This seminar explores the intersection of game theory and computer science in the study of teamwork. Traditional approaches in these fields perceive teams either as cooperative units with shared goals or as groups of self-interested agents where cooperation is necessary for individual success. However, these models fall short in capturing the nuances of human teamwork, where collaboration is not always in everyone's interest and binding agreements are often unattainable.

A novel game-theoretical model will be introduced, making collaboration in teamwork optional, allowing for the assessment of team outcomes, and letting players decide their level of engagement. Additionally, a multiagent multi-armed bandit (MA-MAB) framework will be presented, where agents empirically learn strategic behaviour approximating the Nash Equilibrium. The analysis will demonstrate how agents exhibit human-like behaviour patterns, considering the impact of team heterogeneity, task typology, and assessment difficulty on their strategies.

Alejandra López de Aberasturi is a PhD student in Artificial Intelligence at IIIA-CSIC, with research interests in Reinforcement Learning (RL) and the application of agent-based models to tackle human challenges such as resolving moral dilemmas. She holds a Master's degree in Artificial Intelligence from the Polytechnic University of Catalonia and a Master's degree in Physics and Mathematics applied to biology from the University of Granada. She also has a Bachelor's degree in Physics from the University of Granada.