Proof of Standing for Token Commons – Optimum Learning for Best Allocation of Decision and Minting Rights
Our main theorem shows that when the probability that each individual observes some other individual from the recent past converges to one as the social network becomes large, unbounded private beliefs are sufficient to ensure asymptotic learning. This theorem therefore establishes that, with unbounded private beliefs, there will be asymptotic learning in almost all reasonable social networks.
We also show that for most network topologies, when private beliefs are bounded, there will not be asymptotic learning. In addition, in contrast to the special case where all past actions are observed, asymptotic learning is possible even with bounded beliefs in certain stochastic network topologies exist.
The game is then characterized by two features: (i) the signal structure, which determines how informative the signals received by the individuals are; (ii) the social network structure, which determines the observations of each individual in the game.
Our objective is to identify that group of nodes that have the right standing (information) to make a decision - verify a transaction – allocate a resource or right – given that they have the right kind of information (expertise) for that situation (neighborhood) – they can all see past behaviors and have equal preferences – (the preservation of the commons).