Maximilian Puelma Touzel

The $\textit{Silicon Society}$ Cookbook: Design Space of LLM-based Social Simulations

Aurélien Bück-Kaeffer

Studies attempting to simulate human behavior with …

2026-04-29

arXiv (preprint)

Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions

Matthew Riemer

Tommaso Tosato

This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and sho… (see more)uld be required to obtain behavioral certification before making decisions that affect economic markets. This is because integrating these agents into society could collapse the legal evidentiary distinction between competition and collusion among independent firms without eroding the economic harm distinction. Experiments with DeepSeek-R1 agents in the Bertrand oligopoly pricing domain reveal a tendency towards tacit collusion that persists even when humans prompt the agents not to collude. We further show that the chain-of- thought of these agents can be steered toward either extremely collusive or highly competitive behavior in a way that is not semantically detectable by another LLM analyzing the reasoning traces. As a result, deploying reasoning agents for market decisions leads to collusive economic outcomes without any evidence of conspiracy or intent. Thus, certification based on observed behavior in representative situations is necessary to prevent collusion. We provide preliminary evidence that such agents can be steered in a generalizable way toward efficient competitive equilibria. However, developing a comprehensive behavioral certification will be required before these models can be deployed in real-world markets while ensuring their stability and efficiency.

2025-12-31

International Conference on Machine Learning (Accept (regular))

Position: Time to Close The Validation Gap in LLM Social Simulations

Aurélien Bück-Kaeffer

LLM-based social simulations—in which many language model agents interact over multiple turns—are rapidly proliferating across policy an… (see more)alysis, epidemiology, and computational social science. Yet the field lacks consensus on how to validate these simulations, with evaluation methods that are sparse, inconsistent, and rarely shared across disciplinary silos. We argue this creates a serious risk: premature deployment of unvalidated simulators in high-stakes domains. Our position is that the field must pivot from expansion to consolidation, prioritizing methodological standardization—shared benchmarks, open data, and reproducible evaluation protocols grounded in social science and complex systems research. We outline a concrete research program organized around specific learning problems/benchmarks, providing a path toward answering the fundamental question: when are LLM social simulations useful modelling objects?

2025-12-31

International Conference on Machine Learning (Accept (regular))

$\texttt{BluePrint}$: A Social Media User Dataset for LLM Persona Evaluation and Training

Aur'elien Buck-Kaeffer

Je Qin Chooi

Dan Zhao

Large language models (LLMs) offer promising capabilities for simulating social media dynamics at scale, enabling studies that would be ethi… (see more)cally or logistically challenging with human subjects. However, the field lacks standardized data resources for fine-tuning and evaluating LLMs as realistic social media agents. We address this gap by introducing SIMPACT, the SIMulation-oriented Persona and Action Capture Toolkit, a privacy respecting framework for constructing behaviorally-grounded social media datasets suitable for training agent models. We formulate next-action prediction as a task for training and evaluating LLM-based agents and introduce metrics at both the cluster and population levels to assess behavioral fidelity and stylistic realism. As a concrete implementation, we release BluePrint, a large-scale dataset built from public Bluesky data focused on political discourse. BluePrint clusters anonymized users into personas of aggregated behaviours, capturing authentic engagement patterns while safeguarding privacy through pseudonymization and removal of personally identifiable information. The dataset includes a sizable action set of 12 social media interaction types (likes, replies, reposts, etc.), each instance tied to the posting activity preceding it. This supports the development of agents that use context-dependence, not only in the language, but also in the interaction behaviours of social media to model social media users. By standardizing data and evaluation protocols, SIMPACT provides a foundation for advancing rigorous, ethically responsible social media simulations. BluePrint serves as both an evaluation benchmark for political discourse modeling and a template for building domain specific datasets to study challenges such as misinformation and polarization.

2025-09-26

ArXiv (preprint)

SandboxSocial: A Sandbox for Social Media Using Multimodal AI Agents

Gayatri Krishnakumar

Busra Tugce Gurbuz

Austin Welch

Andreea Musulan

Hao Yu

Ethan Kosak-Hine

Tom Gibbs

Camille Thibault

Dan Zhao

The online information ecosystem enables influence campaigns of unprecedented scale and impact. We urgently need empirically grounded approa… (see more)ches to counter the growing threat of malicious campaigns, now amplified by generative AI. But, developing defenses in real-world settings is impractical. Social system simulations with agents modelled using Large Language Models (LLMs) are a promising alternative approach and a growing area of research. However, existing simulators lack features needed to capture the complex information-sharing dynamics of platform-based social networks. To bridge this gap, we present SandboxSocial, a new simulator that includes several key innovations, mainly: (1) a virtual social media platform (modelled as Mastodon and mirrored in an actual Mastodon server) that enables a realistic setting in which agents interact; (2) an adapter that uses real-world user data to create more grounded agents and social media content; and (3) multi-modal capabilities that enable our agents to interact using both text and images---just as humans do on social media. We make the simulator more useful to researchers by providing measurement and analysis tools that track simulation dynamics and compute evaluation metrics to compare experimental results.

