FINANCIAL/ABM Policy desk
JS core ≡ Python model Star on GitHub

Stage F · Greenwashing policy desk

Heavier fines,or clearer sight?

Two hundred anonymous synthetic firms choose every 60 trading days between real improvement, inflating their report and doing nothing, by net present value.

Full-model results · stage FResearch result · sourcedDetails ↓

  1. 01

    Robust ordering

    Information > penalty > baseline

  2. 02

    Transparency threshold

  3. 03

    Magnitudes are a range

Live wire

    Replay · simplified dynamics · not a research result

    02The firm sphere

    200 synthetic firms · colour = this period’s choice · size = market cap

    Simulated day 0

    Drag to rotate · click a firm

    04Policy window · days 1200–3500

    No market feedback · not a research result

    05Decision spectrogram

    Rows: 200 firms by size class and market cap · columns: 58 decisions over 14 years

    Show data table

    06Audits and turns

    DayFirmEventDetail

    No audit has caught anyone yet.

    Panel ①

    Firm decision One period · live

    Each dot is one firm

    Choices by size class

    What each option is worth this period

    Panel ②

    Simplified dynamics No market feedback · not a research result

    Mean true level (SD)
    Mean inflation (SD)
    Show data table
    The cost of simplification: against the full model

    Panel ③

    Full-model results (stage F) Research result · sourced

    1. 01

      Robust ordering

    2. 02

      Transparency threshold

    3. 03

      Magnitudes are a range

    Decay sets the magnitude; the ordering agrees at both tested ends

    Show data table

    Four primary questions (Holm-adjusted)

    Dose–response

    Phase map 2

    Phase map 1

    Source

    Method and limits

    What this sandbox is, and is not