이번 세미나에서는 1부 3장 Coordination and Incentives를 다룹니다.

Chapter 3. Coordination and Incentives

아래 요약한 내용을 다시 한 번 읽어 봅니다. 강조한 부분은 주의를 좀 더 기울여 여러 번 반복해서 읽어 봅니다.

• What makes a good ecosystem? More coordination. Because more coordination means more efficiency.
• We can offer the right incentives for people to reveal preferences or types truthfully. If the goal is to make sure the participants are always honest, we can do it in two steps:
  1. Ensure that being honest is consistent with rationality and intelligent assumptions.
  2. Offer the right incentives to reveal preference or types truthfully. We call this “incentive compatibility”.
• There are 2 types of incentive compatibility.
  • Participants being truthful is the best response, irrespective of what other participants say or do. We call this Dominant Strategy Incentive Compatibility (DSIC).
  • Participants being truthful is the best response, given their expectation of other participants’ choices. We call this Bayesian Incentive Compatibility (BIC)
• Ideally, we want to evoke the DSIC mechanism. It is not always easy. Thus, the BIC is the next best alternative. An example is to use BIC in improvement proposals and governance voting. In this approach, a minimum quorum of votes is required to be staked in the improvement proposals before the proposal goes to a governance vote by super-users. Balancer developed a similar voting mechanism smart contract called Snapshot to achieve this.
• Externalities of Incentives
  • Two externalities are moral hazard and adverse selection.
• Moral hazard occurs when someone increases their exposure to risk when insured, especially when a person takes more risks because someone else bears the cost of those risks. A party makes a decision about how much risk to take, while another party bears the costs if things go badly, and the party insulated from risk behaves differently from how they would if they were fully exposed to the risk. A possible solution is that the principal creates disincentives to prevent agents from “cheating”.
• Rug Pull as a Moral Hazard
  • Rug pull is an unexpected removal of help from someone and leaving the other in a difficult situation. Rug pull in DeFi is where the liquidity provider removes liquidity from the system, leaving users with valueless tokens.
• Adverse Selection
  • Adverse selection is a result of ineffective price signals through asymmetric information(one party having more information or different information compared to the other).
  • A solution is for the party to screen for or use proxies to signal the type of information that the party is lacking.

• If everyone recognises that their negative cost affects the social community, this can be very useful information to collectively decide the best way to manage social goods. For example, open source protocols and decentralised finance. The key to success is to align incentives via tokenisation and to have proper governance in place. Proper governance entails power, responsibility and capacity to punish bad actors.
  In designing these tokenised and decentralised systems, we can realign these incentive structures and design the governance (decision making and punishment) mechanisms. Equal power and equal starting points are difficult to come by in the real world. In a decentralised tokenised world, therein lies a solution to these problems.