The impact factor game: an agent-based exploration of self-citation influence and interdisciplinary dynamics on impact metrics

Abstract The manipulation of the Impact Factor (IF) through editorial decisions inflating self-citations is a growing concern in academic publishing. This work introduces an agent-based model simulating journals as rational agents competing for IF ranking positions in a zero-sum game, generating synthetic citation networks that reproduce key patterns such as the Matthew Effect and the specialization of manipulation strategies across interconnected disciplines. By comparing IF calculation policies with and without self-citations, statistical analysis of simulation results demonstrates that excluding self-citations significantly reduces incentives for manipulative tactics, offering computational evidence for developing policies that promote scientific integrity within the academic publishing ecosystem.