Related Experiment Video
Updated: Sep 13, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
Optimal number of agents in a collective search and when to launch them
Hugues Meyer1,2, Heiko Rieger2
1University of Nottingham, School of Physics and Astronomy, Nottingham NG7 2RD, United Kingdom.
Abstract:
Search processes often involve multiple agents that collectively search a randomly located target. While increasing the number of agents usually decreases the time at which the first agent finds the target, it also requires resources to create and sustain more agents. In this paper, we raise the question of the optimal timing for launching multiple agents in a search in order to reach the best compromise between minimizing the overall search time and minimizing the costs associated with launching and sustaining agents. After introducing a general formalism for independent agents in which we allow them to be launched at arbitrary times, we investigate by means of analytical calculations and numerical optimization the optimal launch strategies to optimize the quantiles of the search cost and its mean. Finally, we compare our results with the case of stochastic resetting and study the conditions under which it is preferable to launch new searchers rather than resetting the first one to its initial position.
Related Concept Videos
Collisions in Multiple Dimensions: Problem Solving
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
Elastic Collisions: Case Study
Elastic Collisions: Introduction
Collisions in Multiple Dimensions: Introduction
The Availability Heuristic
Cluster Sampling Method
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

