AI Syndicate
Monte Carlo Simulator — Live Dashboard
Tweak the dials to watch the wealth transition matrix, concentration, and inequality update in real time.
Simulation Controls
Shape the syndicate dynamics
More companies = smoother deciles, but heavier compute.
Year 1 occurs after the first growth step.
Higher runs = tighter convergence; set to 1 for a single run view.
If enabled, companies can drop to zero (die) during simulation.
Applied each year, shrinks by decay factor over time.
Adds slope × (decile/9); higher decile (worse) dies more if slope > 0.
Each year multiplies base by this factor (e.g., 0.90 = 10% less each year).
Selects a portfolio at entry year and holds to end.
Higher = stronger tilt toward top deciles (k=0 is random).
Top 10% share
Mean across runs, as share of total wealth.
Gini
Mean inequality of final-year distribution.
Death rate
Mean share of companies that died by final year.
VC multiple
Mean portfolio multiple (sum exit / sum entry).
VC median
Median portfolio multiple across runs.
VC 30× hit rate
Share of portfolios with ≥30× winners.
Transition probabilities
Top 10% share across runs
Gini across runs
Final distribution (one run)
VC portfolio multiple across runs
VC per-company multiples
Deaths by year
Methodology snapshot
Each year every alive company draws growth ~ Uniform(min_g, max_g) and compounds. Ranks → deciles are computed on values after growth; transition probabilities use deciles at your selected start/end years. Top-10% share and Gini are taken on the final-year values.
If “Enable death probability” is on, each year we first compute current deciles, then apply a death chance p = (base × decay^t) + slope × (decile / 9), clamped to [0,1]. Higher decile = worse. Positive slope means worse deciles die more; decay < 1 reduces the base hazard over time. Dead companies drop to zero and stay zero, and zeros remain in the rankings (they sit at the bottom deciles).