82,891
edits
Finite and Infinite Games: Difference between revisions
no edit summary
Amwelladmin (talk | contribs) No edit summary |
Amwelladmin (talk | contribs) No edit summary |
||
| Line 42: | Line 42: | ||
Let me throw in some original research here: historically-focused games are ''fine'': there is no harm and much reward to be had from a game of football, as long as everyone understands the “theatricality” of what is going on; but to apply finite, backward-looking techniques to the “resolution” of ''infinite'' scenarios — necessarily forward-looking, indeterminate problems (in that you don’t even know that there is a problem, let alone what it is) is where you will get into bother. | Let me throw in some original research here: historically-focused games are ''fine'': there is no harm and much reward to be had from a game of football, as long as everyone understands the “theatricality” of what is going on; but to apply finite, backward-looking techniques to the “resolution” of ''infinite'' scenarios — necessarily forward-looking, indeterminate problems (in that you don’t even know that there is a problem, let alone what it is) is where you will get into bother. | ||
[[File:Normal vs fat-tailed distribution.png| | [[File:Normal vs fat-tailed distribution.png|350px|thumb|right|The ostensible similarity between normal and fat-tailed distributions, yesterday.]]It is deceptive in that finite techniques may work perfectly well much of the time, because even infinite environments largely function by reference to established order, existing rules and what is already known: they look, for the most part, like finite games — it’s just that they don’t have to, and are liable to change without notice. As long as they behave themselves, a finite approach is efficient, effective, centrally controllable and provides consistency and certainty. This is why unimaginative business leaders are so fond of sporting metaphors. | ||
This, we think, is just an other way of noting that the middle of a [[normal distribution]] resembles the middle of a “fat-tailed” distribution and the same approaches will work passably well for both, as long as the events fall within the middle, which for the most part they do. | This, we think, is just an other way of noting that the middle of a [[normal distribution]] resembles the middle of a “fat-tailed” distribution and the same approaches will work passably well for both, as long as the events fall within the middle, which for the most part they do. | ||