Genetic Algorithm Optimization

Making Optimal Choices, or Just Making Choices? Part 2

Thursday, March 18, 2010 by DMUU Training Team

In my last blog entry I introduced the notion that optimal decision making wasn’t ‘on the radar’ for many clients in Australasia, and laid out a couple of ideas why. I too once focussed on Monte Carlo simulation rather than decision evaluation, but last year the most obscure event changed that.

Call me a nerd of you will, but I like modelling problems in Excel. There is skill involved in setting up a problem such that the model assumptions aren’t too gross, and an art to making the model elegant. This elegance can be very important to optimisation problems, but more on that later. My first homemade optimisation problem was generated by motorcycle racing! MotoGP, to be precise. A friendly tipping competition with friends was formed at the start of the 2009 season with the following structure:

Entrants played the role of Team Manager.
Team Managers had a fixed budget to spend on riders.
Either a few good riders could be purchased, or many lesser riders, or something in between.
The team that had accumulated the most points at the end of the season was the winner and received kudos!

Although the future results could not be known of course so I set up and ran the optimisation with Evolver after the event to see what the optimal team selection would have been. Historical data could have been used to discover the type of rider mix that tended to be optimal and thus make an informed decision for this competition. The risk in having only a few riders was that any misfortune would have a big negative impact on the points won, whereas a team consisting of many (cheaper) riders was less likely to suffer such a fate. This downside scenario will be modelled into the 2010 MotoGP Team Manager predictive, optimised model (currently in production)!

What has this to do with the corporate world? Replace “team” with portfolio and “riders” with “assets”, “shares” or “projects” and you have a classic portfolio optimisation model. I hadn’t created this model with business applications in mind but I realised that was precisely what I was doing. An instant later I realised just how useful Evolver would be in many decision scenarios even though it doesn’t incorporate uncertainty (RISKOptimizer does).

In the next instalment I will further explore some practical applications for Evolver and you’ll see just how universally appropriate it can be.

» Making Optimal Choices, Part 1

Rishi Prabhakar
Trainer/Consultant

Making Optimal Choices, or Just Making Choices? Part 1

Tuesday, March 16, 2010 by DMUU Training Team

Something has troubled me for some time regarding the choices being made in risk land. I train and work with many clients whom have adopted Monte Carlo simulation techniques (via @RISK for Excel) into the day-to-day running of their businesses. By doing so they (hopefully) now have a good understanding of the exposure they are facing be it in project cost estimation, discounted cash flow analysis or, well, anything really. But this is only one facet of risk and decision assessment, specifically dealing with the descriptive statistical output from a simulation. What of the decision evaluation component? Why aren’t more of my customers analysing the decisions they make, or better yet actually optimising them? I have a few ideas why.

If you’re in business you have to make decisions. Big ones, little ones, yes/no, multiple state and continuous value decisions. Decisions that impact other decisions in simple or complex dependency structures. But are you making the best decisions possible? I’m sure important decisions aren’t being made completely randomly (I hope!) but I see many companies who rely completely upon qualitative techniques for their decision making (experience, gut feel, etc.) which of course means optimality is no more than a hoped for outcome rather than something that is actually being worked towards.
Firstly the decision model must be identified and then quantified, and this can be a difficult task. There is a level of modelling aptitude necessary for effective modelling that goes beyond merely knowing Excel and its functions, and into the construction of logical mathematical descriptions of possibly complicated processes. Relevant decisions need to be identified and the impact of those decisions combined into a formula that can be mathematically optimised. A critical component to all this is the knowledge that spreadsheet models can actually be optimised, and that in cases where Excel’s Solver fails there are Palisade products (Evolver and RISKOptimzer) that can perform optimisations under virtually any circumstance.

I too used to focus on Monte Carlo simulation rather than decision evaluation, and this was mainly a product of the clients I was dealing with almost exclusively when I first worked for Palisade. In my next blog I’ll tell you why that changed and also get a little more into the nuts and bolts of optimisation.

