Neural Network

Health Care Management: Decision Making at Two Levels

Tuesday, June 1, 2010 by Holly Bailey

Reading recent reviews of two books on healthcare caused me to realize that in spite of the rapidly increasing number of clinical studies that use risk analysis and neural networks to sort out the best treatment choices, there has been very little published on how to use quantitative tools like decision trees and Monte Carlo software to manage health care better. Given the recent national debates on health care reform, this is actually quite surprising.

There's health care management, and then there's health care management. On the macro level, decision evaluation focuses on the organization. Marian C. Jennings's Health Care Strategy for Uncertain Times (2000) prescribes ways for corporate health care managers to reshape the ways their organizations deal with uncertainty by adopting the same quantitative techniques used in the commercial realm by enterprises like investment firms and utility companies. On the micro level, health care management focuses on you, your body. Thomas Goetz's The Decision Tree (2010) prescribes how to apply a number of these same decision analysis techniques to your own health.

Essentially, what both books are saying is, "Look, the only certainty is uncertainty. But you have some numbers. Here are the tools to turn those numbers into plans you can reasonably rely on." These tools shouldn't be news to you as a reader of this blog, but apparently, if the popularity of Goetz's book and renewed attention to Jennings's are any indication at all, the health care management arena is plenty ripe for quantitative decision support tools.

Another take on the BP Oil Spill

Friday, May 28, 2010 by Steve Hunt

We are pleased to introduce you to consultant and trainer Sandi Claudell, today’s featured guest blogger. Sandi is CEO of MindSpring Coaching, and has been a valued Palisade Six Sigma Partner for quite some time. She is a Six Sigma Master Black Belt (Motorola), and is a Lean Master (Toyota Motors - Japan) among other notable achievements.

--Steve Hunt

Part 1: The Platform Disaster

Much has been said about the disastrous BP oil spill in New Orleans. If we use the theory of probability and reliability then have too many different companies responsible for a very complex construction and operation added to the chance of failure.

There is probably a cultural issue at work where each entity wanted to give the other what they wanted to hear rather than the truth. (For historic and recent examples: NASA Challenger and recent Toyota Prius problems). When we lose sight of quality and reliability of parts, construction, maintenance, testing under ALL conditions rather than the obvious few, etc. then we run high risks of failure. When you build 100+ wells and avoided disasters . . . perhaps people fool themselves into thinking there never WILL be a disaster. They don’t look at a model that demonstrates the longer you go without such an event (given the input factors of how each element can and will fail) the closer you come to the event we all want to avoid.

They may or may not have used an integrated Systems Design . . . not simply an engineering system but the system on how individuals work together, communicate with each other, act as a conforming unit or a more self-directed autonomous unit looking for and generating solutions outside the box. A team that is innovative and willing to look at all the possibilities and create a breakthrough design that was / is more mistake proof.

If they had used DFSS (Design for Six Sigma) then their designs would be more robust taking into consideration all the necessary safety precautions for human life as well as immediate response to a potential failure. As part of DFSS we use a statistical tool call Design of Experiments (Strategy of Formulations, Central Composites, etc.) where we can try very complex interactions (factors) with minimal effort / cost and maximum statistical accuracy. DoE creates prediction equations that allow us to model and ask questions of what would happen under different conditions. More importantly we can look at many different quality metrics (responses, outcomes, etc.) with the same experimental trial. If we replicate the test then we can even forecast what elements cause variation (very hard to detect in highly complex systems without the use of statistics).

If they had used an FMEA (Failure Mode Effect Analysis . . . a tool used in Six Sigma) then they could have anticipated failures and put error proofing devices in place to detect and/or respond to potential faults BEFORE it is irreversible. If we add a Monte Carlo simulation to potential working conditions then the model forecasts probability plots and identifies key factors that will be critical to success or failure.

Perhaps they did indeed use a Monte Carlo using Crystal Ball. It is a good product but if they used Palisade’s @RISK and added some of the other tools provided by Palisade such as RISK Optimizer, Neural Tools, etc. then they could have analyzed the system in other dimensions besides a simple Monte Carlo, thus uncovering weaknesses BEFORE designing and/or building the platform and well.

