  Social choice is the theory about collective decision towards social welfare
starting from individual opinions, preferences, interests or welfare. The field
of Computational Social Welfare is somewhat recent and it is gaining impact in
the Artificial Intelligence Community. Classical literature makes the
assumption of single-peaked preferences, i.e. there exist a order in the
preferences and there is a global maximum in this order. This year some
theoretical results were published about Two-stage Approval Voting Systems
(TAVs), Multi-winner Selection Rules (MWSR) and Incomplete (IPs) and Circular
Preferences (CPs). The purpose of this paper is three-fold: Firstly, I want to
introduced Social Choice Optimisation as a generalisation of TAVs where there
is a max stage and a min stage implementing thus a Minimax, well-known
Artificial Intelligence decision-making rule to minimize hindering towards a
(Social) Goal. Secondly, I want to introduce, following my Open Standardization
and Open Integration Theory (in refinement process) put in practice in my
dissertation, the Open Standardization of Social Inclusion, as a global social
goal of Social Choice Optimization.
