Blas Kolic

👽

GitHub - blas-ko/IndependentHaltingCascadeModel: Code for Independent Halting Cascade (IHC) model of Kolic et al. (2025) "Incentivized Network Dynamics in Digital Job Recruitment" Code for Independent Halting Cascade (IHC) model of Kolic et al. (2025) "Incentivized Network Dynamics in Digital Job Recruitment" - blas-ko/IndependentHaltingCascadeModel

Github repo: github.com/blas-ko/Inde...

ArXiv version: arxiv.org/abs/2410.09698

🚨 Paper alert 🚨

New ABM simulating recruitment dynamics via incentivized recommendations. For a given opening, an agent may recommend it to peers or apply. If someone is hired, everyone in the chain is rewarded. We also reproduce chain-length distributions of classic experiments.

📰 t.co/QDjIV4NnfJ

Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models In this paper, we present the first systematic comparison of Data Assimilation (DA) and Likelihood-Based Inference (LBI) in the context of Agent-Based Models (ABMs). These models generate observable t...

While limited to one ABM, this work fills a critical gap in the ABM calibration literature, providing the first structured comparison of DA and LBI for latent state inference.

Kudos to Marco, Corrado, and Gianmarco for such a wonderful collaboration!

Hope you enjoy it
👉 arxiv.org/abs/2509.17625

⚖️ Essentially:
➖ DA: Great for macro-level patterns. Easy to apply, doesn’t need a formal likelihood.
➖LBI: Superior for micro-level accuracy, but needs explicit likelihoods (often hard to derive).

➡️ Trade-off between generality and precision.

📊 Main results:
➖ At the agent level, LBI outperforms DA in reconstructing latent opinions. LBI is more accurate and robust to model errors.
➖ At the aggregate level, both methods perform similarly well → DA remains competitive for forecasting population-level trends.

We test this using the Bounded-Confidence Model of opinion dynamics, where agents interact only if their opinions are sufficiently close, resulting in nonlinear updates.

⚙️ Scenarios:
➖ Observed: agent interactions
➖ Latent: agent opinions
➖ Noisy opinions
➖ Mis-specified model parameters

Can we recover the latent agent states (e.g., opinions) from observed data in an ABM?

🆎 First systematic comparison between:
➖ Data Assimilation (DA) → Approximate, model-agnostic
➖ Likelihood-Based Inference (LBI) → Precise, but model-specific

🚨 Fresh from ArXiv:
“Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models”
with @marcopangallo.bsky.social, @c0rrad0.bsky.social, & @gdfm.bsky.social
👉 arxiv.org/abs/2509.17625

Amazing collab with Fabián Aguirre-Lopez and the data science crew at Sinnia, Mexico.

📄 Journal: doi.org/10.1093/comn...

📝 ArXiv (OA): arxiv.org/abs/2206.14501

💻 Code & plots: github.com/blas-ko/Twit...

Last month, we published
"From chambers to echo chambers: quantifying polarization with a second-neighbor approach applied to Twitter’s climate discussion" 🌍🔥

We find stable climate echo chambers despite ~90% weekly user churn, and show how events like #FridaysForFuture can disrupt polarization.

very nasty, indeed

🌱