User Generated Content (UGC) is re-shaping the way people watch video and TV, with millions of video producers and consumers. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and developing new business opportunities. To better understand the impact of UGC systems, we have analyzed YouTube, the world’s largest UGC VoD system. Based on a large amount of data collected, we provide an in-depth study of YouTube and other similar UGC systems. In particular, we study the popularity life-cycle of videos, the intrinsic statistical properties of requests and their relationship with video age, and the level of content aliasing or of illegal content in the system. We also provide insights on the potential for more efficient UGC VoD systems (e.g. utilizing P2P techniques or making better use of caching). Finally, we discuss the opportunities to leverage the latent demand for niche videos that are not reached today due to information filtering effects or other system scarcity distortions. Overall, we believe that the results presented in this paper are crucial in understanding UGC systems and can provide valuable information to ISPs, site administrators, and content owners with major commercial and technical implications. Categories and Subject Descriptors Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

scholars who argue that Simon’s work changed during the Cold War. This is due to the fact that Simon seemingly changed the substance of his research in the 1950s. This paper argues that Simon did not change in any signiªcant way, but was lead by his interest in decision making and rationality into areas of economics, political science, sociology, psychology, organization theory, and computer science. He used elements of different disciplines to address his overall interest and there is therefore a considerable continuity in Simon’s work. This paper also provides part of a background for the recent increase of interest in Simon’s ideas by providing some details of the RAND Corporation and the Ford Foundation’s support of scientiªc research through the post war years in general, and their connections to the behavioral science research

This article argues that the agent-based computational model permits a distinctive approach to social science for which the term “generative ” is suitable. In defending this terminology, features distinguishing the approach from both “inductive ” and “deductive ” science are given. Then, the following specific contributions to social science are discussed: The agent-based computational model is a new tool for empirical research. It offers a natural environment for the study of connectionist phenomena in social science. Agent-based modeling provides a powerful way to address certain enduring—and especially interdisciplinary—questions. It allows one to subject certain core theories—such as neoclassical microeconomics—to important types of stress (e.g., the effect of evolving preferences). It permits one to study how rules of individual behavior give rise—or “map up”—to macroscopic regularities and organizations. In turn, one can employ laboratory behavioral research findings to select among competing agent-based (“bottom up”) models. The agent-based approach may well have the important effect of decoupling individual rationality from macroscopic equilibrium and of separating decision science from social science more generally. Agent-based modeling offers powerful new forms of hybrid theoretical-computational work; these are particularly relevant to the study of non-equilibrium systems. The agentbased approach invites the interpretation of society as a distributed computational device, and in turn the interpretation of social dynamics as a type of computation. This interpretation raises important foundational issues in social science—some related to intractability, and some to undecidability proper. Finally, since “emergence” figures prominently in this literature, I take up the connection between agent-based modeling and classical emergentism, criticizing the latter and arguing that the two are incompatible. � 1999 John Wiley &

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Background. Power distributions appear in numerous biological, physical and other contexts, which appear to be fundamentally different. In biology, power laws have been claimed to describe the distributions of the connections of enzymes and metabolites in metabolic networks, the number of interactions partners of a given protein, the number of members in paralogous families, and other quantities. In network analysis, power laws imply evolution of the network with preferential attachment, i.e. a greater likelihood of nodes being added to pre-existing hubs. Exploration of different types of evolutionary models in an attempt to determine which of them lead to power law distributions has the potential of revealing non-trivial aspects of genome evolution. Results. A simple model of evolution of the domain composition of proteomes was developed, with the following elementary processes: i) domain birth (duplication with divergence), ii) death (inactivation and/or deletion), and iii) innovation (emergence from non-coding or non-globular sequences or acquisition via horizontal gene transfer). This formalism can be described as a birth, death and innovation model (BDIM). The formulas for equilibrium frequencies of domain families of different size and the total number of families at equilibrium are derived for a general BDIM. All asymptotics of equilibrium frequencies of domain families possible for the given type of models are found and their appearance depending on model parameters is investigated. It is proved that the power law asymptotics appears if, and only if, the model is balanced, i.e. domain duplication and deletion rates are asymptotically equal up to the second order. It is further proved that any power asymptotic with the degree not equal to-1 can appear only if the...

We present a theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales. Instead, we assume that an attribute’s subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory and is its rank within the sample. We assume that the sample reflects both the immediate distribution of attribute values from the current decision’s context and also the background, real-world distribution of attribute values. DbS accounts for concave utility functions; losses looming larger than gains; hyperbolic temporal discounting; and the overestimation of small probabilities and the underestimation of large probabilities.

The remarkable successes of the physical sciences have been built on highly general quantitative laws, which serve as the basis for understanding an enormous variety of specific physical systems. How far is it possible to construct universal principles in the cognitive sciences, in terms of which specific aspects of perception, memory, or decision making might be modelled? Following Shepard (e.g., 1987), it is argued that some universal principles may be attainable in cognitive science. Here we propose two examples: The simplicity principle (which states that the cognitive system prefers patterns that provide simpler explanations of available data); and the scale-invariance principle, which states that many cognitive phenomena are independent of the scale of relevant underlying physical variables, such as time, space, luminance, or sound pressure. We illustrate how principles may be combined to explain specific cognitive processes by using these principles to derive SIMPLE, a formal model of memory for serial order (Brown, Neath & Chater, in press), and briefly mention some extensions to models of identification and categorization. We also consider the scope and limitations of universal laws in cognitive science.

We propose an equilibrium model for firm size distribution in an industry with a constrained essential input. The model applies when the population of firms is small and homogeneous and the supply of the necessary input factor is perfectly inelastic. We argue that although the Gibrat assumption obtains, this does not result in the lognormal distribution because of the entries, exits and mergers of firms competing for the inelastic essential resource. Using our own thirty-two-year database of firms, we test the broken-stick, or random-ordered-interval, model that we call the Whitworth distribution, successfully applied by others to a number of data sets, including the abundances of bird species. We propose the Whitworth as the basic model of the equilibrium distribution of firm sizes for such supply-constrained industries, and find it fits our thirty-one-year database best. JEL Classification: L11.

Abstract—User generated content (UGC), now with millions of video producers and consumers, is re-shaping the way people watch video and TV. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and generating new business opportunities. Compared to traditional video-on-demand (VoD) systems, UGC services allow users to request videos from a potentially unlimited selection in an asynchronous fashion. To better understand the impact of UGC services, we have analyzed the world’s largest UGC VoD system, YouTube, and a popular similar system in Korea, Daum Videos. In this paper, we first empirically show how UGC services are fundamentally different from traditional VoD services. We then analyze the intrinsic statistical properties of UGC popularity distributions and discuss opportunities to leverage the latent demand for niche videos (or the so-called “the Long Tail ” potential), which is not reached today due to information filtering or other system scarcity distortions. Based on traces collected across multiple days, we study the popularity lifetime of UGC videos and the relationship between requests and video age. Finally, we measure the level of content aliasing and illegal content in the system and show the problems aliasing creates in ranking the video popularity accurately. The results presented in this paper are crucial to understanding UGC VoD systems and may have major commercial and technical implications for site administrators and content owners. Index Terms—Interactive TV, human factors, exponential distributions, log normal distributions, pareto distributions, probability, copyright protection. I.

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