Interaction Studies - Volume 7, Issue 1, 2006

A trait is of adaptive value if it confers a fitness advantage to its possessor. Thus adaptiveness is an ahistorical identification of a trait affording some selective advantage to an agent within some particular environment. In results reported here we identify a trait within natural language discourse as having adaptive value by computing a trait/fitness covariance; the possession of the trait correlates with the replication success of the trait’s possessor. We show that the trait covaries with fitness across multiple unrelated discursive groups. In our analysis the trait in question is a particular statistically derived word-in-context, that is, a word set. Variation of the word-usage is measured as the relative presence of the word set within a particular text, that is, the percentage of the text devoted to this set of words. Fitness is measured as the rate in which the text is responded to, or replicates, within an online environment. Thus we are studying the micro-evolutionary dynamics of natural language discourse.

This paper explores a structure ubiquitous in grammar, the embedded tree, and develops a proposal for how such embedded structures played a fundamental role in the evolution of consonants and vowels. Assuming that linguistic capabilities emerged as a cognitive system from a simply reactive system and that such a transition required the construction of an internal mapping of the system body (cf. Cruse 2003), we propose that this mapping was determined through articulation and acoustics. By creating distinctions between articulators in the vocal tract or by acoustic features of sounds, and then embedding these distinctions, the various possible properties of consonants and vowels emerged. These embedded distinctions represent paradigmatic options for the production of sounds, which provide the basic building blocks for prosodic structure. By anchoring these embedded structures in the anatomy and physiology of the vocal tract, the evolution of phonology itself can be explained by extra-linguistic factors.

Most current modeling for evolution of communication still underplays or ignores the role of local action in spatialized environments: the fact that it is immediate neighbors with which one tends to communicate, and from whom one learns strategies or conventions of communication. Only now are the lessons of spatialization being learned in a related field: game-theoretic models for cooperation. In work on altruism, on the other hand, the role of spatial organization has long been recognized under the term ‘viscosity’. Here we offer some simple simulations that dramatize the importance of spatialization for studies of both cooperation and communication, in each case contrasting (a) a model dynamics in which strategy change proceeds globally, and (b) a spatialized model dynamics in which interaction and strategy change both operate purely locally. Local action in a spatialized model clearly favors the emergence of cooperation. In the case of communication, spatialized models allow communication to arise and flourish where the global dynamics more typical in the literature make it impossible. Simulations make a dramatic case for spatialized modeling, but analysis proves difficult. In a final section we outline some of the surprises of spatial dynamics but also some of the complexity facing attempts at deeper analysis.

To understand evolutionary aspects of communication is to understand the evolutionary development of the meaning relations between language and the world. Such meaning relations are established by the application of the interactive systems of semantic games. Subsumed under the evolutionary framework of repeated games, semantics in such games refers to the cases in which stable meanings survive populations of strategically interacting players. The viability of compositionality, common ground and salience in such evolutionary games is assessed. Foundationally, the discussion is rooted in Charles S. Peirce’s pragmatist philosophy.

Previous simulation work on the evolution of communication has not shown how a large signal repertoire could emerge in situated agents. We present an artificial life simulation of agents, situated in a two-dimensional world, that must search for other agents with whom they can trade resources. With strong restrictions on which resources can be traded for others, initially non-communicating agents evolve/learn a signal system that describes the resource they seek and the resource they are willing to offer in return. A large signal repertoire emerges mainly through an evolutionary process. Agents whose production and comprehension abilities rely on a single mechanism fare best, although learning enables agents with separate mechanisms to achieve some measure of success. These results demonstrate that substantial signaling repertoires can evolve in situated multi-agent systems, and suggest that simulated social interactions such as trading may provide a useful context for further computational studies of the evolution of communication.