# Variational Inference of Parameters in Opinion Dynamics Models

arXiv: https://arxiv.org/abs/2403.05358 · HTML: https://mlsanity.com/paper/2403.05358/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 74 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 23 of 23)

- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro](https://mlsanity.com/paper/1912.11554/) (2019)
- [Simulation Intelligence: Towards a New Generation of Scientific Methods](https://mlsanity.com/paper/2112.03235/) (2021)
- [Block Neural Autoregressive Flow](https://mlsanity.com/paper/1904.04676/) (2019)
- [Learning Opinion Dynamics From Social Traces](https://mlsanity.com/paper/2006.01673/) (2020)
- [On learning agent-based models from data](https://mlsanity.com/paper/2205.05052/) (2022)
- [Black-box Bayesian inference for economic agent-based models](https://mlsanity.com/paper/2202.00625/) (2022)
- [Emergence of polarized ideological opinions in multidimensional topic spaces](https://mlsanity.com/paper/2007.00601/) (2020)
- [Differentiable Agent-Based Simulation for Gradient-Guided Simulation-Based Optimization](https://mlsanity.com/paper/2103.12476/) (2021)
- [Cascade-based Echo Chamber Detection](https://mlsanity.com/paper/2208.04620/) (2022)
- [Opinion dynamics in social networks: From models to data](https://mlsanity.com/paper/2201.01322/) (2022)
- [Bayesian calibration of differentiable agent-based models](https://mlsanity.com/paper/2305.15340/) (2023)
- [Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs](https://mlsanity.com/paper/2302.11835/) (2023)
- [Opinion Dynamics in Social Networks with Hostile Camps: Consensus vs. Polarization](https://mlsanity.com/paper/1508.05034/) (2015)
- [Modeling how social network algorithms can influence opinion polarization](https://mlsanity.com/paper/2102.00099/) (2021)
- [Likelihood-Based Methods Improve Parameter Estimation in Opinion Dynamics Models](https://mlsanity.com/paper/2310.02766/) (2023)
- [Variational Inference with Agent-Based Models](https://mlsanity.com/paper/1605.04360/) (2016)
- [Vaccination strategies against COVID-19 and the diffusion of anti-vaccination views](https://mlsanity.com/paper/2009.13674/) (2020)
- [Modeling limited attention in opinion dynamics by topological interactions](https://mlsanity.com/paper/2003.08620/) (2020)

## Built on by (most cited first, 4 of 4)

- [Bayesian calibration of stochastic agent based model via random forest](https://mlsanity.com/paper/2406.19524/) (2024)
- [Automatic Differentiation of Agent-Based Models](https://mlsanity.com/paper/2509.03303/) (2025)
- [Bias and Identifiability in the Bounded Confidence Model](https://mlsanity.com/paper/2506.11751/) (2025)
- [Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models](https://mlsanity.com/paper/2509.17625/) (2025)

## Abstract (arXiv)

Despite the frequent use of agent-based models (ABMs) for studying social phenomena, parameter estimation remains a challenge, often relying on costly simulation-based heuristics. This work uses variational inference to estimate the parameters of an opinion dynamics ABM, by transforming the estimation problem into an optimization task that can be solved directly. Our proposal relies on probabilistic generative ABMs (PGABMs): we start by synthesizing a probabilistic generative model from the ABM rules. Then, we transform the inference process into an optimization problem suitable for automatic differentiation. In particular, we use the Gumbel-Softmax reparameterization for categorical agent attributes and stochastic variational inference for parameter estimation. Furthermore, we explore the trade-offs of using variational distributions with different complexity: normal distributions and normalizing flows. We validate our method on a bounded confidence model with agent roles (leaders and followers). Our approach estimates both macroscopic (bounded confidence intervals and backfire thresholds) and microscopic (200 categorical, agent-level roles) more accurately than simulation-based and MCMC methods. Consequently, our technique enables experts to tune and validate their ABMs against real-world observations, thus providing insights into human behavior in social systems via data-driven analysis.