q: The study listing is updated. Publishing will charge $85.71 (8,571 credits) and start recruiting 60 US participants. Ready to publish? A: I'd like to preview the survey first --- ## Assistant *2026-09-16T11:59:02.433157+00:00* Here's the participant preview link -- it lets you take the survey exactly as a participant would see it, without recording a response: **[Preview the survey](https://www.expectedparrot.com/respond/human-surveys/1805d25a-ba1b-450e-b835-3c385d52df07?save_response=false)** You can also view the admin dashboard here: **[Admin view](https://www.expectedparrot.com/home/human-surveys/1805d25a-ba1b-450e-b835-3c385d52df07/prolific-studies/6aaa83c575d8b07d56d09017)** Take a look and let me know when you're ready to publish, or if you'd like any changes to the questions or flow. --- ## User *2026-09-16T12:02:40.650426+00:00* ok - let's launch it --- ## Assistant *2026-09-16T12:02:52.642544+00:00* The study is live on Prolific and recruiting. $85.71 charged. **What's running:** - 60 slots open for US participants (approval rate >= 95%) - Screener filters out non-chronic-medication users at Q1 - Expecting 25-30 qualified completions **Monitor & collect:** You can track progress on the [admin dashboard](https://www.expectedparrot.com/home/human-surveys/1805d25a-ba1b-450e-b835-3c385d52df07/prolific-studies/6aaa83c575d8b07d56d09017). Recruitment for a short, well-paid survey like this typically fills within a few hours. Once responses come in, I can pull the results and build the simulation-vs-human comparison table -- that's the core deliverable from the plan. Just let me know when you'd like to check on responses or are ready to analyze. **Tool calls:** - `Bash` - `description`: Publish Prolific study -- charges $85.71, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop import json coop = Coop() result = coop.publish_prolific_study( hum... --- ## User *2026-09-16T12:19:03.484479+00:00* great - let's get the responses and analyze them --- ## Assistant *2026-09-16T12:19:20.827179+00:00* 53 responses collected. Let me pull the data and separate qualified from screened-out participants. **Tool calls:** - `Bash` - `description`: Check Prolific study status and response count, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop import json coop = Coop() status = coop.get_prolific_study( human_s... - `Bash` - `description`: Pull human survey responses, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop import json coop = Coop() # Pull responses for the human survey res... - `Bash` - `description`: Save Prolific results and list columns, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop coop = Coop() responses = coop.get_human_survey_responses( human_su... - `Bash` **failed** - `description`: Export answer data and check screener breakdown, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " from edsl import Coop coop = Coop() responses = coop.get_human_survey_responses( human_su... ``` Exit code 1 Traceback (most recent call last): File "", line 23, in File "/usr/local/lib/python3.12/collections/__init__.py", line 1135, in __getitem__ raise KeyError(key) KeyError: 'answer.screener' Exported to data/prolific_export.csv ``` - `Bash` - `description`: Check exported CSV structure, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && head -5 data/prolific_export.csv - `Bash` - `description`: Full breakdown of qualified Prolific responses, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " import csv from collections import Counter with open('data/prolific_export.csv') as f: re... Excellent data -- 50 qualified responses out of 54 (93% screen-through, much better than the 50% we estimated). The results are fascinating. Let me build the comparison analysis and report. **Tool calls:** - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_b/analysis/plot_style.py Now let me build the comparison plots and the report. First, the analysis script: **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_b/analysis/comparison_plots.py - `Bash` - `description`: Generate comparison plots, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 analysis/comparison_plots.py Let me inspect the key plots: **Tool calls:** - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_b/writeup/plots/comparison-summary.png - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_b/writeup/plots/comparison-trusted-source.png - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_b/writeup/plots/comparison-message-effectiveness.png - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_b/writeup/plots/comparison-switching-openness.png - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_b/writeup/plots/comparison-primary-concern.png Plots look clean. Now let me load the report authoring skill and write the comparison report. **Tool calls:** - `Skill` - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_b/writeup/report.md Now I need to resolve the pre-compilation review decision before compiling. **Tool calls:** - `Skill` - `Bash` - `description`: Preview quality review plan and detected spotters, `command`: katz report review-plan --report /home/user/sessions/topic_cvs-simulation/study_b/writeup/report.md - `Bash` - `description`: Create 3-model review panel, `command`: ep models create --profile report-review --count 3 --output /home/user/sessions/topic_cvs-simulation/study_b/edsl_jobs/report-review.models.ep - `Bash` - `description`: Build review Jobs package with images, `command`: katz report review-jobs --report /home/user/sessions/topic_cvs-simulation/study_b/writeup/report.md --models /home/user/sessions/topic_cvs-simulation/study_b... - `Bash` - `description`: Get exact cost for review package, `command`: ep jobs cost /home/user/sessions/topic_cvs-simulation/study_b/edsl_jobs/report-review.jobs.ep --- ## User *2026-09-16T12:24:31.206782+00:00* Q: Optional pre-delivery quality review: three frontier models check the report for numerical consistency, claim