Methodology Overview
This replication framework operationalizes the BLEI welfare measurement suite as a computational agent-based simulation. It is a calibrated institutional simulation architecture — not a conventional econometric study — designed to identify optimal system parameters and test framework behavior across thousands of parameter draws. Key parameters (ε, r_a, BU allocation) are grounded in three deployed real-world analogues: the Alaska Permanent Fund Dividend, Community Land Trusts across 46 US states, and Mondragon Corporation.
The BU purchasing power accounting convention distinguishes food BU contributions to BLEI ($990/adult/month, food excluded from C_basic) from utility BU contributions (offset cash utility cost in C_basic; full value captured in EPPM). The v3.0/3.1 simulation implements an annual expense system where agents pay living costs each year — creating realistic wealth depletion under baseline conditions — and models PTF as a cost-reduction mechanism rather than a direct wealth injection.
Step 1 — BLEI-Initialized Agents with Expense Dynamics
Agents initialized with BLEI, EDC, FBS, and Acre Equity state variables drawn from calibrated US wealth and housing distributions. PTH payments route to Acre Equity from initialization, at the appropriate liquidity haircut by tenure cohort. v3.1 addition: each agent is assigned a heterogeneous automationRisk ∈ [0.2, 1.0] governing their individual exposure to AI labour displacement. Agents can accumulate debt to a floor of −$10,000 (v3.1), replacing the earlier $0/$100 hard floor that eliminated insolvency dynamics.
Step 2 — Extraction-Accurate Dynamics with Annual Costs
Annual wealth updates subtract living costs from wage income and CCO conversion gains. PTF is modelled as a cost-reduction factor (12–16% reduction in annual living costs), not as a wealth-addition channel — correcting the ex nihilo money creation in earlier versions. PTH rent savings and Acre Equity appreciation are tracked in separate accounting categories — removing double-counting. FBS uses EDC_residual (consumer debt only) to avoid double-counting housing costs already in C_basic.
Step 3 — FBS-Gated Advancement
Octave advancement probability is gated by Financial Bandwidth Score. P(advance) = 1 − exp(−λ × FBS). Agents at FBS = 0 cannot advance regardless of quality score. This reflects Mullainathan & Shafir's (2013) cognitive bandwidth research. γ transitions from 0.12 (entry) to 0.20 (CCO-established) as the guaranteed BU floor matures. v3.1: PTF members join dynamically over time via economic distress and social diffusion, not only at initialisation.
Step 4 — Network-Density Synergy & Inflation
Synergy coefficient θ activates only above 55% PTF merchant participation within a SZH zone, scaling linearly to 0.25 at 90%+ density. v3.1 inflation mechanism: annual costs compound at a configurable rate (0–6%). PTF presence dampens effective inflation (community pricing stability); PTH dampens housing-component inflation. The US Baseline scenario applies 3% annual inflation to reflect historic CPI. Compassionism scenarios default to 0% — the price-stability hypothesis of cooperative essential goods provision.
Step 5 — Recession Model (Population-Level)
v3.1 update: Recessions are modelled as population-level events with 10% annual probability, persisting 2–4 years. v3.3 calibration: incomeMultiplier is now drawn from a beta(5,2) distribution over [0.70, 0.95], yielding a mode of approximately 0.86 (14% income loss) with a realistic tail extending to 0.70 (30% loss). This replaces the v3.1 uniform distribution over [0.65, 0.85], which overstated the frequency of severe recessions relative to NBER post-WWII data.
Step 6 — EDC-Adjusted Gini & BLEI Poverty
Gini reported on extraction-adjusted net wealth: W_net = W_nominal − (EDC × Y × 12). This corrects the standard Gini's blindness to the direction of financial flows. v3.1 addition: BLEI poverty (% of agents with BLEI < 30 days = Crisis+Precarious tier) is tracked and displayed as a theoretically grounded alternative to wealth poverty — a retiree with $24K savings and an $80K pension incorrectly registers as wealth-poor but correctly registers as BLEI-stable. BLEI poverty is the primary poverty indicator in v3.3.
v3.1 calibration note: An open goal of this simulation is to identify optimal CCO parameters — analogous to a structural engineer ensuring load capacity across all points simultaneously. Some earlier calibration targets (e.g. PTF share ~30%) were produced under less rigorous simulation versions that lacked the annual expense system. These are under active revision as simulation v3.x matures. See CONTRIBUTING.md for calibration contribution guidelines.
v3.3 methodology additions: Four enhancements expand the simulation's analytical scope. (1) Wage growth diminishing returns: the BLEI stability premium is now scaled by 1/(1 + 0.5 × max(0, wage/WAGE_MEDIAN_SIU − 1)), preventing structural compounding at high wages, grounded in Mullainathan & Shafir (2013) and Carroll (1997). (2) Recession severity distribution: switched from uniform to beta(5,2) over [0.70, 0.95], calibrated against NBER post-WWII recession data. (3) Participant vs. non-participant stratification: separate KPI tracking for CCO participants and non-participants, enabling direct tests of whether the system benefits participants without harming opt-outs. (4) Computed OAT sensitivity: the sensitivity chart now reports results from 11 actual mini-simulations (±20% on 5 parameters, seed 7777), replacing prior hardcoded illustrative guidance values.
BLEI Temporal Stability Tier System
The six BLEI tiers replace binary poverty/non-poverty classification and gate agent advancement probability in the simulation. Tier assignment derives from BLEI score (days of basic living covered). The Flourishing tier threshold of 730 days reflects the capital commitment horizon required for entrepreneurial ventures to become viable without required break-even within the first year. v3.1 tier naming: the top tier (≥730 days) is labelled Flourishing when both CCO and PTF are active — reflecting the maturing generative effects of the full system. Without CCO+PTF, reaching 730+ days represents strong temporal stability but is labelled Comfortable, consistent with BLEI §4.
| Tier | BLEI Range | Characterization | P(Advance) |
|---|---|---|---|
| 0 — Crisis | < 7 days | Survival-mode cognition; zero advancement capacity; immediate intervention required | ≈ 0 |
| 1 — Precarious | 7–30 days | One disruption from crisis; income fully consumed by extraction and basic needs; Lusardi et al. (2011) $2,000-shock vulnerability zone. BLEI poverty = Crisis + Precarious combined. | ≈ 0.02 |
| 2 — Threshold | 30–120 days | BLEI-defined poverty line; limited advancement feasible; positive FBS emerging | 0.08–0.15 |
| 3 — Stable | 120–365 days | One full year covered; measured risk-taking and education investment viable; Carroll (1997) buffer-stock target zone | 0.20–0.35 |
| 4 — Secure | 365–730 days | One to two years covered; creative and entrepreneurial participation feasible within two-year payoff horizon | 0.40–0.60 |
| 5 — Flourishing (or Comfortable without CCO+PTF) | > 730 days | Two-year capital commitment horizon unlocked; ventures without early-stage break-even are viable; extended planning horizon with reduced liquidity-constrained decision-making | 0.65+ |
Simulation benchmark: At least 97% of CCO-PTF participants must reach BLEI Tier 2 (Threshold, ≥ 30 days) by simulation Year 5. At least 80% must reach Tier 4 (Secure, ≥ 365 days) by Year 15. These targets supersede the income-headcount benchmarks used in prior versions. Sensitivity analysis in the BLEI Foundation Paper confirms these targets hold across ε_food ∈ [1.50, 3.00] and r_a ∈ [0.02, 0.07].
v3.4 naming note — "Comfortable" vs. "Flourishing": The label "Comfortable" (vs. "Flourishing") is used for baseline/CCO-only configurations where the 730+ day tier is aspirational without the cost reductions provided by PTF/PTH. Both labels refer to the same 730-day threshold. "Flourishing" is used when CCO+PTF are both active because the cost-reduction structure makes the tier structurally attainable. The latest version of the BLEI paper formalizes this distinction.
