  Agents are systems that optimize an objective function in an environment.
Together, the goal and the environment induce secondary objectives, incentives.
Modeling the agent-environment interaction using causal influence diagrams, we
can answer two fundamental questions about an agent's incentives directly from
the graph: (1) which nodes can the agent have an incentivize to observe, and
(2) which nodes can the agent have an incentivize to control? The answers tell
us which information and influence points need extra protection. For example,
we may want a classifier for job applications to not use the ethnicity of the
candidate, and a reinforcement learning agent not to take direct control of its
reward mechanism. Different algorithms and training paradigms can lead to
different causal influence diagrams, so our method can be used to identify
algorithms with problematic incentives and help in designing algorithms with
better incentives.
