Host Selection through Collective Decision
In this article, we present a collective decision-making framework inspired by biological swarms and capable of supporting the emergence of a consensus within a population of agents in the absence of environment-mediated communication (stigmergy). Instead, amplification is the result of the variation of a confidence index, stored in individual memory and providing each agent with a statistical estimate of the current popularity of its preferred choice within the whole population. We explore the fundamental properties of our framework using a combination of analytical and numerical methods. We then use Monte Carlo simulation to investigate its applicability to host selection in the presence of multiple alternatives, a problem found in application migration scenarios. The advantages of self-organization and the use of statistically predictive methods in this context are also discussed.
collective decision-making algorithms design experimentation agent-based systems