  Complex simulators have become a ubiquitous tool in many scientific
disciplines, providing high-fidelity, implicit probabilistic models of natural
and social phenomena. Unfortunately, they typically lack the tractability
required for conventional statistical analysis. Approximate Bayesian
computation (ABC) has emerged as a key method in simulation-based inference,
wherein the true model likelihood and posterior are approximated using samples
from the simulator. In this paper, we draw connections between ABC and
generalized Bayesian inference (GBI). First, we re-interpret the accept/reject
step in ABC as an implicitly defined error model. We then argue that these
implicit error models will invariably be misspecified. While ABC posteriors are
often treated as a necessary evil for approximating the standard Bayesian
posterior, this allows us to re-interpret ABC as a potential robustification
strategy. This leads us to suggest the use of GBI within ABC, a use case we
explore empirically.
