In this presentation, Professor Elisa Cavatorta develops a novel conjoint analysis to study the preferences and priorities of ordinary citizens regarding the components of potential peace agreements between people in conflict. Her application is the Israeli-Palestinian conflict. This approach elicits preferences over competing issues, reveals acceptable and unacceptable trade-offs and visualizes the Zone Of Possible Agreement (ZOPA): the set of mutually acceptable peace deals, and within that set identifies cooperative bargaining solutions. She then studies the expected net benefits that ZOPA deals might bring about and the extent to which expected outcomes drive support for peace. This approach reduces the complexity of multi-attribute negotiations by revealing `hidden’ combinations of resolutions that receive most support on each side and could be fruitful agreements to negotiate. The approach has implications for conflict resolution and negotiations of all kinds.
Elisa Cavatorta is an Associate Professor in Political Economy at King’s College London. She studies conflict, the formation of individual preferences and beliefs, and policy evaluation. Her research is at the interface between economics, psychology and computer science. Empirically, it uses applied econometrics and data science methods to provide policy relevant answers to questions in the fields of development and public policy. She has expertise in the evaluation of policy interventions and the development of new measurement tools for applied research on human behaviour.
She addresses questions like: how and why living in a violent context shapes the formation of people’s preferences and beliefs? What factors drive the willingness and the ability to negotiate and cooperate? How can we foster conflict-resolution skills in children through the use of technology? How can technology help peacemaking?
She uses a variety of methods and data analysis techniques, among which lab and field experiments, RCTs, interactive surveys, Machine Learning and Natural Language Processing.