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Framework for Inferring Following Strategies from Time Series of Movement Data
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
whether each individual uses local-agreement-system or dictatorship-like
strategy 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.Comment: This is the revised version of the preprint entitled "Inferring
Coordination Strategies from Time Series of Movement Data" following
reviewers' suggestion