A Chronicle of Futures Foretold

By Catherine

Written by Catherine Bolgar

Wouldn’t it be nice to have a crystal ball to warn us when the next crisis will happen?

Buffalo on Wall Street

While it isn’t a crystal ball, Swiss university ETH Zurich has developed a scientific platform to predict when bubbles develop. Didier Sornette, professor of entrepreneurial risks at ETH Zurich, created the university’s Financial Crisis Observatory in 2008 as a reaction to the global financial crisis.

I became very angry about what I read that predicting such a crisis wasn’t possible,” he says. “We knew a crisis was coming. We knew this impression of great wealth and mastering of the economy was an illusion.”

Dr. Sornette notes that he doesn’t predict the crash. “We diagnose the bubble before the end, when a crash confirms it existed,” he says.

Normal up-and-down cycles aren’t the same as the booms and busts of bubbles. The existence—or nonexistence—of bubbles ends up in arguments about what is normal vs. abnormal, compared with history, price-equity ratios or an excessive growth rate of the stock market.

What should the normal growth rate be: 5%, 15%, 20%? What could justify that last year the return was 20%? Was it irrational or was it new technology?” Dr. Sornette says. “People find reasons that justify the observed price.”

Rather than pick a number, Dr. Sornette’s model looks for superexponential growth. Regular growth is exponential because of the effects of compounding. With superexponential growth, the growth rate itself is growing.

The bubble is when rate of return accelerates,” he says. “It’s a positive feedback loop. In normal circumstances, the higher the price, the lower the demand. In a bubble, the higher the price, the larger the demand and therefore there’s a larger subsequent growth rate. It’s due to a crowd effect, or herding, because it’s so tempting to imitate the others and to run after the bonanza of the time.”

By coming up with a scientific model with verifiable metrics, Dr. Sornette hopes the resulting evidence will help the “is-it-a-bubble-or-not” debate move from being philosophical or political to being scientific. Similarly, it wasn’t until the existence of the ozone hole was scientifically proven that an international agreement to ban chlorofluorocarbons was adopted.

William White, who was a member of the executive committee of the Bank for International Settlements, and some of his BIS colleagues warned that a crisis was about to hit in 2007.

It seems what we have to focus on is the systemic fragilities that are building up in the economy, as opposed to looking for any trigger point,” he says. “My way of looking at it is increasingly to see the economy as a complex adaptive system. It shares the characteristics of other complex adaptive systems: we know they are inherently vulnerable to crises and that crises occur on regular basis, though the literature says big crises come infrequently but little crises come frequently. In something as complex as the international financial system, things are going to go wrong.”

Cyclical downturns are “events that clear out the system,” he says. “We probably have had 25 years of too little tolerance for downturns. Every time one threatened or happened, we just threw huge amounts of monetary intervention and expansion at it.”

Economic Bubble

The longer bubbles persist and the larger they are, the more likely they are to spread into other sectors. “It’s because of the wealth effect,” Dr. Sornette says. “During a bubble everybody feels rich.”

This makes it hard to stop bubbles. People love them while they’re inflating, and policy makers don’t want to declare an end to the party. One goal of scientifically declaring the existence of a bubble is to force the hand of policy makers to take actions that will deflate or plateau the bubble before it expands to the point of triggering a big, messy crisis.

Financial crises are particularly difficult because the fears of sudden failures can turn into a sort of reverse bubble, with losses feeding new losses. In addition, “a bank that is in bad shape finds it difficult to raise new equity and so is reluctant to make loans, which hurts the real economy,” says Paul Klemperer, economics professor at Oxford University in the U.K.

To remedy this, contingent convertible bonds were designed so that, when things go bad for a bank, they turn into shares or equity. The question is, “‘when does the conversion happen?’ In practice, when regulators say so,” Dr. Klemperer says. “Can we trust regulators to say so in time? We need these bonds to convert automatically, so regulators have to take action to stop conversion, not start it.”

He and co-authors Jeremy Bulow, professor of economics at Stanford University, and Jacob Goldfield, a former senior partner at Goldman Sachs, have proposed a new form of hybrid capital for banks, equity recourse notes, which automatically convert debt into equity when a bank loses market capitalization.

For Dr. Sornette, diagnosing bubbles with scientific metrics is another way to automatically help regulators and policy makers act: “They will do the right thing, if they’re forced to by the scientific evidence.”

For more from Catherine, contributors from the Economist Intelligence Unit along with industry experts, join The Future Realities discussion.