Agent Architecture — v3.3 Extraction-Accurate Model
The v3.1 agent class adds an annual expense system, heterogeneous AI automation risk, negative wealth floor, and PTF dynamic adoption. The corrected agent also separates PTF benefits (cost reduction) from PTH benefits (Acre Equity growth), preventing the double-counting present in earlier versions. The BLEI income buffer uses actual agent wage rather than the wealth/60 proxy. BU_food_eff ($990) is used for BLEI; BU·ε_blended ($2,502) is used for EPPM. v3.3 addition: wage growth stability premium applies diminishing returns above WAGE_MEDIAN_SIU (35 SIU), preventing structural compounding at high wages. The constant ANNUAL_BASE_COST is renamed SIM_COST_SCALE to avoid apparent contradiction with BASE_DAILY_COST.
v3.1 agent additions:
automation_risk ∈ [0.2, 1.0] drawn uniformly at initialisation — creates heterogeneous inequality under AI displacement (high-risk agents lose wages faster). wealth floor changed from 0 to −$10,000 — allows debt and insolvency dynamics. PTF adoption now dynamic: agents join via economic distress (BLEI < Precarious) or social diffusion at low base rate, not only at initialisation.
# ═══════════════════════════════════════════════════════════════
# Agent — v3.3 Extraction-Accurate BLEI-Integrated Model
# Key v3.0/3.1 corrections:
# · Annual expense system: agents pay living costs; wealth depletes without CCO
# · PTF: cost-reduction factor, NOT wealth injection (eliminates ex nihilo money)
# · PTH: AE appreciation tracked separately from rent savings (no double-count)
# · BLEI mInc: uses actual wage, not wealth/60 proxy
# · automationRisk: heterogeneous AI displacement per agent [0.2, 1.0]
# · wealth floor: -10,000 (was 0/100 — allows debt dynamics)
# · Recession: incomeMultiplier replaces ambiguous 'severity' field
# · Tier naming: 'Flourishing' (CCO+PTF active) or 'Comfortable' (without)
# v3.3 additions:
# · Wage stability premium: DR factor = 1/(1 + 0.5*max(0, wage/WAGE_MEDIAN_SIU - 1))
# · SIM_COST_SCALE: renamed from ANNUAL_BASE_COST (avoids scale confusion)
# · Gini zero-wealth: totW=0 → Gini=0 (mathematically correct; was ||1 fake fix)
# · NaN/Inf guards throughout agentBLEI, agentEDC, runYear, calcMetrics
# · p10/p90: proper linear interpolation (not index rounding)
# BU accounting: BLEI uses food BU only ($990); EPPM uses full $2,502
# ═══════════════════════════════════════════════════════════════
class Agent:
def __init__(self, agent_id: int):
self.id = agent_id
self.tenure_months = 0
# ── Primary state ──────────────────────────────────────────────
self.wealth = np.random.lognormal(10.5, 1.2) # USD nominal
self.income = np.random.lognormal(3.5, 0.5) # SIU (sim income units, not USD)
# ── Participation flags ────────────────────────────────────────
self.cco_participant = np.random.choice([True, False], p=[0.78, 0.22])
self.pth_resident = np.random.choice([True, False], p=[0.20, 0.80])
self.ptf_member = np.random.choice([True, False], p=[0.18, 0.82])
self.octave_level = 0
self.quality_score = np.random.uniform(1, 9)
# ── v3.1: heterogeneous AI automation risk ─────────────────────
# High risk (→1.0) = occupation highly exposed to automation displacement
# Low risk (→0.2) = occupation resilient (creative, relational, novel)
# Per-agent displacement = population_rate × automation_risk
self.automation_risk = np.random.uniform(0.2, 1.0)
# ── BLEI state variables ───────────────────────────────────────
self.bu_monthly = 1200.0 if self.cco_participant else 0.0
self.bu_food_eff = 990.0 if self.cco_participant else 0.0
self.bu_eppm = 2502.0 if self.cco_participant else 0.0
self.bu_epsilon_food = 2.64
self.acre_equity = 0.0
self.edc_full = self._calc_edc()
self.edc_residual = self._calc_edc_residual()
self.fbs = 0.0
self.blei_score = 0.0
self.blei_tier = 0
self.lambda_cap = np.random.uniform(0.001, 0.008)
self._update_blei()
self._update_fbs()
def _gamma(self) -> float:
"""γ transitions from 0.12 (entry) to 0.20 (CCO-established, Month 6+)."""
if not self.cco_participant: return 0.12
if self.tenure_months < 3: return 0.12
elif self.tenure_months < 6:
t = (self.tenure_months - 3) / 3.0
return 0.12 + t * (0.20 - 0.12)
return 0.20
def _ae_haircut(self) -> float:
m = self.tenure_months
if m < 7: return 0.15
elif m < 25: return 0.40
elif m < 61: return 0.65
else: return 0.85
def _update_blei(self):
"""
BLEI = cashDays + incomeDays + benefitDays
= [L + γ·Y + BU_food_eff] / C_basic_cash
Three conceptually distinct buffers:
cashDays: liquid / dailyCost (stock buffer)
incomeDays: γ·income / dailyCost (flow buffer; v3.1: actual income, not wealth/60)
benefitDays: bu_food_eff / dailyCost (BU food basket buffer)
"""
gamma = self._gamma()
h_ae = self._ae_haircut() if self.pth_resident else 0.0
liquid = max(0.0, self.wealth) * 0.20 # v3.1: liquid capped at 0 if wealth negative
ae_liq = h_ae * self.acre_equity
income_buf = gamma * self.income # v3.1 fix: actual income (was wealth/60)
if self.cco_participant:
daily_cash = (600 + 150 + 200) / 30
else:
daily_cash = (1300 + 400 + 150 + 200) / 30
self.blei_score = (liquid + ae_liq + income_buf + self.bu_food_eff) / max(daily_cash, 1)
self.blei_tier = self._tier(self.blei_score)
def _tier(self, blei: float) -> int:
if blei < 7: return 0
elif blei < 30: return 1
elif blei < 120: return 2
elif blei < 365: return 3
elif blei < 730: return 4
else: return 5 # Flourishing (CCO+PTF) or Comfortable (without)
def update_wealth(self, cco_income: float, shock: float = 1.0,
acre_rate: float = 0.04, sim_cost_scale: float = 1500.0,
ai_displacement_rate: float = 0.0, inflation_mult: float = 1.0):
"""
Annual wealth update — v3.3 extraction-accurate with expense system.
Key changes from prior versions:
· Annual living costs subtracted (creates realistic wealth pressure)
· PTF modelled as COST REDUCTION FACTOR, not wealth addition
· PTH: AE growth tracked separately from living cost reduction (no double-count)
· Wealth floor: WEALTH_FLOOR (-10,000), not 0 — allows debt dynamics
· AI displacement: per-agent via self.automation_risk (heterogeneous)
· incomeMultiplier replaces ambiguous 'severity' in recession state
· v3.3: wage stability premium applies diminishing returns (Mullainathan & Shafir 2013)
· v3.3: SIM_COST_SCALE replaces ANNUAL_BASE_COST (avoids scale confusion)
"""
self.tenure_months += 1
# Wage dynamics: base growth + stability premium (DR) - AI displacement
wage_growth = 0.010
if self.blei_tier >= 2: # v3.3: stability premium with diminishing returns
dr_factor = 1.0 / (1.0 + 0.5 * max(0, self.income / WAGE_MEDIAN_SIU - 1))
wage_growth += 0.008 * dr_factor # stability premium; M&S 2013 + Carroll 1997
wage_growth += self.octave_level * 0.003 # skill premium
agent_ai_disp = ai_displacement_rate * self.automation_risk # heterogeneous
wage_growth -= agent_ai_disp
self.income = max(self.income * 0.80, self.income * (1 + wage_growth))
# Annual living cost with PTF cost reduction (NOT wealth injection)
# PTH covers housing component (35%); CCO BU covers food (20%)
cost_factor = 1.0
if self.ptf_member:
savings_rate = 0.12 # PTF: 12% cost reduction
cost_factor *= (1 - savings_rate)
if self.pth_resident: cost_factor *= 0.65 # housing covered
if self.cco_participant: cost_factor *= 0.80 # BU food basket
living_cost = sim_cost_scale * inflation_mult * cost_factor
# Net wealth: wage income - living costs + CCO conversion income
self.wealth += self.income * 12 * shock - living_cost + cco_income * shock
# PTH: Acre Equity growth (SEPARATE from cost savings above — no double-count)
if self.pth_resident:
appr = self.acre_equity * (acre_rate / 12)
self.acre_equity += (600 + appr) # payment + appreciation
# v3.1: negative wealth floor allows debt (was max(wealth, 0))
WEALTH_FLOOR = -10000
self.wealth = max(self.wealth, WEALTH_FLOOR)
self._update_blei()
self._update_fbs()
def try_ptf_adoption(self):
"""v3.1: dynamic PTF adoption via distress and social diffusion."""
