I’ve found trends on Google news interesting for some time. For example, did net news predict a housing bubble?
As online sources of such social data get richer, and control and normalization issues are solved or at least made transparent, they could become a useful input to behavioral models. Already, I find them to be a useful reality check, for seeing how long it takes for events to show up on popular radar, and whether things that seem big are really big in the public mind.
A cool portal for worldwide geological map information has just been launched. OneGeology is a portal, into which regional geologic survey agencies can link their data through a map interface. Users can access map info through a web interface akin to Google Maps. Regional and global layers can be added easily, as in a GIS tool. Here’s a map of rock age for Afghanistan, overlayed on NASA Modis imagery, generated in just a minute or two:
Two quick visualizations of the scenario data behind the war game:
First, a map of what happens to the concentration of atmospheric CO2 as a function of an emissions target to which participating regions adhere (y axis) and the regions participating (enumerated on the x axis). This is basically an elaboration of everyone plays. The color scheme on the surface shows atmospheric CO2 concentration in 2100 in slightly cryptic units (TonC). The important thing to know is that the bluest end of the scale (all in, aggressive targets) succeeds in staying under 466ppm, the reddest end (no action) is a disaster at up to 750ppm, and the green and turquoise bands are near 2x CO2. Click the graphic for a larger version.
Second, a visual version of some of the data in the reduction illusion. Bubble size is proportional to regional CO2 emissions in 2015.
Is the cup half empty or half full? It seems to me that there are opportunities to get tripped up by even the simplest emissions math, as is the case with the MPG illusion. That complicates negotiations by introducing variations in regions’ perception of fairness, on top of contested value judgments.
A fundamental tension in the war game arose from the fact that, regardless of development and equity considerations, all large nations would have to make cuts eventually, because there simply isn’t enough atmosphere to go around. Consider the following graph, showing the tradeoff in emissions cuts for a two-region world.
The x-axis shows the size of emissions reductions in a major region, representing a quarter of business as usual emissions (think US or China). The y-axis shows how much the rest of the world would have to cut to meet a global target, given the action of the first region. You can see from the solid orange line that meeting an 80% global cut requires at least a 20% cut from the major region. Otherwise, it’s not physically possible to balance the carbon account (unless free-air capture, a thermodynamic loser, becomes viable). A global target of a 90% cut from current levels – not an implausible requirement in the long run – requires at least 60% participation from the major nation above. Clearly the developing country position of do-nothing-but-grow can be no more than a transient if we are to make real progress on mitigation.
After seeing the presentation around it, Eli Kintisch of Science asked me whether it was realistic to assume that 2050 climate is already locked in. (Keep in mind that we were living in 2015.) I guessed yes, then quickly ran a few simulations to verify. Then I lost my train of thought and lost track of Eli. So, for what it’s still worth, here’s the answer.
Over breakfast this morning (day three), I heard from several participants that the war game was “too easy” – that is, negotiators were too free to agree to aggressive commitments which their real-world constituents or bosses wouldn’t support. That wasn’t my observation in the China team room, and in closing statements it seemed that teams were taking their roles very seriously indeed. It’s not quite the Zimbardo Prison ExperimentÂ but it’s very real in some ways.
The following are some of the more poignant comments from players, with a little editorial license on my part. Hopefully I have attributions and the general drift correct; please comment if I don’t. (Actually, please comment either way!). I’ve colored a few points that I regard as particularly critical.
It was a little surprising that teams didn’t resist the underlying scenario of the game, which includes an aggressive 80% reduction from 2005 emissions by 2050.Â This may have been a recognition of theÂ critical need, pointed out by Sharon Burke,Â to look past what is politically feasible and keep the real goal in sight (i.e. emissions that will achieve stable climate).
Drew Jones of the Sustainability Institute stumbled on a great opportunity for model-based decision support. There are lots of climate models and integrated assessment models, but they’re almost always used offline. That is, modelers work between negotiations to develop analyses that (hopefully) address decision makers’ questions, but during any given meeting, negotiators rely on their mental models and static briefing materials. It’s a bit like training pilots in a flight simulator, then having them talk on the radio to guide a novice, who flies the real plane without instruments.
MIT researchers have developed a cool digital drawing board that simulates the physics of simple systems:
You can play with something like this with Crayon Physics or Magic Pen. Digital physics works because the laws involved are fairly simple, though the math behind one of these simulations might appear daunting. More importantly, they are well understood and universally agreed upon (except perhaps among perpetual motion advocates).
I’d like to have the equivalent of the digital drawing board for the public policy and business strategy space: a fluid, intuitive tool that translates assumptions into realistic consequences. The challenge is that there is no general agreement on the rules by which organizations and societies work. Frequently there is not even a clear problem statement and common definition of important variables.
However, in most domains, it is possible to identify and simulate the “physics” of a social system in a useful way. The task is particularly straightforward in cases where the social system is managing an underlying physical system that obeys predictable laws (e.g., if there’s no soup on the shelf, you can’t sell any soup). Jim Hines and MIT Media Lab researchers translated that opportunity into a digital whiteboard for supply chains, using a TUI (tangible user interface). Here’s a demonstration:
There are actually two innovations here. First, the structure of a supply chain has been reduced to a set of abstractions (inventories, connections via shipment and order flows, etc.) that make it possible to assemble one tinker-toy style using simple objects on the board. These abstractions eliminate some of the grunt work of specifying the structure of a system, enabling what Jim calls “modeling at conversation speed”. Second, assumptions, like the target stock or inventory coverage at a node in the supply chain, are tied to controls (wheels) that allow the user to vary them and see the consequences in real time (as with Vensim’s Synthesim). Getting the simulation off a single computer screen and into a tangible work environment opens up great opportunities for collaborative exploration and design of systems. Cool.
Next step: create tangible, shareable, fast tools for uncertain dynamic tasks like managing the social security trust fund or climate policy.