Incentive alignment for accuracy

The design approach starts with the decision to compensate public goods contributors through coin inflation (and not through NFTs or project tokens, for example). As explained previously, this incentive structure creates a dynamic where it is in the self-interest of every participant in the ecosystem to have an accurate estimate of the economic impact of public goods, since inaccurate estimates (both overestimates and underestimates) hurt the credibility, and long-term viability, of the currency as a store of value.

Each validator in the protocol therefore has an incentive to represent data as accurately as possible, and even indicate her own limitations and biases in coming up with the estimate. All other participants have an incentive to share as much relevant data as is necessary (as well as not hide any relevant data) for validators to make an accurate estimate. Everyone also has the incentive to challenge incorrect or misleading estimates, thus adding another layer of integrity to estimates.

Contrast this with the dynamics in speculative or financial markets; there, the information dynamics follow Warren Buffett’s old adage: “be fearful when others are greedy, and greedy when others are fearful.” In other words, traders in a market benefit from an information asymmetry: if they know something that others don’t, they can profit from that knowledge and would rather not share it with others. Moreover, individuals would share the kind of information that is likely to benefit them financially, often making investment moves in direct opposition to their stated claims. For example, if a trader wants to sell a stock, she would extoll the company and say that it has a bright future — hoping others buy in so that she can sell at a higher price. Similarly, if a trader believes a stock will outperform, and therefore would like to buy more of the stock at a lower price, she would spread FUD about the company so that others sell. It is therefore nearly impossible to know who presents factual information and who is making claims merely to improve their portfolio position. Even when traders agree on the data itself, they may interpret (or “spin”) it completely differently — based on how the interpretation may benefit them financially.

Of course, the same logic that applies to the stock market also applies to prediction markets, NFTs, project tokens, and so on. Since all these involve scarcity (of stocks, tokens, etc.), the interaction between traders is adversarial — with winners and losers. Our protocol, on the other hand, is designed to create dynamics where everyone in the ecosystem benefits and therefore everyone’s incentives are aligned — everyone can agree on the data, share information freely, and each would interpret it with the goal of getting the most accurate estimate. That of course doesn’t mean that everyone will always agree on what the estimate should be, but they would have an incentive to figure out why their views diverge, and seek to get to the truth.

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