{"query_echo":{"q":"","filters":{"analysis_type":["simulation study"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'simulation study' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":1,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=simulation+study","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"prio-001447","url":"https://climateinsightsdirectory.org/document/prio-001447","title":"Focused Model Selection for Markov Chain Models With an Application to Armed Conflict Data","publisher":{"name":"Peace Research Institute Oslo","organization":"peace-research-institute-oslo"},"document_type":"research paper","language":"en","date_published":"2019","source_url":"https://cdn.cloud.prio.org/files/7a672b87-aae4-44a1-82ea-9d2d9c324512/HAUG_JK_FinalVersion.pdf?inline=true","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","analysis_type"],"summary":{"text":"The author develops a Focused Information Criterion (FIC) for dynamic multinomial logit models, specifically designed to select models that provide the most precise estimate of a specific focus parameter rather than the model closest to the true data-generating mechanism. The thesis establishes the asymptotic theory for maximum likelihood estimators under model misspecification for these models, proving they are consistent and approximately normally distributed. Through simulation studies, the author verifies that the FIC effectively identifies models with the lowest true mean squared error for various focus parameters. The methodology is applied to the Militarized Interstate Disputes (MID) dataset (1950\u00102010), focusing on the probability of conflict escalation. The analysis indicates a tendency for higher democracy levels to lower escalation probabilities, although these findings are not statistically significant.","ai_generated":true,"unit":"prio-001447#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Focused Information Criterion (FIC) is designed to select models based on the precision of the focus parameter estimator, specifically aiming to minimize the mean squared error (mse), which is the sum of variance and squared bias.","ai_generated":true,"unit":"prio-001447#insight-1","date_modified":"2026-10-23"},{"text":"Simulation results demonstrate that the FIC successfully selects the model with the lowest true mean squared error for different types of focus parameters, including transition probabilities and long-term probabilities.","ai_generated":true,"unit":"prio-001447#insight-2","date_modified":"2026-10-23"},{"text":"In the application to MID data (1950\u00102010), the FIC selected simpler models (M3 and M5) for estimating escalation probabilities, whereas the Akaike Information Criterion (AIC) preferred a more complex 'wide model' (M0).","ai_generated":true,"unit":"prio-001447#insight-3","date_modified":"2026-10-23"},{"text":"The analysis of the MID dataset suggests a tendency for increased democracy levels to reduce the probability of conflict escalation, but this effect was not found to be statistically significant.","ai_generated":true,"unit":"prio-001447#insight-4","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":[],"frame_perspective":[],"analysis_type":["methodological development","simulation study","empirical application"]}]}