iiinova — where your digital self keeps evolving

Blas Kolic @blas-ko.bsky.social · 10/12/2025

🚨 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

110

Blas Kolic @blas-ko.bsky.social · 06/10/2025

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

arxiv.org

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...

012

Blas Kolic @blas-ko.bsky.social · 06/10/2025

⚖️ 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.

100

Blas Kolic @blas-ko.bsky.social · 06/10/2025

📊 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.

120

Blas Kolic @blas-ko.bsky.social · 06/10/2025

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

110

Blas Kolic @blas-ko.bsky.social · 06/10/2025

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

110

Blas Kolic @blas-ko.bsky.social · 06/10/2025

🚨 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

1104

Blas Kolic @blas-ko.bsky.social · 22/09/2025

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...

doi.org

Validate User

000

Blas Kolic @blas-ko.bsky.social · 22/09/2025

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.

doi.org

Validate User

140