A DYNAMIC ADDITIVE AND MULTIPLICATIVE EFFECTS NETWORK MODEL WITH APPLICATION TO THE UNITED NATIONS VOTING BEHAVIORS

Area of Science:

• Social Network Analysis
• Political Science
• Statistical Modeling

Background:

• Analyzing United Nations voting behavior requires models that account for temporal dependencies and network dynamics.
• Existing network models, like the additive and multiplicative effects network model (AMEN), may not fully capture time-varying network structures and missing data.

Purpose of the Study:

• To introduce a dynamic regression model for correlated, time-varying networks.
• To extend the additive and multiplicative effects network model (AMEN) with temporal dynamics and missing data accommodation.
• To analyze United Nations General Assembly voting data from 1983-2014.

Main Methods:

• Developed a dynamic extension of the additive and multiplicative effects network model (AMEN).
• Incorporated a temporal structure to model network evolution over time.
• Accounted for two types of missing data, allowing for time-varying network sizes.
• Validated model components through simulations.

Main Results:

• Simulations confirmed the necessity of the model's components.
• Applied the model to UN General Assembly voting data (1983-2014).
• Identified significant factors explaining country voting behaviors.
• Estimated additive and multiplicative effects revealed dynamic foreign policy positions and alliances.

Conclusions:

• The dynamic network model effectively captures temporal dependencies in international relations.
• The model provides insights into the evolution of foreign policy and country alliances.
• This approach enhances understanding of voting behaviors in multilateral organizations.