This page describes some of the most influential academic artifacts I’ve come across, organized by area of study. Most of the work is not related to my primary research but it did help lead me to where I am today as a researcher. Although it looks like a simple list of references, many of these materials were very influential to me at the time I decided to start a PhD in Sociology.
Book: “Social and Economic Networks” by Matthew O. Jackson – get chapters 2, 4, and 9 online free. This book is great because it talks about some of the fundamentals of network science from an economic and social perspective. The free chapters talk a bit about game theoretic models of social influence.
Coursera Course: “Social and Economic Networks: Models and Analysis” with Matthew O. Jackson. Great introductory online course with a few of the same studies from Dr. Jackson’s book (listed above).
Book: “Social Influence Network Theory” is a book by Noah E. Friedkin and Eugene C. Johnsen. This book gives a network and mathematical approach to small group social influence that is rooted in classical sociological theory.
Book: “Aid on the Edge of Chaos: rethinking international cooperation in a complex world” by Ben Ramalingam. Ben works with the Overseas Development Institute and runs the Humanitarian Innovation Fund in the UK. This book focuses on providing a background on international development and complexity science for beginners. He uses a series of case studies to point out the oft-vocalized failures of aid, and finally makes a call-to-action for development professionals to use ideas from complexity science when thinking about aid delivery. He also calls for complexity scientists to create an applied complexity framework for international development. Criticism of this book typically focuses on a lack of specific plans, but I attribute this more to the limitations of the theoretical field of complexity science more than details of the book. Regaurdless, this book was great for me as a newbie to international development and complexity science. Will Ben’s work lead to a revolution in the way we approach development? I’m interested and excited to find out!
Article: “From best practice to best fit: understanding and navigating wicked problems in international development” by Ben Ramalingam. This article was written after Ben’s book “Aid on the Edge of Chaos” to establish some specific approaches the DFID (think USAID for the UK) can use to help solve problems. The article was written after a preliminary study was performed, so I’m interested to see what the next report out of this line of research will determine. If it works, there may be concepts that we can apply at USAID here in the US.
Article: “Dangerous Tales: Dominant Narratives on the Congo and their Unintended Consequences” by Severine Autesserre looks at explanations of failure gained from several years of ethnographic research on the aid situation in the Democratic Republic of Congo. The situation in this area is bad due to warring factions and exploitation of mineral resources, and foreign aid over the last decade has only proved to make things worse. The article focuses on how limited communication between people and organizations in the foreign aid community can cause information about what is happening converge on a common narrative. Severine describes how this narrative came about and why it is necessary, but also why this result led to yet more tragedy in the area. The story she tells is heartbreaking and amazing; her ideas question the way aid organizations operate and ask bigger questions about the biases we all share as humans. This article had a large influence on my current research interests in opinion dynamics.
Article: “Prospect Theory: An Analysis of Decision Under Risk” by Daniel Kahneman and Amos Tversky in 1979. This article is credited with creating the field of behavioral economics, and was perhaps one of the largest contributions leading to Daniel Kahneman’s Nobel Prize in Economics. The article focuses on the way humans analyze prospects, or decisions with some probabilistic risk of gains and losses. It also reveals statistically significant phenomena found in a simple exercise asked of a number of test subjects.
Book: “Thinking, Fast and Slow” by Daniel Kahneman discusses the life work of Kahneman and Tversky and also manages to detail some of the most fascinating psychology research published in the past couple decades, all related to cognitive biases. I’m now interested in exploring how these biases can evolve at the macro level with many people interacting to make decisions, solve problems, and develop personal identities.
YouTube Videos: Sociology 150A Lectures 3-5 at Berkeley by Rob Willer (now at Stanford). These are great intro lectures and you can tell Dr. Willer is really passionate about the subject. If you are interested in an introduction to cognitive biases, this is the place to go.
Book: “Gang Leader for a Day” by Sudhir Venkatesh. Sudhir is now a professor of Sociology at Columbia specializing in Urban Ethnography. This book is his personal story as he explores the street gangs of Chicago by befriending a leader of the Black Kings during the peak of the crack cocaine epidemic in 1990. The book became a NYT best-seller when it was released in 2008 and has since garnered much criticism over the way he handled some situations described in the book and the methods used to collect data. The book is fascinating and professor Venkatesh certainly expresses a passion for ethnography while also tying in his personal story of a Sociology graduate student.
Lecture Notes: Stanford CS229: Machine Learning. This is the most popular course at Stanford and also a must for anyone interested in this field from an academic perspective. The course notes take an approach rooted in fundamentals of probability and estimation theory rather than that of a practitioner. You can look at it as an extension of the Machine Learning course on Coursera with an academic spin.
Coursera Course: Machine Learning with Andrew Ng at Stanford (now Chief Scientist at Baidu). Andrew Ng is also a co-founder of Coursera and an expert in deep learning. This course is great for beginners interested in a practitioners perspective on ML.
Article: “The Origins of Morphogenesis” by Alan Turing. This is one of Turing’s (credited with creating the field of computer science) last papers before his unfortunate death. The article proposes a basis by which all organisms start to develop structural form with just a small number of cells. Each similar cell has simple rules that determine chemical reactions within itself and rules that govern diffusion of chemicals between cells. Turing showed through mathematical analysis how these simple rules could result in what we see empirically in developing organisms. Rather than a breakthrough for biological study of morphogenesis, I found it interesting how he proposed the model and described the assumptions on which he based it. The foray into such a different field of study is inspiring and curious; this paper is sometimes referred to as the beginning of the field complexity science. This is a highly controversial field of study integrating mathematical (and now computational) models with classical approaches. Most of the criticism comes from those who find it to be a largely useful field of study – trying to explain mathematically something that operates in environments too complex to model. It should also be noted that many researchers do this work under the guise of other names to avoid this criticism 🙂
Book: “Studies of Mind and Brain” by Stephen Grossberg. This book is essentially a collection of papers by Stephen Grossberg compiled for completeness. I find it full of interesting ideas from different fields of study and extremely helpful for understanding in particular the cognitive and neural theory called Adaptive Resonance Theory (ART for short). ART is a neural network designed to explain how humans draw attention to different aspects of the world through perception. Although Grossberg has a grandiose vision for this theory, I feel that the possibility of validating it as a cognitive or neural theory seems very difficult. That said, it has the potential to explain aspects of human cognition that I feel some other popular theories may lack. The most useful parts for me are ideas from winner-take-all concepts rooted in complexity science. For now it is just an idea book for me, but who knows how I’ll use it in the future.
Coursera Course: Organizational Analysis with Daniel A. McFarland at Stanford University. Daniel McFarland is a professor of Education at Stanford and the course won the 2013 “best MBA Mooc” award. For me this course serves as a great introduction to organizational science. With no background in sociology this was a great resource for me to learn some about the field and get an idea of qualitative analysis. I may be referring back to this online course quite a bit.