calibration, simulation-boundary language, and visual integrity. Approximately $0.15-0.25 across 3 model calls, about 2 minutes. Run it or skip? A: Run review --- ## Assistant *2026-09-16T12:24:48.977068+00:00* $0.05 -- within the approved estimate. Running the review. **Tool calls:** - `Bash` - `description`: Run 3-model quality review, `command`: ep run /home/user/sessions/topic_cvs-simulation/study_b/edsl_jobs/report-review.jobs.ep --output /home/user/sessions/topic_cvs-simulation/study_b/data/report... - `Bash` - `description`: Ingest and normalize review issues, `command`: katz report review-ingest --results /home/user/sessions/topic_cvs-simulation/study_b/data/report-review.results.ep --report /home/user/sessions/topic_cvs-sim... The optional quality review failed to parse again (0/3 reviewers parseable -- same pattern as study_a). Deterministic checks will still validate the report. Let me compile and check. **Tool calls:** - `Bash` - `description`: Check if report CSS exists, `command`: ls /home/user/sessions/topic_cvs-simulation/study_b/writeup/report.css 2>/dev/null && echo "CSS exists" || echo "CSS missing" - `Bash` - `description`: Compile report to HTML with embedded resources, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b/writeup && pandoc report.md -o report.html --standalone --embed-resources --css=report.css - `Bash` - `description`: Verify CSS branding in compiled HTML, `command`: grep -c "ep-green" /home/user/sessions/topic_cvs-simulation/study_b/writeup/report.html && echo "CSS branding embedded" || echo "CSS branding missing" - `Bash` - `description`: Run deterministic report check, `command`: ep report check --root /home/user/sessions/topic_cvs-simulation/study_b Report check passed. Now verify the workflow: **Tool calls:** - `Bash` - `description`: Attest Prolific publication gate, `command`: ep workflow gate attest prolific-study-published --root /home/user/sessions/topic_cvs-simulation/study_b --by user --evidence "Prolific study 6aaa83c575d8b07... - `Bash` - `description`: Verify all remaining workflow gates, `command`: ep workflow verify --root /home/user/sessions/topic_cvs-simulation/study_b All gates passed. Let me present the report. **Tool calls:** - `Bash` - `description`: Present the final comparison report, `command`: ep present /home/user/sessions/topic_cvs-simulation/study_b/writeup/report.html The comparison report is complete. Here's what the Prolific pilot revealed about the AI simulation: **Where the simulation got it right:** - Doctor is the most trusted source (100% sim vs. 82% human) -- rank confirmed - Doctor-recommendation message is preferred (95% sim vs. 50% human) -- rank confirmed **Where it diverged meaningfully:** - **Switching openness was way off** -- the simulation predicted a cautious population (55% hesitant), but real patients are dramatically more open (50% very open, 0% not open). This is the biggest miss. - **Side effects vs. efficacy flipped** -- simulation said efficacy was the top concern (60%); real patients worry most about side effects (50%) - **Cost savings messaging matters** -- the simulation dismissed it (0%), but 26% of real patients found it most persuasive **The strategic takeaway:** The simulation correctly identified the physician as the key trust lever, but overstated consensus on every dimension. A messaging strategy should pair doctor-recommendation framing with concrete cost information -- something the simulation alone would have missed. Total cost across both studies: $86.42 ($0.71 simulation + $85.71 Prolific). --- ## User *2026-09-16T12:44:14.842229+00:00* Great - can we make a new set of digital twins based on the prolific study that in turn can reproduce our original study? --- ## Assistant *2026-09-16T12:44:38.676674+00:00* That's a great idea -- calibrating digital twins from real human data and testing whether they reproduce the Prolific findings better than the hand-crafted personas did. Let me set this up. **Tool calls:** - `Bash` - `description`: Allocate study_c in the CVS topic, `command`: ep study start --root . --topic "cvs-simulation" - `Bash` - `description`: Scaffold study_c with neutral template, `command`: ep study scaffold /home/user/sessions/topic_cvs-simulation/study_c Now let me check exactly what data we have from each Prolific respondent to design the twin construction: **Tool calls:** - `Bash` - `description`: Inspect Prolific respondent profiles for twin construction, `command`: cd /home/user/sessions/topic_cvs-simulation/study_b && python3 -c " import csv from collections import Counter with open('data/prolific_export.csv') as f: ro... Good data -- 50 respondents, each with a condition type and 5 attitudinal responses. Let me design how to turn this into calibrated twins. **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_c/plan.md - `Bash` - `description`: Present study_c plan, `command`: ep present /home/user/sessions/topic_cvs-simulation/study_c/plan.md --- ## User *2026-09-16T12:46:22.079383+00:00* Q: The plan creates 50 AI twins from Prolific respondent profiles, encoding their attitudes as personality traits (not their literal answers -- that would be circular). Then runs the same survey and does a three-way comparison. The key design choice is translating answers into traits rather than embedding them directly. Proceed? A: This seems good but let's do it in a new study so the old study is fixed --- ## Assistant *2026-09-16T12:46:26.133861+00:00* That's exactly the setup -- study_b stays untouched, and this is study_c. Let me build it. **Tool calls:** - `Bash` - `description`: Set up workflow gates for study_c, `command`: ep workflow setup --name "data-calibrated-twins" --root /home/user/sessions/topic_cvs-simulation/study_c --json '{"gates":[{"name":"plan-approved","descripti... - `Bash` - `description`: Attest plan approval, `command`: ep workflow gate attest plan-approved --root /home/user/sessions/topic_cvs-simulation/study_c --by user --evidence "User approved attitudinal-trait persona a... Now let me build the agent list and survey. First, the script that translates Prolific responses into persona traits: **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_c/edsl_jobs/build_agents.py - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_c/edsl_jobs/build_survey.py - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_c/edsl_jobs/build_jobs.py - `Bash` - `description`: Build all EDSL objects: agents, survey, jobs, `command`: cd /home/user/sessions/topic_cvs-simulation/study_c/edsl_jobs && python3 build_agents.py && python3 build_survey.py && python3 build_jobs.py - `Bash` - `description`: Get exact cost for 300-call simulation, `command`: ep jobs cost /home/user/sessions/topic_cvs-simulation/study_c/edsl_jobs/jobs.ep - `Bash` **failed** - `description`: Run 50 calibrated twins x 6 questions, `command`: ep run /home/user/sessions/topic_cvs-simulation/study_c/edsl_jobs/jobs.ep --output /home/user/sessions/topic_cvs-simulation/study_c/data/results.ep ``` Exit code 1 { "status": "error", "error": { "code": "RUN_ERROR", "message": "Job execution failed: Agent trait key(s) {'condition_type'} conflict with question name(s). Please rename the trait(s) or question(s) to avoid collisions. For example: agent.rename({'condition_type': 'agent_condition_type'})\n\n\nException raised when there's an invalid value in job-related operations.\n\n This exception indicates that a parameter or value used in job configuration\n or execution is invalid, out of range, or otherwise inappropriate.\n\n Common causes include:\n - Invalid question names in job configuration\n - Incompatible survey and scenario combinations\n - Invalid parameter values for job construction\n\n To fix this error:\n 1. Check the parameter values in your job configuration\n 2. Ensure that all question names exist in the survey\n 3. Verify that survey and scenario combinations are compatible\n\n\nFor more information, see: https://docs.expectedparrot.com/en/latest/jobs" } } ``` --- ## User *2026-09-16T12:49:24.659043+00:00* Q: Running 50 data-calibrated twins through 6 questions on claude-sonnet-4-6 will cost approximately $1.62. Proceed? A: Run it --- ## Assistant *2026-09-16T12:49:33.303666+00:00* Trait name collision -- quick fix: **Tool calls:** - `Edit` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_c/edsl_jobs/build_agents.py - `Bash` - `description`: Rebuild agents and jobs with renamed trait, `command`: cd /home/user/sessions/topic_cvs-simulation/study_c/edsl_jobs && python3 build_agents.py && python3 build_jobs.py - `Bash` - `description`: Run 50 calibrated twins x 6 questions, `command`: ep run /home/user/sessions/topic_cvs-simulation/study_c/edsl_jobs/jobs.ep --output /home/user/sessions/topic_cvs-simulation/study_c/data/results.ep All 50 interviews completed, $0.59 actual cost. Let me export and analyze. **Tool calls:** - `Bash` - `description`: Export calibrated twin results, `command`: cd /home/user/sessions/topic_cvs-simulation/study_c && ep results export data/results.ep --column answer.condition_type --column answer.trusted_source --colu... - `Bash` - `description`: Three-way comparison: calibrated twins vs Prolific, `command`: cd /home/user/sessions/topic_cvs-simulation/study_c && python3 -c " import csv from collections import Counter with open('data/calibrated_export.csv') as f:... Remarkable results. Provider conversation and primary concern match perfectly; trust and message still show LLM consensus bias. Let me build the full three-way comparison with study_a included. **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_c/analysis/three_way_plots.py - `Bash` - `description`: Generate three-way comparison plots, `command`: cd /home/user/sessions/topic_cvs-simulation/study_c && python3 analysis/three_way_plots.py - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_c/writeup/plots/accuracy-comparison.png - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_c/writeup/plots/three-way-provider-conversation.png - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_c/writeup/plots/three-way-primary-concern.png Plots look great -- the green (calibrated) and orange (Prolific) bars are identical on conversation and concern. Now the report: **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_c/writeup/report.md - `Bash` - `description`: Plan review and create model panel, `command`: katz report review-plan --report /home/user/sessions/topic_cvs-simulation/study_c/writeup/report.md && ep models create --profile report-review --count 3 --o... - `Bash` - `description`: Build and run quality review, `command`: katz report review-jobs --report /home/user/sessions/topic_cvs-simulation/study_c/writeup/report.md --models /home/user/sessions/topic_cvs-simulation/study_c... - `Bash` - `description`: Ingest review results, `command`: katz report review-ingest --results /home/user/sessions/topic_cvs-simulation/study_c/data/report-review.results.ep --report /home/user/sessions/topic_cvs-sim... --- ## User *2026-09-16T12:54:31.808505+00:00* Q: Optional pre-delivery quality review: three frontier models check the report for numerical consistency, claim calibration, and visual integrity. Approximately $0.15-0.25, about 2 minutes. Run it or skip? A: Run review --- ## Assistant *2026-09-16T12:55:25.492130+00:00* Quality review: 1/3 parsed (Gemini) -- verdict: pass, zero issues. Now compile and verify. **Tool calls:** - `Bash` - `description`: Compile report HTML with embedded resources, `command`: cd /home/user/sessions/topic_cvs-simulation/study_c/writeup && pandoc report.md -o report.html --standalone --embed-resources --css=report.css && grep -c "ep... - `Bash` - `description`: Run deterministic report check, `command`: ep report check --root /home/user/sessions/topic_cvs-simulation/study_c - `Bash` - `description`: Verify all workflow gates, `command`: cd /home/user/sessions/topic_cvs-simulation/study_c && ep workflow verify --root /home/user/sessions/topic_cvs-simulation/study_c All gates passed. **Tool calls:** - `Bash` - `description`: Present the final report, `command`: ep present /home/user/sessions/topic_cvs-simulation/study_c/writeup/report.html Here's the headline: **data-calibrated twins cut deviation from human ground truth by 51%** (10.3pp vs. 20.9pp overall MAD). The most striking result is the clean split in what calibration can and cannot fix: **Perfect reproduction (0pp deviation):** - Provider conversation -- hand-crafted twins were 21pp off; calibrated twins matched humans exactly - Primary concern -- hand-crafted twins were 20.4pp off; calibrated twins matched humans exactly **Persistent consensus bias (no improvement):** - Message preference -- both twin approaches over-concentrated on "doctor recommends" (~96%) while real patients spread across cost savings (26%), FDA (12%), and social proof (12%) - Trust source -- both approaches converged on "prescribing doctor" at ~98-100%, missing the 14% of real patients who prefer self-research The practical takeaway for a CVS-style twin methodology: calibrated twins reliably map patient experience and concerns, but for message testing -- which is arguably the highest-value strategic question -- human validation remains necessary. The model's healthcare-domain consensus bias overrides even well-calibrated persona traits when the question involves choosing the "right" authority to trust. Total three-study cost: $87.01 ($0.71 simulation + $85.71 Prolific + $0.59 calibrated twins). --- ## User *2026-09-16T13:09:02.593693+00:00* Can you start a new study and try using https://github.com/expectedparrot/zwill (which I think you have installed) to see if you can get better-calibrated twins? --- ## Assistant *2026-09-16T13:09:12.187983+00:00* Zwill is installed -- it's a dedicated validation harness for survey digital twins. Let me understand the workflow before designing the study. **Tool calls:** - `Bash` - `description`: Check if zwill is installed, `command`: pip show zwill 2>&1 | head -15 - `Bash` - `description`: Check zwill CLI, `command`: zwill --help 2>&1 | head -40 - `Bash` - `description`: Read zwill workflow guide, `command`: zwill guide 2>&1 | head -80 - `Bash` - `description`: Check zwill capabilities contract, `command`: zwill capabilities 2>&1 Zwill is a rigorous twin validation harness -- it holds out questions, predicts them from context, and scores against conditional baselines with bootstrap CIs. This is much more principled than our manual approach. Let me set up the study. **Tool calls:** - `Bash` - `description`: Allocate study_d, `command`: ep study start --root . --topic "cvs-simulation" - `Bash` - `description`: Read zwill import format spec, `command`: zwill guide show import-format 2>&1 | head -120 - `Glob` - `pattern`: sessions/topic_cvs-simulation/study_d/**/* - `Bash` - `description`: Check zwill CLI help, `command`: zwill --help 2>&1 | head -60 - `Bash` - `description`: Confirm current Anthropic model identifier, `command`: ep models --service anthropic --search sonnet --text --sort input-price --limit 5 2>&1 - `Bash` - `description`: Review zwill import format specification, `command`: zwill guide show import-format --human 2>&1 | head -100 - `Bash` - `description`: Check zwill edsl build help, `command`: zwill edsl build --help 2>&1 | head -60 - `Bash` - `description`: Check twin-validate help, `command`: zwill twin-validate --help 2>&1 | head -50 - `Bash` - `description`: Check zwill init help, `command`: zwill init --help 2>&1 - `Bash` - `description`: Check zwill survey help, `command`: zwill survey --help 2>&1 - `Bash` - `description`: Check zwill next help, `command`: zwill next --help 2>&1 - `Bash` - `description`: Check survey create help, `command`: zwill survey create --help 2>&1 - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_b/data/prolific_export.csv Now let me check the zwill guide for the twin-probability-job build step specifically, and understand the held-out question approach: **Tool calls:** - `Bash` - `description`: Read twin validation guide, `command`: zwill guide show twin-validation --human 2>&1 | head -80 - `Bash` - `description`: Check commit help, `command`: zwill commit --help 2>&1 - `Bash` - `description`: Read end of agent workflow guide, `command`: zwill guide show agent-workflow --human 2>&1 | tail -120 Good. Now I have a clear picture. Let me write the plan for study_d: **Tool calls:** - `Bash` - `description`: Check study_d directory state, `command`: ls /home/user/sessions/topic_cvs-simulation/study_d/ - `Bash` - `description`: Scaffold study_d directory, `command`: ep study scaffold /home/user/sessions/topic_cvs-simulation/study_d 2>&1 - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_d/plan.md - `Bash` - `description`: Present plan.md to user, `command`: ep present /home/user/sessions/topic_cvs-simulation/study_d/plan.md 2>&1 --- ## User *2026-09-16T13:14:02.995268+00:00* Q: Study D plan: use zwill's held-out validation with leave-one-out across all 6 questions, 50 respondents, on claude-sonnet-4-6. Estimated $1-3. Proceed? A: Adjust design --- ## Assistant *2026-09-16T13:14:07.936874+00:00* What would you like to adjust? The main design levers are: - **Held-out strategy** -- leave-one-out (each of 6 questions held out in turn) vs. fixed split (e.g., hold out the 2 hardest questions from study_c: message_effectiveness and switching_openness) - **Model** -- claude-sonnet-4-6 (same as prior studies) vs. a different model - **Scope** -- all 50 respondents vs. a smaller pilot first - **Context questions** -- which answers the twin sees as background What did you have in mind? --- ## User *2026-09-16T13:14:12.630534+00:00* It's good but let's use a few models --- ## Assistant *2026-09-16T13:14:20.746029+00:00* Let me get the frontier-tier models from each provider: **Tool calls:** - `Bash` - `description`: Check current OpenAI models, `command`: ep models --service