[TMP-014] Agent based modeling of the Human-AI ecosystem

Contact person: Pierluigi Contucci (pierluigi.contucci@unibo.it) 

Internal Partners:

1. Università di Bologna UNIBO, Pierluigi Contucci, pierluigi.contucci@unibo.it
2. Central European University (CEU), Janos Kertesz, kerteszj@ceu.edu

The project aims at investigating systems composed by a large number of agents belonging to either human or artificial types. The plan is to study, both from the static and the dynamical point of view, how such a two-populated system reacts to changes in the parameters, especially in view of possible abrupt transitions. We plan to pay special attention to higher order interactions like three body effects (H-H-H, H-H-AI, H-AI-AI and AI-AI-AI). We hypothesized that such interactions are crucial for the understanding of complex Human-AI systems. We analyzed the static properties both from the direct and inverse problem perspective. This study will pave the way for further investigation of the system in its dynamic evolution by means of correlations and temporal motifs.

Results Summary

The progressive advent of artificial intelligence machines may represent both an opportunity or a threat. In order to have an idea of what is coming we propose a model that simulates a Human-AI ecosystem. In particular, we consider systems where agents present biases, peer-to-peer interactions and three body interactions that are crucial, and describe two humans interacting with an artificial agent and two artificial intelligence agents interacting with a human. We focus our analysis by exploring how the relative fraction of artificial intelligence agents affect that ecosystem. We find evidence that, for suitable values of the interaction parameters, arbitrarily small changes in such percentage may trigger dramatic changes for the system that can be either in one of the two polarised states or in an undecided state.

Tangible Outcomes

1. Human-AI ecosystem with abrupt changes as a function of the composition. Contucci P, Kertész J, Osabutey G (2022) PLOS ONE 17(5):e0267310 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0267310