For diverse h, within the base of logarithm of contagion measure I(p), we are going to get: log two units are bits, derived from binary; log three units are trits, derived from trinary; log e = ln units are nats, derived from all-natural logarithm; log 10 units are Hartleys, derived from a researcher in this field. Roughly speaking the volume of information and facts necessary to describe a technique, define the complexity of this. Suppose to have a sequence s1 , s2 , . . . , sn provided independently by a supply, with probabilities p1 , p2 , . . . , pn respectively. We’re enthusiastic about the weighted averageFrontiers in Psychology | http://www.frontiersin.orgNovember 2015 | Volume 6 | ArticleCipressoModeling behavior dynamicsquantity of contagion, obtained from every single si , for i = 1, . . . , n. Taking into consideration a long run (assume, N) we are going to have N observation, of course independent, and a total contagion I given by I =I N n i=1 I NNote that S(B) = entropy, therefore S (B)n i = 1 pi log1 pirepresent the source’sN pi logn i=1 1 n1 pi; i.e., for each and every si (i = 1, . . . , n),1 pi=N pi log=n i = 1 pilog1 pi1 pi.Considering that limn0 n log = 0, we will define pi log = 0, if pi = 0. We defined the contagion as function of probabilities of events p1 , p2 , . . . , pn . Say this set P and define the entropy of thisn 1 distribution as S (P) = i = 1 pi log pi . Generalizing S(P) in the continuous as an alternative to within the discrete, we are going to obtain 1 S (P) = P (x) log P(x) dx.Now we endeavor to assume to entropy when it comes to “expected value.” Speaking of probability, contemplating the discrete distribution p1 , p2 , . . . , pn , we’ve got pi 0 and n= 1 pi = 1, exactly where taking into consideration a i continuous distribution P(x) we’ve got P(x) 0 and P(x)dx = 1. Hence, defining the set h1 , h2 , . . . , hn as H, the anticipated value n of H might be E [H] = i = 1 hi pi for discrete distribution p1 , p2 , . . . , pn , or E [H (x)] = H (x)P (x) dx for continuous distribution P(x). Taking into consideration preceding benefits on entropy and contagion, we get S (P) = E I p , i.e., the expected worth from the contagion of our distribution is the entropy of your probability distribution. A Shannon’s simple model is composed of three components: a supply (who send a message), a channel (where the message run), along with a receiver (who acquire the message) (Shannon, 1948). Within a far more common model Shannon considers also the noise inside the channel and decoding and encoding systems (Shannon, 1949). Shannon, in the very first a part of his paper, define a discrete model, assuming a sequence of h1 , h2 , . . . , hn letters, sent by way of a channel (disturbed by a noise) after which encoded, by the supply. Then the receiver decode the Salinomycin (sodium salt) biological activity symbols in information. Behavior comply with the exact same modeling system where information are encoded and modeled as a sequences. Assuming that the elements by way of the channel are single elements (like gestures), we could define an “alphabet” of this channel. Generally we can take into account a set of symbols r1 , r2 , . .