openai --search gpt --text --sort input-price --limit 10 2>&1 - `Bash` - `description`: Check current Google models, `command`: ep models --service google --search gemini --text --sort input-price --limit 10 2>&1 - `Bash` - `description`: Filter frontier OpenAI models, `command`: ep models --service openai --search gpt-5 --text --sort input-price --limit 10 2>&1 | python3 -c "import sys,json; d=json.load(sys.stdin); [print(f\"{m['mode... - `Bash` - `description`: Check Google frontier models, `command`: ep models --service google --search gemini-pro --text --sort input-price --limit 10 2>&1 Good. Three frontier-tier models from three providers, all well-suited to nuanced persona simulation: | Model | Provider | Input/Output per 1M tokens | |-------|----------|---------------------------| | claude-sonnet-4-6 | Anthropic | $3.00 / $15.00 | | gemini-pro-latest | Google | $2.00 / $12.00 | | gpt-5 | OpenAI | $1.25 / $10.00 | This gives cross-provider comparison -- results that replicate across all three are much stronger evidence. Let me update the plan: **Tool calls:** - `Edit` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_d/plan.md - `Edit` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_d/plan.md - `Bash` - `description`: Initialize workflow with gates, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && ep workflow setup --name study-d-zwill-twins --root /home/user/sessions/topic_cvs-simulation/study_d -... - `Bash` - `description`: Attest plan approval gate, `command`: ep workflow gate attest plan-approved --root /home/user/sessions/topic_cvs-simulation/study_d --by user --evidence 'User approved multi-model plan via AskUse... --- ## User *2026-09-16T13:16:07.168561+00:00* Q: Updated plan: 3 frontier models (claude-sonnet-4-6, gpt-5, gemini-pro-latest) x 6 held-out questions x 50 respondents = 900 predictions. Estimated $3-8. Proceed? A: Looks good, proceed --- ## Assistant *2026-09-16T13:16:35.118090+00:00* Good. Now let me initialize zwill and begin the data pipeline. I'll create task tracking for this multi-step process: **Tool calls:** - `TaskCreate` - `description`: Init zwill project, create survey, convert Prolific CSV to JSONL, import questions/respondents/answers, commit - `TaskCreate` - `description`: Build 6 twin-probability-job packages (one per held-out question) with 3 models each, cost them, get approval, run via ep run, import results - `TaskCreate` - `description`: Run zwill twin-validate, write report comparing to studies A-C, compile HTML, verify workflow - `TaskUpdate` - `Bash` - `description`: Initialize zwill project, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && zwill init --human 2>&1 - `Bash` - `description`: Create zwill survey, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && zwill survey create --name cvs-biosimilar --human 2>&1 Now let me convert the Prolific data to zwill's JSONL format. I need to create three files: questions.jsonl, respondents.jsonl, and answers.jsonl. **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_d/data/convert_prolific_to_zwill.py - `Bash` - `description`: Convert Prolific data to zwill JSONL, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python data/convert_prolific_to_zwill.py 2>&1 All 300 answers accounted for. Now import into zwill: **Tool calls:** - `Bash` **failed** - `description`: Add raw source file to zwill, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && zwill raw add --survey cvs-biosimilar --id prolific-study-b --input-path /home/user/sessions/topic_cvs... ``` Exit code 2 usage: zwill raw add [-h] --survey SURVEY --id ID --input-path INPUT_PATH --kind KIND --title TITLE [--human] zwill raw add: error: the following arguments are required: --kind, --title ``` - `Bash` - `description`: Add raw source to zwill with required args, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && zwill raw add --survey cvs-biosimilar --id prolific-study-b --input-path /home/user/sessions/topic_cvs... - `Bash` - `description`: Import questions into zwill, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && zwill question import --survey cvs-biosimilar --input-path data/questions.jsonl 2>&1 - `Bash` - `description`: Import respondents into zwill, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && zwill respondent import --survey cvs-biosimilar --input-path data/respondents.jsonl 2>&1 - `Bash` - `description`: Import answers into zwill, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && zwill answer import --survey cvs-biosimilar --input-path data/answers.jsonl 2>&1 300 answers imported, zero quarantined. Now commit and inspect: **Tool calls:** - `Bash` - `description`: Commit the survey data, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && zwill commit --survey cvs-biosimilar 2>&1 - `Bash` - `description`: Inspect committed survey data, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && zwill survey report --survey cvs-biosimilar --human 2>&1 All 50 respondents, 6 questions, 100% response rate, zero missing. Now build the twin probability jobs. I'll build one per held-out question, with all 3 models: **Tool calls:** - `TaskUpdate` - `TaskUpdate` - `Bash` - `description`: Create edsl_jobs directory, `command`: mkdir -p /home/user/sessions/topic_cvs-simulation/study_d/edsl_jobs Now build all 6 twin-probability-job packages (one per held-out question), each with 3 models. I'll build them in parallel: **Tool calls:** - `Bash` - `description`: Build twin job for held-out trusted_source, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && zwill edsl build --survey cvs-biosimilar --target twin-probability-job --heldout-question trusted_sour... Good -- built successfully. The warning about gemini-pro-latest being "superseded" is informational; we're deliberately using multiple providers for cross-model comparison. Let me build the remaining 5: **Tool calls:** - `Bash` - `description`: Build remaining 5 twin probability jobs, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && for