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

Abstract

A key aspect of collective systems resides in their ability to exhibit coherent behaviors, which demonstrate the system as a single unit. Such coherence is assumed to be robust under local interactions and high density of individuals. In this paper, we go beyond the local interactions and we investigate the coherence degree of a collective decision under different interaction models: (i) how this degree may get violated by massive loss of interaction links or high levels of individual noise, and (ii) how efficient each interaction model is in restoring a high degree of coherence. Our findings reveal that some of the interaction models facilitate a significant recovery of the coherence degree because their specific inter-connecting mechanisms lead to a better inference of the swarm opinion. Our results are validated using physics-based simulations of a locust robotic swarm.

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

“…Adding δ s allows us to include in the model the probability p s that an individual spontaneously switches its heading direction (i.e. the sign of u i ptq) 2 , inspired by previous studies on symmetry-breaking (Huepe et al 2011;Chen et al 2016;Khaluf et al 2018). This spontaneous switching directly contributes to spontaneous exploration by each agent.…”

“…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.…”

“…To put this relationship into focus, we assume the agent not to be able to cancel noise but only to adapt its social feedback. The advantages of modulating the social feedback have been previously investigated in systems of collective motion (Torney et al 2009;Shklarsh et al 2011;Khaluf et al 2018), foraging (Pagliara et al 2018;Pitonakova et al 2018;Talamali et al 2019a;Rausch et al 2019), and collective decision-making (Talamali et al 2019b).…”

“…Furthermore, network-theoretic concepts were applied to analyse the impact of the number of interactions on flocking dynamics and collective response to an oscillating signal (Shang and Bouffanais 2014;Mateo et al 2017Mateo et al , 2019. Similarly, Khaluf et al (2017aKhaluf et al ( , 2018 highlighted the role of different interaction models in enabling the system to restore a specific level of social feedback necessary for convergence to a collective decision.…”

Scale-Free Features in Collective Robot Foraging

“…Many natural collective behaviors were modeled using artificial collective systems, including bird flocking [22], locust marching [23] or cockroach aggregation [24]. However, among the most prominent challenges is the collective foraging task [18].…”