How ethnocentrism evolves: a simulation of evolutionary dynamics

Cooperation is a paradox—it just doesn’t make sense. Why should I help you when there’s no direct benefit for me? Artem, Professor Tom Shultz, and I have been working for quite some time on a paper about cooperation, and we’re psyched to announce that it’s finally been published in The Journal of Artificial Societies and Social Simulation (JASSS). JASSS is an open web journal, so you can view the full text of our article for free on their website. Or you could skip the 8000 or so words and check out this summary post.

Conditional cooperation and emotional profiles

I haven’t been delving into evolutionary game theory and agent-based modeling for very long, and yet I find that in that little time something quite eerie happens once I’m immersed in these models and simulations: I find myself oscillating between two diametrically opposed points of view. As I watch all of these little agents play their games using some all-too-simplistic strategy, I feel like a small God*. I watch cooperators cooperate, and defectors defect oblivious to what’s in their best interest at the moment. Of course, in the end, my heart goes out to the cooperators, who unfortunately can’t understand that they are being exploited by the defectors. That is what pushes me at the other end of the spectrum of omniscience, and with a nudge of empathy I find myself trying to be a simpleton agent in my over-simplified world.

In that state of mind, I begin to wonder what information exists in the environment, in particular information about the agents I am going to play against. I suppose I’m able to access it and use it to condition my move. Admittedly, that makes me a bit more complex than my original simpleton, and that complexity is likely to come at a cost, but I leave it to evolution to figure out whether the trade-off is worthwhile.

Monoids, weighted automata and algorithmic philosophy of science

The Algorithmic Thinkers
original art by Auguste Rodin & Eric Joyner.

If pressed to find a passion and unifying theme behind my interests, I would say that my goal is to emancipate theoretical computer science from the current tyranny of technology and engineering, and restore it to its original position of asking and helping find answers for fundamental questions in science and philosophy. I’ve already written on progress toward an algorithmic theory of biology, wherein I permitted myself to foray into the philosophy of science. I want to continue the expedition with this post because I think that cstheory can be painlessly integrated into philosophy as an extension of analytic philosophy — algorithmic philosophy.

Micro-vs-macro evolution is a purely methodological distinction

On the internet, the terms macroevolution and microevolution (especially together) are usually used primarily in creationist rhetoric. As such, it is usually best to avoid them, especially when talking to non-scientists. The main mistake creationist perpetuate when thinking about micro-vs-macro evolution, is that the two are somehow different and distinct physical processes. This is simply not the case, they are both just evolution. The scientific distinction between the terms, comes not from the physical world around us, but from how we choose to talk about it. When a biologist says “microevolution” or “macroevolution” they are actually signaling what kind of questions they are interested in asking, or what sort of tools they plan on using.

Cooperation and the evolution of intelligence

One of the puzzles of evolutionary anthropology is to understand how our brains got to grow so big. At first sight, the question seems like a no brainer (pause for eye-roll): big brains make us smarter, more adaptable and thus result in an obvious increase in fitness, right? The problem is that brains need calories, and lots of them. Though it accounts for only 2% of your total weight, your brain will consume about 20-25% of your energy intake. Furthermore, the brain from behind its barrier doesn’t have access to the same energy resources as the rest of your body, which is part of the reason why you can’t safely starve yourself thin (if it ever crossed your mind).

So maintaining a big brain requires time and resources. For us, the trade-off is obvious, but if you’re interested in human evolutionary history, you must keep in mind that our ancestors did not have access to chain food stores or high fructose corn syrup, nor were they concerned with getting a college degree. They were dealing with a different set of trade-offs and this is what evolutionary anthropologists are after. What is it that our ancestors’ brains allowed them to do so well that warranted such unequal energy allocation?

Cliodynamics: A Future for History?

What is history? And what, if any, are its practical uses? These are the questions I’ve been pondering since being introduced to Cliodynamics – which claims to make history into  “an analytical, predictive science.” To that end, I wish to address two questions: is it possible to make history into “an analytical, predictive science?” And is it desirable, for the purposes of attaining greater knowledge or understanding, to do this?

Toward an algorithmic theory of biology

When you typically think of computer scientists working on questions in biology, you probably picture a bioinformatician. Although bionformatics makes heavy use of algorithms and machine learning, and its practitioners are often mildly familiar with computational complexity (enough to know that almost everything they study is NP-complete), it doesn’t really apply computational thinking to understand or building theories in biology. Instead, it often generates or analyzes petabytes of blind data that biologists subsequently use to generate or test traditional verbal or physics-inspired hypotheses. The 2nd workshop on Natural Algorithms and the Sciences took a completely different approach.

The workshop was held on May 20th and 21st by Princeton’s Center for Computational Intractability and attracted speakers from biology, computer science, engineering, math and elsewhere. The meeting had a heavy focus on theoretical computer science and a return to the founding spirit of Alan Turing by tackling big fundamental questions in the sciences. It saw applications of computational complexity, computability theory, machine learning, distributed and parallel computing, and information theory. Although the mandate of the workshop is broader than looking at biology, most of the talks returned to questions in the biological sciences. I greatly enjoyed my time at the workshop, and intended to live blog the event. However, a poor internet connection at my residence, other time commitments, and the vast amount of ideas I wanted to cover instead translated into a series of seven posts (this is the eighth) that spanned the last three weeks. To make reading (and later reference) easier, this post is a TL;DR of the last seven posts. Each section is a short summary of a post and a list of the talks discussed; at the end I include a partial bibliography for further reading. Click through on the headings to learn more and join the discussion on specific topics!

Machine learning and prediction without understanding

Big data is the buzzword du jour, permeating from machine learning to hadoop powered distributed computing, from giant scientific projects to individual social science studies, and from careful statistics to the witchcraft of web-analytics. As we are overcome by petabytes of data and as more of it becomes public, it is tempting for a would-be theorist to simply run machine learning and big-data algorithms on these data sets and take the computer’s conclusions as understanding. I think this has the danger of overshadowing more traditional approaches to theory and the feedback between theory and experiment.