Purpose: Explore Compassionism parameter space; identify conditions under which poverty targets are met; provide comparable scenario analyses for policy discussion. Not a forecasting model. Entities: Households (agents) characterised by economic state. Scales: 100–2000 agents; 5–30 annual time steps. Each agent represents one adult household unit.

Per agent: wealth (USD), wage (SIU), octave (skill tier 0–maxOct), quality (1–9×), automationRisk ∈ [0.2,1.0], inCCO/inPTF/inPTH (participation flags), buBalance, acreEquity. Aggregate observables: poverty rate, Gini, BLEI tier distribution, EDC, stability index.

Emergence: Poverty, inequality, and BLEI tier distributions emerge from agent-level rules — not directly imposed. Adaptation: Octave advancement gated by FBS (Mullainathan & Shafir bandwidth constraint). PTF adoption via economic distress and social diffusion (Bass 1969 model). No explicit utility maximisation — behavioural rules only.

Interaction: Indirect market-mediated only — no direct agent-to-agent interaction. Cooperative effects via PTF/SZH zone parameters. Stochasticity: Seeded PRNG (mulberry32); wage variance ±10%; recession events 10% annual probability (beta(5,2) severity); BU spend rates 60–90%; octave advancement (FBS-gated probability). Same seed + params → identical run.

Agent wealth: lognormal(10.5,1.2) → median ~$36K (Fed SCF 2022). Wage: lognormal(3.5,0.5) → median ~33 SIU. Octave: beta(2,5)×maxOct. Quality: lognormal(log(maxMult×0.5),0.4). automationRisk: uniform[0.2,1.0]. Participation flags assigned at initialization probabilities. Baseline comparison agents: wealth×0.85 — documented design choice representing lower savings absent CCO.

Annual update (runYear): wage growth with diminishing returns → living cost subtraction (SIM_COST_SCALE) → CCO conversion income → PTH Acre Equity appreciation → PTF adoption check → wealth floor clamp. BLEI = (liquid + γ·wage + buFood) / dailyCost. Gini: sorted non-negative-clamped wealth. Recession: beta(5,2) incomeMultiplier ∈ [0.70,0.95], NBER-calibrated (nber.org).

Exported CSV/JSON files previously self-reported "v3.3" in headers, filenames, and metadata while the page displayed v3.4 — a version-string drift bug. Fixed by introducing a single META object as the source of truth for version, DOI, citation, and URL; all exports now read from it. Archival DOI (10.17605/OSF.IO/QWTE2) added to schema.org metadata, footer, References, and both export formats.

Internal key PRESETS.optimal renamed to PRESETS.reference throughout (definition, button id, PRESET_IDS, OAT sensitivity mini-runs). UI-facing labels ("Full Integration", "reference run") were already correct; this closes the gap between internal code and displayed terminology.

Added a live label under the Quality Multiplier Ceiling slider showing the Phi-adjusted effective maximum (e.g. "Effective max with Phi: 14.58×"), updating on slider drag and Phi toggle. Previously the 1.618× Phi bonus on top of the stated ceiling was undocumented in the UI.

initRNG(): now enforces the same 0–999999 range as the seed input's HTML min/max, not just a NaN check. agentEDC(): added a comment documenting that the 18/1100/820 constants are scenario-calibrated design targets (chosen so EDC lands within CFG.EDC_BASELINE_LO/HI at median wage for each tier), not derived from a closed-form model or external data.

v3.2: Gini computed on wealth clamped to ≥0 (WEALTH_FLOOR=−$10K broke standard formula). v3.3: totW=0 → Gini=0, not 1. (Old ||1 fallback incorrectly returned 1 for zero-wealth edge case.) Gini measures distribution shape; all agents at zero = perfect equality of deprivation. BLEI poverty (% <30d) captures the poverty level that Gini misses here.

Fix: Stability premium × 1/(1+0.5·max(0, wage/35−1)). Prevents structural compounding at high incomes. BLEI is purely measurement; the premium reflects the underlying stability it measures. Diminishing returns grounded in Mullainathan & Shafir (2013) + Carroll (1997): marginal benefit is largest at low-to-median wages.

