Mediation Centrality in Adversarial Policy Networks
Conflict resolution often involves mediators who understand the issues central to both sides of an argument. Mediators in complex networks represent key nodes that are connected to other key nodes in opposing subgraphs. Here we introduce a new metric, mediation centrality, for identifying good media...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2019-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/1918504 |
| Summary: | Conflict resolution often involves mediators who understand the issues central to both sides of an argument. Mediators in complex networks represent key nodes that are connected to other key nodes in opposing subgraphs. Here we introduce a new metric, mediation centrality, for identifying good mediators in adversarial policy networks, such as the connections between individuals and their reasons for and against the support of controversial topics (e.g., state-financed abortion). Using a process-based account of reason mediation we construct bipartite adversarial policy networks and show how mediation defined over subgraph projections constrained to reasons representing opposing sides can be used to produce a measure of mediation centrality that is superior to centrality computed on the full network. We then empirically illustrate and test mediation centrality in a “policy fluency task,” where participants generated reasons for or against eight controversial policy issues (state-subsidized abortion, bank bailouts, forced CO2 reduction, cannabis legalization, shortened naturalization, surrogate motherhood legalization, public smoking ban, and euthanasia legalization). We discuss how mediation centrality can be extended to adversarial policy networks with more than two positions and to other centrality measures. |
|---|---|
| ISSN: | 1076-2787 1099-0526 |