Robot swarm democracy: the importance of informed individuals against zealots

By: G. De Masi, J. Prasetyo, R. Zakir, N. Mankovskii, E. Ferrante, E. Tuci – March 2021

This is a supplementary document for the Special Issue of Swarm Intelligence 2020 Paper titled Robot swarm democracy: the importance of informed individuals against zealots

Table of Contents

  1. Abstract
  2. Videos

Abstract

In this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule.
We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments.
We find that

  1. if the number of zealots for the lowest quality option is not too high, the decision making process is driven towards the highest quality option;
  2. this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots;
  3. when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents.

Keywords: Collective Decision Making, Swarm Intelligence, Swarm Robotics, Stubborn agents

Videos

Simulation results with

Population – 100
σA (red-zealot-pct) – 22.5
σB (blue-zealot-pct) – 1.25
ρA (red-quality) – 1
ρB (blue-quality) – 1.5
Voting Method – Majority rule (k=3)

Result with Physical Robots for all agents disseminating proportionally

Result with Physical Robots for agents disseminating differentially