Part 2: Capping the well head

In Lean there is a whole discipline called “Error Proofing Devices”. As part of the design effort we need to create first and foremost safety and other devices that prevent the error from occurring in the first place. If that line of defense fails then there should be devices built into the process designed to cap the well if your error proofing fails. If that line of defense fails then there should be a disaster response plan created and practiced and tested to ensure that the spill is repaired immediately.

Part 3: Treating the resulting spill

Again, Design of Experiments could test different materials, chemicals and methods to find the right combination to contain or otherwise manage the resulting oil spill. Trying one chemical only may be the age old definition of madness . . . trying the same thing over and over again expecting different results. Again, a robust design of experiments could aid in the process of finding a solution that is most effective and with multiple tests on the same samples ensure that is it the most safe for the environment and the population most directly in the path of the oil spill. These tests are ideally run years before such a spill however, doing something now is better than simply standing by and watching it happen.

Last but not least:

Management (Executives down to line managers) should have coaches. Coaches who can speak to the culture, the systems design, the tools and methods used in Lean Six Sigma and who can verify data analysis and help with the accurate interpretation of the data. These coaches should be independent . . . not a full time employee of the corporation as they are more likely to speak the truth and highlight risks as well as opportunities.

Now BP and all the other entities may have done some of what I mentioned above. But I would assume they must have left out one or more of the listed items or we wouldn’t be looking at the oil traveling into the wetlands around New Orleans right now. Hindsight is always brilliant but we can learn from our mistakes. We can create better cultures, systems, error proofing devices, Experimental Designs etc.

BIO:

Sandi Claudell is CEO of MindSpring Coaching. She is a Master Black Belt in Six Sigma, a Lean Master and has worked as a consultant for many companies to initiate worldwide improvements. For more information or to contact Sandi please visit http://www.mindspringcoaching.com/.

Neural Nets Writ Small

Friday, May 7, 2010 by Holly Bailey

Of all the statistical analysis techniques I receive news alerts for, the neural network flashes up on my screen most often. While I, like many of you, really enjoy the big-screen futuristic applications of neural nets--prediction of sun storms is a splendid recent example--there is a quieter trend ramping up at a more down-to-earth level. The nano level,that is the itsy-bitsy, teeny-weeny, the molecular level.

For at least the past five years, the nanotechnology industry has been predicting and prototyping ways to incorporate neural networks into nano-machines. This innovation has proved to be very handy for sensing devices. The nano-sensor combines receptor particles with electronics controlled by a neural network algorithm. The neural net sorts through the sensor responses to uncover patterns that trigger alerts.

This year there was a flurry of media attention focused on one of these sensing technologies, the nano-nose, which uses an array of nano-receptors coordinated by a neural network. These sensors are being promoted to sniff out everything from explosives to disease.

One indication of the expected adoption of applications that combine nano with neural is the advertising for neural network algorithms that can downsize to nano. But more than one of the nano-machine innovators has commented on the need to develop more robust statistical analysis techniques to improve the accuracy of the sensors. Which means that there will be more neural network to shrink, which means that the algorithms advertised today may already be outdated.

Whatever the commercial considerations and no matter how blasé we become about technological possibility, there is still a big wow factor in packing a high-powered computing technique into such infinitesimal space, and you can be certain the nano people will be harnessing neural networks to many new kinds of more-mini-than-micro machines.

20 Questions in a New Orbit

Thursday, April 15, 2010 by Holly Bailey

An Ottawa toy developer is trying to make a jet-propelled leap from an online game to space travel. His vehicle? A neural network designed as the back end system for a game of 20 questions. Twelve years ago Robin Burgener wrote a neural net program to train on the sequences of player responses to questions--beginning with Animal? Vegetable? Mineral?--posed by the neural network,

The game is does more than pose simple yes-or-no answers to lead you to a conclusion. The neural network algorithm is able to pose different questions in different orders, and it gets the right answer about 80 percent of the time. ,

Now, apparently, the sky's the limit for Burgener's neural network. He was scheduled to make a presentation late last month at the Goddard Space Flight Centre explaining the potential uses for a neural networked 20 questions on board a space craft. These uses center broadly on troubleshooting technical and equipment problems and subsequently anticipating future problems.

