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John Maynard Smith and Eörs Szathmáry argued that human language signified the eighth major transition in evolution: human language marked a new form of information transmission from one generation to another [Maynard Smith J, Szathmáry E. The major transitions in evolution. Oxford: Oxford Univ. Press; 1995]. According to this view language codes cultural information and as such forms the basis for the evolution of complexity in human culture. In this article we develop the theory that language also codes information in another sense: languages code information on their own structure. As a result, languages themselves provide information that influences their own survival. To understand the consequences of this theory we discuss recent computational models of linguistic evolution. Linguistic evolution is the process by which languages themselves evolve. This article draws together this recent work on linguistic evolution and highlights the significance of this process in understanding the evolution of linguistic complexity. Our conclusions are that: (1) the process of linguistic transmission constitutes the basis for an evolutionary system, and (2), that this evolutionary system is only superficially comparable to the process of

Distributed coordination is the result of dynamical processes enabling independent agents to coordinate their actions without the need of a central coordinator. In the past years, several computational models have illustrated the role played by such dynamics for self-organizing communication systems. In particular, it has been shown that agents could bootstrap shared convention systems based on simple local adaptation rules. Such models have played a pivotal role for our understanding of emergent language processes. However, only few formal or theoretical results were published about such systems. This article discusses deliberately simple computational models in order to make progress in understanding the underlying dynamics responsible for distributed coordination and the scaling laws of such systems. In particular, the article focuses on explaining the convergence speed of those models, a largely underinvestigated issue. Conjectures obtained through empirical and qualitative studies of these simple models are compared with results of more complex simulations and discussed in relation with theoretical models formalized using Markov chains, game theory and Polya processes. 1

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Over the past years, several computational models have been introduced to study the coordination of communication between distributed agents. Although these models have given valuable insights into the mechanisms required for letting agents develop a successful communication system, few theoretical results have been obtained which substantiate these findings. In this paper we introduce a theoretical framework which allows us to analyze and compare different existing models in a uniform way. Therefore we only look at the observable behavior of an agent and not at the internal mechanisms that cause that behavior. In particular, we define an agent’s response function and argue that a stability analysis of its fixed points reveals crucial information about the convergence properties of the dynamical system of interacting agents. 1

We study how decentralized agents can develop a shared vocabulary without global coordination. Answering this question can help us understand the emergence of many communication systems, from bacterial communication to human languages, as well as helping to design algorithms for supporting self-organizing information systems such as social tagging or ad-word systems for the web. We introduce a formal communication model in which senders and receivers can adapt their communicative behaviors through a simple reinforcement learning mechanism that adjusts each agent’s vocabulary: the ways it associates words with meanings. We analyze the model’s dynamics in terms of collective convergence conditions and convergence speed. Our main result on the convergence conditions is that for a given number of meanings, there exists a threshold of the number of words below which the agents can’t converge to a shared vocabulary. We also give the time needed for the agents to converge to a fully communicable, shared vocabulary system; specifically, we show that we can lower convergence time by allowing the agents to use more words than necessary in order to jump out of the threshold point. Finally, the effect of reinforcement learning rates of the agents on the convergence are analyzed, showing there exists a range of learning rates for the agents to achieve the best convergence time. 1.

Theories of the evolutionary origins of language must be informed by empirical and theoretical results from a variety of different fields. Complementing recent surveys of relevant work from linguistics, animal behaviour and genetics, this paper surveys the requirements on evolutionary scenarios that derive from mathematical evolutionary biology. It presents a number of simple but fundamental models from population genetics, evolutionary gametheory and social evolution theory, and evaluates their applicability to natural language. This review yields a list of required elements of evolutionary explanations in general, and of explanations for language and communication in particular. 1

journal homepage: www.elsevier.com/locate/COGNIT

This dissertation studies multiagent agreement problems – problems in which a population of agents must agree on some quantity or behavior in a distributed manner. Agreement problems are central in many areas, from the study of magnetism (Ising model), to understanding the diffusion of innovations (such as the diffusion of hybrid corn planting in Illinois), to modeling linguistic change. The thesis of this dissertation is that the ability for agents to optimally allocate resources towards 1) gaining information from which to infer the agreeing population’s global agreement state (“information gathering”) and 2) effectively using that information to make convergence decisions that move towards agreement (“information use”), are the fundamental factors that explain the performance of a distributed agreement-seeking collective, and that variations on these processes capture all prevalent styles of agreement problems. In this dissertation we develop a taxonomic framework that organizes a wide range of agreement problems according to constraints on information gathering and information use. We explore two specific instances of agreement problems in more depth; the first modulates information gathering by constraining the ability of agents to communicate; the second modulates information use by constraining the ability of agents to