1. Introduction: Bitcoin, Behavioral Economics, and Cryptoeconomics

Let’s start from the origins, Bitcoin. The concept of incentive design in blockchain originates from the original Bitcoin whitepaper by Satoshi Nakamoto, and is well summarized by Andreas Antonopoulos in his book and videos. Nakamoto used incentive design to achieve a previously-unattained goal: a scientifically-solid, secure, decentralized digital currency. Nakamoto’s design incentivizes miners to secure the network and disincentivizes defection from the protocol’s proper operations. Furthermore, it reasonably aligns the incentives of all stakeholders: miners, users, and developers contributing to the ecosystem. Its open-source nature ensures that an organized attack is not very lucrative, by enabling stakeholders to recognize the attack and defect to other chains. Seen another way, Nakamoto found a clever game-theoretic solution to the classic Byzantine Generals’ Problem, by paying the generals a salary as long as they act honestly, but garnishing that salary if they are caught trying to cheat.¹

On historical examination, Nakamoto’s game theoretic assumptions are surprisingly mild. Bitcoin can be successfully disrupted only if 51% of the mining power cooperates and coordinates, aiming to disrupt it.² Crucially, Bitcoin’s security does not depend on any “Homo Economicus” assumption that humans are ruthless optimizers and ultra-rational. Rather, even if people are lazy, and even if some malicious coalitions are formed, the system would still be secure. Compared to modern assumptions like those of Steemit or Augur, the assumptions of Bitcoin are much more realistic and uncontroversial. (Also, Bitcoin’s security guarantees are comparatively quite strong, and have been mathematically proved; see footnote 2.)

Since 2009, incentive design has gotten much more sophisticated. Blockchain systems today find ever-cleverer ways to apply incentive structures to more complex systems:

ZCash and other cryptocurrencies share the basic incentive structure pioneered by Bitcoin, as do Ethereum and other second-generation blockchain systems

Gnosis, Augur and other prediction markets attempt to predict the future using a price discovery mechanism: incentivizing users to profit by trying to form accurate predictions of the future, and betting according to these predictions.

Steemit incentivizes users to post interesting tidbits, and/or truthfully vote on the quality of other people’s posts. Other reputation systems incentivize users to “upvote” reputable actors, thus creating a Blockchain analogue for humans’ de-facto reputation systems. (None have proven themselves thus far.)

Numerai incentivizes data scientists to devise good algorithms for trading in financial markets

Futarchy incentivizes users to stake good decisions

Ocean incentivizes users to stake good datasets and to provide added value to existing datasets (think Numerai meets Gnosis)

Polkadot incentivizes stakeholders to make honest decisions in the network (“validators” and “collators”), to look for bad actors (“fishermen”), and to decide who is trustworthy (“nominators”).

Incentive design is considered one of the killer features of blockchain systems

Overall, incentive design has spread to many exciting applications and is considered one of the killer features of blockchain systems. (More accurately, the killer feature is the ability to implement a fine-grained incentive system in a highly-scalable way, which supports tiny and large incentives alike). This is captured in the spirit of writing by the most prominent blockchain innovators. Trent McConaghy writes in a recent blog post:

‘The blockchain community understands that blockchains can help align incentives among a tribe of token holders. Each token holder has skin in the game. But the benefit is actually more general than simply aligning incentives: you can design incentives of your choosing, by giving them block rewards. Put another way: you can get people to do stuff, by rewarding them with tokens. Blockchains are incentive machines. I see this as a superpower. The block rewards function defines what you want network participants to do. Then the question is: what do you want people in your network to do? It has a crucial corollary: how well can you communicate that intent to the machines? This is a devilish detail. Do we really know how to design incentives?’³

This vision is powerful and inspiring. But it might raise more questions that it resolves. Below we explore McConaghy’s question: “Do we really know how to design incentives?”. We answer it with a resounding “No”, and try to offer some ways to turn this “No” to a “Maybe”.