Selfish mining literature has traditionally focused on MDP to probe for theoretically ideal strategies. However, these fail to integrate crucial factors like network latency, difficulty adjustment, and node/pool internal processing delays, which can significantly affect the outcome of a real-world attack.

The Monero Simulator incorporates these elements, plus discrete asynchronous agentic mining pools, into a configurable Monte-Carlo framework capable of sweeping over a broad set of input parameters. Tunable parameters include: pools/hashrate, difficulty adjustment constants, network latency/bandwidth, and pool strategies.

Additionally, we introduce a novel simplification whereby the set of selfish strategy state-diagrams can be modeled with just two, integer input parameters.

Preliminary results corroborate the classic selfish mining model, while challenging the relative profitability of "stubborn" variants.

Finally, the simulator offers a versatile environment for testing selfish mining countermeasures, and could be extended to probe other aspects of Monero's design.