If, as he claims is true, his neural net guessing program can work around responses that are misleading or downright lies, what that would mean for space travelers, he concludes, is that "if a sensor fails, you're able to see past it."

I know what he means, I think, but I myself don't tend to look past sensors.

Neural Nets vs. the Ripple Effect

Thursday, April 1, 2010 by Holly Bailey

About a week ago the Financial Times ran an article about a "new" investment analysis technique that could cut through turbulence in the financial markets: neural network analysis. I thought okay, this isn't new but maybe the application is innovative. Besides, I liked the metaphor the reporter used, a metal ball dropped in a vat of oil and the ensuing ripples that disturb the oil.

The article is about software developed by a Danish investment firm that turned its back on "linear" models to adopt a neural network approach that continually reclassifies investments in a portfolio and then makes suggestions about which equities to buy and which to sell. The proprietary software chews through a heap of data--prices, price-earnings ratio, and interest rates, for starters, and its performance bench mark is the Russell 1000 index.

The test portfolio used to proof the method was acquired in 2007, just before the ball dropped into the oil. For a time it seemed to hold up but then got caught in the turbulence and its undertow. It has now recovered nicely, ahead of the Russell 1000 in fact, and the asset managers are looking for more investors. This is a sweet success story, especially given the demon turbulence looming over the project and the fact that the assets are apparently owned by the Danish state pension plan.

I understood the use of neural network software to counter nonlinear events like market turbulence, and I understood the continual classification and reclassification. But I was intrigued that nowhere in the article was there a mention of risk, risk analysis, or even risk assessment. Maybe it was there all the time, incorporated in the proprietary software, and maybe it just wasn't mentioned. Certainly the asset managers who developed the program were aware they were at risk--they were chewing their nails as their fund slid down right beside all the other funds that were dropping in value. But assessing risk doesn't seem to have been a factor in the firm's new defense against mayhem in the markets.

So. Is it time to shut down your Monte Carlo software? I don't think so. . . .

New Approaches to Risk and Decision Analysis

Wednesday, March 17, 2010 by DMUU Training Team

Risk analysis and decision-making tools are relevant to most organisations, in most industries around the world. This is demonstrated by the speaker line-up at this year's European User Conference, an event at which we believe it is important to bring together customers from a wide range of market sectors.

We are holding 'New Approaches to Risk and Decision Analysis' at the Institute of Directors in central London on 14th and 15th April 2010. As with previous years, the programme aims to provide everyone attending with practical advice to enhance the decision-making capabilities of their organisation. Customer presentations, which offer insight into a wide variety of business applications of risk and decision analysis, include:

CapGemini: Faldo's folly or Monty's Carlo – The Ryder Cup and Monte Carlo simulation
DTU Transport: New approaches to transport project assessment; reference scenario forecasting and quantitative risk analysis
Georg-August University Research: Benefits from weather derivatives in agriculture: a portfolio optimisation using RISKOptimizer
Graz University of Technology: Calculation of construction costs for building projects – application of the Monte Carlo method
Halcrow: Risk-based water distribution rehabilitation planning – impact modelling and estimation
Pricewaterhouse Coopers: PricewaterhouseCoopers and Palisade: an overview
Noven: Use of Monte Carlo simulations for risk management in pharmaceuticals
SLR Consulting: Risk sharing in waste management projects - @RISK and sensitivity analysis
Statoil: Put more science into cost risk analysis
Unilever: Succeeding in DecisionTools Suite 5 rollout – Unilever's story

We will also look at the recently-launched language versions of @RISK and DecisionTools Suite, which are now available in French, German, Spanish, Portuguese and Japanese. Software training sessions will provide delegates with practical knowledge to ensure they can optimise their use of the tools and implement business best practise and methodologies.

