Sammenlign metoder

	Power Transition Analysis	Alliance Network Analysis
Fagområde	International Relations	International Relations
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1980	2012
Ophavsperson	A. F. K. Organski & Jacek Kugler	Skyler Cranmer, Bruce Desmarais & Elizabeth Menninga
Type	Theory-driven observational analysis of war between rising and dominant powers	Network analysis and inferential network modeling of interstate alliances
Oprindelig kilde	Organski, A. F. K., & Kugler, J. (1980). The War Ledger. University of Chicago Press.	Cranmer, S. J., Desmarais, B. A., & Menninga, E. J. (2012). Complex dependencies in the alliance network. Conflict Management and Peace Science, 29(3), 279–313.
Aliasser	Power Transition Theory Analysis, Power Parity and War Analysis, Hegemonic Transition Analysis, Overtaking and War Analysis	International Alliance Networks, Alliance Portfolio Network Analysis, Network Models of Alliance Formation, Interstate Alliance Graph Analysis
Relaterede	3	3
Resumé	Power transition analysis examines when and why war breaks out between a dominant state and a rising challenger as their relative power converges. Originating in A. F. K. Organski and Jacek Kugler's The War Ledger (1980), it holds that the international system is hierarchical and most dangerous not at moments of clear preponderance but when a dissatisfied rising power approaches parity with the dominant state — and it operationalizes this by tracking relative national capabilities over time and relating overtaking to the onset of major war.	Alliance network analysis studies international alliances as a graph of states linked by formal security commitments, and models how that network forms and evolves. Rather than treating each alliance dyad as independent, it uses network science and inferential models such as the exponential random graph model (ERGM) — applied to alliance data by Cranmer, Desmarais, and Menninga (2012) — to capture the complex dependencies, such as a state's tendency to ally with its allies' allies, that ordinary dyadic regression assumes away.