About & Highlights
Who: I am Artem Kaznatcheev, and this is my venue for articles that are more long-form than G+ posts but aren’t quite long enough for more formal publication — although the ideas developed in the posts often lead to that. I frequently invite others to write for the blog, and TheEGG has had multiple contributions from Eric Bolo, Max Hartshorn, Marcel Montrey, Keven Poulin, Thomas Shultz, and Julian Z. Xue; plus single contributions from Forrest Barnum, Piotr Migdal, Abel Molina, and Yunjun Yang. If you want to contribute a post then send me an email and we can discuss if it fits the vision and standards of TheEGG.
Why: There is both a private and a public motive. The public motivation is my belief that blogs — as well as social media and academic Q&A sites — are the way forward for science as a profession. As a medium, this is greatly more interactive than journals, and also faster and more open. My goal is not to educate or entertain — although I hope that this is a frequent by-product — but to provide a window into the actual research that occupies my thought. In addition, my private motivation is that writing helps to clarify my thoughts and the feedback I receive is both encouraging and enlightening.
What: Finding an elevator pitch for my academic interests — and the contents of this blog — is something that I struggle with at every introduction. Instead of trying to give you a short summary, I will provide you with seven broad themes and a few posts that highlight each. I’ll leave finding the summarizing thread to you: if you find it then let me know. I’ve tried to cleave the existing body of posts at relatively self-evident joints, but there is constant overlap between the themes and posts that fall outside any of them.
Algorithmic theory of biology
I am very interested in the methodology that theorists use, and I have noticed that in biology there is a lot of use of physics-inspired and statistics-inspired tools. However, not as many tools are taken from the sort of mathematical and philosophical viewpoints used by theoretical computer scientists — I think cstheory can help biologists. I’ve called this ‘algorithmic’ because these tools do not (necessarily have to) involve using computing machines to simulate or analyze data, which falls under the more common ‘computational’ perspective. This is not a firmly established (sub)discipline, but we are working toward it.
- Software through the lens of evolutionary biology
- Evolution is a special kind of (machine) learning
- Computational complexity of evolutionary equilibria
Bounded rationality in economics and finance
Game theory originated in economics, and is often discussed in the context of perfectly rational agents — the mythical homo economicus. Unfortunately, this sort of rationality does not capture human behavior well, and although there is a great deal of cultural variability, no culture comes all that close to homo economicus. As such, if we want to build models of human economies and finance, we need to take into account bounded rationality. I explore (at least) two different reasons for this bounded rationality, those that stem from evolution and the properties of dynamic systems and those that stem from theoretical computational constraints.
- Mathematics in finance and hiding lies in complexity
- Bounded rationality: systematic mistakes and conflicting agents of mind
- Mathematical models in finance and ecology
Cognitive science and philosophy of mind
Like so many others thinkers, I cannot help but think about thinking — the mind is completely captivating, and the brain is pretty interesting, too. My long collaboration with Thomas R. Shultz’s Laboratory for Natural and Simulated Cognition — in the context of which the EGT reading group predecessor of this blog first started — has made me particularly keen on the interdisciplinary approach of cognitive science. From a scientific perspective, I am primarily interested in the interaction of evolution, development, and learning; and from philosophy, I am interested in how (or if) we can productively study the mind without necessitating the physicalist perspective.
- Defining empathy, sympathy, and compassion
- Transcendental idealism and Post’s variant of the Church-Turing thesis
- Baldwin effect and overcoming the rationality fetish
Evolutionary game theory
Evolutionary game theory (EGT) is the destination reached by taking all rationality out of game theory and replacing it by dynamic systems. This blog was initially focused just on EGT — hence the url — and launched as an extension of the earlier EGT reading group at McGill University. I am excited about all aspects of EGT, but I have a particular interest in the effects of spatial structure, and applications to the study of ethnocentrism. This work is probably the most orthodox part of my thinking, and you will frequently see use of the physics-inspired and statistics-inspired tools that I lamented in the algorithmic theory of biology.
- Hunger Games themed semi-iterated prisoner’s dilemma tournament
- How ethnocentrism evolves: a simulation of evolutionary dynamics (by Max Hartshorn)
- Quasi-magical thinking and the public good (by Marcel Montrey)
Mathematical oncology and theoretical biology
Here’s a truism: tools are of little use if they are never used. Although I touch on all kinds of domains of application, the place where I want to have a deeper understanding — and where I think my insights might be most helpful — is in the study of cancer. This theme has been a recent development for TheEGG, but given that I currently work in a department of mathematical oncology, you will see more and more posts on this topic. Of course, cancer has a natural relationship to important topics of theoretical biology like evolution and multi-circularity. As such, I also addresses more general questions of theoretical biology, usually with a focus to the mathematical tools used.
- Micro-vs-macro evolution is a purely methodological distinction
- Misleading models in mathematical oncology
- Microenvironmental effects in prostate cancer dynamics
Metamodeling and philosophy of science
There is a lot to be gained from the sharing of mathematical and modeling tools across scientific disciplines. In other words, I believe that I can be a theorists without an explicit domain of study. However, this does define a certain implicit domain: the modeling tools and methodologies themselves. Hence, I spill a lot of ink — or keystrokes — on understanding how mathematical modelings works and how it and other forces shape science.
- Three types of mathematical models
- Models and metaphors we live by
- Should we be astonished by the Principle of “Least” Action? (by Abel Molina)
Theoretical computer science and machine learning
The largest part of my formal education has been in computer science, the theoretical aspects of it, in particular. As such, TheEGG frequently touches on cstheory topics, especially those related to machine learning and — less often — quantum information theory. These discussions are seldom just cstheory for cstheory’s sake, and will often explore connections to biology, philosophy, physics, or society.