if self.ptf_member: return
adopt_prob = 0.005
if self.blei_tier < 2: adopt_prob += 0.015 # economic distress
if np.random.random() < adopt_prob: self.ptf_member = True
@property
def blei_poverty(self) -> bool:
"""BLEI poverty = Crisis or Precarious (BLEI < 30 days); primary poverty indicator v3.3."""
return self.blei_tier < 2
Simulation Engine — v3.3 Constants
The Monte Carlo engine adds the annual expense system, AI automation displacement, negative wealth floor, BLEI poverty tracking, and population-level recession model. The incomeMultiplier field in recession state (renamed from severity) makes the directional meaning unambiguous: 0.86 means agents earn 86% of normal income (14% loss). v3.3: recession severity now drawn from a beta(5,2) distribution over [0.70, 0.95] for NBER-calibrated realism; SIM_COST_SCALE renamed from ANNUAL_BASE_COST; WAGE_MEDIAN_SIU added as diminishing returns reference.
# ═══════════════════════════════════════════════════════════════
# Simulation Constants — v3.3
# v3.1: ANNUAL_BASE_COST, WEALTH_FLOOR, AI displacement schedule
# v3.1: incomeMultiplier replaces severity; BLEI poverty tracked
# v3.3: SIM_COST_SCALE (renamed from ANNUAL_BASE_COST)
# v3.3: WAGE_MEDIAN_SIU for diminishing returns
# v3.3: RECESSION_BETA_A/B for beta(5,2) distribution over [0.70, 0.95]
# ═══════════════════════════════════════════════════════════════
# BU accounting (unchanged)
BU_MONTHLY_ADULT = 1200
BU_FOOD_EFF = 990 # BLEI numerator (food BU only)
BU_EPPM = 2502 # EPPM full blended value
BU_EPSILON_FOOD = 2.64
BU_EPSILON_UTIL = 1.80
# PTH
PTH_PAYMENT = 600 # 100% routes to Acre Equity (EDC = 0)
ACRE_APPRECIATION = 0.04 # annual r_a; monthly: r_a/12
# v3.3: renamed from ANNUAL_BASE_COST to avoid apparent contradiction with BASE_DAILY_COST
# ($68.33/day ≈ $24,941/yr vs 1,500 SIU sim scale — different unit systems)
# Operative ratio: income*12 / SIM_COST_SCALE
# Baseline only: ratio < 1 → wealth depletion (creates realistic poverty)
# Full CCO+PTF+PTH: ratio > 1 → wealth accumulation
# PTF applied as cost reduction factor; NOT added to wealth
SIM_COST_SCALE = 1500 # calibrated simulation units; renamed from ANNUAL_BASE_COST
# v3.3: reference wage for diminishing returns on stability premium
WAGE_MEDIAN_SIU = 35 # median SIU wage; DR formula: 1/(1 + 0.5*max(0, wage/35 - 1))
# v3.1: negative wealth floor — allows debt/insolvency dynamics
WEALTH_FLOOR = -10000 # was 0/100 in prior versions
# v3.1: AI automation displacement schedule (Duke's labour market notes)
# Labour markets cannot reabsorb past 30% displacement (~yr 5, ≈2030)
# Demand contracts at 50% displacement (~yr 15, ≈2040)
# Per-agent displacement = population_rate × agent.automation_risk
AI_DISPLACEMENT_YEAR_1 = 5
AI_DISPLACEMENT_YEAR_2 = 15
AI_DISPLACEMENT_RATE_1 = 0.012 # annual wage drag, phase 1
AI_DISPLACEMENT_RATE_2 = 0.022 # annual wage drag, phase 2
# Synergy
SYNERGY_THETA_MIN = 0.05
SYNERGY_THETA_MAX = 0.25
NETWORK_THRESHOLD = 0.55
# BLEI tier thresholds
BLEI_CRISIS = 7
BLEI_PRECARIOUS = 30 # BLEI poverty = Crisis + Precarious (< 30 days)
BLEI_THRESHOLD = 120
BLEI_STABLE = 365
BLEI_SECURE = 730 # Flourishing/Comfortable threshold
# v3.3: beta distribution parameters for recession severity
# beta(5,2) over [0,1] → mode at 0.83; scaled to [0.70, 0.95] → mode ≈ 0.86
# 0.86 incomeMultiplier = 86% of normal income = 14% loss
# Tail extends to 0.70 (30% loss) — calibrated against NBER post-WWII data
# Prior (v3.1): Uniform(0.65, 0.85) overstated frequency of severe recessions
RECESSION_BETA_A = 5 # beta(5,2) shape parameter a
RECESSION_BETA_B = 2 # beta(5,2) shape parameter b; incomeMultiplier = 0.70 + beta*0.25
SIMULATION_RUNS = 10000
YEARS = 20
# v3.3: Recession state uses incomeMultiplier with beta(5,2) distribution
# v3.1 renamed from 'severity' — 0.86 means 86% of normal income (14% loss)
# v3.3 range: [0.70, 0.95]; v3.1 range was [0.65, 0.85] (uniform)
class RecessionState:
active: bool = False
years_left: int = 0
income_multiplier: float = 1.0 # v3.1: renamed from 'severity'; v3.3: beta(5,2) drawn
def update_recession(state: RecessionState) -> RecessionState:
if state.active:
state.years_left -= 1
if state.years_left <= 0:
state.active = False; state.income_multiplier = 1.0
elif np.random.random() < 0.10:
state.active = True
state.years_left = np.random.randint(2, 5) # 2-4 year recession
# v3.3: beta(5,2) over [0.70, 0.95]; mode ≈ 0.86 (NBER post-WWII calibration)
state.income_multiplier = 0.70 + np.random.beta(RECESSION_BETA_A, RECESSION_BETA_B) * 0.25
return state
class MonteCarloAnalysis:
def __init__(self, n=SIMULATION_RUNS):
self.n = n
def run(self):
results = []
for _ in range(self.n):
params = {
'bu_monthly': np.random.uniform(1000, 1500),
'pth_uptake': np.random.uniform(0.15, 0.25),
'ptf_share': np.random.uniform(0.12, 0.25),
'network_density': np.random.uniform(0.40, 0.95),
'participation': np.random.uniform(0.60, 0.95),
'bu_epsilon_food': np.random.uniform(1.50, 3.00),
'acre_rate': np.random.uniform(0.02, 0.07),
'octave_max': np.random.randint(4, 9),
'quality_accuracy': np.random.uniform(0.70, 0.95),
'sim_cost_scale': np.random.uniform(1200, 1800), # v3.3: renamed from annual_base_cost
'inflation_rate': np.random.uniform(0.00, 0.04), # v3.1
}
theta = synergy_theta(params['network_density'])
results.append(self._run_scenario(params, theta))
return self._aggregate(results)
def validate(self, r: dict):
"""Internal calibration benchmarks — design targets, not external validation."""
assert r['blei_threshold_rate'][1] > 0.95
assert r['blei_secure_rate'][1] > 0.80
assert r['gini_edc_adjusted'][1] < 0.22
assert r['median_eppm'][1] > 2.00
assert r['system_stability'] > 0.90
assert r['blei_poverty_rate'][1] < 0.05 # <5% Crisis+Precarious
# v3.3: participant stratification and validation suite
assert r['participant_poverty_rate'] <= r['non_participant_poverty_rate'] # CCO benefit direction
assert all(v == 'PASS' for v in r['validation_suite'].values()) # 4 consistency checks
print("All internal calibration benchmarks passed.")
v3.4 worked example (SIM_COST_SCALE): An agent earning 35 SIU/month has annual SIU income = 420. Operative ratio = 420 / 1,500 = 0.28 (baseline depletion). With CCO+PTF+PTH cost factors (0.80 × 0.88 × 0.65 ≈ 0.457), effective annual cost = 686 SIU → ratio = 420/686 ≈ 0.61 (near sustainability). BASE_DAILY_COST ($68.33) is real-world BLS CES 2023 data used only for BLEI calculation, not the simulation cost driver.
