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For the last couple of months I have been spending most time working on my thesis. It has been actually very enjoyable, for the most part. There have been some days where it has been somewhat painful. The quick update is that it is going well and that I still hope to submit very soon. As expected, there are many experiments, and tasks, and analysis that I would like to expand into. But I’m going to have to leave it for later. As Inman keeps reminding me, there is no such thing as a finished thesis…
But this post is not really about the thesis. I wanted to mention my increasing interest in the economy as an evolving complex dynamical system. This interest is not entirely new for me. I first became involved in the computational study of economic processes modeled as dynamic systems of interacting agents when I was assisting Prof. Raul Jimenez back in Universidad Simon Bolivar. However, it was the book “The Origin of Wealth: Evolution, Complexity and the Radical Remaking of Economics” by Eric D. Beinhocker - as brought to my attention by Chrisantha - which has gotten me thinking more seriously about the area again.
Prof. Tesfatsion has a very useful online resource on the area. These are some of the interesting questions that she raises: “Why have particular observed regularities evolved and persisted despite the absence of top-down planning and control?” and “How can computational models be used to gain a better understanding of economic systems through a better understanding of their full range of potential behaviors over time (equilibria plus basins of attraction)?”
I have also been most interested with Friederich von Hayek’s and Adam Smith’s work for a while. In fact, I have been recommending many Venezuelans to read Hayek’s seminal “Road to Serfdom” for the last several months. These gentleman wondered about the self-organizing capabilities of decentralized market economies. This is still an unresolved concern. One which I find ever so necessary to resolve.
Perhaps what I find most interesting about computational economic models is the potential to introduce all of the tools from dynamical, complex, adaptive, evolutionary, embedded, enactive and other biologically-inspired systems trade, as Beinhocker points out in his book.
I’m particularly interested in `out of equilibrium’ economics. Traditional economic models have been based around homogeneous all-knowing agents in a discrete-time world. And most of the analysis has been applied to when the market settles down to its equilibrium. Brian Arthur and some other people have done a lot of interesting work with their Santa Fe Institute artificial stock market (SFI-ASM). But from what I have seen so far, there is still a lot of space for improvement. There is also a lot of space for understanding these models using dynamical systems analysis.
That’s it for the moment on computational economics. But I would like to mention a couple of other side things before I disappear into my latex files. I have also been reading “On writing well: the classic guide to writing nonfiction” by William K. Zinsser. This book complements very well W. Strunk and E.B. White’s “The Elements of Style”, as the author suggests.
It’s only until you read something as well written as this book that you realise how awful most of us write. And I’m not talking merely about blogs and things online, I’m talking about many papers and books out there. Zinsser puts it this way: “The man or woman snoozing in a chair with a magazine or a book is a person who was being given too much unnecessary trouble by the writer”. On the contrary, every paragraph in this book is refreshing and it gives me more energy to keep reading the next.
Anyways, I do hope to improve my writing with it. What I like about this book the most is that he acknoledges that good writing is hard. “A clear sentence is no accident” is the way he puts it. There is a lot of emphasis on clarity, simplicity, brevity and humanity. He also stresses the importance of how the written text ’sounds’. Also the necessity of pruning, rewriting, reading aloud and rewriting over again, several times. All of these suggestions are obvious. But it’s worth being reminded about them often. He recommends reading White and Strunk’s book every year. I agree. I also think this is true for his book.
Well, as it turns out this post was about all sorts of things and nothing at the same time. So while I’m at it, why not tell you the one other thing, after the thesis, that has been taking up most of my time: planning for the future. That’s right. Short and medium term future is what’s at stake. A lot of brainstorming about concrete research projects that I would like to carry out, with whom and where. I think I know pretty well what I want. More on this later.
It is so sad that I have been too busy working to update my blog. Sad because keeping the notebook updated helps me stay focus on the bigger picture. Here’s a brief update on what I have been up to.
I have been mostly working on what I think will be the 5th and last experimental chapter of my thesis, a subset of which I am planning to submit to the upcoming ECAL conference with deadline a couple of weeks away (9th of April). The work is on the evolution, and more importantly for me the analysis, of an agent that can learn to associate food with temperature in a 2D environment during its lifetime, while overcoming the difficulties encountered by previous work (Yamauchi, Beer, Tuci, Blynel, Floreano, Phattanarasri, and Chrisantha). I’m particularly interested in attempting to understand the agent’s learning behavior in the language of animal learning theory (is the agent doing classical or instrumental conditioning?), as well as the ‘memory trace’ in the internal dynamics.