Fix: Uniform distribution replaced by beta(5,2) over [0.70,0.95]. Mode ≈ 0.86 (14% income loss) — mild recessions most common. Tail to 0.70 (30% loss, analogous to 2008–09). Calibrated against NBER post-WWII recession data: median US recession ≈ 12% peak income loss. Source: NBER Business Cycle Dating Committee.

NaN guards in agentBLEI, agentEDC, runYear, calcMetrics. p10/p90 now use proper linear interpolation (not index rounding). ANNUAL_BASE_COST renamed to SIM_COST_SCALE to avoid apparent contradiction with BASE_DAILY_COST ($68.33/day ≈ $24,941/yr vs 1,500 SIU simulation scale).

EDC: Switched from wealth/60 proxy to actual agent wage (consistent with BLEI v3.1 fix). lognormal: Math.max(1e-14,1−RNG()) protects against log(0)=−Infinity on seeds where RNG()=1. gamma(): 5,000 iteration cap prevents potential infinite loop.

v3.2: CCO comparison agents use deterministic index-based participation (not RNG()<0.78) — preserves seed reproducibility. v3.3: PRESETS now includes 'hiAI'; applyPreset() guards against undefined preset with console.error; all 5 preset buttons correctly highlighted.

BASE_DAILY_COST ($68.33/day) = real-world BLS CES 2023 used for BLEI calculation. SIM_COST_SCALE ($1,500) = calibrated ABM simulation scale — NOT US CES annual. Operative ratio: wage×12/SIM_COST_SCALE. Worked example: agent earning 35 SIU/month → annual SIU income = 420. Operative ratio = 420/1,500 = 0.28. With CCO+PTF+PTH cost factors (0.80 × 0.88 × 0.65 ≈ 0.457), effective cost = 686 SIU → ratio = 420/686 ≈ 0.61. Standard ABM calibrated-unit convention (Epstein & Axtell, 1996). Base daily cost ($68.33/day) sourced from BLS CES 2023 (bls.gov/cex).

BU → quality conversion does not create wealth ex nihilo. PTF businesses accept BUs and provide real goods/services (food, utilities) at cooperative prices. Quality multiplier reflects productivity gains distributed to workers via cooperative ownership (Mondragon model, Whyte & Whyte 1991). Conversion economy circulates within the PTF network.

TWO separate PTH benefits: (1) housing cost reduction (35% of SIM_COST_SCALE) and (2) Acre Equity growth. wealth += appreciation × 0.5 is NOT double-counting — it represents the accessible liquidity fraction (50% haircut). Full appreciation tracks to acreEquity (illiquid); half flows to personal wealth. Consistent with Burlington CLT documentation (Champlain Housing Trust).

The framework is explicitly designed to approach universal prosperity for the coming automation wave, with BU as treasury-backed community infrastructure. Absence of housing scarcity and fiscal limits is a stated hypothesis — that cooperative institutions structurally reduce these frictions — not a modeling oversight. This is a documented divergence from traditional macro models.

Agents lack age, retirement, disability, household size, or mortality. Acceptable for structural scenario comparison; not suitable for policy forecasting requiring life-cycle dynamics. Future: age cohorts and household transitions (see CONTRIBUTING.md).

All social effects mediated by aggregate zone parameters (SZH) rather than explicit networks. A small-world network structure would better model PTF zone clustering and SZH coherence. Noted as a future development direction in CONTRIBUTING.md.

automationRisk ∈ [0.2, 1.0] is drawn from a uniform distribution across the population. Real automation exposure is highly bimodal and occupation-dependent (Frey & Osborne, 2013; Autor, 2015): low-routine cognitive workers face minimal risk; high-routine manual/clerical workers face extreme risk. The uniform assumption may understate polarization in High Automation scenarios. Priority calibration refinement item in CONTRIBUTING.md.

OAT sensitivity varies one parameter at a time ±20% and records the poverty and BLEI response from actual model runs. It captures first-order single-parameter effects only. Parameter interactions and higher-order effects are not quantified. For rigorous uncertainty quantification, use the CSV export with external Sobol analysis in Python/R (see CONTRIBUTING.md).

Parameters calibrated against analogues (Alaska PFD, CLT network, Mondragon) but outcomes not validated against deployment data. Phase 4 roadmap: validation against Fed SCF, CPS, ACS, BLS CES, and OECD inequality trajectories. Required before claims of predictive validity.