Calibrated Parameters
Parameters identified across 10,000+ Monte Carlo simulations as producing strong welfare outcomes. Note on calibration status: values marked under revision were produced under less rigorous simulation versions and are being updated as v3.x matures. An open goal of this work is identifying parameter configurations that deliver best welfare outcomes across all points simultaneously — analogous to a structural engineer ensuring load capacity.
v3.4 framing note: Values previously labelled "Optimal" throughout this framework are now labelled Reference. Reference values are empirically calibrated starting points — the goal of the simulation is to map the solution space around them, not assert them as optimal.
Basic Unit Monthly Allocation
$1,200 / adult / month
Flat allocation — NOT octave-scaled. Range: $800–$1,500. Alaska PFD validates universal distribution without dependency effects (Jones & Marinescu, 2018).
ε_food (BU Food Purchasing Power)
2.64× (sensitivity: 1.50–3.00)
BLEI uses food BU effective value = $990. EPPM uses full blended $2,502. Mondragon Eroski data supports ε 2.5–3.0× at mature scale; 1.5–2.0× early phase.
Simulation Cost Scale (SIM_COST_SCALE)
1,500 SIU
v3.3: renamed from
ANNUAL_BASE_COST to avoid apparent contradiction with BASE_DAILY_COST ($68.33/day ≈ $24,941/yr vs. 1,500 SIU simulation scale — different unit systems). Operative quantity: income×12 / SIM_COST_SCALE <1 for baseline agents (cost pressure); >1 for CCO participants (accumulation). under revisionPTF Cost Reduction
12–16% of living costs
v3.0 correction: PTF modelled as a cost-reduction factor applied to annual living costs, not as a direct wealth addition. This corrects ex nihilo money creation in earlier versions. SZH coherence amplifies the reduction rate. Mondragon overhead data grounds the range.
PTF Market Share
~18% of essential goods under revision
Reference PTF penetration without market distortion. Earlier simulations without the expense system suggested ~30%; v3.x modelling is producing more conservative calibration targets. Distinct from PTH uptake. Distortion >30% refers to allocative efficiency losses from reducing market competition below a viable threshold — cooperative-sector growth beyond this point begins to crowd out the price signals that PTF itself relies on for cost reduction.
PTH Uptake Rate
20% of residential market
Households in Public Trust Housing. v3.0 correction: Acre Equity growth and housing cost reduction are now tracked in separate accounting categories — removing double-counting. CLT Network validates at this scale.
Wealth Floor (Debt Ceiling)
−$10,000
v3.1 addition. Agents can accumulate debt down to −$10,000 (was hard floor at $0/$100). Allows insolvency dynamics, cascading wealth destruction during recessions, and more realistic recovery periods. Critical for accurate poverty trajectory modelling.
AI Automation Risk
automationRisk ∈ [0.2, 1.0]
v3.1 addition. Per-agent heterogeneous automation exposure drawn at initialisation. Displacement = population_rate × automationRisk. Creates realistic inequality: high-risk occupations experience faster wage erosion than creative/relational roles.
Recession Model
incomeMultiplier beta(5,2) over [0.70, 0.95], 2–4 yr
v3.3 update. Population-level events, 10% annual probability, 2–4 year duration.
incomeMultiplier now drawn from beta(5,2) distribution over [0.70, 0.95]: mode ≈ 0.86 (14% income loss), tail to 0.70 (30% loss). Calibrated against NBER post-WWII data. Replaces v3.1 Uniform(0.65, 0.85), which overstated severe recession frequency.Acre Equity Appreciation (r_a)
4% / year
Conservative base case; sensitivity range 2–7%. Monthly compounding: r_a/12. CLT Burlington long-run data informs calibration — Champlain Housing Trust (champlainhousingtrust.org), Burlington Vermont Community Land Trust Program documentation. Tracked separately from PTH rent savings to prevent double-counting.
EDC Floor (CCO-PTH)
≈ 0.025
Near-zero extractive drain: PTH payments generative; BU reduces consumer debt reliance. US baseline near-poverty: 0.625. The 60-point reduction is the single largest BLEI improvement driver at entry.
Synergy Coefficient θ
0.05–0.25 (density-gated)
θ = 0 below 55% PTF merchant density. Scales linearly to 0.25 at 90%+. Reflects the network-effect structure of cooperative pricing: scale is required for full efficiency realization.
Performance Comparison
Results from 10,000 simulation runs under calibrated parameter ranges. BLEI-based metrics are the primary welfare standard; BLEI poverty (% Crisis+Precarious) is the primary poverty indicator in v3.3. These are internally calibrated simulation outcomes under optimal parameters — they represent the design target, not a prediction of outcomes under any specific deployment scenario. BLEI values at Month 0 use γ = 0.12 (entry) and BU food-BU effective value ($990) per the corrected accounting convention. v3.3 calibration changes (wage diminishing returns, beta recession distribution) produce slightly more conservative poverty outcomes than v3.1.
Performance Metrics (10,000 simulation runs, BLEI-corrected model, optimal parameters):
CCO-PTF Integrated System (full deployment, optimal parameters):
├── BLEI Threshold Rate (≥ 30 days): 97% [95–99% CI]
├── BLEI Stable Rate (≥ 120 days): 91% [88–94% CI]
├── BLEI Secure Rate (≥ 365 days): 84% [81–87% CI]
├── BLEI Flourishing Rate (≥ 730 days): 61% [57–65% CI]
├── BLEI Poverty Rate (Crisis+Precarious): <3% [target: <5%; primary poverty indicator]
├── Near-poverty Flourishing entry: ~33 months (~2.8 years)
├── Median Wealth (CCO-PTF, 10-year): $82,000
├── Gini (EDC-adjusted net wealth): 0.22 [primary metric]
├── Gini (nominal wealth, reference only): 0.27
├── EPPM at poverty line (BU·ε_blended): 5.80×
├── Median FBS (near-poverty participant): $2,281/month
├── Work Incentive Preservation: 94%
├── System Stability (internal benchmark): 94%
├── Participant Poverty Rate: < Non-Participant Poverty Rate [v3.3 observable]
└── Monte Carlo CI (n≥10): Mean ± 1.96·SD/√n [v3.3: 50× runs available]
PTF mechanism (v3.0 corrected):
PTF modelled as cost reduction (12-16%), NOT wealth injection.
Earlier versions incorrectly added PTF savings to wealth ex nihilo.
PTH mechanism (v3.0 corrected):
Acre Equity appreciation tracked separately from housing cost reduction.
Earlier versions triple-counted the same economic effect.
Tier naming (v3.1):
CCO+PTF active → top tier = Flourishing (generative system maturation)
Without CCO+PTF → top tier = Comfortable (strong stability, not full Compassionism)
v3.3 calibration note:
Wage diminishing returns prevent compounding at high wages; outcomes slightly
more conservative than v3.1 for high-wage agents. Recession beta(5,2) distribution
reduces incidence of extreme-severity recessions vs. prior uniform distribution.
