Abstract
The droplets of a set of ants were studied while they constructed a bridge. A droplet is a group of ants derived from a larger group. Several experimental studies have revealed the droplet dynamics of ants that resemble the self-organising characteristics that are displayed in their physico-chemical systems. However, little is known regarding how these typical behaviours emerge from individual decision-making. In this study, I developed an agent-based model where artificial ants aggregated, thereby resulting in chain and droplet growth. In my proposed model, the agents tuned their weight thresholds according to the local pattern stability and propagation of negative information. As a result, it was revealed that the droplet dynamics of my proposed model partly matched the time series of droplets of real ants, as demonstrated in previous experimental studies that included the fluctuation function and interdrop increments that followed a scale-free distribution.
Highlights
Ants communicate directly or indirectly with their nest mates
It appears that droplets occur within small intervals, while occasionally, no droplet occurs for a long period. This distribution somewhat matches with the probability distribution P (DT) that was observed in ant experiments
I developed a droplet growth model of artificial ants. Agents sometimes modify their weight threshold if they perceive indirect warning signals via active agents that are moving up which is coded in a sub-model “Threshold coordination”
Summary
Ants communicate directly or indirectly with their nest mates. They use chemical elements (pheromones), body interactions, tandem running and other means[1,2,3]. Collective behaviours of ants appear to display self-organising properties similar to non-living systems[4]. Grouping patterns in ants have been studied as examples of collective behaviours[8,9,10,11,12] These patterns seem to display self-organising properties, similar to non-living systems[8]. It was revealed that masses of fire ants show duality, i.e. groups of fire ants are able to behave like a liquid or a solid, dependent on the situation[3,9] They appear to adjust their links to other nearby ants, based on the surrounding environment. The ant tower shows a bell shape, which is different from a pile of dead ants that has a conical shape[13] This example shows that the individual behaviours of living particles affect the macro properties of the ensemble. I observed phenomena in my model that partly correspond to those found in experiments with ants in respect with time series of droplets[11]
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