With over 100 delegates from around the world attending, the event is also a good opportunity to network and knowledge-share with risk professionals from around the world.

» Complete programme schedule, more information on each presentation,
and registration details

Palisade is proud to announce our first Health Risk Analysis Forum in San Diego on March 31st 2010

Wednesday, March 10, 2010 by DMUU Training Team

Why attend?

This one-day forum is a great way to find out how others in the Healthcare Industry are using our software, as well as to learn new approaches to the problems Healthcare professionals face every day. We will have six software training sessions, and six real-world case studies presented by industry experts covering risk and decision analysis from all angles specific to the Healthcare sector.

You will also see how new versions of @RISK, PrecisionTree, RISKOptimizer, TopRank, NeuralTools, StatTools, and other Palisade software tools work together to give you the most complete picture possible in your situation.

Who should attend?

Professionals in risk and financial analysis in: Care Equipment & Services, Pharmaceuticals, Biotechnology & Life Sciences, Hospital Care & Management, or related services

How much?

For a limited time, the cost for attending the Health Risk Analysis Forum is has been discounted $100.

$295 covers all sessions, continental breakfast, lunch and a cocktail networking reception. Attendees will also receive a welcome package that includes a 15% discount on their next software purchase.

Please contact Jameson Romeo-Hall at jromeo-hall@palisade.com if you are interested in attending.

Location
The Westin Gaslamp Quarter
910 Broadway Circle
San Diego, CA 92101
(619) 239-2200

Book your room at a discounted rate (subject to availability.)

The Rise of the NOMFET

Friday, February 5, 2010 by Holly Bailey

By now we've become accustomed to the marvels of neural network technology and, in fact, inured to the advances it brought in statistical analysis with its computational simulations of nerve cells. Its many everyday applications--especially in online retailing--seem kind of ho-hum, and we'd be put out if for some reason they weren't in use. Wasn't it only four or five short years ago that neural nets themselves were big news?

Last week there was more big news about neural networks: a French research team's announcement of an "organic" transistor that mimics a brain's synapse. Neural network computing is based on computational stand-ins for biological neurons, and linking these neurons with electronic synapses currently requires at least seven transistors. One new "organic" transistor can take the place of those.

The key here is nano. Tiny. Tinier than tiny. The new transistors are made of nanoparticles of gold and pentacene on a plastic substrate. The resulting connector is called a nanoparticle organic memory field-effect transistor: a NOMFET.

Not only will the NOMFETs accelerate the performance of neural network circuits, but because the human brain uses 10 to the fourth times as many synapses as neurons, the space saving NOMFETs will help make possible a generation of computers inspired by the human brain.

The rise of the NOMFET may also make possible another kind of advance, one that I find a little scary to contemplate. Because its built on plastic, the NOMFET could potentially be used to link a computer with living tissue. Get back, Frankenstein.

A Downturn for the Better

Thursday, January 7, 2010 by Holly Bailey

Honoring a time-honored tradition for the turn of the year, I've been looking back over the year just past to do a little retrospective trend-spotting. Here's one that took me by surprise: in spite of the downturn in the economy, there was also a downturn in online fraud. It's counterintuitive--historically, hard times are correlated with rising crime--but apparently true.

Late last year, DigitalTransactions, an online publication catering to businesses engaged in the "electronic exchange of value," reported that the results of a survey of principals in these businesses showed an overall decline in fraud of about 1 percent.

The survey, sponsored and carried out annually by a California risk management company, is the first in its eleven-year history to show a fraud rate this low. In 2009 North American merchants were expected to lose (a mere) $3.3 billion, in contrast to their loss of $4.0 billion in 2008.

What's behind this good-news downturn? Probably not increased honesty. There was no data on attempted fraud, and the assumption is that the increased use of automated fraud detection tools cut the merchant's losses. The level of sophistication of these tools has ratcheted up to the level where neural network classification, risk analysis, and statistical analysis of correlated data can take place in real time during the processing of a transaction. Furthermore, the combination of operational risk software with device identification of the purchaser's computer now make it difficult for a single computer to mob an online merchant with multiple bogus orders.

