Do proof-of-stake blockchains trend toward decentralization over time?

As blockchain technology transitions toward proof-of-stake consensus models, a pressing question arises — will these systems maintain decentralization, or will rewards disproportionately pool among large players at the expense of broader participation?

Dr. Wenpin Tang, a leading researcher of blockchain incentives, analyzed these dynamics in proof-of-stake (PoS) systems using advanced mathematical models. His findings highlight and begin to unpack the complex forces at play.

In pure PoS chains like Ethereum, miners bid using their coin balances for validation rights with no trading allowed between miners. Winners earn more coins as rewards. This seems to favor large players, but Dr. Tang explains it’s more nuanced:

The key takeaway is it will be different for large and small miners. For large miners (e.g. Binance or Musk), their shares will be stable e.g. if they have 10% initial shares, they will also be close to 10% in the end. That is not the case for small miners (e.g. many retailer miners), their shares suffer from fluctuations. If they have 0.01% initial shares, they may end up with 0.0001% or 0.1%, say — with the downward probability being higher than the upward probability.

So while giants remain steady in this pure PoS system, small miners face significant volatility with a long-term trend toward loss of stake. Dr. Tang notes this could lead to greater reliance on large validators for blockchain upkeep.

Introducing trading to the ecosystem, however, has a profound effect. When miners can trade coins, new dynamics emerge. Dr. Tang modeled a “market impact” approach where selling drops prices and buying lifts them. The math then showed trading enforcing decentralization over time.

This, however, presumes a “homogenous” group of miners validating the network, meaning that all are acting to optimize their positions. “The analysis presumes miners have identical incentives and information,” Dr. Tang says, “but reality is far messier.”

Equally vital is moving beyond perfect rationality assumed in most models. “Real decisions come from ‘feeling,’ not calculated optimization,” Tang explains. “Thi

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Author: Jacob Oliver

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