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Simulating Societies
blamanj writes "Most of us were exposed fairly early to Conway's game of Life.
A few simple rules produce a fascinating variety of behavior. Now, it
appears that similar simulations can predict the behavior of populations and human societies."
Damn!! I wanted to submit that comment!!
The question is: is this inspired by Asimovs' excellent work or is it a completely new approach???
Do not beware of the Mule. Beware of Daneel R. Olivaw and his friend Giskard R. Relentlov..... They are much more dangerous....
It's not just that the simulations use
simple rules, but we humans use simple rules
too because we are simple minded and are usually
driven by simple heuristics. It's not suprising
that the simulated behaviour closely matches
real behaviour. Fot it to be otherwise would
take a level of intelligence we don't seem
to have.
Problem is, the Club of Rome predicted that everyone in the Western world would either be starving to death or choked in their own waste by the far-off year 2000. Looking out my window today, I see that things are far from perfect, but we have a higher population, more food, and in many respects less pollution than we did in 1975. So the CoR's models were dead wrong.
sPh
You're correct about the models not necessarily being reversable - meaning that you can't predict history from the future. However, the correct method of verifying a simulation as correct is to verify the simulation results against known data. In the article, where it talks about the Anasazi, they describe writing the simulation and then letting it run through they years that they have data about the Anasazi (where the villiages are, the water availability, etc) and comparing it to reality. As described, they got quite close to reality. Villiages ending up in the same spot as reality over 50% of the time, etc. etc. etc.. Remember, it is very hard to determine the cause (or stimulus) from the effect without additional data. However, if the cause (stimulus) is known, the effect is usually fairly easy to guess.
If we were to try to build a model of today's history, you would want to build the model, seed it like the world was in the 1700's or earlier and let it run, and see how often it ended up correct. If it wasn't quite accurate, figure out where your model is wrong, fix, and repeat.
In the Asimov stories, what Hari Seldon was doing was to come up with a set of "formulas" (stored in the prime radiant) which accurately simulated history. The more accurate the formulas and the data you have, the more correct you are going to be. Hari and the members of the Foundation were constantly working on tweaks to better account for errors in the simulation. The hard part is dealing with the truly random influences. For instance, in the article when they talked about the Anasazi, they used real weather data instead of simulating it. I suspect if the weather data was simulated, the simulation would not have been as accurate on a year-to-year basis, although if the weather simulation was realistic enough I suspect that the outcome would have been similar.
Thinking back about Psychohistory as put forward by Asimov, I think that the only thing which really stretches for me is the accuracy (within a few months) of the events which he predicted-- taking into account the numerous variables which have such a rare occurance (such as an asteroid hitting a planet wiping everything out, or another major random event), that it would be throw the accuracy of small-scale events off. It seems logical that you can be accurate on a large scale on a simulation (over many thousands of years) or on a small scale (over a hundred years or so), but not both with the same simulation.
The major problems with the models is that they are not very good at handling technological change that in turn makes fundamental changes in the values the models use to make its predictions.
For example, let's say your population growth model includes a value for "food value produced per acre of land". If something comes along that allows more food to be produced per acre, then that'll skew the models to hell.
This actually happened. A new strain of wheat (?) was produced a few years ago that was able to survive in much tougher conditions, and that single-handedly staved off starvation in India.
The same with waste levels. recycling has become much more prevelent, and modern cars are so much better that they're actually starting to _clean_ the air that passes through them.
The models were accurate the day they were published, but the run conditions have changed since.
DG
Want to learn about race cars? Read my Book
The police were perfect in the simulation, that is, they only threw corrupt people in jail. Only thing that changed was the likelihood of being caught. Now, I would be interested of the results with imperfect and indeed corrupt cops.
The problem with the "running forward from 1900" test is that the model includes, both explicitly and subconsciously, the model maker's view and understanding of the world that already exists. Including the events that occured between 1900 and 2000, say. So of course you would expect it to show reasonably accurate results for that time period - otherwise it would have been discarded during the development phase. However, that is no guarantee that the model is accurate outside the limits of that perception of the world.
I ran into exactly this problem myself. I developed several system dynamics models that seemed to give a good simulation of the population and wealth of the City of Chicago from 1950 to 1980. But when I ran them starting with the base data for similar cities, I got meaningless results. What seemed on first examination to be a general model of city population was actually just a condensed way of displaying the known state of one particular city.
So stronger tests than just "run forward to known state" are needed. Some argue that human events include irreversible processes, so perhaps the "run backwards" test is not valid. But more is needed than a demonstration between two known states.
sPh
First, the book is full of examples, but nowhere to Epstein and Axtell give you enough information to actually reproduce their results (a classic mark of shady science).
Second, there are parts of the book where they draw conclusions from things that are obviously simulation artifacts (ie. if you change the grid size, these effects disappear or are mitigated severely).
