re than doubles the earlier 3% and increases the excitement). We obtain the corresponding source entropy; H (421, bonus, null) = p(421) log2[p(421)] - p(bonus) log2[p(bonus)] - p(null) log2[p(null)] = 0.4313 bit/symbol. (4.12) We observe that the entropy H (421, bonus, null) = 0.4313 bit/symbol is more than the double of H(421, other) = 0.183 bit/symbol, a feature that indicates that the new game has more diversity in outcomes, which corresponds to a greater number of "exciting" possibilities, while the information of the game, I(N), remains unchanged. Thus, entropy can be viewed as representing a measure of game "excitement," while in contrast infor- mation is a global measure of game "surprise," which is not the same notion. Consider, indeed, two extreme possibilities for games: Game A: the probability of winning is very high, e.g., pa(win) = 90%; Game B: the probability of winning is very low, e.g., (win) = 0.000 01%. One easily computes the game entropies and information: H(A) = 0.468 bit/symbol Iwin(A) = 0.152 H(B) = 0.000 002 5 bit/symbol Iwin(B) = 23.2 bit. We observe that, comparatively, game A has significant entropy and low information, while the reverse applies to game B. Game A is more exciting to play because the player wins much more often, hence a high entropy (but low information). Game B has more of a surprise potential because of the low chances of winning, hence a high information (but low entropy). 4.3 Language entropy Language entropy Here, we shall see how Shannon's entropy can be applied to analyze languages, as primary sources of word symbols, and to make interesting comparisons between different types of language. As opposed to dialects, human languages through history have always possessed some written counterparts. Most of these language "scripts" are based on a unique, finite-size alphabet of symbols, which one has been trained in early age to recognize and manipulate (and sometimes to learn later the hard way!). 7 Here, I will conventionally call symbols "characters," and their event 