Fortunately, I have managed to succesfully evolve small circuits for the task (much smaller than all previous attempts in the literature) even though many aspects of the tasks are more complex: they have to be able to remember for several re-tests and be able to re-learn different environments during their lifetime. The smallness of the circuit (4 nodes) has encouraged its in-depth dynamical systems analysis (as opposed to the typical statistical measures, correlation and ablation studies).
I’m still hard at work analysing the agent and writing it up, but I will have a draft to share with whoever wants to read it fairly soon.
I have also been collaborating with Nathaniel and Tom (colleagues from the CCNR) on a review paper on the notion of autonomy in the cognitive sciences for the same conference. There is a lot of material already there, it’s a matter mostly of narrowing it down and making it as concise as possible. We have been meeting on and off for the last 3 months, that has been really useful and interesting. I would definitely like to engage in more collaborations of that sort.
I’m also getting my presentation ready for the IEEE conference in Hawaii, and that’s even closer! I leave on the 3oth of this month. Still need to work on it a lot more.
Will be sending our first call for papers for the workshop that Eldan and I are organising in Portugal next week. It’s very exciting. I think there is a lot of space for really interesting discussions, new directions and collaborations to take place from that workshop.
There are a couple of other things, also important and interesting, which I won’t go into now. Laters.
Officially I have started to work on my doctoral dissertation. Here’s a first attempt at a ‘mindmap’ of how I see it at the moment. This will most likely not make any sense to anybody apart from me (probably like the rest of my posts too).
This term has been unusually busy in terms of teaching and marking for me: 92 hours which ended up being more like 180. Just finished marking today around 100 assignments on logic and reasoning. All that activity has made me have a hard time focusing on my research. The experience, however, has been great. Teaching first year students can be very rewarding indeed. A good proportion of them are very alert and highly motivated. Even though it was my first time teaching that course, some of the students gave me positive feedback. Furthermore, even though I am not a fan of the topic by any stretch of the imagination, I actually feel encouraged to teach it again next year. I know I could do much better. In any case, the good news is that it is almost over now.
With the IEEE Artificial Life symposium deadline approaching, I think there is some chance of working on a small project that I have been thinking for a while. It is based on ideas from Inman - basically he gave an initial attempt at it many years ago, got partial results but did not proceed much further. It is the simplest form of learning we can think of - and it deals directly with how ‘hardwired’ small circuits (with nevertheless a continuum of possible different time-scales) can ‘implement’ ‘weight-like changing’ mechanisms. The idea is to evolve a CTRNN network to produce Hebbian learning behavior: “Nodes which tend to be either both positive or both negative at the same time will have strong positive weights while those which tend to be opposite will have strong negative weights. It is sometimes stated more simply as ‘neurons that fire together, wire together.’” (definition loosely taken from Wikipedia). The beauty of this work is that it would allow for a very thourough dynamical analysis of how the ‘Hebbian-like-learning’ is implemented in a small circuit. Which is not always possible for two different reasons: (a) the task has other complications or (b) the circuit is not small enough. For all of these reasons, this research should provide the underlying foundations for the work on evolving dynamical systems for learning behavior in general (particularly the one I have been doing until now)… The catch, is that for this reason I have partially postponed the writing of the journal paper on the results from my summer research visit with Randy for after the deadline.
Plans for the next 3 months: start of 3rd (and hopefully last) year of my PhD.
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With less than two weeks left of my stay in Bloomington, I’m now hurrying to get everything that I planned for in. These days my time has been mostly occupied by:
In trying to analyse a dynamical system that remembers features from a continuum of possible stimuli, one of the key factors that I will have to take into consideration is that it can not be perfect. If there are only 2 (or any other finite) number of things to be learned then we could expect perfection. If the system were a computer, with an infinitely long tape and so on, then we could also expect certain degree of perfection. But when we are dealing with a system that decays over time, then the decay of the memorised stimuli has to come into play and be explained. This is indeed the case in the evolved circuits. And it is intuitively the case in living organisms… so the task now is to find some information and references about this.