Rishi Prabhakar
Trainer/Consultant

Free Webcast this Thursday: "Simulating the U.S. Economy: Where will we be in 100 years?"

Tuesday, January 26, 2010 by DMUU Training Team

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.

In this free webcast, Dr. William Strauss models the next 100 years, based on the last century's data. The experiment in this webcast 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? The experiment uses @RISK’s risk analysis and Monte Carlo techniques 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 Evolver (genetic algorithm optimization software) 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.

Palisade is pleased to host this presentation from Dr. William Strauss.

» Complete abstract of "Simulating the U.S. Economy: Where will we be in 100 years?"
» Register now (FREE)
» View archived webcasts

Free Live Webcast this Thursday: Simulating the U.S. Economy: Where will we be in 100 years?

Monday, January 25, 2010 by DMUU Training Team

This Thursday, 28 January 2010 at 11am ET, Dr. William Strauss, President of FutureMetrics, will present a free live webcast entitled, "Simulating the U.S. Economy: Where will we be in 100 years?" Sign up now to attend the webcast.

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 free live webcast 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 webcast 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 (risk analysis software using Monte Carlo techniques) 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 Evolver (genetic algorithm optimization using Monte Carlo simulation) 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.

----
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).

» Register now for this FREE live webcast
» View archived webcasts

Predicting Customer Will

Tuesday, January 12, 2010 by Holly Bailey

If hindsight is twenty-twenty, foresight--at least in the world of market research--still has a ways to go. Simulation, both with Monte Carlo software and with a conjoint simulation approach, has been used by market researchers for some time now. Recently David G. Bakken,who maintains a blog on the Smart Data Collective site, pointed out that the drawback of these models is that even those that incorporate random number generation are static. That is, the inputs and the coefficients determine the model outcomes.

What's wrong with deterministic models? Nothing, I gather, except for the limitation that those that are applied to marketing research questions tend to treat the target customers, the companies devising product strategies, and their affiliates in advertising and PR as blocs that make decisions without benefit of individual will.

Agent-based models, which were born in the social sciences, simulate the interactions of multiple players, each of whom will act, absolutely rationally, in his or her own best interests. Bakken believes that agent-based modeling used in tandem with traditional risk analysis models or evolutionary programming methods such as genetic algorithms, offers a more dynamic means of accounting for the future behavior of potential customers.

On the face of it, Bakken's proposal seems to have merit. If the technique works for the social sciences, maybe it will work for marketing research. After all, what is marketing if not a commercial application of social science?

Data Issues Part 1

Tuesday, January 12, 2010 by DMUU Training Team

In a recent public training workshop (for @RISK for Excel) I was reminded of an unusual fact regarding data.

Commonly @RISK for Excel is used to fit distributions to historical data for use in risk modelling, and it sure beats wildly guessing obscure parameters. However there are (naturally) a litany of woe-inducing problems with all historical data sets: non-stationary data series, extreme values/outliers, data recording errors, seasonality and heteroskedasticity to name a few. Excessive ‘cleansing’ of the data set is commonly prescribed, but the statistician in me cringes to even type those words! Quality control and transforming the data will help to eliminate most of those problems, but what about outliers?

In the early Naughties I was working for a large Australian bank, forecasting their daily call centre volumes for the purpose of planning staff levels and predicting service levels. A particular call centre averaged 30,000 calls per weekday. Yet on September 12th, 2001, calls dropped to less than 10,000. Along with the rest of the world, Australians were watching the terrorist attacks on television and the internet rather than calling to fix spelling mistakes in their contact details or transfer small sums of money between accounts. But what to do with that data point? Presuming the forecasting model is not intended to include such extreme events as terrorist attacks then the point could simply be filtered out of the data set and not thought of again.

But now consider a process that should include rarer events, such as flood damage or operational risk, as one of the risks in a model. If you have 10 years of good data (say), but the set includes an event that should only occur every 100 years. This level of impact is thus drastically overrepresented in the data and any fitted distribution will be biased toward such extremes. Yet the data point can not be completely ignored as such values can occur and the simulation models must have the capacity to sample such values (though with a reasonable likelihood). In this case the artistry that is fitting distributions to data comes to the fore. The data point could be removed from the set but not from our decision making process.

