Token Engineering In Practice

In order to design successful cryptoeconomic primitives, we must know in what settings cryptoeconomic primitives do and do not work in practice.

Case Studies

Barriers to Success in Practice

Markets and Price-Finding

The theoretical design of cryptoeconomic primitives often relies on the price-finding property of markets, which follows from the Efficient-Market Hypothesis (EMH). EMH, however, is a useful model, but is believed by many to not actually describe the behavior of real-world markets.

In particular, here is a list of possible criticisms of cryptoeconomic primitives:

Prediction Markets

  • These are similar to real-world financial markets (e.g. for trading derivatives). As such, they suffer from some of the same problems. Some factors that might reduce price-finding ability:
    • low liquidity, due to no interest in participation
    • groupthink and "wishful thinking" biases.
    • incentives from outside the system
  • prediction markets have an expiration time: the time when the measured event happens and the tokens are liquidated. Shorter-expiration markets should provide better price-finding than long-expiration markets (all other things being equal), because:
    • short-term markets are closer to the fundamental value of the commodity, and reduce the ability to speculate.
    • if an actor is a good predictor, short-term markets are guaranteed to return winnings quickly, while long-term markets might take a long while to return winnings (even though the market is liquid). Thus, because of the time value of money, shorter markets provide better returns.
Unless otherwise stated, the content of this page is licensed under Creative Commons Attribution-ShareAlike 3.0 License