Analyzing Computational Models
Bibliographic Data
| ID | 6231695 |
|---|---|
| Authors | David A Siegel (0000-0003-1619-6119, Duke University, corresponding author) |
| Year | 2018 |
| Volume | 62 |
| Issue | 3 |
| Pages | 745-759 |
| Publication date | 2018-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Journal of Political Science (JOURNAL) |
| Journal identifiers | ISSN: 0092-5853 • E-ISSN: 1540-5907 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/ajps.12364 |
| OpenAlex | W2805499857 |
| Language | EN |
| Citations received | 5 |
| References cited | 25 |
Computational models have been underutilized as tools for formal theory development, closing off theoretical analysis of complex substantive scenarios that they would well serve. I argue that this occurs for two reasons, and provide resolutions for each. First, computational models generally do not employ the language or modes of analysis common to game‐theoretic models, the status quo in the literature. I detail the types of insights typically derived from game‐theoretic models and discuss analogues in computational modeling. Second, there are not widely established procedures for analysis of deductive computational models. I present a regularized method for deriving comparative statics from computational models that provides insights comparable to those arising from game‐theoretic analyses. It also serves as a framework for building theoretically tractable computational models. Together, these contributions should enhance communication between models of social science and open up the tool kit of deductive computational modeling for theory building to a broader audience
Algorithm · Closing (real estate · Computational complexity theory · Computational model · Computational problem · Game theory · Management science · Mathematical economics · Artificial Intelligence · Computer Science · Electoral Systems and Political Participation · Experimental Behavioral Economics Studies · Mathematics · Qualitative Comparative Analysis Research · Theoretical Computer Science
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| Unique citing works | 5 |
|---|---|
| Citations per year | 1 |
| Citation span | 2021 - 2025 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |