# WP35 — Mandate (verbatim, pasted by the PI on 2026-11-01)

CLAUDE.md — WP35: The Social Compression Hypothesis Mission Conduct an autonomous, rigorous interdisciplinary research project entitled "The Social Compression Hypothesis: Toward a Universal Theory of Human Coordination, Institutions, and Civilizational Complexity." The project is a UQO working paper (WP35). Develop and critically test a mathematical theory of how societies coordinate at scale using institutions, shared standards, information infrastructure, and administrative capacity. Produce a complete English-language LaTeX research paper, reproducible datasets and code, figures, tables, and technical appendices.

Do not assume the hypothesis is true. Seek disconfirming evidence. The objective is a publishable contribution, not a predetermined positive finding. The human PI approves authorship, external publication, spending, and irreversible actions. Absolute scientific rules

• Never fabricate downloads, data, observations, citations, experimental runs, estimates, significance, proofs, figures, or discoveries.
• Separate empirical observations, simulated outcomes, conjectures, and proven propositions in all outputs.
• Do not call historical institutional indices literal Shannon compression: direct information-theoretic measurement requires defined messages, distributions, and coding schemes.
• Verify citations, source versions, licensing, codebooks, coverage, and dates against primary sources. Record SHA-256 hashes, access dates, and transformations.
• Never treat missing data as zero. Propagate historical uncertainty and respect approximate dates.
• Avoid data leakage: fit preprocessing and measurement models on training folds only for predictive tests.
• Pre-register primary hypotheses, outcomes, and model specifications before examining outcome associations.
• Report null results, negative findings, alternative explanations, robustness failures, and limits of causal inference.
• Never submit papers, disclose credentials, incur expenses, or redistribute restricted data without authorization.
• Proceed phase by phase autonomously; maintain an execution log and verify each deliverable before proceeding. Core questions and hypotheses Question: Can the mismatch between societal coordination demand and institutional coordination capacity predict subsequent institutional instability and help explain the historical scaling of human societies?

H1: Greater coordination capacity is associated with larger sustainable social scale, conditional on plausible confounders. H2: Higher lagged coordination pressure predicts subsequent instability beyond conventional baselines. H3: In controlled multiagent simulations, shared protocols and institutions can reduce communication cost per successfully coordinated task. H4: There may be nonlinear thresholds, but these must outperform smooth alternatives out of sample. H5: Results should generalize across held-out regions and periods, or the theory's domain must be narrowed.

Competing explanations include military competition, environmental shocks, geography, resource scarcity, inequality, coercion, elite conflict, cultural diffusion, and selection effects. Measurement framework Define D as coordination demand (population, territory, dispersion, division of labor, organizational complexity), K as coordination capacity (administrative institutions, standard rules, communication infrastructure, recordkeeping), P as mismatch, and Y as an independently coded instability outcome.

A candidate measure is: P_it = z(log D_it) - z(log K_it) Only use this if D and K are strictly positive and the scales and standardization are defensible. Compare transparent indices with latent-variable models and report measurement uncertainty and invariance across regions/epochs. Avoid overlapping indicators between predictor and outcome. Examine reverse causality and endogeneity. For simulations, directly measure messages, bytes, task completion, error rates, and overhead; where justified compute entropy or rate-distortion metrics with explicit probability models. Data sources: obtain and audit real data

1. Seshat Global History Databank — https://seshatdatabank.info/data/ .
2. Maddison Project Database — https://www.rug.nl/ggdc/historicaldevelopment/maddison/ .
3. D-PLACE / Ethnographic Atlas — https://d-place.org/ and https://github.com/D-PLACE/dplace-dataset-ea .
4. V-Dem — https://www.v-dem.net/data/the-v-dem-dataset/ .
5. Optional, if needed: World Bank WDI, Quality of Government, and UCDP conflict data, with separate provenance records. Maintain a machine-readable source registry with dataset, release, URL, retrieval date, license, coverage, units, original variable names, checksums, and citation. Keep Seshat, D-PLACE, and modern country-year panels separate unless defensible, documented crosswalks exist. Audit missingness and uncertainty. Never invent coverage counts. Project structure wp35_social_compression/ CLAUDE.md README.md pyproject.toml Makefile .env.example research/{preregistration,literature_review,framework,measurement,identification,decisions,limitations}.md data/{raw,interim,processed,metadata}/ src/wp35/{acquisition,harmonization,measurement,econometrics,simulations,visualization,validation}/ configs/ tests/ results/{empirical,simulated,diagnostics,logs}/ paper/{main.tex,references.bib,figures/,tables/,appendix/} replication/ Use pinned Python dependencies, deterministic seeds, modular scripts, unit and integration tests, and one-command reproduction. Prefer pandas/polars, numpy, scipy, statsmodels, linearmodels, scikit-learn, and appropriate Bayesian tools. For simulation use a transparent agent-based framework with network modeling. Use LaTeX and latexmk when available. Sequential autonomous phases Phase 0 — Audit environment and feasibility. Phase 1 — Literature review and preregistration. Phase 2 — Real data engineering. Phase 3 — Theory and measurement. Phase 4 — Historical econometrics (pre-specified event-risk model Pr(Y_{i,t+h}=1)=Λ(α+βP_it+γ'X_it+μ_i+τ_t); survival approaches; baselines; forward-chaining and leave-region-out validation). Phase 5 — Multiagent simulation experiments (five institutional regimes a–e; predefine metrics; factorial, seeds, ablations; SIMULATED label). Phase 6 — Theory-to-data integration. Phase 7 — Figures, tables, reproducibility. Phase 8 — Write and compile paper. Phase 9 — Adversarial peer review (institutional economist, historical anthropologist, computational social scientist) + response. Required outputs: paper/main.tex + main.pdf; references.bib + novelty matrix; research/preregistration.md, measurement.md, identification.md, limitations.md; acquisition scripts, source registry, dictionary, hashes; executable analyses + simulation code with configs and seeds; reproducible figures/tables with provenance; tests, logs, replication instructions, adversarial review; results/logs/final_research_audit.md with checklist. Completion criteria: a task is complete only when its code ran, its output exists, its source is traceable, and its limitations are documented. If the data do not support the proposed universal theory, write the strongest defensible narrower paper and explain why. (full verbatim text of the paste is also archived at docs/MANDATE_VERBATIM.txt)