So the good news is not about improvements in human nature. It's about improving the defenses of this booming sector of the economy.

Digital Eyes on Alien Life

Wednesday, December 9, 2009 by Holly Bailey

University of Chicago geoscientist Patrick McGuire has big plans for Mars. Previously he worked on an imager for a Mars orbiter that could identify different types of soil and rock by detecting infrared and other wavelengths, and now he is drawing on that experience to develop a space suit with digital "eyes" and a neural network that rides on the hips of the spacesuit and can sort out living biological material from other matter.

The digital eyes will detect and plot colors, and the neural net, which is known as a Hopfield neural network, will compare these color patterns to a database of information previously gathered from that area of planet in order to make an animal-vegetable-mineral determination.

This complex AI system has already been tested at the Mars Desert Research Station in Utah, and McGuire and his colleagues were satisfied that the Hopfield algorithm could learn colors from just a few images and could recognize units that had been observed earlier.

McGuire's concept is that a human wearing this neural network could simply walk around the red planet and record every nearby object, rapidly gathering information.

Obviously, such a clothing item awaits a manned Mars mission. But in the meantime, why not have the next Rover suit up?

The Cat is Out of the Bag

Thursday, December 3, 2009 by Holly Bailey

At November's supercomputing conference in Portland, Oregon, IBM announced that its researchers working with a team from Stanford University had succeeded in developing an accurate simulation of human brain function. The simulation will be capable of emulating sensation, perception, action, interaction and cognition.

This algorithm simulating a living neural network, called BlueMatter (spelled as one word like everything else in computerese these days) is an important milestone in IBM's mission to build a cognitive computing chip because it begins to advance large-scale simulation of a cortical neural network and it synthesizes neurological data. BlueMatter is built with Blue Gene (two words for this pun in the singular) architecture, which, in combination with specialized MRI images, allowed the team to create a wiring diagram of the human brain. This map of the brain is, according to IBM's press release "crucial to untangling its vast communication network and understanding how it represents and processes information."

To be more accurate, what BlueMatter has thus far demonstrated is the potential to achieve neural network technology that operates on the scale of complexity of the human brain. The algorithm's current simulation approximates the cortical system of a cat. Hence, the title of the paper announcing IBM's accomplishments: "The Cat Is Out of the Bag." Even so, this is an operations research accomplishment that dwarfs such mundane analytical tasks as option valuation, value-at-risk, or reserve estimation.

One of the goals of the company's cognitive computing program is to create a chip that operates with the energy efficiency of the human brain (20 watts). But in order to emulate the brain activity of a cat, the research team had to bring out one of the largest supercomputers in the world, the IBM Dawn Blue Gene/P--which comprises about 150 thousand processors and contains 144 terrabytes of main memory.

This cat came out of a pretty big bag.

New Approaches to Risk & Decision Analysis at the 2010 Conference in London

Friday, November 13, 2009 by DMUU Training Team

Following on from the resounding success of the last Palisade Risk Conference in London, which attracted over 110 attendees from industry and academia, the 2010 Palisade Risk Conference will be taking place on April 14th-15th. The location for this event will again be the Institute of Directors on Pall Mall, London, and already there are a number of exciting presentations confirmed from the likes of Unilever, Pricewaterhouse Coopers and Halcrow.

The 2010 Palisade Risk Conference will be a two-day forum which will cover a wide variety of innovative approaches to risk and decision analysis. Featuring real-world case studies from industry experts, best practices in risk and decision analysis, risk analysis software training, and sneak previews of new software in the pipeline, the event is also an excellent opportunity to network with other professionals and find out how they’re using Palisade risk analysis solutions to make better decisions.

Call for Papers

If you have an unusual or interesting application of Palisade software which you would like to present, please send a short abstract to cferri@palisade.com. The closing date for abstracts to be submitted is Friday, 11th December, 2009.

A Random Walk

Friday, October 23, 2009 by Holly Bailey

It has struck me often that the target of statistical analysis writ large is randomness. Because it is the fundamental concern of all the techniques for decision making under uncertainty that I try to track--risk analysis, genetic algorithm optimization, and neural network prediction--I assumed I understood the term randomness pretty well.

