  In science, macro level descriptions of the causal interactions within
complex, dynamical systems are typically deemed convenient, but ultimately
reducible to a complete causal account of the underlying micro constituents.
Yet, such a reductionist perspective is hard to square with several issues
related to autonomy and agency: (1) agents require (causal) borders that
separate them from the environment, (2) at least in a biological context,
agents are associated with macroscopic systems, and (3) agents are supposed to
act upon their environment. Integrated information theory (IIT) (Oizumi et al.,
2014) offers a quantitative account of causation based on a set of causal
principles, including notions such as causal specificity, composition, and
irreducibility, that challenges the reductionist perspective in multiple ways.
First, the IIT formalism provides a complete account of a system's causal
structure, including irreducible higher-order mechanisms constituted of
multiple system elements. Second, a system's amount of integrated information
($\Phi$) measures the causal constraints a system exerts onto itself and can
peak at a macro level of description (Hoel et al., 2016; Marshall et al.,
2018). Finally, the causal principles of IIT can also be employed to identify
and quantify the actual causes of events ("what caused what"), such as an
agent's actions (Albantakis et al., 2019). Here, we demonstrate this framework
by example of a simulated agent, equipped with a small neural network, that
forms a maximum of $\Phi$ at a macro scale.
