This paper presents research into conflict analysis, utilizing Hidden Markov models to capture the patterns of escalation in a conflict and Markov chains to forecast future escalations. Hidden Markov models have an extensive history in a wide variety of pattern classification applications.
In these models, an unobserved finite state Markov chain generates observed symbols whose distribution is conditioned on the current state of the chain. Training algorithms estimate model parameters based upon known patterns of symbols. Assignment rules classify unknown patterns according
to the likelihood of known models generating the observed symbols. The research presented here utilized much of the Hidden Markov model methodology, but not for pattern classification, rather to identify the underlying finite state Markov chain for a symbol realization. Machine coded newswire
story leads provided event data that served as the symbol realization for the Hidden Markov model. Fundamental matrices derived from the Markov chain led to forecasts that provide insight into the dynamic behavior of the conflict and describe potential futures of the conflict in probabilistic
terms, to include the likelihood of conflict, the time to conflict, and the time in conflict.
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