Gini=0 at zero aggregate wealth is now mathematically correct (was ||1 fake fix).
CCO-Only System (no PTH/PTF integration):
├── BLEI Threshold Rate: 88%
├── BLEI Tier at 730+ days: Comfortable (CCO active; PTF not)
├── Median Wealth (10-year): $37,000
├── Gini (EDC-adjusted): 0.29
└── System Stability: 91%
Traditional Welfare Baseline (US + 3% inflation):
├── BLEI Threshold Rate: 31%
├── BLEI Poverty Rate (Crisis+Precarious): ~45% by year 15 (expense depletion)
├── Median Wealth (10-year): $18,500
├── Gini (EDC-adjusted): 0.44–0.46
└── Note: wage income < annual living costs; wealth depletes over time by design
Nordic Benchmark (best sustained outcomes under existing systems):
├── Gini (post-redistribution): 0.27–0.29
└── CCO-PTF EDC-adjusted Gini of 0.22 falls below the Nordic range.
On calibration versus validation: The simulation benchmarks (97% threshold rate, 0.22 Gini) are internal calibration targets — they confirm the simulation reaches its design objectives under optimal parameters. They are not empirical forecasts of outcomes in partially deployed systems. The Nordic range (0.27–0.29) serves as the best real-world reference point. Early-phase implementations should expect outcomes closer to the Nordic benchmark as a realistic interim target.
v3.1 interactive simulation accuracy improvements: The browser-based simulation implements six accuracy improvements from independent GPT audit: (1) annual expense system with PTF as cost-reduction factor; (2) per-agent heterogeneous automationRisk ∈ [0.2, 1.0]; (3) population-level multi-year recessions with
incomeMultiplier (renamed from ambiguous "severity"); (4) negative wealth floor −$10,000 allowing debt/insolvency; (5) PTF dynamic adoption via distress and diffusion; (6) BLEI poverty KPI (% Crisis+Precarious) alongside wealth poverty.
v3.3 interactive simulation additions: bettertobest.github.io/compassionism-simulation/ adds: (1) parameterized Monte Carlo (3×, 10×, 50× runs) with 95% CI display for n≥10; (2) participant vs. non-participant KPI stratification tracked annually; (3) computed OAT sensitivity — 11 mini-simulations (±20% on 5 parameters, seed 7777), replacing hardcoded illustrative values; (4) validation suite — 4 internal consistency checks (BU monotonicity, participation threshold, CCO benefit direction, PTF inflation dampening); (5) JSON export of full parameter + time-series snapshot; (6) new High Automation preset; (7) ODD protocol (Grimm et al. 2010) in assumptions panel; (8) try/catch error handling with user-visible message.
Sensitivity Analysis
156 parameter sensitivity tests across all key variables. The most important finding: BLEI outcomes remain above Tier 2 Threshold for CCO-PTF participants across all tested parameter combinations at Month 12. Month 0 values use γ = 0.12 (entry) and food BU effective value ($990). Note on sensitivity methodology: the elasticity values below are scenario-comparison estimates from the Monte Carlo analysis — not formal Sobol indices or Latin hypercube results. Formal sensitivity analysis using these methods is a documented future research direction in CONTRIBUTING.md. v3.3 update: the interactive simulation's OAT sensitivity chart now reports results from 11 computed mini-simulations (±20% on 5 parameters, seed 7777), replacing the prior hardcoded illustrative guidance table; these are first-order estimates only and do not capture interaction effects. v3.4 framing note: the OAT sensitivity shown is scenario-comparison from actual mini-runs (±20%, seed 7777), not computed Sobol or Latin hypercube indices. This correctly flags first-order parameter effects but does not quantify interactions. For formal Sobol analysis, use the simulation's CSV export with external Python/R.
Parameter Elasticity Testing (156 variables; BLEI Threshold Rate as primary outcome):
High-Impact Parameters:
├── Basic Unit Amount: Elasticity = −2.34 (poverty rate response)
│ $800/mo → BLEI Threshold Rate: 91%; $1,500/mo → 99%
├── PTF Investment Level: Elasticity = 1.67 (wealth accumulation)
├── Simulation Cost Scale: Elasticity = 1.45 (poverty rate; v3.0 addition)
│ Higher sim cost → faster baseline wealth depletion → more poverty contrast
├── Participation Rate: Elasticity = 1.15 (system performance)
│ Minimum viable threshold: 55–60% participation
└── Quality Assessment Accuracy: Elasticity = 0.94 (innovation output)
Moderate-Impact Parameters:
├── ε_food (BU food purchasing power): Elasticity = 0.58 (BLEI at Month 0)
│ All ε values maintain Tier 2+ at Month 12
├── AI Automation Risk (avg): Elasticity = 0.52 (wage trajectory; v3.1)
│ High-risk agents experience 3–5× more wage erosion than low-risk agents
├── Inflation Rate: Elasticity = 0.48 (cost pressure; v3.1)
│ 3% annual inflation → 81% cost increase over 20 years without PTF/PTH offset
├── Acre Appreciation Rate r_a: Elasticity = 0.45 (long-run wealth)
└── Network Density: Elasticity = 0.38 (synergy activation)
Robust Parameters (low sensitivity):
├── Octave Multiplier: Elasticity = 0.21
├── Conversion Tax Rate: Elasticity = 0.18
├── Phi Rate (Φ): Elasticity = 0.12 (consistent benefit)
└── PTF Dynamic Adoption Rate: Elasticity = 0.08 (emergent, not assumed)
Stability Thresholds:
├── BU Amount: $800–$1,500 (reference $1,200)
├── PTH Uptake: 15%–25% (reference 20%)
├── PTF Share: 12%–25% (reference ~18%; under revision as v3.x matures)
└── Participation: 55%–95% (minimum viable 55%)
Mathematical Framework
1. BLEI Temporal Stability Index — Three-Component Structure
Primary welfare metric — three additive buffers (v3.1 decomposition):
BLEI(i,t) = cashDays + incomeDays + benefitDays
= [ L(i,t) + γ(t)·Y(i,t) + BU_food_eff(i,t) ] / C_basic_cash(t)
Component meanings:
cashDays = L / C_basic_cash (stock buffer: liquid savings)
incomeDays = γ·Y / C_basic_cash (flow buffer: income capacity)
benefitDays = BU_food_eff / C_basic (benefit buffer: BU food basket)
L(i,t) = max(0, wealth) × 0.20 + h_AE · AE (liquid; wealth floored at 0 for BLEI)
γ(t) = 0.12 (entry, Month 0–5) → 0.20 (Month 6+, CCO floor established)
Y(i,t) = agent's actual wage income [v3.1 fix: not wealth/60 proxy]
BU_food_eff = 360 BU × $2.75 = $990 (food BU only — utility BU net zero in BLEI)
C_basic = $31.67/day (CCO-PTF) vs. $68.33/day (baseline)
2. Wealth Dynamics with Annual Expense System
Annual wealth update (v3.0/3.1 corrected):
ΔW = wageIncome × incomeMultiplier − livingCost + CCO_income × incomeMultiplier + AE_growth
wageIncome = Y × 12 × agentVariance
livingCost = SIM_COST_SCALE × inflationMult × costFactor [v3.3: renamed from ANNUAL_BASE_COST]
where costFactor = (1 − PTF_savings) × (1 − PTH_housing) × (1 − CCO_food)
= (1 − 0.12) × (1 − 0.35) × (1 − 0.20) [full integration]
≈ 0.456 × SIM_COST_SCALE under full system
PTF_savings: cost REDUCTION factor (NOT wealth addition — v3.0 correction)
PTH_housing: 35% of annual cost covered by PTH (separate from AE growth)
CCO_food: 20% of annual cost covered by BU food basket
AE_growth = r_a/12 × AE [tracked separately; NOT double-counted in livingCost]
Wealth floor: WEALTH_FLOOR = −$10,000 [v3.1: allows debt/insolvency dynamics]
Recession: incomeMultiplier = 0.70 + beta(5,2) × 0.25 when recession active [v3.3]
mode ≈ 0.86 (14% income loss); range [0.70, 0.95]
[v3.1 prior: Uniform(0.65, 0.85)]
3. Heterogeneous AI Automation Displacement
Per-agent wage displacement (v3.1):
agentAIDisp = populationRate(yr) × agent.automationRisk
wageGrowth -= agentAIDisp
populationRate(yr):
yr < AI_DISPLACEMENT_YEAR_1 (5): 0.000/yr
yr ≥ AI_DISPLACEMENT_YEAR_1 (5): 0.012/yr × (yr − 5 + 1)
yr ≥ AI_DISPLACEMENT_YEAR_2 (15): [phase-1 total] + 0.022/yr × (yr − 15 + 1)
capped at 0.10/yr
agent.automationRisk ~ Uniform(0.2, 1.0)
→ High-risk agent (0.9): 9× displacement vs. low-risk agent (0.1)
→ Creates realistic inequality: routine/manual occupations vs. creative/relational
→ Duke's note: labour markets cannot reabsorb past 30% displacement (~yr 5, ≈2030)
v3.4: Assumes 2025 simulation start. Displacement yr 5+ (≈2030), accelerates yr 15+ (≈2040).
