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where the inequality follows from the fact that conditioning reduces differential entropy, and the nal equality from the fact that X1 and X2 are independent. Substituting the expressions for the differential entropies of a normal random variable, we obtain 1 1 log(2 e)n |A| > log(2 e)n |B|, 2 2 which is equivalent to the desired lemma. Lemma 9.6.3 For two n-dimensional random vectors X and Z, |KX+Z | 2n |KX + KZ |. Proof: From Lemma 9.6.1, 2(KX + KZ ) KX+Z = KX Z 0, (9.131) (9.130) (9.129)

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Distributed Database Management Systems by Saeed K. Rahimi and Frank S. Haug Copyright 2010 the IEEE Computer Society

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of complex systems and are widely used in physics and various domains of biology, including neuroscience (Amit, 1989) and population dynamics (May, 1981) A set of elements is speci ed to represent the basic units (eg, neurons, people) in the process under consideration Each element can adopt a nite number of discrete states (eg, activated vs inhibited, pro- vs antiabortion) The elements are arranged in a spatial con guration, the most common of which is a twodimensional grid The state of an element at t + 1 depends on the states of the neighboring elements at time t The exact form of this dependence is speci ed by so-called updating rules The dynamics of cellular automata depend on the nature of the updating rule and on the format of the grid dictating the neighborhood structure.

Differential entropy bound on discrete entropy. Let X be a discrete random variable on the set X = {a1 , a2 , . . .} with Pr(X = ai ) = pi . Show that 2 1 1 H (p1 , p2 , . . .) log(2 e) pi i 2 ipi + . 2 12

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Two classes of cellular automata models are used to characterize social processes In both, elements represent individuals in a social system In one, personal characteristics change as a result of updating rules This approach explores changes in attitudes and opinions that occur as a result of social interaction In the other class, individuals maintain stable characteristics but may change their physical location This approach has revealed the emergence of spatial patterns on the basis of stable values and preferences Shelling (1969, 1971), for instance, developed an updating rule specifying that an individual who has more dissimilar than similar neighbors will move to a different random location Simulations based on this simple rule demonstrated the emergence of spatial patterns corresponding to social segregation Both classes of models reveal the emergence of regularities and patterns on a global level that were not directly programmed into the individual elements.

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These regularities and patterns typically take the form of spatial con gurations, such as coherent minority opinion clusters that emerge from an initial random distribution of opinions Regularities may also appear as temporal patterns, including such basic trajectories as the development of a stable equilibrium ( xed-point attractor), alternation between different states (periodic attractor), and apparent randomness (deterministic chaos) Cellular Automata and Social Processes Cellular automata models are useful for exploring different social interaction rules and the generation of societal level phenomena as a result of such rules (cf Hegselman, 1998; Messick & Liebrand, 1995; Nowak, Szamrej, & Latan , 1990) In these applications, the neighborhood structure is intended to capture the structure of interdependence among individuals (Thibaut & Kelley, 1959) Indirect interdependence exists when an individual s actions have consequences, intended or unintended, for other people This form of interdependence is often examined in the context of social.

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