Game-Theoretic Randomness for Blockchain Games
It addresses fairness in decentralized games, though it appears incremental as it builds on existing randomness generation approaches.
The paper tackles the problem of generating fair randomness in deterministic, multi-agent blockchain games, proposing a game-theoretic method that aligns incentives to minimize manipulation, resulting in only slight distribution skews even when miners attempt to cheat.
In this paper, we consider the problem of generating fair randomness in a deterministic, multi-agent context (for instance, a decentralised game built on a blockchain). The existing state-of-the-art approaches are either susceptible to manipulation if the stakes are high enough, or they are not generally applicable (specifically for massive game worlds as opposed to games between a small set of players). We propose a novel method based on game theory: By allowing agents to bet on the outcomes of random events against the miners (who are ultimately responsible for the randomness), we are able to align the incentives so that the distribution of random events is skewed only slightly even if miners are trying to maximise their profit and engage in block withholding to cheat in games.