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T h e I n c r e a s i n g Va l u e o f I n t a n g i b l e A s s e t s i n O r g a n i z a t i o n s

where the expectation is taken over X. De nition 1.3 (Entropy of Continuous Random Variable) On the other hand, the entropy of a continuous random variable X with probability density function f(X) is given by H ( X ) = - f ( x) log 2 ( f ( x))dx

(1.52)

For simplicity, we shall assume discrete random variable unless otherwise speci ed. De nition 1.4 (Joint Entropy) The joint entropy of two random variables X1, X2 is de ned as H ( X 1 , X 2 ) = - p( x1 , x2 ) log 2 ( p( x1 , x2 )) = -e [log 2 ( p( X 1 , X 2 ))] (1.53)

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General Ledger However, the new economy with its premium on intangible value-creating mechanisms demands more from our performance measurement systems Today s system must have the capabilities to identify, describe, monitor, and fully harness the intangible assets driving organizational success The Balanced Scorecard provides a voice of strength and clarity to intangible assets, allowing organizations to benefit fully from their astronomical potential The Strategy Story Could there possibly exist a more passionately discussed and debated subject on the business landscape than strategy While military strategy has been with us for millennia and continues to influence our thinking witness the ever-popular Art of War by Sun Tzu business strategy is a relatively new phenomenon with its greatest contributions arriving in the twentieth century Despite its brief tenure the topic has spawned hundreds of books, thousands of scholarly articles, and countless gurus each espousing their version of the Holy Grail of strategy.

where the expectation is taken over (X1, X2). De nition 1.5 (Conditional Entropy) The conditional entropy of a random variable X2 given X1 is de ned as H ( X 2 X 1 ) = - p( x1 , x2 ) log 2 ( p( x2 x1 )) = -e [log 2 ( p( X 2 X 1 ))]

(1.54)

your brand. As Eric puts it, Twittering a lot about nothing will increase your velocity but decrease your signal-to-noise ratio. And while the latter is not directly factored into the in uence calculation at Twitalyzer, in our experience if you start to ramble about nothing you will lose followers very, very quickly.

where the expectation is taken over (X1, X2). After introducing the de nitions of entropy, let s look at various properties of entropy. They are summarized as lemmas below. Please refer to the text by Cover and Thomas [30] for the proof. The rst lemma gives a lower bound on entropy. Lemma 1.2 (Lower Bound of Entropy) H (X ) 0 Equality holds if and only if there exists x0 X such that p(x0) = 1. (1.55)

Strategy is not a subject that can be unraveled by its academic and practical threads to reveal the one right method or version of the truth Every reader of this book, if appropriately prodded, could undoubtedly produce a coherent and cogent definition of strategy Ultimately we all cherish that spirit of discovery and rightly applaud our diversity of ideas, but practically speaking, it makes the study of strategy a frustrating one Fortunately for all of us the one thing that pundits from every strategy corner do agree on is the fact that strategy execution or implementation is far more important than strategy formation I have had the opportunity to sit in on a number of strategy setting workshops and have always relished the spirited debates, the aha moments of breathtaking clarity.

Proof Directly obtained from Equation (1.51). The following lemma gives an upper bound on entropy for discrete and continuous random variable X. Lemma 1.3 (Upper Bound of Entropy) If X is a discrete random variable, then H ( X ) log 2 ( X ) (1.56)

Equality holds if and only if p(X) = 1/|X|. On the other hand, if X is a continuous random variable, then H (X ) 1 2 log 2 (2pes X ) 2 (1.57)

2 where sX = e[|X|2] and equality holds if and only if X is Gaussian distributed 2 with an arbitrary mean m and variance sX .

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