Social symbol grounding and language evolution

Artificial Life, 2004

In this paper, a multi-agent computational model is proposed to simulate the coevolution of social structure and compositional protolanguage from a holistic signaling system through iterative interactions within a heterogeneous population. We implement an indirect meaning transference based on both linguistic and nonlinguistic information in communications, together with a feedback without direct meaning check. The emergent social structure, triggered by two locally selective strategies, friendship and popularity, has small-world characteristics. The influence of these selective strategies on the emergent language and the emergent social structure are discussed.

2002

ABSTRACT This paper discusses the evolution of language as an emerging phenomenon with both genetic and social components that are shaped under evolutionary pressure. Communication between relatives is seen as an act of kinship-driven altruism and the chances of survival of such behavour discussed from a Neo-Darwinist point of view.

2004

Abstract This paper presents a multi-agent system which has been developed in order to test our theories of language evolution. We propose that language evolution is an emergent behaviour, which is influenced by both genetic and social factors and show that a multi-agent approach is thus most suited to practical study of the salient issues.

icmas, 1998

Multi-agent models of language evolution usually involve agents giving names to internal independently constructed categories. We present an approach in which the creation of categories is part of the language formation process itself. When an agent does not have a word for a particular object it is allowed to use the existing name of another object, close to the original one as defined by an analogy function. In this way, the names in the shared lexicon that has evolved in a collective way, directly yield the different object ...

2004

Addressing the limitations of current computational models, in this paper, we present a multi-agent computational model to simulate the coevolution of lexicon and syntax (simple word order) during the transition from a holistic signaling system to a compositional language through iterative interactions within a heterogeneous population. An indirect meaning transference based on both linguistic and nonlinguistic information in communications, together with a feedback without direct meaning check, is implemented in communications. Based on this model, the influences of simple exogenous social structures, such as structure with popular agent and inter-group communication, on language emergence are studied. Besides, under the assumption of geographic limited communication, a phenomenon of "global polarization, local convergence" is simulated during language emergence. In the end, necessary linguistic and social structure related future directions are pointed out.

Human language, over its evolutionary history, has emerged as one of the fundamental defining characteristic of the modern man. However, this milestone evolutionary process through natural selection has not left any 'linguistic fossils' that may enable us to trace back the actual course of development of language and its establishment in human societies. Lacking analytical tools to fathom the critical essentials of evolutionary mechanism of cultural transmission, we seek the recourse of simulation study as another useful method of enquiry into the evolutionary trajectory of language. In this paper we use a toy model to understand an interesting feature of language evolution, namely, the scenario in which words gets fixed in a population of language users. We obtain simulation for the replicator dynamics that characterise the time rate of change of various words in the given language, using genetic algorithm to simulate the dynamics. We infer that two of the prime determinant...

ACL 2010, 2010

Computational Methods in Science and Technology, 2011

Computational modelling with multi-agent systems has become an important technique in studying language evolution. We present a brief introduction into this rapidly developing field, as well as our own contributions, which include an analysis of the evolutionary naming game model. In this model, communicating agents, which try to establish a common vocabulary, are equipped with an evolutionarily selected learning ability. Such a coupling of biological and linguistic ingredients results in an abrupt transition: upon a small change of the model control parameter, a poorly communicating group of agents with small learning abilities transforms into almost perfectly communicating group of agents with large learning abilities. Genetic imprinting of the learning abilities progresses through the Baldwin effect: initially linguistically unskilled agents learn a language, which creates a niche where there is an evolutionary pressure for the increase of learning ability. Under the assumption that communication intensity increases continuously with finite speed, the transition is split into several transition-like changes. It shows that the speed of cultural changes, that sets an additional characteristic time scale, might be yet another factor affecting the evolution of language. In our opinion, this model shows that linguistic and biological processes have a strong influence on each other and this influence certainly has contributed to an explosive development of our species.

Wiley Interdisciplinary Reviews Cognitive Science, 2014

In the absence of direct evidence of the emergence of language, the explicitness of formal models which allow the exploration of interactions between multiple complex adaptive systems has proven to be an important tool. Computational simulations have been at the heart of the field of evolutionary linguistics for the past two decades, particularly through the language game and iterated learning paradigms, but these are now being extended and complemented in a number of directions, through formal mathematical models, language-ready robotic agents, and experimental simulations in the laboratory.For further resources related to this article, please visit the WIREs website.Conflict of interest: The author has declared no conflicts of interest for this article.

Language and Linguistics Compass, 2008

Large linguistic databases, especially databases having a global coverage such as The World Atlas of Language Structures (Haspelmath et al. 2005), The Automated Simility Judgment Program (Brown et al., n.d.) or Ethnologue (Gordon 2005) are making it possible to systematically investigate many aspects of how languages change and compete for viability. Agent-based computer simulations supplement such empirical data by analyzing the necessary and sufficient parameters for the current global distributions of languages or linguistic features. By combining empirical datasets with simulations and applying quantitative methods it is now possible to answer fundamental questions such as 'what are the relative rates of change in different parts of languages?', 'why are there a few large language families, many intermediate ones, and even more small ones?', 'do small languages change faster or slower than large ones?' or 'how does the borrowing of words relate to the borrowing of structural features?'