But it turns out that what you think randomness is depends upon what line of work you're in. If you are a statistician, randomness occurs in a repeating process the results of which follow no discernible pattern. If you are a geneticist, randomness applies to genetic mutations that are not controlled by genes. If you are a financier, randomness is blamed for controlling stock prices, which respond instantaneously changes changes in available information.

In all these fields, randomness is closely allied with chance and probability, and the human struggle against chance is epic. We resort to sharper and sharper tools to pare down what appears to be random, and just when we think we have the magic tool--say, Monte Carlo software--or the magic idea--the random walk hypothesis, for example--the definition of randomness changes.

Neural Network Zeros in on Quarks

Monday, October 19, 2009 by Holly Bailey

Having successfully dodged high school physics I would not normally be sucked in by an article on quarks, but this one involved neural network computing, which I do understand pretty well.

It seems that a couple of weeks ago physicists at Fermilab, near Chicago, made the most precise measurement yet of a top quark. A quark is an elementary particle, the most fundamental building block of matter. Quarks come in six flavors (I'm not making this up!), four of which can be produced only by high-energy collisions. Think updated cyclotron. The top quark is one of these four, and first observed in 1995, it is the most recently discovered quark. The physicists--unsatisfied, of course, with having simply identified the particle--wanted to measure it.

It turns out that the way to measure a quark is to observe its decay and work backward from non-quark to quark. This involves heavy-duty statistical analysis of many, many observations. The scientists at Fermilab collected a large set of sample data on quark decay, and then in order to zero in on bona fide quarks, they trained a neural network to identify which particle events were not related to top quark decay. When the neural net had sorted out the quark imitators, the physicists could size up the real quarks more accurately.

The top quark is relatively large for an elementary particle. Until last month it was believed to be about the size of an atom of gold. What is the current estimate? Too daunting a calculation to quote. But if you go to the information the Fermilab has on display, you--or some of you, anyway--will begin to get the picture.

Introduction to DecisionTools Suite 5.5 Products: Software training at the NYC Conference

Monday, October 12, 2009 by DMUU Training Team

Palisade is gathering trainers from our New York and London offices to present software training seminars next week at the 2009 Palisade Conference: Risk Analysis, Applications, & Training. The conference is set to take place on 21 - 22 October at the Hyatt Regency in Jersey City, 10 minutes by PATH from Manhattan's Financial District.

Take this convenient and inexpensive opportunity to learn from Palisade’s trainers and software developers. Learn how to use the elements of the new DecisionTools Suite 5.5 as a comprehensive risk analysis, optimization, and statistical analysis toolkit. See how each of the products in the Suite — @RISK, RISKOptimizer, Evolver, PrecisionTree, TopRank, StatTools, and NeuralTools — can be used to solve practical problems in the real-world.

The conference also features case studies demonstrating how to use @RISK and DecisionTools Suite, from risk management experts in the fields of finance, healthcare and pharmaceuticals, energy, oil and gas, DFSS and Six Sigma, project management, operations management, manufacturing, and more.

See the full schedule for the Conference here.

Next Week: October 21-22 in NYC

Building on the success of last year’s record-breaking event, the conference will offer a wide range of software training, model building, and real-world case study sessions. Last year, the event drew over 150 practitioners and decision-makers from a broad spectrum of industries. The @RISK and DecisionTools software tracks were more popular than ever. This year, we’re expanding software training with sessions that let you walk through examples and try the tools directly. This will enable you to take some new tips back to the office. Please join us in October for a great opportunity to learn and connect with colleagues.

A Neural Network Is Never Too Old to Be Immature

Wednesday, October 7, 2009 by Holly Bailey

Here's a follow-on to my column about evolving neural networks. That one involved deception, this one involves distractiion. That one involved robots, this one involves babies.

