How to model mining competition between miners & uncles?

Question

I would like to really understand pow mining by implementing a simple one, but still struggle.

assume we have five miners, m1,...,m5. Each miner has hash rate, m1=30%, m=20%,m3=15,m4,25, m5=10%, in total 100%

How to simulate (e.g., in simple python code) minig competition between these miners in such a way each 15 seconds a new block is found by one of them taking their hash rate into consideration. for example it could be possible that two or three miners got a block at the same time. so each block is distributed (let us say time dealy) and got confirmed by others. How to model this? decide number of stale/uncle blocks etc

Could anyone help and advise in this.

Answer 1

Edit: I spent a few hours writing some code that simulates block propagation time and uncles. You can find it at my GitHub repo here: https://github.com/lungj/ethtools. The program in question is mine_sim.py. I figured it would be a bit weird to post such a long program into StackExchange.

The rabbit hole can get pretty deep, depending on how deeply you want to model things. In the actual world of mining, miners compute hashes of particular values. When the hashes satisfy some conditions, the data that is hashed can be propagated onto the blockchain. When the blockchain is in equilibrium for difficulty and hash rate and block time is b, for every timestep ts in your simulation, there is a (hashrate / total hashrate) * b / ts) chance that a block is mined by a particular miner.

Here is a simulation that ignores block propagation times. You can, of course, add that. How you do that depends on the properties of the network you want to simulate. You can create a crude uncle calculation by changing TIMESTEP to your block propagation time and, whenever more than one miner finds a block at the same simulation timestep, you can count that as an uncle. If you want a more involved model, for each node, you can store the chain state from the perspective of that node (with the presumption that some nodes are also miners). You can model propagation delays between individual nodes to simulate block propagations more realistically. You would then want to choose a realistic fan-in/fan-out number for each node (or simulate connections being formed and broken) and perhaps model geographic distribution of various nodes. And, of course, you could go much more deeply than that, even.

#!/usr/bin/env python3
import random

class Miner(object):
    def __init__(self, name, hashrate):
        '''Initialize a new miner named name with hashrate measured in hashes per second.'''
        self.name = name
        self.hashrate = hashrate

MINERS = [
    Miner('Alice', 30e6),
    Miner('Bob', 20e6),
]

TIMESTEP = 0.01     # Timestep of simulation
BLOCKTIME = 15      # Average number of seconds per block

TOTAL_HASHPOWER = sum([miner.hashrate for miner in MINERS])
time = 0

while True:
    for miner in MINERS:
        if (random.random() * (TOTAL_HASHPOWER / miner.hashrate)) < (TIMESTEP / BLOCKTIME):
            print("t=%0.3f: %s mined a block" % (time, miner.name))
    time += TIMESTEP