How the Living Earth Simulator Will Work

Simulations allow us to test and experience one system or process through the functioning of another. A U.S. Air Force flight simulator allows aspiring fighter pilots to test-drive an F-22 Raptor without endangering themselves -- or a $200 million aircraft. Likewise, a Resusci Anne CPR training dummy stands in for an unresponsive victim without risking a human life.

People have turned to models and simulations since time out of mind. Models of both people and animals often turn up at prehistoric sites, and the ancient Egyptians, Greeks and Romans all built models of their vehicles and buildings. In addition to ceremonial uses, these miniatures served as tools for teaching and planning -- much like the models we use today.

Humans also developed the means to copy more than mere physical forms. They learned to simulate systems. The ancient astrolabe, for instance, served as an indispensable astronomical tool for more than 2,000 years and is a working model of the night sky and the position of the stars. The user plotted colossal, interstellar movements while holding the device in the palm of his or her hand and manipulated the data to gauge time, location and distances.

The astrolabe was essentially an analogue computer, a pre-digital device that incroporated electrical, hydraulic or mechanical systems to simulate another system. The Monetary National Income Analogue Computer (MONIAC) stands as another classic example of analogue computing. Built in 1949 by engineer and economist Bill Phillips, the MONIAC used the flow of colored water through pipes, drains and pumps to simulate the British economy.

Digital computing, however, changed everything. Just consider meteorology, the scientific study of atmosphere and weather. Computer advancements allowed meteorologists to move beyond mere observation-based predictions and implement numerical weather prediction (NWP) models, in which computers pull past and present atmospheric data to construct predictive models of future weather.

The science of weather prediction is far from perfect, but better equations, more powerful computers and a widening array of atmospheric data sets continue to improve the accuracy of our simulations.

But can we really simulate the world itself? To find out, we have to travel the waters of big data.

Simulations feed on external data. In the case of weather simulations, the computer models require an expansive diet of both past and present atmospheric readings -- everything from the temperature in Aberdeen, Scotland, to Earth's current distance from the sun. It all comes together to form a more complete picture of the world's weather.

Humans have amassed vast collections of data on a range of topics, yet in most cases these data sets stand apart from one another. Just imagine human knowledge as a vast field littered with puddles. Each puddle represents a collection of data: economic data here, political data there -- all of them separate from the other puddles.

But the rain continues to fall and the puddles of data continue to swell, to the tune of 2.5 quintillion bytes per day [source: IBM]. (To give you an idea of how crazy that number is, some people have conservatively estimated that all the words ever spoken by humans equal 5 quintillion bytes of data.)

All that new data comes from climate sensors, social media hubs, digital media Web sites, online transaction records, cell phone GPS signals and countless other sources. The information about the world pours in at an exponential rate. In fact, according to IBM, 90 percent of the data in the world today was created in the last two years alone.

So the rain falls. The data pools swell and spread, overlapping and merging until there are no more pools -- just the vast sea of information we call big data.

To better understand the value of big data, think of it in terms of three v's: variety, velocity and volume. It encompasses data of all varieties, is generated in real time and amasses in volumes that stagger the imagination -- to the tune of petabytes. That's a million gigabytes, sufficient space to stash a 32-year-long MP3 file [source: BBC].

Can we really build a simulation of the world from this growing wealth of data? The men and women behind the FutureICT Project believe we can -- and all for a mere 1 billion euros ($1.3 billion).

So here we are, up to our necks in a sea of big data with a staggering inability to see the big picture it illustrates. Lucky for us, several major technology players have already staked their claims on the big data frontier.

NASA and multinational networking giant Cisco Systems are developing a $100 million Planetary Skin, independent of the FutureICT project. This integrated system of air, sea, ground and space sensors will allow the space agency to capture, analyze and interpret global environmental data for a more complete, real-time sense of planet Earth.

Yet even the Planetary Skin is just a fraction of what the FutureICT teams hope to achieve. The project is the brainchild of Dirk Helbing, a sociologist, mathematician and physicist who specializes in modeling and simulation at the Swiss Federal Institute of Technology in Zurich.

Helbing's early work in the 1990s focused on urban traffic, specifically how to prevent the cascading small traffic events that ultimately lead to large-scale congestion. Today, the interweaving roads he aims to chart are those of society, technology, economy and environment -- where the stakes range from financial crisis and political upheaval to nuclear war.

The German-born modeler compares the goals of the project to that of the European Organization for Nuclear Research's Large Hadron Collider, even going so far as to describe FutureICT as a "knowledge collider." In the same way that physicists at the famous particle accelerator attempt to answer fundamental questions about mass and matter, FutureICT hopes to reveal the underlying sociological and psychological laws that underpin human civilization.

After all, there is no grand theory of how society works. Just think back to the puddles analogy: Our previous lack of data made it impossible for the social sciences to develop a systematic science of human society -- much less keep up with globalization and technological change.

