Pre-computed step-by-step influence campaign for each candidate, derived from Dijkstra shortest-path analysis on the 68-actor causal influence graph, weighted by influence_weight × causal confidence. Phases are ordered by cascade dependency — complete Phase 1 before Phase 2 unlocks. Probability impacts are additive estimates based on Grade-A historical effect sizes. Status tracks execution progress.

Track live influence operations across Planning → In Progress → Executed → Measuring stages. Each card represents a specific actor engagement. Cascade dependencies are shown — some operations cannot start until predecessors complete. Drag cards between columns as operations advance. Click any card to expand the full action brief.

Force-directed graph of the 68-actor invisible primary network. Node size = influence weight; edge color = relationship type; node color = access difficulty (green=accessible, yellow=2-hop, orange=intermediary required, red=difficult). Click any node to see the optimal access path and approach strategy. Toggle 'Grade A only' to filter to causally confirmed edges.

Real-time nomination probability for all candidates, updated as operations are logged. The 'what would move the needle most' section below each bar is the key action signal — it identifies the single highest-expected-value intervention available right now. Use 'Scenario' to model what happens if a specific actor commits to any candidate.

Log and interpret incoming signals — endorsements, public statements, donor meetings, fundraising reports, polling shifts. Each signal is assessed for its downstream cascade effect on nomination probabilities. Use the manual entry form to log new intelligence; the system will recompute affected probabilities automatically.