Improving the Reliability of Consumer Electronics Products Through Realistic Simulation

By Harish

Realistic simulation for Electronic products

Early product failures and product recalls are very costly. They result in loss of revenue, litigation, and brand devaluation among others. Hardware recalls are often costlier than software recalls as software patches can be easily downloaded and installed once flaws come to light. But recalls and early product failures tend to happen over and over again. Why? Because engineering teams are constantly under the gun to improve product performance, reduce form factors, and reduce time to market, all while cutting costs. In order to mitigate risk engineers need to develop a deeper understanding of the product behavior under real operating conditions and quickly evaluate design trade-offs based on overall system behavior.

Physical tests provide an excellent means to understand product behavior. However, physical testing is expensive and time consuming. Simulation provides a cheaper and faster alternative to physical tests. It is critical to strike the right balance between physical tests and simulation during product development. In order to get the maximum bang for your buck, simulations should be deployed starting early in the design cycle when physical prototypes are not available and the design is not fully developed. The earlier you find flaws, the earlier you can fix them. Since the cost of fixing flaws grows exponentially through the design cycle (figure below), identifying and fixing design flaws early in the design cycle is super critical.

Relative Cost of fixing errors in embedded Systems

Relative cost of fixing errors in embedded systems

Not all simulation tools are created equal. You don’t need any answer. You need the right answer. For that, you need simulation tools that most closely depict reality. And you need answers fast. Hence you need product testing and validation tools with industry leading physics and solver technology to obtain accurate solutions faster in order to help you improve product design, ensure product reliability and reduce time to market. Accurate depiction of material behavior and physics of failure are essential to obtaining realistic results. Such capabilities are critical in predicting the behavior of materials such as glass, adhesives, and polymers that have high propensity for damage.

Consumer electronic products, especially mobile and portable devices such as smartphones, tablets and laptops, are subjected to a variety of operating conditions. The devices need to be designed to protect them from damage. Engineers need to ensure that “portable” doesn’t mean “breakable.”

Tablet drop

The challenge is to design a light-weight product that can withstand not just the loading cycles associated with regular usage, but also abusive loading scenarios that are encountered less frequently (According to surveys and insurance claim statistics, drop and water damage constitute the two most frequent causes of damage for mobile devices.). Simulation should be employed at the ideation, product development, and failure analysis stages in order to improve product quality and reduce time to market. Refer to this  case study to learn how a leading manufacturer of consumer electronics used simulation to improve the keystroke feel and to enhance frame rigidity while reducing weight .

Tablet drop simulation

While drop during daily usage is a concern for mobile devices, transportation drops are the main concern for office equipment. The engineers are faced with the challenge of identifying the structural members that are most susceptible to damage and to improve their damage resistance while reducing the overall weight of the structure. Refer to the ebook below to know how a leading manufacturer of office equipment designed a low cost printer that can withstand a series of transportation drop tests.

The examples above provide a snapshot of applications leveraging SIMULIA Abaqus technology   to successfully improve product durability while satisfying other constraints such as weight and cost.

More example related to  how engineering teams are using virtual testing to predict stresses, optimize design performance and reduce time to market can be read in  this ebook .

Alan Turing, Another #DDay Engineering Hero

By Aurelien

Alan Turing60 years ago died Alan Turing, a great mind considered to be the father of computer science. On this occasion, the University of Reading announced that they passed the famous Turing test (although leading to much debate and controversy as one could expect).

But Alan Turing is also known as playing a key role in the resolution of WWII, in particular his contribution to the effort at Bletchley Park. There, he created an incredibly efficient signal intelligence operation to simplify the reading of messages encrypted using the German Enigma Machine. The Allies had to know if Germany was taking the bait on the many deceptions being deployed, such as Operation Fortitude, a deception strategy to let Germans believe that the landings would actually happen in North of France instead of Normandy. As many other engineers who contributed to the incredible innovations of the D-Day, we should pay tribute to Turing for the same.

Of course, Alan Turing also has a special connection with us, as a scientific software company.

Put simply, Alan Turing’s test could be summed up in this quote:

A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.

What I like in this definition is how it relies on human perception to assess whether a machine is declared intelligent or not. In fact, it has a lot of commonalities with 3DEXPERIENCE itself. Indeed, if you’d transpose this definition of artificial intelligence to realistic visualization and simulation, you’d end up in a definition of what a Lifelike Experience can be:

YouTube Preview Image

A visionary mind, Turing envisioned a digital world where computers could not only simulate lifelike experiences but could even be a part of those experiences, as he mentioned during a not well-known lecture broadcast on the BBC:

It might for instance be said that no machine could write good English, or that it could not be influenced by sex-appeal or smoke a pipe. I cannot offer any such comfort, for I believe that no such bounds can be set.

Check out this nice infographic about Alan Turing, courtesy of Jurys Inn Manchester Hotel:

Alan Turing Infographic courtesy of Jurys Inn Hotels



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