25 books like Historical Dynamics

Strictly speaking, there is very little math in this short book, but it nevertheless details precise models that yield loads of insight.

Using simple machines with sensors and motors, Braitenberg shows us how easy it is to generate behaviors that look purposeful and even emotional, and how hard it would be to guess how those behaviors were generated if we didn’t already know. This is a book I come back to again and again, not only for its valuable lessons, but also for its beautiful prose.

The models in this may be fictions, but, as Braitenberg advises, fiction is a necessary part of science “as long as our brains are only minuscule fragments of the universe, much too small to hold all the facts of the world but not too idle to speculate about them.” 

In 2016 I went to a conference in Leuven, Belgium, on computational approaches to understanding science. There I presented a model showing how selection for productivity (good old “publish or perish”) could, over time, degrade the quality of methods used by scientists.

I also met Cailin O’Connor, a philosopher and game theorist who was also studying science with formal models, with a focus on equity, or lack thereof. In this terrific book, Cailin uses game theory and evolutionary dynamics to consider how some social institutions lead to entrenched inequality among people or social classes, as well as how one might combat the forces of unfairness. 

I had the good fortune to go to graduate school at UC Davis, where I got to know Peter Richerson, who co-led a group of people working on cultural evolution.

Pete, along with his long-time collaborator Rob Boyd, pioneered the theoretical framework of dual inheritance theory, or how genes and culture act as twin transmission channels for human evolution. In this book, they use mathematical models to explore the various ways in which humans might learn from one another, and how natural selection can shape the evolution of a psychology that facilitates various forms of social learning.

This book, more than any other, launched contemporary research on cultural evolution.  

I have always been fascinated by how people join and leave groups.

What are the benefits of joining a particular group? Which group should I join? What happens if someone wants to join a group, but its current members don’t want them to? I once thought such questions were merely qualitative, and when I was a graduate student I thought I’d be the first to tackle them quantitatively.

I was humbled when I stumbled upon this book, written years earlier, in which two behavioral ecologists review game theoretic models that address questions of just this sort, starting simple, and building up models of increasing nuance and complexity. I think anyone interested in the dynamics of group formation in humans or other animals should read this book. 

This book turned the field of Terrorism Studies on its head. Historical sociologist Lisa Stampnitzky demonstrates that the legion of terrorism experts who rose to prominence in North America, Western Europe, and Israel in the 1970s were not neutral analysts of political violence. Rather, through their intellectual work, much of it funded with government grants, terrorism scholars helped construct the contemporary meaning of terrorism as a threat to society fundamentally different from other forms of violence, crime, and political activity. This book made it clear that we can’t understand the history of “terrorism” without analyzing the history of the term itself, and how the use of this term in law, academia, politics, international relations, and popular culture has shaped political power and violent conflicts between states and insurgents.

As a math student I found economics a slippery subject and, therefore, was hesitant to read any book on the subject. 

Theory of Value is a short, formal manuscript, that includes the definition of an economy. It was the first book I read in economics and I loved it. It induced me to move to New York and to study the field.

This looks like it’s the sternest and most boring book ever, but I love Steedman’s cool-and-collected ability to address the implications of the obvious: You can only do one thing at a time. You only have two hands. And when you’re with one set of belongings, you’re neglecting all the other stuff you own.

In the vast array of quantitative finance relative to financial markets instruments and related risks, the case of credit or counterparty risk remains by far the most complex one, and thus, unsurprisingly, the least mastered by financial markets professionals.

A lot has been done, but a lot remains to be done: covering this is precisely the goal of this book. In a nutshell, the main obstacle to succeed in developing grounded and useful models of default prediction is due to the fact that a default is (fortunately) a rare event, in other words, with a (very) low probability of occurrence, and statistical tools are uncomfortable with very low probability levels. Hence the need of this book, to help the practitioner to go ahead in this matter.

One of the earliest books on this subject, Uri Alon presents an engaging account of biological networks. Focussing on transcriptional networks, and their motifs, the book illustrates the nexus between network structures and functions. The second edition of the book launched a few years ago and has some updated content and new material on interesting functionalities such as fold change detection. Uri Alon is a very accomplished scientist, mentor, and a leader in the field of biological networks/systems biology.

By now, you may notice that I like to recommend textbooks. I use this bestseller for my course in Financial Machine Learning at Northwestern University, but really, nobody interested in financial machine learning hasn’t read this book. The topics are highly relevant to every investor or trader – I read it at least 5 times to digest every nugget and have put them to very productive use in my trading as well as in my fintech firm predictnow.ai. It covers basic techniques such as random forest to advanced techniques such as Hierarchical Risk Parity, which is a big improvement over traditional portfolio optimization methods.

Marcos used to be Head of Machine Learning at AQR (AUM=$143B), and now is the Global Head of Quant Research at Abu Dhabi Investment Authority. He is also very approachable to his readers and students. There was seldom an email or message from me to which… show more.