acceptable outcomes. and each have a set of available moves, which choose the outcome from a table with as many rows as has moves and as many columns as has moves. is allowed full knowledge of 's move, and must pick moves in response so that the outcome is acceptable. Since many games pose no difficulty for , the table is chosen so that no outcome is repeated in any column, which ensures that in the corresponding game any change in 's move means a change in outcome, unless has a move to keep the outcome from changing. With this restriction, if never changes moves, the outcome fully depends on 's choice, while if multiple moves are available to it can reduce the variety of outcomes, if the table allows it, dividing by as much as its own variety of moves. The law of requisite variety is that a deterministic strategy for can at best limit the variety in outcomes to , and only adding variety in 's moves can reduce the variety of outcomes: "only variety can destroy variety". For example, in the table above, has a strategy (shown in bold) to reduce the variety in outcomes to , which is in this case. It is not possible for to reduce the outcomes any further and still respond to all potential moves from , but it is possible that another table of the same shape would not allow to do so well. Requisite variety is necessary, but not sufficient to control the outcomes. If and are machines, they cannot possibly choose more moves than they have states. Thus, a perfect regulator must have at least as many distinguishable states as the phenomenon it is intended to regulate (the table must be square, or wider). Stated in bits, the law is . In Shannon's information theory, , , and are information sources. The condition that if never changes moves, the uncertainty in outcomes is no less than the uncertainty in 's move is expressed as , and since 's strategy is a deterministic function of set . With the rules of the game expressed this way, it can be shown that . Ashby described the law of requisite variety as related to the tenth theorem in Shannon's Mathematical Theory of Communication (1948): This law (of which Shannon's theorem 10 relating to the suppression of noise is a special case) says