Did I mention their lack of understanding of basic computer science issues? (Their formal training is in the social sciences).
For a pair of scholars at the esteemed Brookings Institute, you would would expect more. Unfortunately, you wouldn't get it.
Don't buy their book.
i've done this in other research, it checks out, it's pretty neat to calculate the length of posts/conversations etc, rank them and graph them, and see a zipf distribution pop out. anybody else out there doing this?
The Journal of Artificial Societies and Social Simulation (JASSS). It is on-line. It is free. It is great.
Belief is the currency of delusion.
Check out this cellular automaton which I made which makes some cool graphics:
www.geocities.com/enriqueeder/trip.html
Each pixel is a cell in the automaton. Each cell has 3 quantities each of which has a value between 0 and 255. The quantities correspond to the amount of red, green and blue in the color of the cell.
The color of each cell in the next frame of the simulation depends on its current color and the color of its neighbors in the current frame. The rule is that each quantity (red, green and blue) has an enemy or inhibitor quantity. For example green is by default the enemy of red, so the more green a cell's neighbors have in the current frame, the less red that cell will have in the next frame. Red is also the enemy of blue, and blue is the enemy of green. So each quantity has an enemy.
The simulation is seeded with a randomly colored cell by clicking on the black screen. To run the simulation, click the Go button. To stop it, click the Stop button. To advance just one frame click the Step button.
If you click the Design button, a window will pop up where you can modify the parameters of the calculation. The Neighbors amount determines how much the amount of the enemy quantity in a cell's neighbors affects that cell in the next frame. The Self amount determines how much the cell stays true to its current color. The Enemy amount affects how much one quantity is affected by its enemy quantity. The Direction button flips the quantities' enemies.
The unexpected result is trippy swirling patterns as red chases green, green chases blue and blue chases red.
/.
A fatal flaw of this simulation (as a model of real society, that is) is that it includes the "Cincinatus" characters - the incorruptible agents - but does not include the "Dillingers" - agents who are not deterred by punishment, of themselves or of others.
I have found over the years that people who are not influenced by "common sense" (or even an informed sense of self-preservation) are much more common than incorruptible people. Luckily (perhaps) these people more commonly are obsessed with greed than killing, or we'd have a lot more mayhem and a few less rich people.
Thus, the simultation should include agents that are not influenced by the arrest rate, and the model will probably become cyclic instead of trending to a fixed equilibrium.
Your statement that "the simulation is accurate" is unfounded, as any serious study of real behaviour in a police state will show. The Chinese shoot homosexuals and drug addicts; yet they still occur just as frequently as in other nations with less draconian laws. The US is "soft on crime" according to the Immoral Minority, yet our crime rates continue to drop.
But of course, anyone who thinks humans are simple agents with simple motivations is very unobservant.
--Charlie
The word "chaordic" is used as defined by Dee Hock (the person behind VISA) at http://www.chaordic.org and in his book "Birth of the Chaordic Age", which is essentially processes at the boundary between CHAos and ORDer and the social implications for how to design effective and responsive organizations for a dynamic society. The focus will be specially on computer simulations to support part of the goal defined here http://www.chaordic.org/who_hist.html#FourCond of: "Development of visual and physical models of chaordic organizations so that people have something to examine, experiment with, and compare to existing organizations. The models must contain the ethical and spiritual dimensions generally lacking in current models. In addition, computer simulations will need to be created to allow people to quickly see how clarity of purpose and principles allow institutions to self-organize, evolve over decades, and link in new patterns for an enduring constructive society."
People are invited to join the mailing list if they want at this page http://mail.freesoftware.fsf.org/mailman/listinfo/ simulchaord-discuss
if you want to contribute to project related discussions or submit
snippets of code (with the understanding contributions will be archived
and can be incorporated into the project under the GPL license). I have been posting some artificial life links there related to modelling social systems to get things started -- one of the first was a link to the Atlantic Monthly article discussed in this Slashdot thread. For now, I am using
use the list to record my own musings on related simulation issues
including design, architecture, and use cases. I will also be posting my experiences as I try to create such simulations. Feel free to lurk for a while or chime in.
Here is a page leading to the entire mailing list archives (aroudn twenty messages so far): http://mail.freesoftware.fsf.org/pipermail/simulch aord-discuss/
The main project page is here: http://savannah.gnu.org/projects/simulchaord/ Cooperative development of releases of code is hosted on Savannah using CVS although I haven't yet put up any content (files or homepage) besides what's archived in the mailing list.
At the moment I am looking at using Swarm http://www.swarm.org as the base -- although I may just use Python instead -- or even use both for different aspects.
A 21st century issue: the irony of technologies of abundance in the hands of those still thinking in terms of scarcity.