Politics, uncertainty and interoperability challenges: the potential for sensemaking to improve multi-agency approaches
Generative AI, network connection concept by NAITZTOYA.
August 2022
Briefing Note 22
Professor Christopher Baber, University of Birmingham
Professor Andrew Howes, University of Birmingham
Dr James Knight, University of Birmingham
Professor Heather Marquette, University of Birmingham
SOC ACE Project: Interoperability, multi-agency sensemaking and the potential of AI for more politically feasible & effective strategies and operations
PUBLICATION SUMMARY
This briefing note sets out the results of initial scoping research into the potential for sensemaking for tackling challenges that arise when multi-agency teams are tasked with tackling the same problems – in this case, serious organised crime (SOC), illicit finance and corruption – but with unclear and potentially competing (or even conflicting) mandates and incentives.
Despite the interest expressed in early consultation with policymakers and practitioners for better understanding issues to do with politics, particularly when it comes to political will, there appears to be a fairly typical interoperability challenge. This needs to be overcome if we are to find better ways to bring together a wider range of evidence, data, actors, frames and so on to increase our knowledge, test our assumptions, construct new and better hypotheses, anticipate consequences and manage complexity, in order to more effectively target resources and efforts.
We look at the role that sensemaking can play in helping to better uncover the ways in which different agencies frame the problem to be addressed and the resources to which they have access, focusing in particular on framing effects where each agency tries to make sense of a problem in terms of its own assumptions, beliefs, desired consequences, expected risks and so on. We consider how different definitions of the problem and different approaches to sensemaking can lead to differences in consequences (intended or unintended) and potential for conflict between agencies, and suggest an approach to better understanding these issues in the next stage of the research for improved multi-agency analysis and decision-making.
