  Interactions between social animals provide insights into the exchange and
flow of nutrients, disease, and social contacts. We consider a chamber level
analysis of trophallaxis interactions between carpenter ants
(\textit{Camponotus pennsylvanicus}) over 4 hours of second-by-second
observations. The data show clear switches between fast and slow modes of
trophallaxis. However, fitting a standard hidden Markov model (HMM) results in
an estimated hidden state process that is overfit to this high resolution data,
as the state process fluctuates an order of magnitude more quickly than is
biologically reasonable. We propose a novel approach for penalized estimation
of HMMs through a Bayesian ridge prior on the state transition rates while also
incorporating biologically motivated covariates. This penalty induces
smoothing, limiting the rate of state switching that combines with appropriate
covariates within the colony to ensure more biologically feasible results. We
develop a Markov chain Monte Carlo algorithm to perform Bayesian inference
based on discretized observations of the contact network.
