# Cobalt: Optimizing Mining Rewards in Proof-of-Work Network Games

arXiv: https://arxiv.org/abs/2307.04695 · HTML: https://mlsanity.com/paper/2307.04695/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 10 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Multi-Armed Bandits with Correlated Arms](https://mlsanity.com/paper/1911.03959/) (2019)
- [Decentralization in Bitcoin and Ethereum Networks](https://mlsanity.com/paper/1801.03998/) (2018)
- [Perigee: Efficient Peer-to-Peer Network Design for Blockchains](https://mlsanity.com/paper/2006.14186/) (2020)
- [On Light Spanners, Low-treewidth Embeddings and Efficient Traversing in Minor-free Graphs](https://mlsanity.com/paper/2009.05039/) (2020)
- [Modeling the Impact of Network Connectivity on Consensus Security of Proof-of-Work Blockchain](https://mlsanity.com/paper/2002.08912/) (2020)
- [Hop-Constrained Metric Embeddings and their Applications](https://mlsanity.com/paper/2106.14969/) (2021)
- [Less is More: Fairness in Wide-Area Proof-of-Work Blockchain Networks](https://mlsanity.com/paper/2204.02461/) (2022)
- [Strategic Latency Reduction in Blockchain Peer-to-Peer Networks](https://mlsanity.com/paper/2205.06837/) (2022)

## Abstract (arXiv)

Mining in proof-of-work blockchains has become an expensive affair requiring specialized hardware capable of executing several megahashes per second at huge electricity costs. Miners earn a reward each time they mine a block within the longest chain, which helps offset their mining costs. It is therefore of interest to miners to maximize the number of mined blocks in the blockchain and increase revenue. A key factor affecting mining rewards earned is the connectivity between miners in the peer-to-peer network. To maximize rewards a miner must choose its network connections carefully, ensuring existence of paths to other miners that are on average of a lower latency compared to paths between other miners. We formulate the problem of deciding whom to connect to for miners as a combinatorial bandit problem. Each node picks its neighbors strategically to minimize the latency to reach 90% of the hash power of the network relative to the 90-th percentile latency from other nodes. A key contribution of our work is the use of a network coordinates based model for learning the network structure within the bandit algorithm. Experimentally we show our proposed algorithm outperforming or matching baselines on diverse network settings.