Grad Students

I went through the Systems Science PhD program at Portland State at the end of the last century. I just got back from a quick trip down there to discuss two different research projects, and to give an impromptu presentation on one of them.

I currently teach in a College of Business. It is a great job, with superb colleagues and fun students…and yet. Most business schools don’t really have grad students. They have students going for their MBA’s. The difference is, most folks going for an MBA have been out in business for a while, have a job and a family, and are getting the degree as a way of moving up into management, or into upper management. It’s a straightforward practical program for those who have a life.

In other disciplines, like Systems Science, the grad students are there to continue to develop as researchers in their academic discipline. They often don’t have families. They often don’t have jobs outside the school, unless you count ones that require you to wear a paper hat. They very often spend their time working for their professors, doing research and teaching.

That struck me as I was sitting in the Systems Science building at PSU, listening to the grad student, and student/professor discussions that went on around me. My right ear was getting snippits of a discussion of biosystems simulation, one that ranged from cell diffusion to the language of bees, and the issues associated with writing code to support it. My left ear was picking up discussions of symmetry breaking in physics and information theory, and of frozen accidents in evolution. In my presentation, on agent based simulation, the discussion ranged from fractal networks to random boolean networks to the desired level of expertise in a field. Afterwards, I sat in a group that discussed the importance of good data and consistency in phenotype definitions for GWAS analysis. Note that all of these were associated with actual research issues, and weren’t just late night beer-fueled gabfests. Those came later.

Good as my job is, I miss that kind of wide-ranging, yeasty, no limits discussions.

Bruce Schneier on Security

TED talks are an excellent set of 20min vids on interesting topics by interesting people. Here is security expert Bruce Schneier on emotions, models, and reality.

Robotalk – Much less than meets the eye

Or ear.

IEEE Spectrum has an article on two robots learning to communicate.

Lingodroid mapping maps

The thing is, as far as I can tell from the IEEE article (the press versions being less than useful), the two ‘bots were programmed to map their surroundings (presumably to a fine distance scale) using laser and sonar sensors. They were programmed to exchange gross location data, said data being a random string of consonant-vowel pairs. They were programmed to use this information to establish range-and-bearing data, and to exchange that data via a random string of consonant-vowel pairs. They were programmed to play games like “let’s go to pize”, and to compare their locations when each had thought they’d gotten there.

As a result of this programming, they were able to adjust their internal tables that mapped precise locations to coarse positioning words (the positions were coarse, not the words), such that they generally agreed that this region of precise mapping should be designated as that region of coarse mapping.

This is an interesting development, but it’s more along the lines of a good application of fuzzy logic than it is “development of language.” Essentially, they are creating ‘lingustic variables’ and then defining various membership functions on those variables. They have been programmed to be able to adjust those membership functions so that both of them agree on their shape and location.

So, did they reinvent fuzzy logic, or did they just apply it as programmed? IEEE doesn’t say.

Systems, General Systems, Systems Dynamics, and the Earth 2

Good artists copy, great artists steal. I just leech. Once again, Vandana Singh has come up with an excellent essay over on Strange Horizons. There, she talks about the importance of not just preserving biodiversity, but restoring it. This is important, because an ecosystem is a tightly woven mesh, pulled taut over an uneven, spikey, even, fitness surface. Cut one thread and the mesh twists and distorts.

Obama vs Osama 2

There are two other issues that I skipped over in my original writeup. First is the question of mixed vs pure strategies. Second is the question of value assignments.

In game theory, when you don’t have a dominant strategy, or a saddlepoint (where both players have no incentive to move), you have two choices. First is to go with a pure strategy. That means you pick the high payoff strategy and stick with it. That automatically means that you will lose a certain percentage of the time. Not only that, your opponent can detect this, and change their strategy accordingly. Your second choice is to go with a mixed strategy, playing the two feasible strategies in combination to maximize your payoff. You automatically lose part of the time, but you will pick a secondary strategy when a secondary strategy is needed, just often enough to make it worthwhile. The problem comes when you have a one-shot game — you can’t depend on the wins and losses evening out in the long run, because there is no long run. Traditional analysis suggests you still use some random number generator to decide what to do, even if it’s only the once. Depending on the relative likelihoods, this might be a good idea. Since I doubt that President Obama used formal game theory to make his decision, I think it’s also unlikely that he rolled a D20 die to determine the outcome.

The second issue is that of assigning values to the outcomes. My values were highly subjective, not to say, rigged. You may conclude that pattern-bombing Abbottabad would have a larger downside than shown. That’s fine. That’s perfect.

