  There is an analogy between machine learning systems and economic entities in
that they are both adaptive, and their behaviour is specified in a more-or-less
explicit way. It appears that the area of AI that is most analogous to the
behaviour of economic entities is that of morally good decision-making, but it
is an open question as to how precisely moral behaviour can be achieved in an
AI system. This paper explores the analogy between these two complex systems,
and we suggest that a clearer understanding of this apparent analogy may help
us forward in both the socio-economic domain and the AI domain: known results
in economics may help inform feasible solutions in AI safety, but also known
results in AI may inform economic policy. If this claim is correct, then the
recent successes of deep learning for AI suggest that more implicit
specifications work better than explicit ones for solving such problems.
