Talks @ LASIGE: Elias Fernández Domingos

Title: Delegation to Autonomous Agents Promotes Cooperation in Collective Risk Dilemmas

Speaker: Elias Fernández Domingos (ULB – Brussels)
When: March 30, 14h00
Where: 6.3.27
(Coffee break included)

Abstract: Home assistant chat-bots, self-driving cars, drones or automated negotiations are some of the several examples of autonomous (artificial) agents that have pervaded our society. These agents enable the automation of multiple tasks, saving time and (human) effort. However, their presence in social settings raises the need for a better understanding of their effect on social interactions and how they may be used to enhance cooperation towards the public good, instead of hindering it. To this end, we present an experimental study of human delegation to autonomous agents and hybrid human-agent interactions centered on a non-linear public goods dilemma with uncertain returns in which participants face a collective risk. Our aim is to understand experimentally whether the presence of autonomous agents has a positive or negative impact on social behaviour, fairness and cooperation in such a dilemma. Our results show that cooperation increases when participants delegate their actions to an artificial agent that plays on their behalf. Yet, this positive effect is reduced when humans interact in hybrid human-agent groups. Also, we show that humans have biases towards agent behaviour, assuming that they will contribute less to the collective effort. In general, we find that delegation to autonomous agents might act as long-term commitment devices, which prevent both the temptation to deviate to an alternate (less collectively good) course of action, as well as limiting responses based on betrayal aversion.

Bio: Elias is currently a Postdoctoral researcher (FNRS fellow) at the ULB – Brussels. He is interested in the origins of cooperation in social interactions and how can we maintain it in an increasingly complex and hybrid human-AI world. In his research he applies concepts and methods from (Evolutionary) Game Theory, Behavioural economics and Machine Learning to model collective (strategic) behaviour and validate it through behavioural economic experiments.