Samuel Martin-Gutierrez

Andrea Cera Palatsi, Samuel Martin-Gutierrez, Ana S. Cardenal et al.

Large language models (LLMs) are increasingly used both to make decisions in domains such as health, education and law, and to simulate human behavior. Yet how closely LLMs mirror actual human decision-making remains poorly understood. This gap is critical: misalignment could produce harmful outcomes in practical applications, while failure to replicate human behavior renders LLMs ineffective for social simulations. Here, we address this gap by developing a digital twin of game-theoretic experiments and introducing a systematic prompting and probing framework for machine-behavioral evaluation. Testing three open-source models (Llama, Mistral and Qwen), we find that Llama reproduces human cooperation patterns with high fidelity, capturing human deviations from rational choice theory, while Qwen aligns closely with Nash equilibrium predictions. Notably, we achieved population-level behavioral replication without persona-based prompting, simplifying the simulation process. Extending beyond the original human-tested games, we generate and preregister testable hypotheses for novel game configurations outside the original parameter grid. Our findings demonstrate that appropriately calibrated LLMs can replicate aggregate human behavioral patterns and enable systematic exploration of unexplored experimental spaces, offering a complementary approach to traditional research in the social and behavioral sciences that generates new empirical predictions about human social decision-making.

Mauritz N. Cartier van Dissel, Tomáš Lintner, Samuel Martin-Gutierrez et al.

Three theories offer competing predictions about how people respond to growing diversity in their social environment. Contact theory suggests more exposure to out-groups reduces prejudice; conflict theory predicts a stronger in-group preference; structural opportunity theory argues that shifts in behaviour only reflect changes in the opportunity structure rather than in underlying preference. We test these predictions using friendship and rejection nominations from nearly 5,000 students in 228 classrooms, across gender, ethnicity, and socio-economic status. We estimate individual preference using a multilevel model based on the Wallenius hypergeometric distribution, which accounts for the finite, asymmetric pool of potential ties. Results show that for ethnicity and socio-economic status, preferences are largely unaffected by classroom composition. For gender, however, same-gender preference strengthens as the out-group increases, supporting conflict theory. This means greater diversity does not necessarily change the intrinsic preference of students toward out-group peers, but creates more opportunities for cross-group interactions.

Jan Bachmann, Samuel Martin-Gutierrez, Lisette Espín-Noboa et al.

Inequalities in social networks arise from linking mechanisms, such as preferential attachment (connecting to popular nodes), homophily (connecting to similar others), and triadic closure (connecting through mutual contacts). While preferential attachment mainly drives degree inequality and homophily drives segregation, their three-way interaction remains understudied. This gap limits our understanding of how network inequalities emerge. Here, we introduce PATCH, a network growth model combining the three mechanisms to understand how they create disparities among two groups in synthetic networks. Extensive simulations confirm that homophily and preferential attachment increase segregation and degree inequalities, while triadic closure has countervailing effects: conditional on the other mechanisms, it amplifies population-wide degree inequality while reducing segregation and between-group degree disparities. We demonstrate PATCH's explanatory potential on fifty years of Physics and Computer Science collaboration and citation networks exhibiting persistent gender disparities. PATCH accounts for these gender disparities with the joint presence of preferential attachment, moderate gender homophily, and varying levels of triadic closure. By connecting mechanisms to observed inequalities, PATCH shows how their interplay sustains group disparities and provides a framework for designing interventions that promote more equitable social networks.