Cohesion in fish schools
Collaboration: Vishwesha Guttal
Fish are often found to interact with one fish at a time, in a simple pairwise manner. It is not obvious how fish schools stay cohesive and polarized with only a pairwise interaction. In this study, we develop a spatial-model for the movement of a fish school where individual fish spontaneously change direction and speed, copy the velocity of a neighbor -- alignment and move towards a nearby neighbor -- cohesive move. We investigate the propensity of fish schools to stay cohesive and polarized. We also search for possible mechanisms that could lead to the experimentally observed speed vs polarization correlation.
Observing and inferring a collective
When studying collective phenomena in social organisms, it is common practice to collect data, as the organismal system exhibits the group-level behavior. Attempts are made, using the data collected, to infer the underlying rules that the organisms follow. However, is it always possible to infer these rules accurately? Will the interactions between the organisms and the collective dynamics they exhibit render inferring these rules difficult? We explore the conditions under which the system is completely 'observable' and 'inferrable' using a simple model system made up of composite agents.
Moving through a crowd
Is it possible to take advantage of the similarities between traffic and granular flows, to solve a complex problem in one field using the existing knowledge in another?
We explore this question in the context of the traffic problem of moving through a crowd, where an elite agent with priority for motion tries to maneuver through a dense crowd of agents that are otherwise inert: similar to the movement of an ambulance through a dense assembly of vehicles. We propose a traffic rule for the inert agents, inspired from the nature of granular flows, that will help maximize the motion of an elite agent through the crowd.
Noise induced phenomena
Collaboration: Vishwesha Guttal,
Interactions between social organisms are probabilistic in nature, which gives rise to a noisy group-level dynamics. We discovered that the finite-size noise observed (And recorded) encodes interesting information about the nature of the interactions. We analyze the noise in several different organisms and experimental conditions and compute the jump moments corresponding to the fluctuations, using which we decipher the underlying collective dynamics of the organisms.
Configurational space mixing
Mean field models are often employed to study the collective behavior exhibited by organismal systems which assumes 'fully connectedness' -- an agent can perceive all agents in the system. However, we know that organisms have finite field of vision and only interact with nearby neighbors. If that is the case, when is any mean-field analysis valid? We observe that the agents exhibit, what we call, configurational space mixing, due to the probabilistic nature of the interactions. We show how this mechanism may allow the agents to access information available in the entire system from just its neighbors---resulting in 'meanfield-ness'.