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I built this to provide transparency around enforcement activities. It uses [mention tech stack, e.g., Mapbox/Leaflet] and a verification system to prevent false positives. Feedback on the verification logic is welcome.
I'm working on (and hold IP around) an architecture pattern for P2P contest and oracle-resolved systems that focuses on deterministic settlement, dispute containment, and exactly-once execution between outcome resolution and payout.<p>The goal is to eliminate:
- replay / double-settlement conditions
- ambiguous resolution states
- arbitration loops caused by partial failures or conflicting outcomes<p>The pattern introduces a reconciliation layer that gates settlement, enforces finality, and holds contested states for resolution before funds move.<p>I'm curious if anyone here has implemented or seen similar patterns in:
- prediction markets
- fintech / escrow platforms
- marketplaces with disputes
- gaming / contest systems<p>Interested in architectural feedback, pitfalls, or pointers to teams working on this class of problem.
We prove that identifying decision-relevant coordinates in a decision problem is coNP-complete. Finding the minimum sufficient coordinate set is also coNP-complete.<p>Formally: given state space S = X_1 × ... × X_n and utility U : A × S → Q, a coordinate set I is sufficient if s_I = s'_I implies Opt(s) = Opt(s'). Checking whether I is sufficient reduces to TAUTOLOGY. Finding minimum I reduces to the same.<p>Main results:<p>SUFFICIENCY-CHECK is coNP-complete
MINIMUM-SUFFICIENT-SET is coNP-complete (Sigma_2^P structure collapses)
ANCHOR-SUFFICIENCY (fixed coordinates) is Sigma_2^P-complete
Dichotomy: polynomial when |minimal set| = O(log |S|), exponential when Omega(n)
Tractable cases: bounded |A|, separable U(a,s) = f(a) + g(s), tree-structured coordinates
Engineering consequence: over-modeling is not laziness. Determining which configuration parameters matter requires solving coNP-complete problems. Including everything costs O(n). Minimizing costs Omega(2^n). For large n, over-specification is optimal.<p>This explains: config files that grow forever, heuristic feature selection (AIC/BIC/CV), absence of "find minimal config" tools. These are not tooling failures. They are optimal responses to intractability.<p>2760 lines of Lean 4 proofs. 106 theorems. Zero sorry.