Interesting what turned out to be the best scenario.
The scenario with ’Low technology’ seems to be the most reasonable one with an initial decrease in quality of life that rebounds in the later run of the model. That seems to happen, as prices remain much lower than those observed in the ’Average’ and ’Positive’ scenarios, although not at zero as the ’Negative’ one
Where the scenario »Low technology« is defined as follows.
• a massive solar flare from the sun disables all electronic equipment
• a bioengineered super-nutritional food is available
This is breakthrough. I'm telling you, it is a game changer, and it was coded by women -- which is also significant.
Simulation of human life combined with reality capture and BIM software are going to change the game. So-much-so that the market price of commodities like copper and aluminum are going to become of GLOBAL interest.
This could not have come at a better time than when scientists are freaking out about the Great Barrier Reef being almost completely dead.
I can tell you're excited by this, but I can't be sure as to why. Which "game" do you think this project is going to change? Bearing in mind that simulating/predicting mass psychology/behaviour has been mainstream in the field of economics for quite some time.
Because it is not so focused on people as much as it is with "people in cities." Cities are the future of low carbon emissions. So specifically simulating and visualizing the happiness of people within cities is huge. There is great potential for this work.
Even though you can't draw any meaningful conclusions from this, I think this is a great effort. The results are weird in the sense that the results in the scenarios don't resemble our current outcomes. A couple of comments on that:
1) Models are hard to get right. You need to find the right balance between simplifications and reality. The results indicate something is not calibrated or functioning the same as our current state.
2) Models are intended to be simplifications. We draw meaningful results from very simple models (e.g. supply and demand) but without modeling the complex interactions it's hard to say what impact could a certain policy have on the system. Agent-based models are a way to do this and the computing power available is allowing us to experiment with this.
Re: #1. We may find that modeling and seeing outcomes similar to our own is very hard to get to. This may be due to model specification, or it could be that given the rules that govern our own interactions and intentions, our current state may be the one we just chanced upon.
This is a hard challenge to tackle but I'm glad that the authors have published this and shared their code. Don't expect to have prescriptive solutions from it anytime soon though.
It's not completely fair to judge the quality of this paper by the first few references (8 and 25, in particular), but it does make me extremely dubious. It's not really appropriate to cite Ed Glaeser in an urban modelling paper (or anywhere, really, other than as a cautionary example), and that interpretation of Bettencourt and West won't bear much scrutiny either. I won't address the…contributions (I work in a department that does a lot of urban modelling and ABM – it's cited in the paper – but I'm not a modelling / ABM person myself)
urschrei, can you please expand on your claim about the appropriateness of citing Ed Glaeser on this topic? I am a layman on the topics of economics and urban planning. Glaeser's Wikipedia page suggests he is an active researcher in this field; what is which you believe makes him unsuitable?
Seems similar to the various Simcity designs. I wish it was available in a form we could interact with. When designing simulations like this of real life, among other problems the initial conditions are hard to generate since real cities grow in historical contexts, not at some recent starting point. I assume the initial conditions probably result in a fairly unpredictable and chaotic end state.
Ooh, I have a new toy now. My main personal obsession right now is a Census/ACS data project (I'd be tempted to post it here, but I'm worried about the HN effect stomping my Linode flat), so something like this is right up my alley.
And it's even written in Python, just like my project!
I can't wait to get home tonight and play with this...
The scifi book Blindsight has a similar concept - simulated environments for disease testing - but instead of creating a new simulation researchers use MMOs. Thinking about real-life examples, I wonder if a game like WoW with history and aspects of civilization would be accurate enough to research disease spreading.
26 comments
[ 3.1 ms ] story [ 62.2 ms ] threadThe scenario with ’Low technology’ seems to be the most reasonable one with an initial decrease in quality of life that rebounds in the later run of the model. That seems to happen, as prices remain much lower than those observed in the ’Average’ and ’Positive’ scenarios, although not at zero as the ’Negative’ one
Where the scenario »Low technology« is defined as follows.
• a massive solar flare from the sun disables all electronic equipment
• a bioengineered super-nutritional food is available
• disease has been totally eliminated
Simulation of human life combined with reality capture and BIM software are going to change the game. So-much-so that the market price of commodities like copper and aluminum are going to become of GLOBAL interest.
This could not have come at a better time than when scientists are freaking out about the Great Barrier Reef being almost completely dead.
Because it is not so focused on people as much as it is with "people in cities." Cities are the future of low carbon emissions. So specifically simulating and visualizing the happiness of people within cities is huge. There is great potential for this work.
Perhaps you have some good ideas, but the form is terrible.
1) Models are hard to get right. You need to find the right balance between simplifications and reality. The results indicate something is not calibrated or functioning the same as our current state.
2) Models are intended to be simplifications. We draw meaningful results from very simple models (e.g. supply and demand) but without modeling the complex interactions it's hard to say what impact could a certain policy have on the system. Agent-based models are a way to do this and the computing power available is allowing us to experiment with this.
Re: #1. We may find that modeling and seeing outcomes similar to our own is very hard to get to. This may be due to model specification, or it could be that given the rules that govern our own interactions and intentions, our current state may be the one we just chanced upon.
This is a hard challenge to tackle but I'm glad that the authors have published this and shared their code. Don't expect to have prescriptive solutions from it anytime soon though.
And by that I mean most economic theory about how to run policy.
And it's even written in Python, just like my project!
I can't wait to get home tonight and play with this...
And an amazing podcast, "This week in virology": http://www.microbe.tv/twiv/twiv-7-viruses-in-video-games/