From the range of distributions that can be selected, the optimal choice should not only represent the remaining data well but also have a tail that samples events in the vicinity of those that have been excluded from the analysis with reasonable probability. No, that’s not always easy to do. But as with many elements of probabilistic modelling it simply must be done in order to provide useful information to decision makers.

Thus the context of the modelling can go a long way to determine the most appropriate steps to take with your data set. If that sounds like a subjective guideline then you read it correctly. Not enough people realise just how important experience and intuition can be in the seemingly prescriptive fields of mathematics and statistics. Fitting distributions to data is no different.

And yet that isn’t the unusual fact I was reminded of in the workshop! But I’ll leave that for Part 2 of my Data Issues blog.

Rishi Prabhakar
Trainer/Consultant

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.

25 Worst Tech Products

Monday, November 30, 2009 by Steve Hunt

A friend and colleague who knows I write a Six Sigma blog sent me a link to an older article on PC World, The 25 Worst Tech Products of All Time that he thought might applicable to Six Sigma.

As first blush, I thought, “What an article on PCWorld.com on the Worst Tech products would have anything to do with Six Sigma?” The answer . . . everything! Particularly after reading the piece, the number 1 or worst product of all time (in their eyes) is American Online. I agree AOL has had its difficulties, but one has to admit the service has had staying power despite this. It’s been around for 20 years, which is a lifetime in the computer world. I don’t know if they utilized Voice of the Customer (VOC) , but they did something right since they are still around.

The article mentions AOL had shown improvements over the previous years. This goes to show us, they had a good idea, but took many years to sort out the bugs and for them to position themselves correctly. At the time of initial development they probably didn’t utilize Design for Six Sigma or another Critical Parameter development methodology, but it appears they may have implemented Lean Six Sigma principles to improve their “inexcusably poor customer service,” “inaccessible dial-up numbers,” and what I’ll call “flawed billing practices.” Please know I am not necessarily agreeing with the article, or being an advocate for AOL, I’m simply pointing out how the company has appeared to have improved its product and service over time.

One can only hope and assume that companies are doing a better job up front vetting their ideas, products and designs . . . with sound initiatives such as Design for Six Sigma. If not, hopefully we won’t seem the on PC World’s next “worst of” list.

If you would like to learn more about Design for Six Sigma, May I recommend either of these two free webinars:

Wayne Winston’s Math and Sports blog debuts on HuffPost

Thursday, November 12, 2009 by DMUU Training Team

Wayne Winston is the newest blogging personality at the Huffington Post! His first post, “The Importance of Schedule Strength in Sports,” appeared yesterday. Wayne will focus on the interface between math and sports, with detailed explanations of statistical analysis and spreadsheet modeling, including @RISK risk analysis models. You can find a link to the Wayne Winston blog from the newly-launched HuffPost Sports.

Wayne is the John and Esther Reese Professor of Decision Sciences at Indiana University’s nationally ranked Kelly School of Business. He has won over 30 teaching awards, and written over 20 journal articles and 15 books. Wayne has consulted for many organizations including the Dallas Mavericks, USA Diving, Cisco, Microsoft, US Army, Eli Lilly, Diamond Consulting, Tellabs and Medtronics. He has also developed online spreadsheet modeling and mathematics courses for Harvard Business School Publishing. And, Wayne is a two time Jeopardy! champion!

Wayne’s latest book, Mathletics, provides an introduction to the use of math by baseball, football, and basketball teams. He has also authored several books published by Palisade, including Financial Models Using Simulation and Optimization I, Financial Models Using Simulation and Optimization II: Investment Valuation, Options Pricing, Real Options & Product Pricing Models, and Decision Making Under Uncertainty with RISKOptimizer.

DMUU Training Team

Wine Aficionado? Six Sigma expert? or both?