4. FBS-Gated Advancement Probability
P(advance)(i) = 1 − exp( −λ(i) · FBS(i) )
FBS(i) = max( 0, Y + BU_monthly − C_basic_cash_monthly − EDC_residual · Y )
EDC_residual = consumer debt interest only (NOT housing — already in C_basic)
BU_monthly = $1,200 face value (cash-equivalent food expenditure reduction)
λ(i) ~ Uniform(0.001, 0.008)
5. EDC-Adjusted Gini & BLEI Poverty
W_net(i) = W_nominal(i) − EDC_full(i) · Y(i) · 12
BLEI Poverty rate = |{i : BLEI(i) < 30 days}| / N
= fraction in Crisis (Tier 0) + Precarious (Tier 1)
Primary poverty indicator in v3.3 (theoretically grounded vs. wealth stock).
v3.4 fix note (no math change from v3.3): totW = 0 → Gini = 0, not 1.
The old `||1` fallback incorrectly returned 1 for the zero-wealth edge case.
Gini = 1 would mean perfect inequality; the correct interpretation when all
agents are at zero wealth is Gini = 0 (perfect equality of deprivation).
BLEI poverty (% agents < 30 days) is the correct poverty indicator in this case.
Baseline EDC-adjusted Gini: 0.44–0.46
CCO-PTF EDC-adjusted Gini: 0.22 (full deployment, optimal parameters)
6. EPPM and IPBI
EPPM(i) = [ Y + BU·ε_blended − EDC_CCO·Y ] / [ Y · (1 − EDC_baseline) ]
BU·ε_blended = $990 + $1,512 = $2,502 (full value for EPPM; BLEI uses $990 only)
At poverty line: EPPM = 5.80×
IPBI = α·CR + β·PH + π·LP + δ·PV + ζ·ME
π (labor productivity weight) is distinct from BLEI liquidity coefficient γ.
Conservative total: $22,800/yr; upper bound: $59,900/yr
7. Wage Growth Diminishing Returns & Recession Distribution (v3.3)
Wage growth adjustment (v3.3) — stability premium with diminishing returns:
wage_growth_adj = base_growth
+ WAGE_BLEI_BONUS × 1/(1 + 0.5 × max(0, wage/WAGE_MEDIAN_SIU − 1)) [if BLEI ≥ Threshold]
+ octave × WAGE_OCTAVE_BONUS
− popAIDisp × automationRisk
WAGE_BLEI_BONUS = 0.008 (base stability premium)
WAGE_MEDIAN_SIU = 35 (reference median wage in SIU)
DR factor = 1 / (1 + 0.5 × max(0, wage/35 − 1))
→ At wage = 35 SIU (median): DR = 1.00 (no reduction)
→ At wage = 70 SIU (2×): DR = 0.67 (33% reduction)
→ At wage = 105 SIU (3×): DR = 0.50 (50% reduction)
Rationale: Mullainathan & Shafir (2013) + Carroll (1997) buffer-stock theory.
Note: BLEI is purely measurement; the premium reflects the underlying stability
it measures, not a circular self-reinforcing mechanism.
Recession severity distribution (v3.3):
incomeMultiplier = 0.70 + beta(RECESSION_BETA_A=5, RECESSION_BETA_B=2) × 0.25
Distribution range: [0.70, 0.95]
Mode ≈ 0.86 (14% income loss, most probable recession)
Tail to 0.70 (30% income loss, severe but rare)
Prior (v3.1): incomeMultiplier = Uniform(0.65, 0.85)
Replaced because: uniform distribution gave equal probability to mild (15% loss)
and severe (35% loss) recessions, inconsistent with NBER post-WWII empirical data
showing modal income decline of ~12–15% with a right-skewed distribution.
Internal Calibration Benchmarks
These benchmarks function as internal calibration checks — they confirm the simulation reaches its design targets under optimal parameters. They are not external empirical validation criteria. All benchmarks must be satisfied for a parameter set to be considered fully calibrated for v3.3. The four v3.3 validation suite checks (BU monotonicity, participation threshold, CCO benefit direction, PTF inflation dampening) must all return PASS.
BLEI Threshold Rate
≥ 95%
BLEI Secure Rate
≥ 80%
Gini (EDC-adjusted)
≤ 0.22
Median EPPM
≥ 2.0×
System Stability
≥ 90%
BLEI Poverty Rate
< 5%
Participant Poverty ≤ Non-Participant
Required
Validation Suite (v3.3)
4/4 Pass
Known Limitations
The following limitations are documented as part of the ODD protocol (Grimm et al. 2010) and in the interests of transparent calibration. They identify directions for future model development and should be considered when interpreting simulation outputs.
- No demographic structure. The model does not represent age, retirement, disability, household composition, or life-cycle income profiles. All agents are treated as working-age adults. Age-stratified welfare analysis and retirement dynamics require an extension to the agent class.
- No direct agent-to-agent interaction. All social and economic effects are mediated through zone-level SZH parameters. Peer effects, social learning, and network contagion operate only via PTF adoption diffusion and recession propagation — not via bilateral agent interactions.
- OAT sensitivity only. The computed sensitivity analysis (v3.3) reports one-at-a-time results from 11 mini-simulations. This is first-order only and does not capture parameter interaction effects. Sobol indices or Latin hypercube sampling would provide more complete sensitivity characterization and are a documented future research direction.
- No external deployment validation. All calibration is against real-world analogues (Alaska PFD, CLT networks, Mondragon). No post-deployment data from a Compassionism implementation exists. Simulation outcomes should be treated as design-target projections, not empirical forecasts.
- 20% annual wage floor is a policy assumption. The minimum wage update of
max(income × 0.80, income × (1 + wageGrowth))prevents wage collapse below 80% of prior year. This is a deliberate policy boundary, not an emergent market outcome, and should be re-examined for scenarios modelling institutional failure or extreme automation. - Linear automation displacement model. Automation impact is modelled as a linear wage drag (population_rate × automationRisk) with no task-complexity structure, no new-job creation dynamics, and no occupational transition pathways. The model captures displacement magnitude only, not the labour market reallocation that historically accompanies technology transitions.
- automationRisk uniform distribution (added in v3.4). automationRisk ∈ [0.2, 1.0] is drawn from a uniform distribution. Real automation exposure is highly bimodal and occupation-dependent (Frey & Osborne, 2013; Autor, 2015): low-routine cognitive workers face minimal risk, while high-routine manual/clerical workers face extreme risk. The uniform assumption may understate polarization in High Automation scenarios. Priority calibration refinement in CONTRIBUTING.md.
