These online modules present many basic and applied issues in cultural evolution and introduce students to methods of dynamical systems theory as applied to the evolution of human systems. Without the aid of mathematical models, human intuitions about dynamic systems of any complexity can be quite faulty. The materials have been developed with self-guided study in mind. Through a variety of online learning methods, students will be able to independently work through the material to gain both a theoretical understanding of the method and practical experience doing it.
The series includes:
– Models of Social Dynamics (Paul Smaldino)
– Animal Cultures (Andrew Whiten, Lucy Aplin, Nicolas Claidiere, Rachel Kendal)
– The Neverending Story (Joseph Stubbersfield, Jamie Tehrani, Oleg Sobchuk)
– Foundations of Cultural Evolution (Adrian Bell)
– Modeling the Dynamics of Cultural Diversification (Bernard Koch, Erik Gjesfjeld)
– Dynamic Models of Human Systems (Russell Genet, Peter Richerson, Cheryl Genet & Charles Efferson)
This tutorial shows how to create very simple simulation or agent-based models of cultural evolution in R, including unbiased and biased transmission, unbiased and biased mutation, migration, blending inheritance, polarisation, cultural group selection, social networks, iterated learning, reinforcement learning and the evolution of social learning.
The field of cultural evolution has emerged in the last few decades as a thriving, interdisciplinary effort to understand cultural change and cultural diversity within an evolutionary framework and using evolutionary tools, concepts and methods. Given its roots in evolutionary biology, much of cultural evolution is grounded in, or inspired by, formal models. Yet many researchers interested in cultural evolution come from backgrounds that lack training in formal models, such as psychology, anthropology or archaeology. This book aims to partly address this gap by showing readers how to create individual-based models (IBMs, also known as agent-based models, or ABMs) of cultural evolution.
In this graduate-level workshop, students will learn about agent-based modeling and how it is applied to study social phenomena in human and animal societies. Agent-based models are widely used to help us understand a wide range of topics, including but not limited to cooperation, social learning, collective problem solving, opinion dynamics and polarization, segregation, the spread of disease, and the emergence of social norms. Students will receive introductions to relevant formal theories in the life and social sciences, and get hands on experience writing and analyzing simulation models using NetLogo, a widely used software package for agent-based modeling.
Five 2-hour lectures demonstrating how simple models of strategic interaction illuminate important topics in the evolution of animal behavior, along with extensive notes.
This course teaches data analysis, but it focuses on scientific models. The unfortunate truth about data is that nothing much can be done with it, until we say what caused it. We will prioritize conceptual, causal models and precise questions about those models. We will use Bayesian data analysis to connect scientific models to evidence. And we will learn powerful computational tools for coping with high-dimension, imperfect data of the kind that biologists and social scientists face.
We created this self-directed course because we’ve seen the positive and meaningful impacts of anthropologists sharing their ideas with a broad global readership. We also recognize that the storytelling style in newspapers, magazines, and online publications—and the path to getting published in these outlets—is very different from what you may be used to in academic publishing. So, we harnessed our experiences and insider knowledge as editors, journalists, and anthropologists. We combined these with writer testimonials and examples of successful anthropological writing from a variety of publications. We distilled it all into seven chapters that will help you write compelling stories for popular publications.