Model how regulatory signals flow from presidential appointments through agencies to crypto industry outcomes. Adjust policy levers, watch propagation in real time, and export your scenario analysis.
Policy Levers represent key decision points in crypto regulation. Each slider controls a node's directive — its intentional policy stance.
Signal Propagation runs continuously. A node's effective signal blends its directive with weighted influence from upstream nodes. Positive = pro-crypto; negative = restrictive.
Edge Weights reflect real authority: Presidential appointments strongly steer agencies; Congress shapes legislation; agencies enforce rules; industry responds.
Quick Scenarios load documented policy postures:
Pro-Crypto Executive — Friendly SEC/CFTC chairs, Treasury engagement, banking accessRestrictive Enforcement — Aggressive SEC, limited banking, no stablecoin frameworkLegislative Clarity — Congress passes market structure & stablecoin billsStatus Quo — Mixed signals, enforcement by regulation continuesExport downloads a JSON scenario file + human-readable summary for reports or further modeling.
This is a qualitative signal-flow model, not a quantitative price predictor. It illustrates how directional policy intent propagates through institutional hierarchies.
Node signals ∈ [−1, 1]. Effective signal = 0.6 × directive + 0.4 × (weighted upstream average). Iteration continues until convergence (Δ < 0.001).
Edge weights are derived from statutory authority, appointment power, and historical influence — not fitted to market data.
Limitations: Omits judicial branch, state regulators, international coordination, market feedback loops, and timing dynamics. Use for structural reasoning, not forecasting.
Source grounding: Inspired by Coin Bureau founder @puckrin's observation (via Cointelegraph) that presidential impact on crypto may be less divergent than assumed — this tool lets you test that hypothesis structurally.