Introduction

The Modeling Tools component focuses on data curation, creation of new metrics, and modeling and forecasting of democratization and autocratization trends using causal and machine learning (ML) approaches.

Our Approach

AIM-3D is an empirically grounded and theoretically informed effort to build an AI-based data, modeling, and knowledge hub that systematically analyzes the trajectories of democratization and autocratization across nations.

Leveraging recent advances in artificial intelligence, AIM-3D seeks not merely to automate analysis, but to integrate computational modeling with established theories of democratic change, enabling new forms of predictive and explanatory insight into political development and decline.

Key Components

Data Curation – Integrating diverse datasets to create a comprehensive foundation for analysis

New Metrics – Developing innovative measures of democratic development and decline

Causal Modeling – Applying causal inference techniques to understand drivers of political change

Machine Learning – Utilizing advanced ML approaches for forecasting and pattern recognition

Theory Integration – Combining computational methods with established theories of democratic change

In order to read more, please click here.

AIM-3D Data Platform Tools

Take a look behind the scenes at the different tools included in our AIM-3D Data platform demo.

NAVAR Causal Score Matrix

At the core of the platform is the NAVAR Causal Score Matrix. It quantifies the learned directional relationships between democratic components, which variables exert influence on others over time, and at what relative strength. In the interactive heatmap, darker cells mark stronger inferred relationships and lighter cells mark weak or negligible relationships.

*The full causal score matrix. Each cell reads as the causal strength from a row variable to a column variable.*

A dual slider above the matrix lets users set a floor and ceiling on the causal strength range they wish to isolate. Users can also use their curser to hover over the cells within the heatmap and access tooltips with more detailed information about each relationship.

*In this example, the user limited the visible relationships to a range of 0.0102 and 0.0744. The cursor tooltip is also visible.*

NAVAR Implied Causal Graph

The implied causal graph turns the matrix into a network of the Granger-causal relationships between variables. Directional arrows show which variable influences which. The thickness of each arrow and the value printed on it encodes the strength of the relationship.

*Users can pan and zoom across the nodes, or select any single node to highlight only the relationships tied directly to it.*

*This shows the implied causal graph zoomed in and all of the relationships of the selected v2xdl_delib node shown in bold.*

Multi-Horizon Forecasting

Due to the model’s predictive nature, it can forecast as well as explain. The multi-horizon forecasting tool charts an index’s historical trend and projects it 10 years forward, with uncertainty shown as nested prediction intervals. Users can select a country and index of interest to visualize the model’s forecasting at different intervals.