Shaked Rafaeli | Searcharxiv

arXiv2019

The Role of A-priori Information in Networks of Rational Agents

Until now, distributed algorithms for rational agents have assumed a-priori knowledge of $n$, the size of the network. This assumption is challenged here by proving how much a-priori knowledge is necessary for equilibrium in different distributed computing problems. Duplication - pretending to be more than one agent - is the main tool used by agents to deviate and increase their utility when not enough knowledge about $n$ is given. The a-priori knowledge of $n$ is formalized as a Bayesian setting where at the beginning of the algorithm agents only know a prior $σ$, a distribution from which they know $n$ originates. We begin by providing new algorithms for the Knowledge Sharing and Coloring problems when $n$ is a-priori known to all agents. We then prove that when agents have no a-priori knowledge of $n$, i.e., the support for $σ$ is infinite, equilibrium is impossible for the Knowledge Sharing problem. Finally, we consider priors with finite support and find bounds on the necessary interval $[α,β]$ that contains the support of $σ$, i.e., $α\leq n \leq β$, for which we have an equilibrium. When possible, we extend these bounds to hold for any possible protocol.

cs.DC↗

arXiv2018

Cheating by Duplication: Equilibrium Requires Global Knowledge

The question of what global information must distributed rational agents a-priori know about the network in order for equilibrium to be possible is researched here. Until now, distributed algorithms with rational agents have assumed that $n$, the size of the network, is a-priori known to the participants. We investigate the above question, considering different distributed computing problems and showing how much each agent must a-priori know about $n$ in order for distributed algorithms to be equilibria. The main tool considered throughout the paper is the advantage an agent may gain by duplication- pretending to be more than one agent. We start by proving that when no bound on $n$ is given equilibrium for Coloring and Knowledge Sharing is impossible. %We prove that when agents have no a-priori knowledge on $n$, or even a known bound, equilibrium for both Knowledge Sharing and Coloring is impossible. We provide new algorithms for both problems when $n$ \emph{is} a-priori known to all agents, thus showing that there are algorithms in which the only way for an agent to gain an advantage is duplication. We further show that for each distributed problem there is an a-priori known range, an upper and a lower bound on $n$, such that if the actual $n$ is guaranteed to lay in that range, equilibrium is possible. By providing equilibria for a specific range, and impossibility results for any larger range, we prove the tight range necessary for equilibrium in: Leader Election, Knowledge Sharing, Coloring, Partition and Orientation.

cs.DC↗