The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site del.icio.us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for “popular ” sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.

Human language is unique among the communication systems of the natural world. The vocabulary of human language is unique in being both culturally-transmitted and symbolic. In this paper I present an investigation into the factors involved in the evolution of such vocabulary systems. I investigate both the cultural evolution of vocabulary systems and the biological evolution of learning rules for vocabulary acquisition.

Speech is considered an efficient communication channel. This implies that the organization of utterances is such that more speaking effort is directed towards important parts than towards redundant parts. Based on a model of incremental word recognition, the importance of a segment is defined as its contribution to worddisambiguation. This importance is measured as the segmental information content, in bits. On a labeled Dutch speech corpus it is then shown that crucial aspects of the information structure of utterances partition the segmental information content and explain 90 % of the variance. Two measures of acoustical reduction, duration and spectral center of gravity, are correlated with the segmental information content in such a way that more important phonemes are less reduced. It is concluded that the organization of reduction according to conventional information structure does indeed increase efficiency.

BLUE PAPER: A RESEARCH ROADMAP FOR DEVELOPING ARTIFICIAL EMBODIED AND COMMUNICATING AGENTS............................................................. 1 1. INTRODUCTION.................................................................................................................... 3 2. Communication and language as complex adaptive systems......................................... 4 2.1 Individual Behaviour.................................................................................................... 4 2.2 Collective Behaviour.................................................................................................... 5 2.3 Language and communication..................................................................................... 5

nformation theory has been discussed as a technique to analyse communicative processes or sequential behaviour of nonhuman animals, as in MacKay (1972), Slater (1973) and Bradbury & Vehrencamp (1998, chapters 13–15) among others. Recently, McCowan et al. (1999) proposed the use of information theory for their study of bottlenose dolphin, Tursiops truncatus, whistles. They discussed several aspects of their analysis techniques. Although we agree about the effectiveness of information theory to analyse unknown sources, we would like to further the discussion of one analysis method used in McCowan et al. (1999). Specifically, we wish to illustrate that Zipf’s law is of little use in the analysis of communication signals. The presence or absence in dolphins and other animals of some features of human language remain intriguing and open questions (Tyack 1999). However, we assert that a Zipf-based technique is methodologically inappropriate to address these questions. McCowan et al. (1999, page 410) noted that ‘Few investigators of animal behaviour have examined the use of first-order entropic analysis known as Zipf’s law or statistic’. In fact, Zipf’s law has been discarded as a linguistic tool, strongly criticized by Miller (1957), Miller & Chomsky (1963) and more thoroughly by Rapoport (1982). McCowan et al. (1999, page 411) also cite the application of Zipf’s law to DNA sequences by Mantegna et al. (1994) ‘with varying interpretations and reliability

The potential advantage of extreme value theory in modeling management phenomena is the central theme of this paper. The statistics of extremes have played only a very limited role in management studies despite the disproportionate emphasis on unusual events in the world of managers. An overview of this theory and related statistical models is presented, and illustrative empirical examples provided. As I am sure almost every geophysicist knows, distributions of actual errors and fluctuations have much more straggling extreme values than would correspond to the magic bell-shaped distribution of Gauss and Laplace.

The Iterated Classification Game (ICG) combines the Classification Game with the Iterated Learning Model (ILM) to create a more realistic model of the cultural transmission of language through generations. It includes both learning from parents and learning from peers. Further, it eliminates some of the chief criticisms of the ILM: that it does not study grounded languages, that it does not include peer learning, and that it builds in a bias for compositional languages. We show that, over the span of a few generations, a stable linguistic system emerges that can be acquired very quickly by each generation, is compositional, and helps the agents to solve the classification problem with which they are faced. The ICG also leads to a different interpretation of the language acquisition process. It suggests that the role of parents is to initialize the linguistic system of the child in such a way that subsequent interaction with peers results in rapid convergence to the correct language. 1

Abstract. The principle of least effort in communications has been shown, by Ferrer i Cancho and Solé, to explain emergence of power laws (e.g., Zipf’s law) in human languages. This paper applies the principle and the informationtheoretic model of Ferrer i Cancho and Solé to genetic coding. The application of the principle is achieved via equating the ambiguity of signals used by “speakers ” with codon usage, on the one hand, and the effort of “hearers” with needs of amino acid translation mechanics, on the other hand. The re-interpreted model captures the case of the typical (vertical) gene transfer, and confirms that Zipf’s law can be found in the transition between referentially useless systems (i.e., ambiguous genetic coding) and indexical reference systems (i.e., zero-redundancy genetic coding). As with linguistic symbols, arranging genetic codes according to Zipf’s law is observed to be the optimal solution for maximising the referential power under the effort constraints. Thus, the model identifies the origins of scaling in genetic coding — via a trade-off between codon usage and needs of amino acid translation. Furthermore, the paper extends Ferrer i Cancho – Solé model to multiple inputs, reaching out toward the case of horizontal gene transfer (HGT) where multiple contributors may share the same genetic coding. Importantly, the extended model also leads to a sharp transition between referentially useless systems (ambiguous HGT) and indexical reference systems (zero-redundancy HGT). Zipf’s law is also observed to be the optimal solution in the HGT case.

We analyze representations of the world attained through an infomax principle by agents acting in a simple environment. The representations obtained by different agents in general differ to some extent from each other in different instances. This gives rise to ambiguities in how the environment is represented by the different agents. We now develop an information-theoretic formalism able to extract a "common conceptualization " of the world for a group of agents. It turns out that the common conceptualization intuitively seems to capture much higher regularities or symmetries of the environment than the individual representations. We formalize the notion of identifying symmetries in the environment- with respect to "extrinsic " operations on the environment as well as with respect to "intrinsic " operations, i.e. the reconfiguration of the agent’s embodiment. In particular, using the latter formalism, we can re-wire an agent to conform to the highly symmetric common conceptualization to a much higher degree than an unrefined agent; and that without having to re-optimize the agent from scratch. In other words, we can "re-educate " an agent to conform to the de-individualized "concept " of the agent group with comparatively little effort.

An approach to information processing based on the excitation of patterns of activity by non-linear active resonators in response to their input patterns is considered. Arguments are presented to show that any computation performed by a conventional Turing machine-based computer, called T-machine in this paper, could also be performed by the pattern excitation-based machine, which will be called P-machine. A realization of this processing scheme by neural networks is considered. In this realization, the role of the resonators is played by neural pattern excitation networks, which are the neural circuits capable of exciting different spatio-temporal patterns of activity in response to different inputs. Learning in the neural pattern excitation networks is also considered. It is shown that there is a duality between pattern excitation and pattern recognition neural networks, which allows to create new pattern excitation modes corresponding to recognizable input patterns, based on Hebbian learning rules. Hierarchically organized, such networks can produce complex behavior. Animal behavior, human language and thought are treated as examples produced by such networks.