About fifty years ago, logician and child psychologist Jean Piaget designed what has become a classic experiment to test the memory and learning of babies. It is a game of hiding and finding, and through it we have discovered that when infants up to 10 months old are repeatedly shown an toy being hidden in a certain place, they continue to look for the toy there, even when they have also seen it hidden in some other place. By the age of one year, however, they get it. They figure out they can look in more than one place.

This experiment has been used for decades by scientists interested in human development, and most recently has led to a finding by a Hungarian team that the ability of young infants to read social cues actually misleads them and causes them to perform worse in the hide-and-seek game. Older babies, however, were still able to read through the deception.

Now scientists at the University of Iowa have used a neural network model to prove that the problem is not the infants' mistaken reading of a social cue but simply distraction claiming the attention of the young infants and thereby disrupting their memory of the actual hiding of the toy.

How, you are wondering, did the UI team verify an internal cognitive process with neural network? Their neural net trained on the responses of the infants in many different versions of the hiding game, and the team then programmed an interruption in the flow of computation so that the computer stopped "paying attention" to the hiding event. Then it too flunked the memory test.

What is the take-home message from this? That you can fool all of the babies only part of their lives? Or that an neural network is never too old to be immature?

The Evolution of Deception

Friday, October 2, 2009 by Holly Bailey

No sooner did I mention an apparent trend toward using neural networks to analyze biological processes than another fascinating study came up on my radar: in Switzerland two engineers and a biologist are using a neural network to simulate the biological evolution of social communication.

The Swiss research employs camera-equipped robots instead of animals, and it sends a group of robots out "foraging." The robots are equipped with light sensors, and the "food" at one end of the foraging area is a lighter color than the "poison" at the other end. The bots are scored for their success at locating the "food," and they "talk about" their success by flashing a blinking blue light. Which, in turn, draws robots that are computationally attracted to the blue light.

After that, the self-prepetuating algorithms of the neural network experiment begin to look a little like genetic algorithm optimization. The robots are randomly "mated," and their neural nets are mingled. In only a few generations, the robots learn to head towards the blue lights. But here there is a hitch: the "food source" is available to only a few robots at a time.

What evolves next? By the 50th generation the robots begin to do what you and I would probably do if we had the advantage of a precious resource we didn't want to share: they deceive. They stop signaling with their blue lights when they find "food" because their signals will draw a crowd of greedy robots.

Interestingly, this story of evolving robots mirrors research done on live chickens in the mid-1990s, except that there was also sex and mating motivation--the real kind--at work in those experiments. Animal behaviorists studying vocal communication observed that roosters will call to notify other chickens about the availability of food only if those other chickens are hens.

In spite of this difference, the evolutionary logic of both the chickens and the robots is the same: why share the good stuff and lose your advantage?

A Neural Network by Any Other Name

Wednesday, September 30, 2009 by Holly Bailey

You say toe-MAY-toe, and I say toe-MAH-toe. But I still know exactly what you mean: a big, round, juicy red veggie that slices up nice for the burgers. But how I know the difference between toe-MAH-toe and toe-MAY-toe is a mystery that has eluded neuroscientists. Until now.

In the many press releases I see on scientific topics, I've noticed a trend toward using neural network technology to analyze its biological namesakes, the neural networks in the human brain. One of the latest examples of this is research from a team of scientists at Hebrew University who have used computational neural networks to analyze the cellular processes by which sensory neurons adjust to differences in speech for the same word.

The differences in the way I say tomato and you say tomato are largely a matter of timing and durations, and these sounds are received by single nerve cells. The neural net algorithms devised by Dr. Robert Gutig and Dr. Haim Sompolinsky identify these differences by classifying the way the single nerve cells respond. This innovation will not only be useful in such speech decoding applications as telephone voice dialing, but they also have promise in treating auditory problems.

Toe-MAY-toe? Toe-MAH-toe? Let's call the whole thing....Naw, the two brain scientists aren't calling anything off. Their neural network is just getting started.

Simulating the U.S. Economy: Where will we be in 100 years?

