One who is interested in philosophy shouldn’t miss this great collection of People with online papers in philosophy compiled by David Chalmers. You may also find out when any new philosophical paper have been added to the cyberspace by visiting Online Papers in Philosophy. You can find good resources on David Chalmers’ site.
Iran Presidential Election and Predictability
1) Friday was a very surprising day for us. Iran’s presidential election got into a very unbelievable result (read + + +). The reformist candidate, Mostafa Moin -who was believed to get many votes- was humbled by fifth-place finish. On the other hand, Mahmoud Ahmadinejah –a fundamentalist mayor of Tehran- got many votes and stood in the second place. He and Rafsanjani will continue their fight next week in the second round of election.
I do not intend to analyze the situation or even inform you about the dark ages we may face if this Ahmadinejad become our president. Instead, I want to concentrate on the predictability of such elections.
2) No one in blogosphere predicted this situation. They guessed that Moin would get the first or second place. But it did not happen and all of us are in a great shock. I hypothesize the following fact:
Internet community does not reflect what is going on the society. Internet polls may have an order of magnitude difference with reality. I guess this becomes truer (in the Fuzzy sense?!) whenever the society is pre-modern or under-development.
Let me discuss it in the notion of statistical learning theory: Consider the whole society as a set A0. Those people who use Internet considerably and discuss about their opinion in that media make a subset of A0, naming it A1. If we consider the general belief as X, we can define probability measure P0 over set A0 that indicates the probability of selecting each instances of X. The same is true for A1 and P1. I hypothesize that one may not estimate E[f(X)] over P0 by making i.i.d. samples from A1 by probability measure P1. Well … a very evident fact?! Yeap! OK!
Anyway, be careful to assess the commonsense using the Internet media.
3) A few days ago, I thought about making something like Fuzzy Cognitive Map to model the society and predict its behavior. I am not that aware of the society modelling literature, but it might be interesting subject to work on. One may talk about the model’s predictability and the effect of model’s error to the prediction. Is the society –that must be modeled- chaotic or stable to a fixed point? Or in other words, can we make a model that even if it has some errors predicts well?
Dark Age
Welcome to the DARK AGE!
Germany! WE ARE COMING!
Iran defeated Bahrain last night and became qualified to attend Germany soccer worldcup 2006.
See a few after-effects of this victory:
(some more pictures from BBC) and a piece of English news!
Blogging after a while
I haven’t written for a while in this blog – I know this is not the way of looking after a blog! There are a few reasons that I didn’t post anything there:
1-My monitor was damaged (its vertical sweeping voltage generator was corrupted) and I couldn’t connect to the Internet using my own computer. I don’t feel easy to post from our lab.
2-I have started writing my thesis and it takes times and more than it mind!
3-I wanted to post a really useful(!) post, but it takes time to prepare one.
Well! That’s all!
PhD Graduate Study and something more
As a M.S. student who is hopefully going to start his Ph.D. soon, this kind of advices are interesting to read: A Short Guide to PhD Graduate Study and What it means to do research.
To read latter: You and Your Research (Richard Hamming)
Myths of Reinforcement Learning
RL is slow?
Large state spaces are hard for RL?!
RL does not work well with function approximation?!
Non-Markovianness invalidates standard RL methods?!
POMDPs are hard for RL to deal with?!
…
In the opinion of Satinder Singh, these are myths of RL. He answers to these myths and expresses that these are not true beliefs. Moreover, there are many other information about reinforcement learning (myths, success, and …) in The University of Michigan Reinforcement Learning Group.
CFP, Job Opening, and Weblogs
I read this Machine Learning mailing list policy for submission that suddenly a thought came to my mind. Read the shortened policy first:
* Meeting announcements. ….
* Publications of books or journal issues. In general, we accept announcements about the publication of multiple papers (e.g., journal issues) or books relevant to machine learning. Announcements about single papers such as articles or dissertations are not usually included.
* Career opportunities. …
* Software releases. …
I wonder if one can announce the release of a new academic weblog to such lists?! I think academic weblogs can change the way we do research and connect with each other significantly. Reading IlliGAL weblog or John Langford’s Machine Learning or a few others is a more useful for me than nth national XYQSD conference that I cannot attend. So, why not introduce such weblogs in our mailing lists?! (Of course, I am against those people who send their homepage or weblog address everywhere irrelevantly.)
Don’t Send them to Prison
Once upon a time, Najmeh Omidparvar (in Persian) and Mohammad Reza Nasab Abdolahi (in Persian) lived together happily. However, the day came that they wrote something wrong in their weblogs, so they must have been jailed. They went to prison to continue their life there – not supposedly to be happy anymore. They are sent to different cells to live alone and think about themselves. Well … they are supposed to be alone. BUT, they were not that alone. Their souls are together, AND they had their child. Yeap! They had their child in the prison! (Read More)
Some Experiments with Memetic Algorithm
A week ago, I tried some simple idea of what is called Memetic Algorithm with my friend, Ali Reza. He is working on Vehicle Routing Problem (VRP) for his MS thesis (industrial engineering) using evolutionary techniques. I told him about MA and its benefits; he became eager about the approach; we find a computer and we implemented a local search to put in the [cultural] evolution cycle. My local search was something like this:
Suppose that we have a route specified by R: c(1)c(2)…c(n) in which c(i) is the index of the ith city (or delivery position). I suggested this simple search:
For every position in the sequence, swap two adjacent cities and calculate the fitness, i.e. R: …c(i-1)c(i)c(i+1)… —> R’: …c(i-1)c(i+1)c(i)… and comparing f(R’) and f(R). If this change was better, accept it as the new individual.
This is very simple and I dare to say it is really mine. I guess someone might discovered (or used) this heuristic in their combinatorial optimization problems. However, I am not aware of such a search. Is there any in the literature?
We did not test it very much on many different problems. We have tested it in a single problem two or three times and in all cases, we got a better results comparing simple genetic algorithm. How much better?! I don’t know what amount is significant in this problem, but we reached (~)8.5 score when simple GA reached 9.5-10.
All of these has been done in less than an hour!
Mmm … As I told before, I do not believe in this type of interpretation of meme. However, I have tested it too!