In addition to a 1 billion euro ($1.3 billion) pledge from the European Union, FutureICT has also acquired the cooperation of dozens of academic institutions, research organizations, supercomputing centers, businesses, industries and government agencies. The team has charted a 10-year course to reinvent how global information and communication technologies work.

On the next page, we'll look at the key components that make up the plan.

The FutureICT project breaks down into three core components. First, there's the Planetary Nervous System, a vast network of sensors that monitor socio-economic, environmental and technological systems. The sensors range from the smart grid power meters in your home to the Dow Jones industrial average to seafloor sonar beacons and mountaintop weather stations. FutureICT is even working with MIT's Media Lab to incorporate smartphone-generated data.

Many of the components for the Planetary Nervous system already exist -- the challenge is to bring them together onto a larger information platform.

The next component is the Living Earth Simulator itself, a meta-model of the world and human society based on information and analysis from the Planetary Nervous System. Don't think of it as a virtual world a la "The Matrix," however. Think of it in terms of a weather forecast that models far more than mere atmosphere.

The ultimate idea is that the Living Earth Simulator will allow us to run simulations that project future events based on specific questions. For instance, the simulator wouldn't answer the question, "What will happen on April 1, 2060?" anymore than it will answer "What will I eat for breakfast on April 1, 2020?" The former question is too broad and the latter is too small. Rather, the massive modeling tool would allow governments, organizations or even individuals to run the parameters and variables required to explore such questions as, "How will an Iranian oil embargo today affect the euro tomorrow?"

The simulator will also boast an open source component that operates much like the iTunes app store. This World of Modeling will enable various scientists and developers to upload their own expert modeling components that map corners of the world. Imagine an expert-generated Wikipedia in which the goal isn't a mere explanation of the world but a simulation of interconnected systems.

Finally, the FutureICT project features a Global Participatory Platform, which will serve as an open framework for citizens, businesses and organizations to share and explore data and simulations powered by the Living Earth Simulator. This aspect of the project will include everything from open debates on simulator projections to smartphone apps that exploit the data.

Will it work? Is this really the big science future of big data? On the next page, we'll get chaotic -- and predictable.

Let's return to the world of weather forecasts for a moment, as well as the world of butterflies and hurricanes. You've probably heard of chaos theory, the mathematical field concerned with the seemingly disorganized behavior of highly dynamic systems. The term originates in 1961 with meteorologist Edward N. Lorenz and his fascination with how the smallest of atmospheric variables can result in drastically different weather models. Yes, it's the butterfly effect, the notion that an insect might flap its wings in Brazil and stir up a tornado in Texas.

Due to its many variables, a dynamic system such as Earth's atmosphere is difficult to predict -- and the Living Earth Simulator aims to forecast outcomes in dynamic systems composed of interwoven dynamic systems. How do we hope to stay afloat in such an ocean of chaos? Plus, consider that the simulated Earth would be one with access to a Living Earth Simulator: a simulation of the world that has access to a simulation of the world. Can even a supercomputer find predictable patterns amid such complexity?

Other critics take aim at our ability to predict virtually anything at all. Nassim Nicholas Taleb's Black Swan Theory takes its name from the fact that before the discovery of Australia, scientific observation suggested that all swans were white. There was no more such thing as a black swan as there was a green or purple one. Then European explorers discovered a black swan Down Under -- an event that was both unpredictable and exceptional.

The black swan was an outlier, existing beyond the realm of reasonable expectation. But the human mind depends on pattern recognition, so, Taleb writes in his Black Swan book, we humans think up explanations for an outlier's occurrence after we encounter it, making it explainable and predictable.

By their very nature, outliers are unpredictable and, according to Taleb, this implies the inability to predict the course of history, given how much outliers such as the market crash of 1987, the demise of the Soviet Bloc and the Sept. 11 terrorist attacks have drastically informed the shape of human events.

Another criticism stems not from the science of predictability but from the stubborn and irrational natures of humans. Assume for a moment that a cabal of supercomputers will one day crunch all our big data and advise us on which choice in a given decision will steer us away from some major catastrophe. Will we listen to the machines?

Columbia University statistician Victoria Stodden argues we might not, especially if we can't comprehend the colossal calculations that went in to the computer decision [source: Weinberger]. Stodden points to scientists' warnings about the dangers of climate change -- and how often these simulation-based warnings go unheeded.

Dirk Helbing and FutureICT remain confident, however, that the Living Earth Simulator will vastly improve humanity's ability to cope sustainably with the challenges of a dynamically changing world. The models won't be perfect, won't glimpse the far future or the minutiae of daily life. But, according to Helbing, they will provide us greater insight into how many systems that make up human society actually work.

Explore the links on the next page to discover even more about the future of technological innovation.