Friday, September 4, 2009 by DMUU Training Team

William Strauss is the President and founder of FutureMetrics. He brings more than thirty years of strategic planning, project management, data analysis, and modeling experience into the company’s stock of knowledge capital. Bill’s professional history includes executive positions as director, president, and senior vice president, as well as positions as senior analyst and field coordinator. He has an MBA (specializing in Finance) and a PhD (Economics).

Dr. Strauss will present a case study at the 2009 the 2009 Palisade Conference: Risk Analysis, Applications, & Training. The conference is set to take place on 21 - 22 October at the Hyatt Regency in Jersey City, 10 minutes by PATH from Manhattan's Financial District.

See the abstract for his case study below, and see the full schedule for the Conference here.

Simulating the U.S. Economy:
Where will we be in 100 years?

There is an assumption that drives all of our expectations for how our economy will be in the future. That assumption is one of endless economic growth. Clearly endless exponential growth is impossible. Yet that is what we base all of our expectations upon. We all agree that zero or negative economic growth is bad (just look around now at the effects of the Great Recession). But we also know logically that 2% or 4% annual growth every year leads to an exponential growth outcome that is unsustainable.

To see where this growth imperative will take us we first have to see how we go to where we are today. This work first models the 20th century. The model is both complex and simple. The basic schematic of the model’s relationships is easy to understand. Furthermore, the core of the model is a simple production function that combines capital, labor, and the useful work derived from energy to generate the output of the economy. Complexity is contained in the solutions to the internal workings of the model. What is unique is that there are no exogenous economic variables. Once the equations’ parameters are calibrated, setting the key outputs to "one" in 1900 results in their time paths very closely predicting the U.S. GDP and its key components from 1900 to 2006.

The experiment in this work is about the future. If the model can very closely replicate the last 100 years, what does it have to say about the next 100 years? From 1900 to 2006 there are periods in which there was parameter switching. (The optimal parameters and the years for the switching were found using a constrained optimization technique.) That suggests that in the future there will also be changes. The experiment uses @RISK’s features to generate new combinations of parameters for each of tens of thousands of runs of the simulation. Changes in the parameters represent potential exogenous policy choices.

The "doing what you did gets you what you got" scenario leads to a surprising and unsettling outcome. The experiments using @RISK do find a path that works. Obviously if it is not "business-as-usual" that leads to a stable outcome, it is some other way. The policy choices that lead to a stable outcome suggest that the future of capitalism is not going to be what we expect it to be.

Please join us in October in New York for software training in best practicies in quantitiative risk analysis and decision making under uncertainty, real world case studies from risk services consultants and experts, and networking with practicioners from many different fields including oil and gas, pharmaceuticals, academics, finances, Six Sigma, and more.

Palisade’s Custom Development Services

Wednesday, August 12, 2009 by DMUU Training Team

Palisade Corporation now offers custom development services. Our consulting team can help you to automate your risk and decision analysis models so they can be easily used by everyone in your company, or even outside of it.

We offer different options that include Excel add-ins, Windows, and Web based applications. Our consultants can help you to design, program and deploy these applications. A typical application might connect an Excel spreadsheet to your company’s database, extract data, then adjust it to probability distributions so they can be used in dynamic risk or optimization models. The structure of reports can be also customized and published as PDFs, or to the Web.

Palisade Custom Development can incorporate Monte Carlo simulation, probability distributions, distribution fitting, graphs, reports, and many other features of @RISK into any Windows-based application. In addition, we can integrate genetic algorithm optimization from RISKOptimizer or Evolver. This allows you to apply powerful, proven analytics to applications outside Excel. Applications can be run in a desktop, network, or Web environment.

You may wish to customize your @RISK or DecisionTools Suite spreadsheet models, restricting access to model components for some users or automating reports and other aspects of your analysis. Using the DecisionTools built-in Excel Developer Kit (XDK) and custom Excel VBA programming language, Palisade can help you build powerful, easy-to-use risk models for one user or for an entire work group.

We are currently working on a new website where you will find more information and project samples. Upcoming posts will discuss examples of custom Excel VBA programming.

» More about Palisade Custom Development

Dr. Javier Ordóñez
Director of Custom Development