Proceedings of the Annual Meeting of the Cognitive Science Society

Predicting the effect of persuasion campaigns is difficult, asbelief changes may cascade through a network. In recentyears, political campaigns have adopted micro-targetingstrategies that segment voters into fine-grained clusters formore specific targetting. At present, there is little evidencethat explores the efficiency of this method. Through anAgent-Based Model, the current paper provides a novelmethod for exploring predicted effects of strategic persuasioncampaigns.The voters in the model are rational and revise their beliefsin the propositions expounded by the politicians inaccordance with Bayesian belief updating through a sourcecredibility model.The model provides a proof of concept and shows strategicadvantages of micro-targeted campaigning. Despite havingonly little voter data allowing crude segmentation, the micro-targeted campaign consistently beat stochastic campaignswith the same reach. However, given substantially greaterreach, a positively perceived stochastic candidate can nullifyor beat a strategic persuasion campaigns.