Research Article
Binary Consensus via Exponential Smoothing
@INPROCEEDINGS{10.1007/978-3-319-03473-7_22, author={Marco Oca and Eliseo Ferrante and Alexander Scheidler and Louis Rossi}, title={Binary Consensus via Exponential Smoothing}, proceedings={Complex Sciences. Second International Conference, COMPLEX 2012, Santa Fe, NM, USA, December 5-7, 2012, Revised Selected Papers}, proceedings_a={COMPLEX}, year={2013}, month={11}, keywords={Consensus Collective Decision-Making Self-Organization Swarm Intelligence}, doi={10.1007/978-3-319-03473-7_22} }- Marco Oca
Eliseo Ferrante
Alexander Scheidler
Louis Rossi
Year: 2013
Binary Consensus via Exponential Smoothing
COMPLEX
Springer
DOI: 10.1007/978-3-319-03473-7_22
Abstract
In this paper, we reinterpret the most basic exponential smoothing equation, = (1 − ) + , as a model of social influence. This equation is typically used to estimate the value of a series at time + 1, denoted by , as a convex combination of the current estimate and the actual observation of the time series . In our work, we interpret the variable as an agent’s tendency to adopt the observed behavior or opinion of another agent, which is represented by a binary variable . We study the dynamics of the resulting system when the agents’ recently adopted behaviors or opinions do not change for a period of time of stochastic duration, called latency. Latency allows us to model real-life situations such as product adoption, or action execution. When different latencies are associated with the two different behaviors or opinions, a bias is produced. This bias makes all the agents in a population adopt one specific behavior or opinion. We discuss the relevance of this phenomenon in the swarm intelligence field.