  Conversation has been a primary means for the exchange of information since
ancient times. Understanding patterns of information flow in conversations is a
critical step in assessing and improving communication quality. In this paper,
we describe COnversational DYnamics Model (CODYM) analysis, a novel approach
for studying patterns of information flow in conversations. CODYMs are Markov
Models that capture sequential dependencies in the lengths of speaker turns.
The proposed method is automated and scalable, and preserves the privacy of the
conversational participants. The primary function of CODYM analysis is to
quantify and visualize patterns of information flow, concisely summarized over
sequential turns from one or more conversations. Our approach is general and
complements existing methods, providing a new tool for use in the analysis of
any type of conversation. As an important first application, we demonstrate the
model on transcribed conversations between palliative care clinicians and
seriously ill patients. These conversations are dynamic and complex, taking
place amidst heavy emotions, and include difficult topics such as end-of-life
preferences and patient values. We perform a versatile set of CODYM analyses
that (a) establish the validity of the model by confirming known patterns of
conversational turn-taking and word usage, (b) identify normative patterns of
information flow in serious illness conversations, and (c) show how these
patterns vary across narrative time and differ under expressions of anger, fear
and sadness. Potential applications of CODYMs range from assessment and
training of effective healthcare communication to comparing conversational
dynamics across language and culture, with the prospect of identifying
universal similarities and unique "fingerprints" of information flow.
