Abstract
How do groups of individuals achieve consensus in movement decisions? Do
individuals follow their friends, the one predetermined leader, or whomever
just happens to be nearby? To address these questions computationally, we
formalize Coordination Strategy Inference Problem. In this setting, a group of
multiple individuals moves in a coordinated manner towards a target path. Each
individual uses a specific strategy to follow others (e.g. nearest neighbors,
pre-defined leaders, preferred friends). Given a set of time series that
includes coordinated movement and a set of candidate strategies as inputs, we
provide the first methodology (to the best of our knowledge) to infer the set
of strategies that each individual uses to achieve movement coordination at the
group level. We evaluate and demonstrate the performance of the proposed
framework by predicting the direction of movement of an individual in a group
in both simulated datasets as well as two real-world datasets: a school of fish
and a troop of baboons. Moreover, since there is no prior methodology for
inferring individual-level strategies, we compare our framework with the
state-of-the-art approach for the task of classification of
group-level-coordination models. The results show that our approach is highly
accurate in inferring the correct strategy in simulated datasets even in
complicated mixed strategy settings, which no existing method can infer. In the
task of classification of group-level-coordination models, our framework
performs better than the state-of-the-art approach in all datasets. Animal data
experiments show that fish, as expected, follow their neighbors, while baboons
have a preference to follow specific individuals. Our methodology generalizes
to arbitrary time series data of real numbers, beyond movement data.
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