CTIA: Explainable and Adaptive Probability Assignment for Cryptographic Micropayment Tickets

In digital systems, micropayments refer to extremely small transactions which occur frequently. These micropayments support a lot of existing systems such as pay-per-API billing, machine-to-machine interaction, and incentive mechanisms in distributed networks. Probabilistic micropayment systems enhance the system, by settling only a subset of transfers, although the rules for these probability are either prefixed or purely cryptographic. Due to this the system loses governance and limits auditability, and leaves with no explanation of the final win/lose outcome. This paper introduces CTIA (Cryptographic Ticket Issuer Agent), focused on a decision-layer component that assigns per-ticket win probabilities from telemetry while leaving the cryptographic ticket format and deterministic lottery verification unchanged. CTIA uses a Random Forest regressor to map telemetry (network load, relay reputation, budget utilization, and recent win rate) to a probability p , and logs SHAP attributions per issuance as an auditable explanation log. We evaluated CTIA under low-probability micropayment regimes under controlled simulations and compared it to static, analytic and alternative machine-learning controllers. With the results we found that CTIA preserves the intended probability and payout scale while providing adaptive and auditable probability assignments. However we noticed that fairness outcomes varied across regimes and were not consistently better than simpler controllers. During the experiment, we also observed that adaptive probability assignment can affect participant-level and temporal reward distribution, but the magnitude and direction of the effect were dependent on the probability regime, controller and evaluation horizon. Thus though this study we would like to position CTIA as an auditable probability-assignment layer.