The MIT Science Impact Collaborative team employs a range of tools and approaches in our work. These include:
Stakeholder Engagement - Effectively engaging stakeholders is critical to the success of efforts to resolve disputes and make decisions that involve science, politics and policy. The MIT Science Impact Collaborative is developing and employing best practices for bringing parties together.
Joint Fact-Finding - Scientific and technical data too often become fodder for disputes and sources of tension. The MIT Science Impact Collaborative’s approach to joint fact-finding helps parties to use shared data as a way to collectively answer questions, clarify situations and seek creative solutions.
Collaborative Adaptive Management - Uncertainty and continuous change necessitate management approaches that involve ongoing monitoring and evaluation, and mechanisms for revising practice and policy in light of new information. The MIT Science Impact Collaborative promotes approaches to adaptive management that are responsive to new data while also recognizing and accounting for political and social realities by involving stakeholders throughout.
Role-Play Simulation Exercises - Exercises allow both stakeholders and researchers to explore emerging situations, tools and approaches for tackling them, and potential solutions in a low-cost, low-risk environment. The MIT Science Impact Collaborative regularly uses role-plays with groups for their own learning and reflection, to foster change, and to advance our research.
Multi-Party Dispute Resolution (Mediation) - Most public policy questions involve multiple stakeholders, and multiple issues. Third party neutrals, or mediators, can play a critical role in helping groups to effectively and efficiently get together to make decisions by offering critical procedural support. Effective mediation and process support is at the heart of what the MIT Science Impact Collaborative does.
Scenario Planning - The future is always uncertain. Scenario planning processes generate multiple possible futures against which proposals can be measured, facilitating the identification of options that will be robust under a range of different conditions. Unfortunately, despite its promise, integrating scenario planning into actual decision-making is proving challenging; identifying best practices for this integration is a goal of the MIT Science Impact Collaborative.