2025-08-15

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (published)

Veracity: An Open-Source AI Fact-Checking System

Taylor Lynn Curtis

William Garneau

Manon Gruaz

Mike Pinder

Li Wei Wang

Sukanya Krishna

Luda Cohen

The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper… (see more) introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications. This paper will showcase Veracity's ability to not only detect misinformation but also explain its reasoning, fostering media literacy and promoting a more informed society.

2025-08-15

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (published)

TRUTH: Teaching LLMs to Rerank for Truth in Misinformation Detection

Hao Yu

Shenyang Huang

2025-07-24

colmweb.org/COLM/2025/Workshop/SoLaR (poster)

A Simulation System Towards Solving Societal-Scale Manipulation

Austin Welch

Gayatri K

Dan Zhao

Hao Yu

Ethan Kosak-Hine

Tom Gibbs

Andreea Musulan

Camille Thibault

Busra Tugce Gurbuz

The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-w… (see more)orld settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. We improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys. We demonstrate the simulator with a tailored example in which we track agents' political positions and show how partisan manipulation of agents can affect election results.

2024-10-16

ArXiv (preprint)

Simulation System Towards Solving Societal-Scale Manipulation

Austin Welch

Gayatri K

Dan Zhao

Hao Yu

Tom Gibbs

Ethan Kosak-Hine

Andreea Musulan

Camille Thibault

Busra Tugce Gurbuz

The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-w… (see more)orld settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. Through a variety of means we then improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys of the agents' political positions. We demonstrate the simulator with a tailored example of how partisan manipulation of agents can affect election results.

2024-10-11

NeurIPS.cc/2024/Workshop/SafeGenAi (poster)

Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada

Anne Imouza

C'ecile Amadoro

Gabrielle Desrosiers-Brisebois

Sacha Lévy

Public health measures were among the most polarizing topics debated online during the COVID-19 pandemic. Much of the discussion surrounded … (see more)specific events, such as when and which particular interventions came into practise. In this work, we develop and apply an approach to measure subnational and event-driven variation of partisan polarization and explore how these dynamics varied both across and within countries. We apply our measure to a dataset of over 50 million tweets posted during late 2020, a salient period of polarizing discourse in the early phase of the pandemic. In particular, we examine regional variations in both the United States and Canada, focusing on three specific health interventions: lockdowns, masks, and vaccines. We find that more politically conservative regions had higher levels of partisan polarization in both countries, especially in the US where a strong negative correlation exists between regional vaccination rates and degree of polarization in vaccine related discussions. We then analyze the timing, context, and profile of spikes in polarization, linking them to specific events discussed on social media across different regions in both countries. These typically last only a few days in duration, suggesting that online discussions reflect and could even drive changes in public opinion, which in the context of pandemic response impacts public health outcomes across different regions and over time.

2024-07-02

ArXiv (preprint)

Scalable Approaches for a Theory of Many Minds

Amin Memarian

Matthew D Riemer

Andrei Mircea

Andrew Robert Williams

Elin Ahlstrand

A major challenge as we move towards building agents for real-world problems, which could involve a massive number of human and/or machine a… (see more)gents, is that we must learn to reason about the behavior of these many other agents. In this paper, we consider the problem of scaling a predictive Theory of Mind (ToM) model to a very large number of interacting agents with a fixed computational budget. Motivated by the limited diversity of agent types, existing approaches to scalable TOM learn versatile single-agent representations for quickly adapting to new agents encountered sequentially. We consider the more general setting that many agents are observed in parallel and formulate the corresponding Theory of Many Minds (ToMM) problem of estimating the joint policy. We frame the scaling behavior of solutions in terms of parameter sharing schemes and in particular propose two parameter-free architectural features that endow models with the ability to exploit action correlations: encoding a multi-agent context, and decoding through an abstracted joint action space. The increased predictive capabilities that have come with foundation models have made it easier to imagine the possibility of using these models to make simulations that imitate the behavior of many agents within complex real-world systems. Being able to perform these simulations in a general-purpose way would not only help make more capable agents, it also would be a very useful capability for applications in social science, political science, and economics.

2024-06-17

ICML.cc/2024/Workshop/Agentic_Markets (poster)