DeSoc’s Dark Heart

DeSoc, for all its talk, has a very dark element as proposed


When analyzing real-world ecosystems, it is desirable to measure how decentralized the ecosystem actually is. To what extent is the ecosystem truly decentralized, and to what extent is the decentralization “fake” and the ecosystem de-facto dominated by one or a small set of coordinating entities?

For example, nominally independent firms may have many major shareholders in common, have directors who are friends with each other, or be regulated by the same government. In the context of token protocols, measuring decentralization of token holdings by looking at on-chain wallets is wildly inaccurate because many people have multiple wallets, and some wallets (e.g., exchanges) represent many people. Moreover, even if addresses could be traced back to unique individuals, those individuals could be socially correlated groups prone to accidental coordination (at best) or intentional collusion (at worst). A better way of measuring decentralization would capture social dependencies, weak affiliations, and strong solidarities. (emphasis in original).

Which votes count for what?

Now suppose a simplified model where Abdu, Shou and Belle are differentiated by a single membership — workplace — and matching funds are available for startups, companies, and open-source projects (again, in the spirit of Gitcoin). Because people from the same workplace have a strong incentive to contribute to their own workplace to maximize matching funds to their company, we should expect them to coordinate. An extreme approach would be to assume that workers fully share goals and fully coordinate their behavior.

The previous example assumes Abdu, Shou and Belle have a single membership: workplace. Yet in almost all applications this would be a vast oversimplification. People have multiple community memberships, cooperative relationships, and even informal intersections. Abdu and Belle might be extended family, Shou and Belle might have attended the same school, or Shou and Abdu might be token-holders of the same layer 1 protocol, and so on. To facilitate cooperation across differences, these correlations in memberships between individuals need to be recognized in a less binary manner.

A Slippery Slope

But rather than treating pre-existing cooperation as a bug we ought to “write over,” the key is to acknowledge it as reflecting partial cooperation that we should harness and compensate for. After all, we are in the business of encouraging cooperation. The trick is to make quadratic mechanisms work alongside pre-existing networks of cooperation, correcting for their biases and tendencies to over-coordinate. SBTs offer a natural way by allowing us to tip the scales in favor of cooperation across differences. As Nobel Laureate Elinor Ostrom famously highlighted, the problem is less coordinating public goods per se but rather one of helping communities made up of imperfectly cooperative but socially connected individuals overcome their social differences to coordinate at scale in broader networks.



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Joshua Gans

Skoll Chair in Innovation & Entrepreneurship at the Rotman School of Management, University of Toronto and Chief Economist, Creative Destruction Lab.