There are multiple use-cases of this decentralized framework. Here a few examples:

– The best developed algorithms are likely to be attractive intraday proprietary trading candidates at real exchanges, including market making strategies when the system supports genuinely more high-frequency (low-latency) actions to be taken;

– The framework already produces increasingly realistic dLOB data that can find use in AI development, and complicated numerical finance applications such as option pricing;

– Real world exchanges can collaborate to test different market microstructure rules and setups to optimise their business via tick sizes and fee structures;

– A bit similarly, regulators may find utility in the framework to maximize market quality metrics, such as volatility, spread, trading volumes, or even lower the probability of flash crashes and other problems.

As the decentralised system adapts more of real world exchange rules, such as more complicated volume tiers to incentivise higher trading volumes, adaptive tick sizes, maker-taker fee structures, self-prevention mechanisms, automatized market surveillance techniques, and perhaps most importantly, connection to real world events, the value of the system upon successful adaptation grows significantly.