  Modelling causal responsibility in multi-agent spatial interactions is
crucial for safety and efficiency of interactions of humans with autonomous
agents. However, current formal metrics and models of responsibility either
lack grounding in ethical and philosophical concepts of responsibility, or
cannot be applied to spatial interactions. In this work we propose a metric of
causal responsibility which is tailored to multi-agent spatial interactions,
for instance interactions in traffic. In such interactions, a given agent can,
by reducing another agent's feasible action space, influence the latter.
Therefore, we propose feasible action space reduction (FeAR) as a metric of
causal responsibility among agents. Specifically, we look at ex-post causal
responsibility for simultaneous actions. We propose the use of Moves de Rigueur
(MdR) - a consistent set of prescribed actions for agents - to model the effect
of norms on responsibility allocation. We apply the metric in a grid world
simulation for spatial interactions and show how the actions, contexts, and
norms affect the causal responsibility ascribed to agents. Finally, we
demonstrate the application of this metric in complex multi-agent interactions.
We argue that the FeAR metric is a step towards an interdisciplinary framework
for quantifying responsibility that is needed to ensure safety and meaningful
human control in human-AI systems.
