RES: Policy Resilience

A paper on the stability of positions in networks published in Network Science. We create a taxonomy of actors engaged in Swiss climate policy. We see how their collaboration networks and coalitions change over time and how their popularity varies.  We employ a class of simulation models (exponential random graphs) that allow us to determine whether a priviledged network position at t1 is associated to such a position at t2 and t3. Among other network metrics we define a Pure Honest Broker statistic and combine it with other innovative metrics such as preference similarity. We find evidence that actors network activity changes across time and that being a broker reduces your popularity across time. Those embedded in strong ties appear less active. Whether brokerage implies a popularity cost (the Janus faced argument) and whether triadic closure implies an activity cost (complacency?) will be addressed in future research.

This work is part of an ongoing project in this area which follows earlier work on exceptional agents oscillating positions. And is linked to more recent work with the same co-authors on resilience in political networks. Also on forthcoming work on resilience in policy networks due in June 2024. 

The roles actors play in policy networks: Central positions in strongly institutionalized fields

Network Science 9/2 by Karin Ingold, Manuel Fischer and Dimitris Christopoulos

Abstract
Centralities are a widely studied phenomenon in network science. In policy networks, central actors are of interest because they are assumed to control information flows, to link opposing coalitions and to directly impact decision-making. First, we study what type of actor (e.g., state authorities or interest groups) is able to occupy central positions in the highly institutionalized context of policy networks. Second, we then ask whether bonding or bridging centralities prove to be more stable over time. Third, we investigate how these types of centrality influence actors’ positions in a network over time. We therefore adopt a longitudinal perspective and run exponential random graph models, including lagged central network positions at t1 as the main independent variable for actors’ activity and popularity at t2. Results confirm that very few actors are able to maintain central positions over time.