Inference of interaction kernels in mean-field models of opinion dynamics

TL;DR

This paper develops a method to infer the interaction kernel in mean-field opinion dynamics models from partial empirical data, proving uniqueness and demonstrating exponential error decay with increased data.

Contribution

It introduces a novel approach for uniquely reconstructing interaction kernels in opinion models using limited data and provides numerical evidence of exponential error decay.

Findings

Unique inference of interaction kernels is guaranteed under certain measurements.

The proposed numerical method accurately reconstructs kernels from limited data.

Error in inference decreases exponentially as data size increases.

Abstract

In models of opinion dynamics, many parameters -- either in the form of constants or in the form of functions -- play a critical role in describing, calibrating, and forecasting how opinions change with time. When examining a model of opinion dynamics, it is beneficial to infer its parameters using empirical data. In this paper, we study an example of such an inference problem. We consider a mean-field bounded-confidence model with an unknown interaction kernel between individuals. This interaction kernel encodes how individuals with different opinions interact and affect each other's opinions. Because it is often difficult to quantitatively measure opinions as empirical data from observations or experiments, we assume that the available data takes the form of partial observations of a cumulative distribution function of opinions. We prove that certain measurements guarantee a precise and unique inference of the interaction kernel and propose a numerical method to reconstruct an interaction kernel from a limited number of data points. Our numerical results suggest that the error of the inferred interaction kernel decays exponentially as we strategically enlarge the data set.