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Related Experiment Video

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The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies

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Published on: August 25, 2023

Pairwise influences in dynamic choice: network-based model and application.

Stefano Nasini^1,2, Victor Martínez-de-Albéniz³

¹IESEG School of Management, Lille, France.

Journal of Applied Statistics

|June 16, 2022

Summary

This summary is machine-generated.

This study introduces a new method to discover networks and analyze how choices spread, even with unknown social structures. It helps understand influence propagation and optimize diffusion strategies.

Keywords:

05C82 62-07 62Fxx 90C10 91G70 Network influences cross-sectional dependencies exponential family of distributions multidimensional panel data music broadcasting industry

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Area of Science:

Social Network Analysis
Econometrics
Information Diffusion Models

Background:

Analyzing decision propagation through social interactions often assumes known network structures.
Inferring individual influences from observed choices requires strong modeling assumptions.

Purpose of the Study:

To propose an integrated approach for network discovery and influence propagation analysis.
To extend vector autoregression for analyzing pairwise influences in dynamic choices.
To analyze lead-lag synchronization in multiple choices within social networks.

Main Methods:

Developed a class of parametric models extending vector autoregression for pairwise influences.
Investigated theoretical properties including conditional moments, parameter sensitivity, identifiability, and estimation.
Applied the methodology to music broadcasting data to infer station-to-station influences.

Main Results:

Successfully uncovered theoretical properties of the proposed models.
Inferred station-to-station influences in music broadcasting networks.
Assessed the propagation effect of initial launching stations for maximizing song diffusion.

Conclusions:

The proposed approach integrates network discovery and influence propagation analysis.
The methodology provides a robust framework for understanding decision diffusion in complex systems.
Empirical application demonstrates the practical utility in optimizing diffusion strategies.