Written during the US-Israel strikes on Iran, examines whether prediction markets on armed conflicts are net informational goods or perverse incentive engines. Dissects the IDF insider trading case where soldiers traded Polymarket positions before strikes, the CFTC's regulatory stance, and the divergent approaches of Kalshi (regulated, avoids conflict markets) versus Polymarket (offshore, lists them freely). Argues the information value is real but the moral hazard is structurally underpriced, and proposes guardrails including delayed settlement and conflict-of-interest screens.

Frames prediction markets as a real-time information layer that complements traditional journalism by aggregating probabilistic forecasts from financially-incentivized participants. Argues that skin-in-the-game accountability produces more accurate signals than commentary-based analysis, with price movements often anticipating news before official announcements. Uses Polymarket and Kalshi as examples and acknowledges COVID-19 as a case where markets underperformed.

Argues that nearly any prediction market tied to a public figure's actions, tenure, or appearances implicitly embeds assassination as a resolution path — what the authors call 'kinetic intervention.' Uses the Charlie Kirk assassination and subsequent Kalshi market voiding as the central case study. Warns that blanket void-on-death rules can backfire by incentivizing violence from losing bettors, and proposes that platforms hire geopolitical risk officers to evaluate resolution wording, monitor anomalous betting signals (BETINT), and build early-warning capacity before tragedy occurs.

Explains the SKC (Srinivasan, Karger, Chen) mechanism for prediction markets on unverifiable outcomes. Markets resolve using crowd consensus as the outcome, with delta-based scoring rewarding participants for moving markets toward final consensus. Enables markets for subjective questions lacking ground truth.

Educational thread on the game-theoretic foundations of prediction markets. Explains why truth-telling is the dominant strategy through incentive compatibility, details how LMSR works as a proper scoring rule, and argues prediction market builders need economists and game theory experts on their teams.

Proposes a mechanism for prediction markets where outcomes cannot be objectively verified. Uses the last reporter's prediction as a reference point, creating incentives for truthful reporting through negative cross-entropy payments. Proves truthful reporting is a perfect Bayesian equilibrium.