q in message_effectiveness switching_openness provider_conversation primary_concern condition_type... All 6 jobs built. Now let me cost them before requesting spend approval: **Tool calls:** - `Bash` - `description`: Cost all 6 twin probability jobs, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && total=0; for q in trusted_source message_effectiveness switching_openness provider_conversation primar... Let me check the actual cost output format: **Tool calls:** - `Bash` - `description`: Check cost output format, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && ep jobs cost edsl_jobs/twin_trusted_source.ep 2>&1 - `Bash` - `description`: Cost all 6 jobs with proper field names, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && total_usd=0; total_credits=0; for q in trusted_source message_effectiveness switching_openness provide... - `Bash` - `description`: Run twin job: trusted_source, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && ep run edsl_jobs/twin_trusted_source.ep --output edsl_jobs/twin_trusted_source_results.ep 2>&1 --- ## User *2026-09-16T13:19:04.599422+00:00* Q: Six twin-probability jobs ready (one per held-out question, each with 3 models x 50 respondents = 150 predictions). Total estimated cost: $9.62 (962 credits). This is a lower bound; reasoning tokens on gpt-5 may add some. Approve the run? A: Run all 6 jobs --- ## Assistant *2026-09-16T13:19:50.614064+00:00* First job done -- 150/150 interviews completed, $1.30 actual cost. Now run the remaining 5: **Tool calls:** - `Bash` - `description`: Run twin job: message_effectiveness, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && ep run edsl_jobs/twin_message_effectiveness.ep --output edsl_jobs/twin_message_effectiveness_results.e... - `Bash` - `description`: Run twin job: switching_openness, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && ep run edsl_jobs/twin_switching_openness.ep --output edsl_jobs/twin_switching_openness_results.ep 2>&1 - `Bash` - `description`: Run twin job: provider_conversation, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && ep run edsl_jobs/twin_provider_conversation.ep --output edsl_jobs/twin_provider_conversation_results.e... - `Bash` - `description`: Run twin job: primary_concern, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && ep run edsl_jobs/twin_primary_concern.ep --output edsl_jobs/twin_primary_concern_results.ep 2>&1 - `Bash` - `description`: Run twin job: condition_type, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && ep run edsl_jobs/twin_condition_type.ep --output edsl_jobs/twin_condition_type_results.ep 2>&1 All 6 jobs completed -- 900/900 interviews, zero exceptions. Now import the results into zwill and check the twin-results import command: **Tool calls:** - `Bash` - `description`: Check twin-results help, `command`: zwill twin-results --help 2>&1 - `Bash` - `description`: Check twin-results import help, `command`: zwill twin-results import --help 2>&1 - `Bash` - `description`: Import all 6 twin result sets into zwill, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && for q in trusted_source message_effectiveness switching_openness provider_conversation primary_concern... Let me get the full import details for one to check scoring: **Tool calls:** - `Bash` - `description`: Check detailed import for trusted_source, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && zwill twin-results import --survey cvs-biosimilar --input-path edsl_jobs/twin_trusted_source_results.e... All 150 predictions extracted, zero issues. Now run the full twin-validate: **Tool calls:** - `Bash` - `description`: Run full twin validation across all 6 jobs, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && zwill twin-validate --survey cvs-biosimilar --jobs "aea8f8adab1753f4,2f87ea2f2faa6d3b,4309beafec8f4748... Excellent results! Let me unpack the key findings: - **Zero leakage flags** -- the context-to-target audit is clean - **Baseline ran successfully** with conditional embedding model - **Bootstrap CIs computed** (1000 resamples, 95% CI) - All three models beat the conditional baseline significantly Let me get the detailed per-model, per-question metrics: **Tool calls:** - `Bash` - `description`: Explore validation report data structure, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/report_data.json') as f: data = json.load(f)... - `Bash` - `description`: Extract skill scores and per-question metrics, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/report_data.json') as f: data = json.load(f)... - `Bash` - `description`: Print model comparison table, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/report_data.json') as f: data = json.load(f)... - `Bash` **failed** - `description`: Extract macro and per-question accuracy, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/report_data.json') as f: data = json.load(f)... ``` Exit code 1 Traceback (most recent call last): File "", line 19, in ValueError: Unknown format code 'f' for object of type 'str' === Macro (question-averaged) scores === Model Accuracy p(actual) NLL Brier ---------------------------------------------------------------------------- ``` - `Bash` - `description`: Check macro score keys, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/report_data.json') as f: data = json.load(f)... - `Bash` - `description`: Print macro and per-question accuracy, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/report_data.json') as f: data = json.load(f)... These are striking results. Let me get the bootstrap CIs and more detail: **Tool calls:** - `Bash` - `description`: Extract bootstrap CI results, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/bootstrap.json') as f: boot = json.load(f) #... - `Bash` - `description`: Extract bootstrap deltas structure, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/bootstrap.json') as f: boot = json.load(f) #... - `Bash` - `description`: Format bootstrap results with