Editing Minimax (section)

{{Short description|Decision rule used for minimizing the possible loss for a worst case scenario}}
{{About|the decision theory concept}}
{{For|company|MiniMax (company)}}
'''Minimax''' (sometimes '''Minmax''', '''MM'''<ref>{{cite report |author=Bacchus, Barua |date=January 2013 |title=Provincial Healthcare Index 2013 |publisher=Fraser Institute |page=25 |url=http://www.fraserinstitute.org/uploadedFiles/fraser-ca/Content/research-news/research/publications/provincial-healthcare-index-2013.pdf}}</ref> or '''saddle point'''<ref>{{cite AV media |people=Professor Raymond Flood |place=[[Gresham College]] |medium=video |url=https://www.youtube.com/watch?v=fJltiCjPeMA&t=12m0s |title=Turing and von&nbsp;Neumann |via=[[YouTube]]}}</ref>) is a decision rule used in [[artificial intelligence]], [[decision theory]], [[combinatorial game theory]], [[statistics]], and [[philosophy]] for ''minimizing'' the possible [[loss function|loss]] for a [[Worst-case scenario|worst case (''max''imum loss) scenario]]. When dealing with gains, it is referred to as "maximin" – to maximize the minimum gain. Originally formulated for several-player [[zero-sum]] [[game theory]], covering both the cases where players take alternate moves and those where they make simultaneous moves, it has also been extended to more complex games and to general decision-making in the presence of uncertainty.