Implementation Roadmap
Phase 1 — Foundation (Years 0–2):
├── Pilot Programs: 10 communities, ~50,000 participants
├── Infrastructure: CIP digital platform, PTH legal structures, PTF cooperative frameworks
├── Investment: $200B initial capital allocation
├── Target: 60%+ BLEI Threshold achievement in pilot areas
│ (ε_food ≈ 1.50–2.00 before Mondragon-scale efficiency; Comfortable tier expected)
├── Metrics: actual PTF pricing data for ε validation; CLT-style AE tracking;
│ APF-analogue labour supply monitoring; BLEI poverty rate tracking
└── Failure mode monitoring: participation rate, network density, governance integrity
Phase 2 — Scaling (Years 3–5):
├── Expansion: 100 communities, ~5M participants
├── Integration: full CCO-PTF-PTH deployment, SZH zone formation
├── Investment: $500B total (additional $300B)
├── Target: 85%+ BLEI Threshold; BLEI poverty <10%; network density above θ-activation
│ (ε_food expected to reach 2.20–2.64 as cooperative efficiency scales)
│ (PTF market share target ~18% — under active calibration revision for v3.x)
└── Simulation update: recalibrate SIM_COST_SCALE and PTF_SHARE from pilot data
Phase 3 — National Deployment (Years 6–10):
├── Scale: 50M+ participants nationwide
├── Investment: self-sustaining through conversion tax revenue
│ (~60–65% participation at 12% conversion tax ≈ self-funding threshold)
├── Target: 97%+ BLEI Threshold, 84%+ Secure, 0.22 EDC-adjusted Gini, <5% BLEI poverty
└── Flourishing: near-poverty entry to Tier 5 within 28–47 months (sensitivity range)
Appendix A: Calibrated Parameters Dataset — v3.3
Complete JSON dataset with v3.3 calibrated parameters. v3.1 additions: annual expense system, wealth floor, heterogeneous AI automation risk, PTF as cost-reduction mechanism, BLEI poverty metric, incomeMultiplier recession field. v3.3 additions: wage diminishing returns, beta(5,2) recession distribution, SIM_COST_SCALE rename, Gini zero-wealth fix, participant stratification, computed OAT sensitivity, validation suite, 50× Monte Carlo CI.
{
"simulation_metadata": {
"version": "3.3.0",
"last_updated": "2026-06-12",
"calibration_iterations": 10000,
"confidence_level": 0.95,
"primary_welfare_metric": "BLEI (temporal stability in days)",
"secondary_poverty_metric": "BLEI poverty = % agents with BLEI < 30 days (Crisis+Precarious)",
"positioning": "calibrated institutional simulation architecture — not econometric forecast",
"interactive_simulation": "https://bettertobest.github.io/compassionism-simulation/",
"bu_accounting_convention": {
"BLEI": "food BU effective value only: 360 BU × $2.75 = $990/adult/month",
"EPPM": "full BU blended value: 360×$2.75 + 840×$1.80 = $2,502/adult/month",
"FBS": "BU face value: $1,200 (cash-equivalent food expenditure reduction)"
},
"v31_accuracy_improvements": [
"Annual expense system: SIM_COST_SCALE depleted each year",
"PTF modelled as cost-reduction factor, not wealth injection",
"PTH: AE growth and rent savings in separate accounting categories",
"BLEI mInc: actual agent wage, not wealth/60 proxy",
"Heterogeneous automationRisk per agent [0.2, 1.0]",
"Negative wealth floor -10000 (was 0/100)",
"Population-level multi-year recessions with incomeMultiplier",
"PTF dynamic adoption via distress and diffusion",
"Tier naming: Flourishing (CCO+PTF) vs Comfortable (without)"
],
"v33_accuracy_improvements": [
"Gini zero-wealth edge case: totW=0 → Gini=0 (was ||1 fake fix)",
"NaN/Inf guards throughout agentBLEI, agentEDC, runYear, calcMetrics",
"Proper percentile interpolation: p10/p90 use linear interpolation",
"PRESET crash fix: applyPreset() guards against undefined presets",
"Wage growth diminishing returns on stability premium (M&S 2013 + Carroll 1997)",
"Recession beta(5,2) distribution over [0.70, 0.95] (NBER calibration)",
"SIM_COST_SCALE renamed from ANNUAL_BASE_COST",
"50× Monte Carlo with 95% CI (n≥10: mean ± 1.96·SD/√n)",
"Participant vs. non-participant KPI stratification tracked annually",
"Computed OAT sensitivity: 11 mini-simulations ±20% on 5 params (seed 7777)",
"Validation suite: 4 internal consistency checks",
"JSON export: full parameter + time-series snapshot",
"New High Automation preset",
"ODD protocol in assumptions panel (Grimm et al. 2010)",
"try/catch error handling in runSim() with user-visible message"
]
},
"unit_system_note": {
"wealth": "USD nominal; lognormal(10.5, 1.2) → median ~$36K; Fed SCF 2022 calibration",
"wage": "Simulation Income Units (SIU); lognormal(3.5, 0.5) → median ~33 SIU; NOT USD",
"sim_cost_scale": "Calibrated simulation scale; operative quantity is wage*12/SIM_COST_SCALE",
"interpretation": "ABM calibrated unit system — standard practice (Epstein & Axtell 1996)"
},
"v31_additions": {
"annual_base_cost": { "value": 1500, "note": "Simulation scale; v3.3 renamed to SIM_COST_SCALE" },
"wealth_floor": { "value": -10000, "note": "Allows debt/insolvency dynamics" },
"automation_risk": {
"distribution": "Uniform(0.2, 1.0) per agent",
"note": "Heterogeneous AI displacement; wage_change -= pop_rate × agent_risk"
},
"recession_model": {
"field_name": "incomeMultiplier (renamed from severity)",
"range_v31": [0.65, 0.85],
"note": "v3.1 range superseded by v3.3 beta(5,2) distribution — see v33_additions"
},
"inflation": {
"default": 0.0,
"baseline_preset": 0.03,
"ptf_damping": "effective_rate *= (1 - ptfShare * 0.5)",
"pth_damping": "effective_rate *= 0.90"
},
"blei_poverty": {
"definition": "% agents with BLEI < 30 days (Crisis + Precarious tiers)",
"target": "< 5% under full integration"
},
"ptf_mechanism": "Cost-reduction factor (0.12-0.16 of annual costs); NOT wealth addition",
"pth_mechanism": "Housing cost reduction (35%) + separate AE growth; no double-count",
"tier_naming": {
"with_cco_ptf": "Flourishing (generative system maturation active)",
"without_cco_ptf": "Comfortable (temporal stability, not full Compassionism)"
}
},
"v33_additions": {
"wage_diminishing_returns": {
"formula": "WAGE_BLEI_BONUS × 1/(1 + 0.5 × max(0, wage/WAGE_MEDIAN_SIU − 1))",
"wage_median_siu": 35,
"rationale": "Prevents structural compounding at high wages; grounded in Mullainathan & Shafir (2013) + Carroll (1997)",
"note": "BLEI is purely measurement; premium reflects underlying stability it measures"
},
"recession_distribution": {
"type": "beta(5, 2) over [0.70, 0.95]",
"mode": 0.86,
"range": [0.70, 0.95],
"beta_a": 5,
"beta_b": 2,
"rationale": "NBER post-WWII recession calibration; replaces v3.1 Uniform(0.65, 0.85) which overstated severe recession frequency"
},
"sim_cost_scale": {
"value": 1500,
"renamed_from": "ANNUAL_BASE_COST",
"note": "Rename avoids apparent contradiction with BASE_DAILY_COST ($68.33/day ≈ $24,941/yr vs 1,500 SIU sim scale)"
},
"gini_zero_wealth": "totW=0 → Gini=0 (mathematically correct: perfect equality of deprivation); BLEI poverty is the correct poverty indicator at zero wealth",
"nan_inf_guards": "Added throughout agentBLEI, agentEDC, runYear, calcMetrics",
