Modeling Campaign Optimization Strategies in Political Elections under Uncertainty
Paul, Kizito Mubiru
Maureen, N. Ssempijja
MetadataShow full item record
In most political campaigns,the overall goal of every candidate is to maximize the number of voters during the election exercise.In such an effort,cost effective methods in choosing the optimal campaign strategy areparamount.In this paper, a mathematical model is proposed that optimize campaign strategies of a political candidate.Considering uncertainty in voter support and cost implications in holding political rallies,we formulate a finite state markov decision process model where states of a markov chain represent possible states of support among voters.Using daily equal intervals,thecandidates‟s decision of whether or not to campaign and hold a political rally at a given location were made using discrete time Markov chains and dynamic programming over a finite period planning horizon.Empirical data was collected from two locations on a daily basis during the campaign exercise.The data collected was analyzed and tested to establish the optimal campaign strategy and costs at the respective locations.Results from the study indicated the existence of an optimal state-dependent campaign strategy and costs at the respective political rally locations.