The impact of agent density on scalability in collective systems: noise-induced versus majority-based bistability

Abstract

In this paper, we show that non-uniform distributions in swarms of agents have an impact on the scalability of collective decision-making. In particular, we highlight the relevance of noise-induced bistability in very sparse swarm systems and the failure of these systems to scale. Our work is based on three decision models. In the first model, each agent can change its decision after being recruited by a nearby agent. The second model captures the dynamics of dense swarms controlled by the majority rule (i.e., agents switch their opinion to comply with that of the majority of their neighbors). The third model combines the first two, with the aim of studying the role of non-uniform swarm density in the performance of collective decision-making. Based on the three models, we formulate a set of requirements for convergence and scalability in collective decision-making.

Coherent collective behaviour emerging from decentralised balancing of social feedback and noise

“…In collective decision-making systems, the interplay between social feedback and noise (e.g. individual exploration) has a crucial role in determining the collective coherence of the group (Khaluf et al 2017b(Khaluf et al , 2018Rausch et al 2019). While a general solution to design any adaptive decision-making system is not yet in reach, our goal is to advance the understanding of the interplay between noise and social feedback in collective systems by taking a bottom-up approach and by using particular case studies as a starting point for the investigation of underlying fundamental properties.…”

The Impact of Interaction Models on the Coherence of Collective Decision-Making: A Case Study with Simulated Locusts

“…For example, a high level of noise may exceed the influence of the feedback loops and hence keep the system in an undecided state. Similarly, applying a strong enough positive feedback around a particular option may push the system to decide in favor of that particular option even in cases of low system densities-i.e., low number of feedback loops [17]. In this paper, we show that the interaction model is a key parameter to tune the balance between feedback loops and noise such that the collective system becomes decided even under significant noise levels.…”

Voluntary Retreat for Decentralized Interference Reduction in Robot Swarms

“…The modeling and regulation of interference in swarm robotic systems is a widely studied topic (e.g., see [8], [20], [21]). The effects of interference on the efficiency of a swarm have been studied experimentally e.g., [4], [9], [22], [23], and by developing analytical models [11], [21]. Techniques to regulate interference range from performing "aggression" maneuvers to break deadlocks [4], [24], partitioning robots among tasks [25], and team size selection before deploying the swarm [26].…”