arXiv subjects

Udoy S. Basak

Publications and source records attributed to Udoy S. Basak.

3 recordsLinked to original sources

arXiv2021

Transfer entropy dependent on distance among agents in quantifying leader-follower relationships

Synchronized movement of (both unicellular and multicellular) systems can be observed almost everywhere. Understanding of how organisms are regulated to synchronized behavior is one of the challenging issues in the field of collective motion. It is hypothesized that one or a few agents in a group regulate(s) the dynamics of the whole collective, known as leader(s). The identification of the leader (influential) agent(s) is very crucial. This article reviews different mathematical models that represent different types of leadership. We focus on the improvement of the leader-follower classification problem. It was found using a simulation model that the use of interaction domain information significantly improves the leader-follower classification ability using both linear schemes and information-theoretic schemes for quantifying influence. This article also reviews different schemes that can be used to identify the interaction domain using the motion data of agents.

nlin.AO↗

arXiv2021

Modes of Information Flow in Collective Cohesion

Pairwise interactions between individuals are taken as fundamental drivers of collective behavior responsible for group cohesion and decision-making. While an individual directly influences only a few neighbors, over time indirect influences penetrate a much larger group. The abiding question is how this spread of influence comes to affect the collective. One or a few individuals are often identified as leaders, being more influential than others. Transfer entropy and time-delayed mutual information are used to identify underlying asymmetric interactions, such as leader-follower classification in aggregated individuals--cells, birds, fish, and animals. However, these conflate distinct functional modes of information flow between individuals. Computing information measures conditioning on multiple agents requires the proper sampling of a probability distribution whose dimension grows exponentially with the number of agents being conditioned on. Employing simple models of interacting self-propelled particles, we examine the pitfalls of using time-delayed mutual information and transfer entropy to quantify the strength of influence from a leader to a follower. Surprisingly, one must be wary of these pitfalls even for two interacting particles. As an alternative we decompose transfer entropy and time-delayed mutual information into intrinsic, shared, and synergistic modes of information flow. The result not only properly reveals the underlying effective interactions, but also facilitates a more detailed diagnosis of how individual interactions lead to collective behavior. This exposes the role of individual and group memory in collective behaviors. In addition, we demonstrate in a multi-agent system how knowledge of the decomposed information modes between a single pair of agents reveals the nature of many-body interactions without conditioning on additional agents.

nlin.AO↗

arXiv2020

An information-theoretic approach to infer the underlying interaction domain among elements from finite length trajectories in a noisy environment

Transfer entropy in information theory was recently demonstrated [Phys. Rev. E 102, 012404 (2020)] to enable us to elucidate the interaction domain among interacting elements solely from an ensemble of trajectories. There, only pairs of elements whose distances are shorter than some distance variable, termed cutoff distance, are taken into account in the computation of transfer entropies. The prediction performance in capturing the underlying interaction domain is subject to noise level exerted on the elements and the sufficiency of statistics of the interaction events. In this paper, the dependence of the prediction performance is scrutinized systematically on noise level and the length of trajectories by using a modified Vicsek model. The larger the noise level and the shorter the time length of trajectories, the more the derivative of average transfer entropy fluctuates, which makes it difficult to identify the interaction domain in terms of the position of global minimum of the derivative of average transfer entropy. A measure to quantify the degree of strong convexity at coarse-grained level is proposed. It is shown that the convexity score scheme can identify the interaction distance fairly well even while the position of global minimum of the derivative of average transfer entropy does not. We also derive an analytical model to explain the relationship between the interaction domain and the change of transfer entropy that supports our cutoff distance technique to elucidate the underlying interaction domain from trajectories.

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