You see, what building this game does is force you to make your assumptions explicit. The same holds true for modeling and simulations and other kinds of games. Many times when people are arguing over something, and it seems like they are arguing past each other, it’s a case of one person having one set of assumptions about how the world works, and another person having a different set of assumptions. Forcing us to bring these assumptions out into the open means we can start arguing about the assumptions, not some presumed second order effect that occurs if they’re true. There are a number of assumptions made in the OvO game. One is, what is the probability of an ISI leak if we tell them. Greater than 50%? 90%? Another is, what’s the difference in payoff among the three strategies, assuming we get OBL. There are others, but they are left as an exercise for the reader.

Obama vs Osama

While I was prepping for a class last week, it struck me that part of the decision-making process for the Osama bin Ladin raid could be modeled as a game theory exercise.

Setting the Stage

CIA believes OBL is living in a compound in Abbotabad, PK. We have three strategies for taking him out.
1. Two B-2 bombers, carrying 16 x 2,000lb bombs each.
2. Helicopter raid by Special Operations Forces
3. Joint raid with PK government forces.

Each of these has advantages and drawbacks. How do we decide what to do? Enter, game theory.

Systems, General Systems, Systems Dynamics, and the Earth 1

I just finished reading an essay by Vandana Singh, Author, Friend, Bloggatrix, and -ePal, over on Strange Horizons. The essay begins halfway up a cliff in the Himalayas, and ends with the idea of rewilding nature and the promise of future essays on the why and the how. I recommend you go over there and read it. Don’t worry, you won’t miss anything — not much happens in this corner of the Internet.

Much of the essay deals with interconnectedness, and the human response to local emergence. I am inspired to talk about the same topic (thanks, Vandana), but in a more formally Systems Science fashion.

God hates shrimp – part 2

Last week, inspired by this shrimp tale, we talked about scientific theories (AKA models that explain observations) and showed how Darwin’s Theory of Evolution, updated via DNA, is a good description of what we see today, and a good enough predictor to use for commercial products that make money, today. What about the past? Is there anything that could disprove the Theory of Evolution as a descriptor of the ancient past? What would it take? Well, based on what we’ve said so far, you’d have to show that its predictions were wrong, or that it contained an irreconcilable internal contradiction, or that your model was better. More to the point, since modern day evolution theory (currently) passes all the tests, you have to show that when it is applied to the past it creates incorrect predictions or logical inconsistencies, or that there is a better model.

God Hates Shrimp – Part 1

This article in Science Daily got me to thinking about evolution and science.

A while back, when I was talking to a colleague about science, she asked what proof scientists would need to disprove evolution. I glibly said “but we know it works”. Which is a cop-out. Let me make another attempt.

First, let me repeat what I’ve said elsewhere about the language of science. To a civilian, the word “theory” means “a hunch”, but to a scientist it means “a model for explaining observed phenomena”. You can’t prove a theory. All you can do is disprove it.

Systems and Afghanistan

Michael Yon links to an interesting article by Rice and Filippelli on using technology to fight corruption in Afghanistan (I am using his version because it’s easier to read). As usual, I have grave doubts about the likely success of any technological solution to a complex societal problem. I have written about the Multiple Perspectives issue before, and I think it applies here.

In a nutshell, systems scientist Hal Linstone posits three Perspectives on any organizational problem — Technical factors (how the associated technology works), Organizational factors (how the rules of the organization are structured) and Personal factors (how key individuals see the problem and the issues surrounding it). In the computer field, most IT people think in terms of technical solutions to problems. Most of the time their solutions don’t work the way they think they should, because of the other two. To pull an example off the top of my head, what is the use of an ultra-secure voting machine in promoting democracy, if the law limits voting to males, and the president of the country thinks it ought to be males with property?

In the Rice and Filippelli article they point out how using cellphones for salary payments to police and soldiers cut out the middle-men, who were all corrupt, and actually sent all the money to bank accounts belonging to actual people — no skimming and no payroll padding. The police and soldiers involved thought they’d gotten a substantial pay raise, when all they got was their true salary.

So, say R&F, why not move more of the payment system onto cellphones? Well, this is why not — the Kabul Bank is in danger of collapsing due to corruption and fraud. A cellphone based economy is subject to the same kinds of problems a paper economy is, just in a different form. If you have a corrupt banking Organization, and Afghanistan’s appears to be very corrupt, the kleptocracy will find ways to steal the peoples money from the bank. And, if the top People involved, like the president and his relatives, view the country as their own personal ATM, there will ultimately be little done to correct it. After all, from the view of the people on top, it is working.

I think that R&F’s point is a useful one, and I’m not saying don’t do it. I am saying that the Technical solution is not a panacea, and that we have to attack the problem on a wide range of fronts. Of course, that assumes that we have the power to do so. In this case, the Organizational kicker is that Afghanistan is a sovereign country that doesn’t need to do what we say.

UPDATE: Here is another example of how making an improvement in technology doesn’t always improve things.