As blockchain technology moves toward proof-of-stake consensus models, a pressing question arises: Will these systems sustain decentralization, or will rewards be disproportionately pooled among major players at the expense of broader participation?
Dr. Wenpin Tang, a leading researcher in the field of blockchain incentives, analyzed these dynamics in proof-of-stake (PoS) systems using advanced mathematical models. His findings highlight and begin to unravel the complex forces at play.
In pure PoS chains like Ethereum, miners offer their coin balance for validation privileges, with no trading allowed between miners. Winners earn more coins as a reward. This seems to favor big players, but Dr. Tang explains that it is more nuanced:
The main conclusion is that it will be different for large and small miners. For large miners (e.g. Binance or Musk), their shares will be stable. For example, if they have 10% initial shares, they will eventually be close to 10%. That is not the case for small miners (e.g. many small miners); their shares are subject to fluctuations. For example, if they have a starting share of 0.01%, they could end up at 0.0001% or 0.1% – where the downside probability is greater than the upside probability.
So while giants remain stable in this pure PoS system, small miners face significant volatility with a long-term trend towards stake loss. Dr. Tang notes that this could lead to an increased reliance on large validators to maintain the blockchain.
However, introducing trade into the ecosystem has a profound effect. When miners can trade coins, a new dynamic arises. Dr. Tang modeled a “market impact” approach in which selling lowers prices and buying increases them. The math then showed that trade forced decentralization over time.
However, this assumes a ‘homogeneous’ group of miners validating the network, meaning they all take action to optimize their positions. “The analysis assumes that miners have identical incentives and information,” says Dr. Tang, “but the reality is much messier.”
Equally important is going beyond the perfect rationality assumed in most models. “Real decisions come from ‘feeling’, not from calculated optimization,” Tang explains. “This chaotic collective behavior requires study.”
In other words, human feelings constitute incentives, and different incentives create heterogeneity among the mining population, which is difficult to explain by pure mathematics. So while Dr.’s equations Tang provides guiding insights, human actions in the real world deliver ultimate results. Dr. Tang uses the term “bounded rationality” – rational thinking that is nevertheless “bounded” by human weaknesses and incentives.
Here is Dr. Tang machine learning plays an important role in analyzing the vast number of idiosyncrasies between different actors on the blockchain. It could cluster and analyze different behaviors and knowledge of miners. The insights gained could help protocol designs better promote decentralization.
This interaction between theory and practice leads Tang to the conclusion:
“Well-structured PoS systems can potentially decentralize wealth. But achieving this requires carefully tailoring rewards and trading parameters – and always taking human imperfection into account.”
While fully decentralized networks remain an ambitious goal, Dr. Tang hopes these can be achieved through careful design considerations. Importantly, it shows the models that do that Doing trend in a favorable direction, and provides at least a partial framework for sustainable network design.
However, mathematical models alone are not enough to tell the whole story. Maintaining broad participation requires a deep understanding of miners’ behavior and incentives. By combining insights from theory and practice, blockchains can still deliver on their promise of fair access and distributed trust. But the path forward will require recognizing social and cognitive nuances beyond the purely technical.