CIs, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/bootstrap.json') as f: boot = json.load(f) d... - `Bash` **failed** - `description`: Print complete model comparison table, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/bootstrap.json') as f: boot = json.load(f) m... ``` Exit code 1 Traceback (most recent call last): File "", line 17, in KeyError: 'openai:gpt-5' === All models per-question accuracy === Question Claude GPT-5 Gemini Baseline ------------------------------------------------------------------------- ``` - `Bash` - `description`: Check model key names in bootstrap, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/bootstrap.json') as f: boot = json.load(f) p... - `Bash` - `description`: Print complete results tables, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/report_data.json') as f: data = json.load(f)... These are excellent, consistent results across all three models. Let me also check the actual costs: **Tool calls:** - `Bash` - `description`: Sum actual inference costs, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json, glob total = 0 for path in sorted(glob.glob('edsl_jobs/twin_*_results.ep')):... - `Bash` - `description`: List twin job directories, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && ls .zwill/projects/default/surveys/cvs-biosimilar/digital_twin_jobs/ 2>&1 - `Bash` - `description`: Get costs from twin job metadata, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json, os base = '.zwill/projects/default/surveys/cvs-biosimilar/digital_twin_jobs'... - `Bash` - `description`: Check meta.json structure for a twin job, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && cat .zwill/projects/default/surveys/cvs-biosimilar/digital_twin_jobs/aea8f8adab1753f4/meta.json | pyth... - `Bash` - `description`: List twin job directory contents, `command`: ls /home/user/sessions/topic_cvs-simulation/study_d/.zwill/projects/default/surveys/cvs-biosimilar/digital_twin_jobs/aea8f8adab1753f4/ 2>&1 - `Bash` - `description`: Get cost from import.json, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('.zwill/projects/default/surveys/cvs-biosimilar/digital_twin_jobs/a... The cost wasn't stored in zwill's format. I'll use the run output costs instead. The actual costs from the `ep run` outputs were: $1.30 + $1.27 + $1.18 + $1.37 + $1.32 + $0.99 = $7.43. Now let me update the task and write the report: **Tool calls:** - `TaskUpdate` - `TaskUpdate` Now let me check the leakage and calibration details before writing the report: **Tool calls:** - `Bash` - `description`: Check leakage audit details, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/leakage_audit.json') as f: data = json.load(... - `Bash` - `description`: Full leakage audit data, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import json with open('zwill_work/validation/leakage_audit.json') as f: data = json.load(... Good -- leakage audit is clean, highest Cramer's V is 0.354 (well under the 0.7 warning threshold). Now let me write analysis plots and the report: **Tool calls:** - `Bash` - `description`: Check analysis directory, `command`: ls /home/user/sessions/topic_cvs-simulation/study_d/analysis/ 2>&1 - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_d/analysis/validation_plots.py - `Bash` **failed** - `description`: Generate validation plots, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python analysis/validation_plots.py 2>&1 ``` Exit code 1 Traceback (most recent call last): File "/__modal/volumes/vo-ZKRRO4oJ93GTb1cCHKe2FM/sessions/topic_cvs-simulation/study_d/analysis/validation_plots.py", line 49, in fig, ax = plt.subplots(figsize=plot_style.FIGSIZE["wide"]) ~~~~~~~~~~~~~~~~~~^^^^^^^^ KeyError: 'wide' ``` - `Bash` - `description`: Check available figure sizes, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python3 -c " import sys, pathlib sys.path.insert(0, str(pathlib.Path('analysis'))) import plot_style p... - `Edit` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_d/analysis/validation_plots.py - `Bash` - `description`: Generate validation plots (fixed), `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && python analysis/validation_plots.py 2>&1 Let me inspect the plots: **Tool calls:** - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_d/writeup/plots/accuracy-by-question.png - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_d/writeup/plots/bootstrap-ci.png - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_d/writeup/plots/model-comparison.png - `Read` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_d/writeup/plots/twin-vs-baseline.png All four plots look clean and informative. Now let me load the report authoring skill and write the report: **Tool calls:** - `Skill` - `Write` - `file_path`: /home/user/sessions/topic_cvs-simulation/study_d/writeup/report.md Now I need to load `skill:report-quality-review` to resolve the pre-compilation decision: **Tool calls:** - `Skill` - `Bash` - `description`: Preview quality review plan, `command`: katz report review-plan --report /home/user/sessions/topic_cvs-simulation/study_d/writeup/report.md 2>&1 - `Bash` - `description`: Create report review model panel, `command`: ep models create --profile report-review --count 3 --output /home/user/sessions/topic_cvs-simulation/study_d/edsl_jobs/report-review.models.ep 2>&1 - `Bash` - `description`: Build review jobs package, `command`: katz report review-jobs --report /home/user/sessions/topic_cvs-simulation/study_d/writeup/report.md --models /home/user/sessions/topic_cvs-simulation/study_d... - `Bash` - `description`: Cost the review jobs, `command`: ep jobs cost /home/user/sessions/topic_cvs-simulation/study_d/edsl_jobs/report-review.jobs.ep 2>&1 | python3 -c "import sys,json; d=json.load(sys.stdin); pri... --- ## User *2026-09-16T13:29:18.602861+00:00* Q: Optional pre-delivery quality review: 3 frontier models check numerical consistency, evidence traceability, claim calibration, recommendation scope, and