Learning in Highly Polarized Conflicts

Abstract

Negotiations are often conducted in highly polarized environments, which are also uncertain and dynamic. However, the intense rivalry involved in these conflicts does not always prevent an agreement from being reached. A recently proposed static model sets out the conditions under which either an agreement is achieved or negotiations break down in this environment [4]. Nevertheless, important aspects related to partial mutual knowledge of players in a dynamic context are not yet been studied. To fill this gap, we develop an extension of the static game to modelling highly polarized conflicts in an uncertain, asymmetric and dynamic environment. In this extension both parties bargain multiple negotiation rounds under uncertain threats that are materialised only if an agreement is not reached. If a negotiation breakdown occurs, each party learns about these threats from the outcome observed in the previous round. This paper presents the most important results, and a short discussion about possible applications. In particular, we provide the conditions that characterise different paths for negotiations held under polarized environments, which matches the observed evolution of many of these conflicts in the real world.