Models and Methods in Cultural and Social Evolution

<p>Chapter 1 The mathematical study of genealogies has yielded important insights in population biology, such as the ability to estimate the time to the most recent common ancestor (MRCA) of a sample of genetic sequences or of a group of individuals. Here we introduce a model of cultural genealogies that is a step toward answering similar questions for cultural traits. In our model individuals can inherit from a variable, potentially large number of ancestors, rather than from a fixed, small number of ancestors (one or two) as is typical of genetic evolution. We first show that, given a sample of individuals, a cultural common ancestor does not necessarily exist. We then introduce a related concept: the most recent unique ancestor (MRUA), i.e., the most recent single individual who is the earliest cultural ancestor of the sample. We show that, under neutral evolution, the time to the MRUA can be staggeringly larger than the time to MRCA in a single ancestor model, except when the average number of learning opportunities per individuals is small. Our results point out that the properties of cultural genealogies may be very different from those of genetic genealogies, with potential implications for reconstructing the histories of cultural traits.</p> <p>Chapter 2 A specific goal of the field of cultural evolution is to understand how processes of transmission at the individual level lead to population wide patterns of cultural diversity and change. Previous models of cultural copying and innovation have assumed that traits are independent of one another and essentially exchangeable. But culture has an architecture: traits bear relationships to one another that affect the transmission process itself. Here we introduce an agent based simulation model to explore the effect of cultural architecture on the process of copying and innovation. We construct a space of all possible traits and assign them pairwise, symmetric relationships of compatibility or incompatibility. We then implement different ways for agents to parse these relationships, called filters. We find that introducing this simple architecture leads to novel results. When individuals copy based on a trait’s features (here, its compatibility relationships) they produce smaller, more homogenous cultures than when they copy based on the cultural model. We also find that the average compatibility of a culture produced by some filters is determined by the variance in compatibility in the space of all possible traits, a cultural analog to Fisher’s Fundamental Theorem of Natural Selection. We discuss the implications of considering cultural architecture in the dynamics of cultural change.</p> <p>Chapter 3 Language shift is when a group of speakers adopts a new language, and occurs as part of the larger phenomenon of language contact. It has long been observed that language change is accelerated during shift situations. The standard explanation for this accelerated change has been the introduction of novel linguistic forms by new speakers during the second language acquisition (SLA) process. This hypothesis is based on historical reconstructions of contact situations and has never been formally tested on empirical data. In this paper, we construct an agent-based model to formalize the hypothesis that L2 speakers are responsible for accelerated language change during language shift. In our simulations, a population experiences demographic change via the birth of native (L1) speakers, recruitment of L2 speakers, and death. However, only L2 speakers have the potential to ‘mutate’ (introduce a new variant) on entering the population. We then parameterize the model using demographic data from Maputo, Mozambique—where rapid shift from Bantu-languages to Portuguese has been occurring for the past forty years—and compare our model predictions to a rare diachronic data set on two linguistic features of Portuguese in Maputo. We find that our basic model is a poor fit to either data set. Next, we modify the model by allowing L2 speakers to introduce a novel variant at any point during their first five years in the population, a feature that represents the fact that the SLA process is not instantaneous (we find support for the five year duration in the literature). We find that the extended SLA model is a good fit to one of our datasets—we discuss plausible reasons for why the other data set is such a mismatch. Finally, we discuss typological differences between contact-induced and non-contact-induced language change and suggest that multiple introductions of a new linguistic variant by different individuals may be the mechanism by which SLA accelerates language change.</p> <p>Chapter 4 From breeding to flocking, synchrony is an important feature of many social behaviors. Different measures of synchrony have been proposed for different behaviors. Here we test how well some of these measures behave when applied to what we term timing and duration variable scalar behaviors (TDVS). These behaviors are those that may vary in timing (onset) and duration, and which can be characterized by a scalar variable at any time. Using agent-based simulations, we compare the effectiveness of four synchrony measures and show that two, the Dispersion Index of Mean Behavior, and the Kappa coefficient, perform best. We also show that population size affects the statistical interpretation of these measures. Finally, we also include additional results to show the relationship between a number of these measures.</p>

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Biology

Grantor

The Graduate School and University Center of The City University of New York

Year dc:date.available

2016

Author dc:creator

• Aguilar, Elliot G

Advisor dc:contributor.advisor

• David Lahti

Committee members dc:contributor.committeemember

• Stefano Ghirlanda
• Mike Hickerson
• Ofer Tchernickovski
• Nina Fefferman

• Behavior and Ethology
• Evolution
• cultural evolution
• language evolution
• agent-based simulation
• synchrony

Repository record dc:identifier

https://academicworks.cuny.edu/gc_etds/1549

OAI identifier oai:identifier

oai:academicworks.cuny.edu:gc_etds-2559