This thesis attempts to provide both an explanatory model and to forecast settlements using large-n statistical analysis and machine learning. By asking the question of how costly signals affect the likelihood of conflict settlement, and drawing upon the literature on bargaining and signaling, it argues that when the challenging actor publicly state their demands and policy desires, they are sending costly signals, revealing information about their willingness to fight. This information is used by conflict parties to recalculate costs of war, causing them to eventually locate an agreement which both parties prefer continued fighting. As such, the mechanism suggests that a greater number of such signals means a greater chance at locating such an agreement, resulting in a greater chance of settlement. Additionally, connecting the signal to the issue at stake, I argue that territorial signals would be especially important, in part because they are often seen as indivisible, suggesting that signals relating to territory would be especially important relative to signals of comparable policy domains. The results are statistically significant in support of the first hypothesis but findno benefit to predictive performance from costly signals. In contracts territorial signals are neither statistically significant, nor contribute to predictive performance.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-529841 |
| Date | January 2024 |
| Creators | Gustafsson, Tobias |
| Publisher | Uppsala universitet, Institutionen för freds- och konfliktforskning |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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