Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Alliance Portfolio Similarity× | Alliance Network Analysis× | |
|---|---|---|
| Fagfelt | International Relations | International Relations |
| Familie | Process / pipeline | Process / pipeline |
| Opprinnelsesår≠ | 1999 | 2012 |
| Opphavsperson≠ | Bueno de Mesquita (Tau-b); Curtis Signorino & Jeffrey Ritter (S) | Skyler Cranmer, Bruce Desmarais & Elizabeth Menninga |
| Type≠ | Dyadic similarity index over alliance commitment profiles | Network analysis and inferential network modeling of interstate alliances |
| Opprinnelig kilde≠ | Signorino, C. S., & Ritter, J. M. (1999). Tau-b or not Tau-b: Measuring the similarity of foreign policy positions. International Studies Quarterly, 43(1), 115–144. DOI ↗ | 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. DOI ↗ |
| Alias | Alliance Portfolio Similarity Scores, S-Score of Alliance Similarity, Tau-b Alliance Similarity, Alliance Profile Similarity | International Alliance Networks, Alliance Portfolio Network Analysis, Network Models of Alliance Formation, Interstate Alliance Graph Analysis |
| Relaterte | 3 | 3 |
| Sammendrag≠ | Alliance portfolio similarity measures how alike two states' overall patterns of alliance commitments are. Each state has a 'portfolio' — the profile of defense pacts, neutrality agreements, ententes, or no tie it holds with every other state — and the similarity of two portfolios is summarized in a single dyadic score. Signorino and Ritter (1999) showed that the long-dominant Kendall's tau-b measure is flawed for this purpose and introduced the S-score as a better-behaved alternative. These scores are a standard proxy for shared interests and have been used to operationalize utilities in expected-utility models of 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. |
| ScholarGateDatasett ↗ |
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