Tuesday, October 27, 2009 by Steve Hunt

I’ve heard of Six Sigma being used in every industry from manufacturing, banking, even baking, but now . . . wine making?

Just the other night I found out a winery is using Six Sigma principles to ensure they are producing the highest quality wine available.

Yes, that’s right . . . Six Sigma Ranch and Vineyards have combined the old-world art of wine making with the science of data driven Six Sigma principles. Why not! Isn’t the origin of Design of Experiments from the agricultural world? That’s where (is that right?) RA Fisher introduced the concepts of replication, randomization, blocking and devise analysis of Variance to separate the sources of variation in the 1920s.

How many times have we read the reviews from a single winery, how some years are better than others, etc., and wondered why they can’t make the quality more consistent? Why not apply Six Sigma to wine making?

I think it makes perfect sense!

Six Sigma Ranch and Vineyards is applying Six Sigma principles in all stages of the process:

Conduct extensive analyses of soil, water and climate to find the most favorable sites for our vineyards.
Choose rootstocks that thrive best in the soil composition of a given vineyard.
Meticulously prune vines to enhance the quality of grapes and to allow consistent ripening.
Apply chemical and sensory analyses to pick the grapes at just the right time to produce optimal flavor in the wine.
Listen to the voice of the customer - whether you are a sophisticated wine drinker with well-defined preferences, a social wine drinker who knows what you like and wants the security of consistency, or you just want a good place to start

The use of Six Sigma in all business process makes good sense. There is talk that Six Sigma is dead, and that people are waiting for the next big thing. The truth of the matter is no matter how you repackage the tools, these tools will be around for decades, because good decisions are based on data analysis and that should never go away. My only hope is that they are using @RISK to analyze their data to make even better decisions.

The next time I am in California or the local wine store, I’ll have to investigate this further.

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.

Targeted Analyses and Compelling Communication: A Formula for Successful Value Creation in Management Science

Monday, October 19, 2009 by DMUU Training Team

Michael A. Kubica is Founder and President of Applied Quantitative Sciences, Inc. He has over 18 years' experience within the healthcare industry, and has been providing quantitative sciences consultancy since 1999. Michael has extensive experience in providing quantitative decision support solutions for leading pharmaceutical, medical device/diagnostics, and biotechnology companies, addressing a wide range of business issues. Prior to establishing AQS, Michael held the position of Vice President, Operations for Magellan Health Services. During his career Michael has also held positions of Director of Quality Management, Regional Director of Business Operations & Finance, and Hospital Administrator. Throughout his career, Michael employed sophisticated quantitative methods to forecast performance, streamline operations, and improve quality. Michael has an MBA and Master’s of Science in psychology. He serves as Adjunct Professor of Research Design and Statistical Analysis at St. Thomas University in Miami, FL. Applied Quantitative Sciences, Inc. (AQS) is a consultancy specializing in assisting medical device, pharmaceutical and biotechnology companies make decisions under conditions of complexity and uncertainty. They are a market leader in providing simulation and optimization models which are used by industry leaders for the purposes of forecasting, new technology valuation, business and strategic planning, supply chain management, and resource planning.

Mr. Kubica will present a case study later this week at the 2009 Palisade Conference: Risk Analysis, Applications, & Training, 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.

Targeted Analyses and Compelling Communication: A Formula for Successful Value Creation in Management Science

The value of quantitative science projects too often remains unrealized for would-be consumers. Despite flawless analyses, sophisticated reports and dazzling presentations, the message goes unheeded by those who could most benefit: If only they understood how to operationalize the results. The clarity with which quantitative scientists view the practical application of results is often paralleled only by their inability to generate that same clarity in their customers. The result is that good management science is at best ignored and worst, misunderstood (and misapplied). This workshop describes steps we as quantitative scientists can take to foster understanding, generate novel insights and stimulate actionable results with our clients.

This 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.

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.

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.

The Discouraging But Enticing Scenario of Clinical Trials

Friday, September 25, 2009 by Holly Bailey

One of the most expensive passages in the long road that a new drug must take to reach the marketplace is the series of mandatory clinical trials. This past summer a "life-sciences advisory company," Value of Insight Consulting, based in Fort Lauderdale, Florida, provided a close look at the factors that make clinical trials so expensive--and so risky.

