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Consensus under Constraints: Modeling the Great English Vowel Shift
Abstract
Human culture is fundamentally tied with language. We argue that the study of language change and diffusion in a society sheds light on its cultural patterns and social conventions. In addition, language can be viewed as a ”model problem” through which to study complex norm emergence scenarios.
In this paper we study a particular linguistically oriented complex norm emergence scenario, the Great English Vowel Shift (GEVS). We develop a model that integrates both social aspects (interaction between agents), and internal aspects (constraints on how much an agent can change). This model differs from much of the existing norm emergence models in its modeling of large, complex normative spaces.
In this study, we present the first agent-based simulation of vowel chain shifts across large communities, providing a parsimonious reinterpretation of Labov's (2007) notions of transmission, diffusion, and incrementation. Labov determined that parent-to-child transmission faithfully reproduces structural patterns such as the Northern Cities Shift (NCS), but adult-to-adult diffusion does not. NCS is transmitted faithfully to new generations of U.S. Inland North children. But St. Louis speakers, depending only on adult-adult contact, only attain an incomplete, unsystematic version. Labov (2007) attributed the difference to children's superior language-learning ability; transmission and diffusion are categorically different processes in that approach. By contrast, our multiagent simulation suggests that such transmission/diffusion effects can be derived by simple density of interactions and simple exemplar learning; we also find that incrementation is a natural outcome of this model. Unlike Labov (2007), this model does not require a dichotomy between transmission and diffusion. While dichotomous assumptions about child versus adult learning may be necessary in other contexts, our results suggest that the NCS effects in Labov (2007) may be explained economically in terms of simple density of interactions between speakers. Our results also provide an agent-based perspective supporting and explicating the notion of speech community.
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
The paper reports experiments to test the hy-pothesis that language is an autonomous evolving adaptive system maintained by a group of dis-tributed agents without central control. The ex-periments show how a coherent lexicon may spon-taneously emerge in a group of agents engaged in language games and how a lexicon may adapt to cope with new meanings that arise or new agents that enter the group. The lexicon has several characteristics of natural language lexicons, such as polysemy, synonymy and ambiguity.
We analyze the ordering dynamics of the voter model in different classes of complex networks. We observe that whether the voter dynamics orders the system depends on the effective dimensionality of the interaction networks. We also find that when there is no ordering in the system, the average survival time of metastable states in finite networks decreases with network disorder and degree heterogeneity. The existence of hubs, i.e., highly connected nodes, in the network modifies the linear system size scaling law of the survival time. The size of an ordered domain is sensitive to the network disorder and the average degree, decreasing with both; however, it seems not to depend on network size and on the heterogeneity of the degree distribution.
From Animals to Animats 4 brings together the latest research at the frontier of an exciting new approach to understanding intelligence.
The Animals to Animats Conference brings together researchers from ethology, psychology, ecology, artificial intelligence, artificial life, robotics, engineering, and related fields to further understanding of the behaviors and underlying mechanisms that allow natural and synthetic agents (animats) to adapt and survive in uncertain environments. The work presented focuses on well-defined models—robotic, computer-simulation, and mathematical—that help to characterize and compare various organizational principles or architectures underlying adaptive behavior in both natural animals and animats.
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This paper presents a computer simulation of the emergence of vowel systems in a population of agents. The agents (small computer programs that operate autonomously) are equipped with a realistic articulatory synthesizer, a model of human perception and the ability to imitate and learn sounds they hear. It is shown that due to the interactions between the agents and due to self-organization, realistic vowel repertoires emerge. This happens under a large number of different parameter settings and therefore seems to be a very robust phenomenon. The emerged vowel systems show remarkable similarities with the vowel systems found in human languages. It is argued that self-organization probably plays an important role in determining the vowel inventories of human languages and that innate predispositions are probably not necessary to explain the universal tendencies of human vowel systems.
The emergence of social conventions in multi-agent systems has been analyzed mainly in settings where every agent may interact either with every other agent or with nearest neighbours, according to some regular underlying topology. In this note we argue that these topologies are too simple if we take into account recent discoveries on real networks. These networks, one of the main examples being the Internet, are what is called complex, that is, either graphs with the small-world property or scale-free graphs. In this note we study the efficiency of the emergence of social conventions in complex networks, that is, how fast conventions are reached. Our main result is that complex graphs make the system much more efficient than regular graphs with the same average number of links per node. Furthermore, we find out that scale-free graphs make the system as efficient as fully connected graphs.
We define the notion of social conventions in a standard game-theoretic framework, and identify various criteria of consistency of such conventions with the principle of individual rationality. We then investigate the emergence of such conventions in a stochastic setting; we do so within a stylized framework currently popular in economic circles, namely that of stochastic games. This framework comes in several forms; in our setting agents interact with each other through a random process, and accumulate information about the system. As they do so, they continually reevaluate their current choice of strategy in light of the accumulated information. We introduce a simple and natural strategy-selection rule, called highest cumulative reward (HCR). We show a class of games in which HCR guarantees eventual convergence to a rationally acceptable social convention. Most importantly, we investigate the efficiency with which such social conventions are achieved. We give an analytic lower bound on this rate, and then present results about how HCR works out in practice. Specifically, we pick one of the most basic games, namely a basic coordination game (as defined by Lewis), and through extensive computer simulations determine not only the effect of applying HCR, but also the subtle effects of various system parameters, such as the amount of memory and the frequency of update performed by all agents.
We use several models of scale-free graphs as underlying interaction graphs for a simple model of Multi-Agent Systems (MAS), and study how fast the system reaches a fixed-point, that is, the time it takes for the system to get a 90% of the agents in the same state. The interest of these kind of graphs is in the fact that the Internet, a very plausible environment for MAS, is a scale-free graph with high clustering and 〈 knn 〉, the nearest neighbor average connectivity of nodes with connectivity k, following a power-law. Our results show that different types of scale-free graphs make the system as efficient as fully connected graphs, in a clear agreement with our previous research (Artif. Intell. 141, pp. 175-181).
Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
Networks grow and evolve by local events, such as the addition of new nodes and links, or rewiring of links from one node to another. We show that depending on the frequency of these processes two topologically different networks can emerge, the connectivity distribution following either a generalized power law or an exponential. We propose a continuum theory that predicts these two regimes as well as the scaling function and the exponents, in good agreement with numerical results. Finally, we use the obtained predictions to fit the connectivity distribution of the network describing the professional links between movie actors.
Social norms The Handbook of Social Economics
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