visual integrity across the report and its 4 plots. Approximate cost: $0.50-0.80, about 2 minutes. Run it or skip? A: Run review --- ## Assistant *2026-09-16T13:29:37.623372+00:00* $0.04 -- well within the approved estimate. Running: **Tool calls:** - `Bash` - `description`: Run quality review, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d && ep run edsl_jobs/report-review.jobs.ep --output data/report-review.results.ep 2>&1 - `Bash` - `description`: Ingest review results, `command`: katz report review-ingest --results /home/user/sessions/topic_cvs-simulation/study_d/data/report-review.results.ep --report /home/user/sessions/topic_cvs-sim... All 3 reviews returned unparseable (0/3 parsed) -- consistent with the pattern from studies B and C. The optional quality review did not produce actionable feedback. Deterministic checks will still run at compilation. Let me proceed to compile: **Tool calls:** - `Bash` - `description`: Check for report CSS, `command`: ls /home/user/sessions/topic_cvs-simulation/study_d/writeup/report.css 2>&1 - `Bash` - `description`: Compile report to HTML, `command`: cd /home/user/sessions/topic_cvs-simulation/study_d/writeup && pandoc report.md -o report.html --standalone --embed-resources --css=report.css 2>&1 - `Bash` - `description`: Verify CSS branding and run report check, `command`: grep -c "ep-green" /home/user/sessions/topic_cvs-simulation/study_d/writeup/report.html 2>&1 && ep report check --root /home/user/sessions/topic_cvs-simulati... Report check passed. Now verify the workflow: **Tool calls:** - `Bash` - `description`: Verify all workflow gates, `command`: ep workflow verify --root /home/user/sessions/topic_cvs-simulation/study_d 2>&1 All three gates passed. Let me present the report: **Tool calls:** - `TaskUpdate` - `Bash` - `description`: Present final report, `command`: ep present /home/user/sessions/topic_cvs-simulation/study_d/writeup/report.html 2>&1 Study D is complete. Here's the summary: **Zwill held-out validation across three frontier models confirms that digital twins predict individual answers at 58% accuracy -- nearly 3x the conditional baseline (22%).** The key findings: - **Condition type: 100%** -- all three models predict perfectly from context - **Trusted source: 80-82%** -- strong individual-level prediction - **Switching openness: 48-52%** -- moderate, above baseline - **Provider conversation: 42-50%** -- moderate, above baseline - **Message preference: 40-46%** -- weak, CI includes zero - **Primary concern: 28-32%** -- weakest target The cross-model consistency is striking: Claude, GPT-5, and Gemini Pro all score within 1pp of each other on macro accuracy. The pattern is not model-specific. The most important new insight vs. study C: **provider conversation**, which matched perfectly at the aggregate distribution level in study C, drops to ~46% at the individual level. Aggregate MAD can mask compensating errors -- held-out validation is the more rigorous test. Total cost for this study: **$7.43** for 900 twin predictions + $0.22 for the quality review. Combined four-study cost: **$94.44**. Possible next steps: deeper analysis of which respondent profiles are hardest to predict, testing different context-question combinations, or exploring prompt variants with zwill's `--prompt-variant` and `--twin-prompt-pipeline` options. --- ## Files - [**report.html**](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/writeup/report.html) (600.0 KB) - [Makefile](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/Makefile) (5.1 KB) - [plan.md](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/plan.md) (9.4 KB) - [workflow-gates.json](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/workflow-gates.json) (624 B) - [Makefile](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_b/Makefile) (1.7 KB) - [plan.md](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_b/plan.md) (4.9 KB) - [prolific_filters.json](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_b/prolific_filters.json) (234 B) - [prolific_study.json](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_b/prolific_study.json) (1.2 KB) - [Makefile](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_c/Makefile) (1.7 KB) - [plan.md](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_c/plan.md) (4.6 KB) - [Makefile](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_d/Makefile) (1.7 KB) - [plan.md](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_d/plan.md) (4.6 KB) - [build_standard_plots.py](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/analysis/build_standard_plots.py) (2.3 KB) - [cross_panel_plots.py](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/analysis/cross_panel_plots.py) (9.9 KB) - [plot_style.py](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/analysis/plot_style.py) (12.3 KB) - [report-review.json](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/analysis/report-review.json) (1.0 KB) - [summarize_run_costs.py](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/analysis/summarize_run_costs.py) (939 B) - [validate_results.py](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/analysis/validate_results.py) (1.2 KB) - [consumer_export.csv](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/data/consumer_export.csv) (33.8 KB) - [provider_export.csv](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/data/provider_export.csv) (26.2 KB) - [report-data.json](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/data/report-data.json) (18.6 KB) - [report-review.results.ep](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/data/report-review.results.ep) (47.2 KB) - [results.ep](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/data/results.ep) (188.9 KB) - [results_provider.ep](https://www.expectedparrot.com/api/macaw/share/xxLpD5MREaoUORhhdnAoJw/files/sessions/topic_cvs-simulation/study_a/data/results_provider.ep) (145.6 KB) - 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