"Optimizing Global Clinical Trials," by Todd Clark, reports on the details of a complex model built with Monte Carlo software that was intended to help a pharmaceutical developer working out product strategy for clinical trials. The company's goal was to choose a country from which to launch trials for a specific drug for a specific kind of cancer. Because the primary factor in locating clinical trials is probable patient enrollment, the report provides country-by-country risk assessments for 54 factors ranging from epidemiological data to satisfaction with existing cancer therapies.

For myself, having no idea how clinic trials are organized, Clark's report is eye-opening. It gives a very clear picture of the constraints under which pharmaceutical development takes place and of the huge budgets behind the process--which helps to justify the high costs of drugs. Risk analysis should have a very happy home in this industry because the value-at-risk is very high and the probabilities are pretty sorry. As Clark reports, “On average, drug sponsors can spend over 13 years studying the benefits and risks of a new compound, and several hundred millions of dollars completing these studies before seeking FDA’s approval. About 1 out of every 10,000 chemical compounds initially tested for their potential as

new medicines is found safe and effective. . . ."

Amazingly enough in light of all this, Clark reports that the number of clinical trials is growing. It doesn't take any statistical analysis to derive from this last fact that when a drug makes it to market and makes it big there, the return on investment is a whopper.

The DNA of Cement

Thursday, September 17, 2009 by Holly Bailey

Last week a team of MIT scientists calling themselves Liquid Stone made a breakthrough (as it were) discovery about cement. The Romans used cement to build their remarkable aqueducts, and the stuff is still in use. In fact it's one the most widely used building materials on the planet. It has a chemical name, calcium-silica-hydrate. But until last week, its molecular structure was unknown.

Scientists have been operating under the assumption that cement is a crystal, but the Liquid Stone group discovered this is not the case. It's a hybrid structure in which the crystal form is interrupted by "messy areas" in which small voids allow water to attach.

By now, you are probably wondering what the composition of cement has to do with risk analysis. The link is Monte Carlo simulation, Liquid Stone used Monte Carlo software harnessed together with an atomistic modeling program to test various scenarios for how water attaches to the cement molecule in the messy areas.

Why is this discovery important? Because the manufacture of cement is accounts for about 5 percent of worldwide carbon emissions. The new knowledge of the composition of cement will enable engineers to tinker with the manufacture of cement to reduce these emissions. Now that Liquid Stone has what it calls the DNA of cement, they can progress to genetic engineering of the messy areas, and predictive statistical analysis will allow them to test various product strategies for replacing various atoms in the cement molecule.

What I love about all this is that apparently, Liquid Stone isn't using risk analysis to get the messy areas better organized,the purpose of it is to figure out how to fit new stuff into the mess.

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.

Bausch & Lomb’s Global Director of DFSS Gets Our Focus

Wednesday, September 2, 2009 by Steve Hunt

As part of Palisade’s membership in the ISSSP, we get to participate in what are called Focused Sessions. For these webcast-like sessions, we are sponsors and exert no editorial control over their content . . . but we decide who the speaker is.

So we’ve decided to put the attendees in good hands! Jeff Slutsky, Global Director of Design for Six Sigma for Bausch & Lomb, will be giving a presentation on September 17th called Probabilistic Project Estimation Using Monte Carlo Simulation.

Registration for the event through the ISSSP is free. This presentation will feature @RISK for MS Project. If you ever wanted to find out more about @RISK for Project in Six Sigma and project estimation, this would be a good venue.

Last summer Jeff presented an excellent free live webcast: DFSS-based Design Optimization using Design of Experiments and @RISK. This is also something that can be viewed for free.

As for recommended reading in the future, Jeff is also the coauthor of Design for Six Sigma in Technology and Product Development. I'd highly recommend it, it is an excellent resource that is often used as the corner stone in many DFSS and Critical Parameter Management courses