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Profit Maximization for Bitcoin Pool Mining: A Prospect Theoretic Approach

Published on Oct 1, 2017 in CIC (Color Imaging Conference)
· DOI :10.1109/CIC.2017.00043
Mehrdad Salimitari2
Estimated H-index: 2
(UCF: University of Central Florida),
Mainak Chatterjee26
Estimated H-index: 26
(UCF: University of Central Florida)
+ 1 AuthorsEduardo L. Pasiliao13
Estimated H-index: 13
(AFRL: Air Force Research Laboratory)
Abstract
It is predicted that cryptocurrencies will play an important role in the global economy. Therefore, it is prudent for us to understand the importance and monetary value of such cryptocurrencies, and strategize our investments accordingly. One of the ways to obtain cryptocurrency is via mining. As solo mining is not possible because of the computational requirements, pool mining has gained popularity. In this paper, we focus on Bitcoin and its pools. With more than 20 pools in the network of Bitcoin and other cryptocurrencies, it becomes challenging for a new miner to decide the pool he must join such that the profit is maximized. We use prospect theory to predict the profit that a specific miner, given his hash rate power and electricity costs, is expected to make from each pool. A utility value is calculated for each pool based on its recent performance, hash rate power, total number of the pool members, reward distribution policy of the pool, electricity fee in the new miner's region, pool fee, and the current Bitcoin value. Then, based on these parameters during a certain time duration, the most profitable pool is found for that miner. We show how the utility values from a pool varies with electricity fee and dollar equivalent of a Bitcoin. To find the accuracy of our predictions, we mine Bitcoin by joining 5 different pools- AntPool, F2Pool, BTC.com, Slushl'ool, and BatPool. Using an Antminer 55 for each pool, we mine Bitcoin for 40 consecutive days. Results reveal that our prospect theoretic predictions are consistent with what we actually mine; however predictions using expected utility theory are not as close.
  • References (11)
  • Citations (11)
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References11
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#1TverskyAmos (Stanford University)H-Index: 92
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