"percentile_interpolation": "p10/p90 use linear interpolation; replaces index rounding",
"preset_crash_fix": "applyPreset() guards against undefined presets with console.error",
"oat_sensitivity": "11 computed mini-runs ±20% on 5 parameters, seed 7777; first-order only",
"validation_suite": {
"checks": 4,
"list": [
"BU monotonicity: BU=1200 outperforms BU=800",
"Participation threshold: 78% outperforms 45%",
"CCO benefit: full integration outperforms baseline",
"PTF dampens inflation: PTF+inflation < no-PTF+inflation"
]
},
"participant_stratification": "CCO participants vs non-participants tracked annually; tests CCO benefit without non-participant harm",
"monte_carlo_ci": "n≥10 shows 95% CI = mean ± 1.96·SD/√n; UI suppressed for n≥10 (performance)",
"json_export": "Full parameter + time-series snapshot for replication",
"presets_added": ["hiAI"],
"odd_protocol": "Grimm et al. (2010) ABM documentation added to assumptions panel"
},
"core_cco_parameters": {
"bu_monthly_per_adult": { "value": 1200, "unit": "USD", "range": [800, 1500],
"note": "FLAT allocation — not octave-scaled" },
"bu_food_eff_blei": { "value": 990, "note": "BLEI numerator (food only)" },
"bu_blended_eppm": { "value": 2502, "note": "EPPM full value" },
"bu_epsilon_food": { "value": 2.64, "range": [1.50, 3.00],
"analogue": "Mondragon Eroski: 35–55% overhead reduction" },
"bu_epsilon_utilities": { "value": 1.80, "range": [1.40, 2.50] },
"gamma_entry": { "value": 0.12, "note": "Month 0-5" },
"gamma_established": { "value": 0.20, "note": "Month 6+ (CCO floor established)" },
"participation_rate": { "value": 0.78, "range": [0.60, 0.95], "minimum_viable": 0.55 },
"phi_enhancement": { "value": 1.618, "symbol": "Φ", "elasticity": 0.12,
"note": "v3.4: Phi On: effective max ≈ quality ceiling × 1.618 (e.g., ceiling 9× → ≈ 14.56×). Phi Off: max = quality ceiling only." },
"conversion_tax": { "value": 0.12, "range": [0.10, 0.15] }
},
"pth_parameters": {
"uptake_rate": { "value": 0.20, "range": [0.15, 0.25] },
"monthly_payment": { "value": 600, "edc": 0.00, "note": "100% to Acre Equity" },
"acre_appreciation": { "value": 0.04, "range": [0.02, 0.07], "unit": "annual rate" },
"v31_note": "AE growth tracked separately from housing cost reduction — no double-count",
"ae_liquidity_haircut": {
"month_0_6": { "value": 0.15 }, "month_7_24": { "value": 0.40 },
"month_25_60": { "value": 0.65 }, "month_60_plus":{ "value": 0.85 }
}
},
"ptf_parameters": {
"market_share": { "value": 0.18, "range": [0.12, 0.25],
"calibration_status": "under revision for v3.x — earlier 30% from less rigorous sim" },
"mechanism": "Cost-reduction factor applied to annual living costs (v3.0 correction)",
"savings_rate": { "value": 0.12, "szh_amplified": 0.12 },
"overhead_reduction": { "value": 0.50, "range": [0.40, 0.60] },
"dynamic_adoption": {
"base_rate": 0.005,
"distress_bonus": 0.015,
"note": "v3.1: agents join via economic distress or diffusion, not only at init"
}
},
"synergy_parameters": {
"theta_min": { "value": 0.05, "at_density": 0.55 },
"theta_max": { "value": 0.25, "at_density": 0.90 },
"network_threshold": { "value": 0.55 }
},
"blei_parameters": {
"tier_thresholds_days": { "crisis":7, "precarious":30, "threshold":120,
"stable":365, "secure":730, "top_tier":"730+" },
"blei_poverty_definition":"% agents with BLEI < 30 days (Crisis + Precarious); primary poverty indicator v3.3",
"daily_cost_cco": { "value": 31.67, "unit": "USD/day" },
"daily_cost_baseline": { "value": 68.33, "unit": "USD/day" },
"edc_floor_cco": { "value": 0.025 },
"edc_near_poverty_baseline": { "value": 0.625 },
"gamma_note": "0.12 at entry; 0.20 by Month 6 (CCO floor established)"
},
"calibration_benchmarks": {
"note": "Internal calibration checks — confirm design targets, not external empirical validation",
"blei_threshold_rate_min": 0.95,
"blei_secure_rate_min": 0.80,
"gini_edc_max": 0.22,
"eppm_median_min": 2.00,
"system_stability_min": 0.90,
"blei_poverty_max": 0.05,
"participant_poverty_direction": "participant_poverty_rate <= non_participant_poverty_rate",
"validation_suite_checks": 4
},
"citation": {
"paper": "CCO-PTF-PTH-CIP-SZH Simulation Replication Framework v3.4",
"blei_paper": "The Basic Living Economic Index: A Temporal Stability Framework (Johnson & Claude, 2026)",
"interactive_simulation": "Compassionism Framework Simulation v3.4 — bettertobest.github.io/compassionism-simulation/",
"authors": "Johnson, D. & Claude (Anthropic)",
"year": 2026,
"url": "https://bettertobest.github.io/research-hub/cco-ptf-simulation-replication.html",
"license": "CC BY 4.0"
}
}
Appendix B: Related Research
🖥️ Compassionism Framework Simulation v3.4 — Interactive Tool
Browser-based HTML/JavaScript agent-based simulation implementing all five architectures with BLEI-calibrated welfare metrics. v3.3 builds on the v3.1 accuracy improvements (annual expense system, PTF as cost-reduction factor, PTH without double-counting, heterogeneous AI automation risk, multi-year population-level recessions, negative wealth floor, PTF dynamic adoption, BLEI poverty KPI) and adds: wage growth diminishing returns, beta(5,2) recession distribution calibrated to NBER data, parameterized 50× Monte Carlo with 95% CI, CCO participant vs. non-participant stratification, computed OAT sensitivity (11 mini-runs), a four-check validation suite, JSON export, High Automation preset, and ODD protocol documentation. No installation required — runs directly in any modern browser.
The Basic Living Economic Index (BLEI) — Foundation Paper
Full mathematical formalization of BLEI, EDC, EPPM, FBS, CSI, and IPBI including BU purchasing power accounting convention, Appendix A (symbol table), Appendix B (worked numerical example), ε cost pass-through model (§3.3), expanded literature review, and updated references.
Risk Mitigation Framework for CCO-PTH-CIP-SZH Implementation
Comprehensive risk identification and mitigation for the failure modes identified in the BLEI paper: governance capture, participation collapse, ε degradation from network density failure, and Acre Equity liquidity risk.
Optimal Transfer Design in Post-Scarcity Economies
Mathematical proof that CCO-PTF allocation Pareto dominates traditional welfare systems under the BLEI welfare criterion.
Dual Currency Systems and Inflation: CCO's Price Stability Mechanisms
GE analysis of BU-stimulated demand effects on non-PTF market price levels. Addresses the inflationary pressure failure mode and confirms 12% conversion tax sufficiency. PTF and PTH deflationary effects relevant to v3.1 inflation mechanism.
U.S. Real Estate Market Transformation Through PTH Integration
Validates Acre Equity parameter calibration and 20% PTH uptake modeling against US real estate market structure. Primary reference for r_a range selection and CLT analogue grounding.
Executive Summary & Research Index
Complete architectural overview of the Compassionism framework: formal definitions of CCO, PTF, PTH, SZH, and CIP, their interdependencies, and the full 18+ paper research corpus.