Language Evolution and Computation Bibliography

What happens when a new social convention replaces an old one? While the possible forces favoring norm change-such as institutions or committed activists-have been identified for a long time, little is known about how a population adopts a new convention, due to the difficulties of finding representative data. Here, we address this issue by looking at changes that occurred to 2,541 orthographic and lexical norms in English and Spanish through the analysis of a large corpora of books published between the years 1800 and 2008. We detect three markedly distinct patterns in the data, depending on whether the behavioral change results from the action of a formal institution, an informal authority, or a spontaneous process of unregulated evolution. We propose a simple evolutionary model able to capture all of the observed behaviors, and we show that it reproduces quantitatively the empirical data. This work identifies general mechanisms of norm change, and we anticipate that it will be of interest to researchers investigating the cultural evolution of language and, more broadly, human collective behavior.

The topics dealt with in this thesis are all part of the general problem of social consensus, namely how a convention flourish and decay and what motivates people to conform to it. Examples range from driving on the right side of the street, to language, rules of courtesy or moral judgments. Some conventions arise directly from the need to coordinate or conform, such as fashion or speaking the same language, others, instead, apply to situations where there is a tension between individual and collective interest, such as cooperation, reciprocity, etc. This thesis is developed around three main questions still open in the research field of collective human behavior: how coexistence of concurrent conventions is possible, why cooperation in real systems is more common than predicted and how a population undergoes collective behavioral change, namely how an initially minority norm can supplant a majority ones. In the first work, we study the impact of concurrent social pressures in consensus processes. We propose a model of opinion competition where individuals participate in different social networks and receive conflicting social influences. The dynamics take place in two distinct domains, which we model as layers of a multiplex network. The novelty of our study lies to the fact that individuals can have different options in the different layers. This naturally reflects a common situation where an individual can possess some different opinions in different social contexts as a result of consensus with other individuals in the one context but not in the other. Our analysis shows that the latter property enriches the system’s dynamics and allows not only for consensus into a single state for both layers, but also for active dynamical states of coexistence of both options. In the second model, we analyze the influence of opinion dynamic in competitive strategical games. Cooperation between humans is quite common and stable behavior even in situations where both game theory and experiments predict defection prevalence. One of the reasons could be just the fact that individuals engaging in strategic interactions are also exposed to social influence and, consequently, to the spread of opinions. We present a new evolutionary game model where game and opinions dynamics take place in different layers of a multiplex network. We show that the coupling between the two dynamical processes can lead to cooperation in scenarios where the pure game dynamics predicts defection and, in some particular setting, gives rise to a metastable state in which nodes that adopt the same strategy self-organize into local groups. In the last work, we present the first extensive quantitative analysis of the phenomenon of norm change by looking at 2,365 orthographic and lexical norms shifts occurred in English and Spanish over the last two centuries as recorded by millions of digitized books. We are able to identify three distinct patterns in the data depending on the nature of the norm shift. Furthermore, we propose a simple evolutionary model that captures all the identified mechanisms and reproduces quantitatively the transitions between norms. This work advances the current understanding of norm shifts in language change, most often limited to qualitative illustrations (e.g., the observation that adoption curve of the new norm follows an ‘S-shaped’ behavior.