Approximate Simulation-Based Verification of Compatibility of the Friedkin-Johnsen Model with Binary Observations
Yu Xing, Aneesh Raghavan, Michael T. Schaub, and Karl H. Johansson

TL;DR
This paper presents an approximate simulation-based method for verifying if Friedkin-Johnsen opinion models can produce observed binary outputs, using finite abstractions to handle continuous parameters.
Contribution
It introduces a novel abstraction approach that simplifies the influence matrix and parameters, enabling verification of opinion models against binary observations.
Findings
Finite abstractions approximately simulate continuous FJ models.
Verification over abstractions can confirm model consistency with observations.
Numerical experiments demonstrate the effectiveness of the proposed framework.
Abstract
We consider a verification problem for opinion dynamics based on binary observations. The opinion dynamics is governed by a Friedkin-Johnsen (FJ) model, where only a sequence of binary outputs is available instead of the agents' continuous opinions. At every time-step we observe a binarized output for each agent depending on whether the opinion exceeds a fixed threshold. The objective is to verify whether an FJ model with a given set of stubbornness parameters and initial opinions can generate the observed binary outputs up to a small error. The FJ model is formulated as a transition system, and an approximate simulation relation of two transition systems is defined in terms of the proximity of their opinion trajectories and output sequences. We then construct a finite set of abstract FJ models by simplifying the influence matrix and discretizing the stubbornness parameters and the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
