Optimal Management of Evolving Hierarchies

Abstract

We study the optimal management of evolving hierarchies of revenue-generating agents. The initiator invests into expanding the hierarchy by adding another agent, who will bring revenues to the joint venture and who will invest herself into expanding the hierarchy further, and so on. The higher the investments (which are private information), the higher the probability of expanding the hierarchy. An allocation scheme specifies how revenues are distributed, as the hierarchy evolves. We obtain schemes that are socially optimal and initiator-optimal, respectively. Our results have potential applications for blockchain, cryptocurrencies, social mobilization, and multilevel marketing. This paper was accepted by Manel Baucells, behavioral economics and decision analysis.