Public Relations Representative for Palisade
I specialize in communications for technical and scientific companies. During my work for Palisade Corporation over the past decade I have kept a close eye on trends in quantitative decision-making techniques. I'm keeping this blog to report where and how I find these techniques--such as risk analysis, risk optimization, decision analysis, neural networks, and statistical analysis--being applied.
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Posted Thursday, July 2, 2009 by
Holly Bailey
It seems reasonable that in a recession, cost containment would become hot topic, and in the world of supply chain management, it's known as "spend control."
I was interested in a recent blog describing spend control in the "fast-moving goods" (what used to be called consumer packaged goods) industry, where taming volatility in cash flow is a huge challenge. In it, the chief procurement officer at DelMonte pointed out that forward contracting for commodities is a standard practice that involves a great deal of decision making under uncertainty--and is therefore wide open to improvement through risk analysis. Apparently this was news to many in Dave McLain's audience.
The key to profitable forward contracting, McLain observed, is to structure contracts so that your suppliers don't push their risk to your company. Not always possible, he acknowledges, but his company uses a variety of operational risk software to identify and anticipate the biggest drivers of operations risk in the commodity markets where they are active. This is a complex set of analyses, and among other techniques, he recommends using Monte Carlo software to go beyond the usual option valuation and look at production forecasting and statistical analysis of historical data on cost and price volatility--from the supplier's standpoint. The risk assessment models will churn out a range of what potential commodity costs could be, so the procurement folks can build caps and locks into their contracts.
All of this requires some analytical knowhow, and the Spendmatters blog promises a primer on the DelMonte approach. It's worth following because it ain't easy, it ain't simple, but it's how you get to spend control.
All of this requires some analytical knowhow, and the Spendmatters blog promises a primer on the DelMonte approach. It's worth following because it ain't easy, it ain't simple, but it's how you get to spend control.
Posted Friday, June 26, 2009 by
Holly Bailey
Question for today: What do you get when you run Monte Carlo software back in time? Answer: You get closer and closer to the wreckage of Air France Light 447.
The U.S. Coast Guard's search for the crash site of the doomed Air France plane was the first major test of its "reverse-drift" modeling program SAROPS (Search and Rescue Optimal Planning System). Earlier this year I reported on one of its first reality tests, the search for two football players whose boat capsized in the Gulf of Mexico, which apparently took place before the software was formally adopted by the Coast Guard. For this search, a Coast Guard team in Portsmouth, Virginia, managed the modeling in close cooperation with French and Braziian rescue teams.
At last report, the reverse risk analysis was performing admirably. Starting with the location of the first object sighted in the water, in this case a seat cushion and some smaller debris, team using SAROPS established the location and the immediate wind and current conditions and then used the history of weather and water since the plane disappeared to estimate thousands of possible paths the seat cushion could have traveled to reach its location. When the next piece of debris surfaced, its data were fed into the program, and the Monte Carlo software spun out a slightly narrower range of retrospectively possible routes.
Although the reconstruction of the crash location sounds laborious, the simulations are extremely fast. The software can spin out ten thousand possible routes in fifteen minutes, and as the possible routes of a number of objects begin to converge, they focus with increased probability on the crash site.
Posted Thursday, June 25, 2009 by
Holly Bailey
Six years ago when Dale Addison was speaking to a group of engineers and trying to pitch "artificial intelligence"--meaning neural networks someone in his audience asked him if it was true that a neural network had once mistakenly classified a T-62 tank as a Volkswagen. Although the incident had occurred years before, Addison seems to admit that there was some truth to the story. At the time, he was dismayed to see how little confidence this technologically sophisticated audience had in neural nets because even by that time the technology had made huge progress since computer scientists started tinkering with it.
Addison is on the faculty of the University of Sunderland, and in spite of his audience's skepticism, he should be feeling fairly smug that many of the applications he foresaw for neural network technology, especially those that involve accurate classification, have been exploited. We have customers who use it on a daily basis for such tricky classification tasks as cancer diagnosis, emergency response systems, exploration and production of gas and oil, and operations research issues in manufacturing.
Nevertheless, Addison still sees resistance to emerging artifical intelligence techniques among engineers and business people, and he is still out there pitching neural nets, especially their use in combination with other new computational analysis methods, such as genetic algorithm optimization and neuro-fuzzy logic. Addison himself is working on some really tough classification problems now in the CASSANDRA project, where the goal is to develop an insider trading and market abuse detection system. He doesn't worry about the tank and the Volkswagen because he's confident that sooner rather than later, a neural network will learn to recognize a suspicious transaction when it sees one.
Posted Friday, June 19, 2009 by
Holly Bailey
As anyone who has read a few of my blog entries knows, probably all too well, I believe that Monte Carlo simulation has been unfairly maligned for its role in derailing the economy. This month in his column for Seeking Alpha, Geoff Considine made this point a lot better than I've been able to make it, and he also made it more fun. Considine, who works for a firm that develops specialized Monte Carlo software for investors, offers a detailed recipe for stress testing your personal investment portfolio.
Of course, his recipe makes use of his company's software. But you could use the same recipe with the same ingredients in any Monte Carlo Excel spreadsheet (and here I don't see any need to hide my own risk analysis affiliation) and bake up the same pie charts.
What's good about his recipe is that it walks you through the assumption stages of model building quite carefully. What's fun about his recipe is that his hypothetical example, which uses a straw man named Bob, is retrospective, and he constructs a model that begins in 2007 and runs forward from there. This means Considine has an opportunity to second-guess the assumptions--such as asset value, value-at-risk, and probabilities of worst-case scenarios--that brought down the house in 2008.
What's good about his recipe is that it walks you through the assumption stages of model building quite carefully. What's fun about his recipe is that his hypothetical example, which uses a straw man named Bob, is retrospective, and he constructs a model that begins in 2007 and runs forward from there. This means Considine has an opportunity to second-guess the assumptions--such as asset value, value-at-risk, and probabilities of worst-case scenarios--that brought down the house in 2008.
Of course, mortality tables being what they are, Bob dies in the end. But Bob doesn't fail, he goes out the smart way.
Posted Monday, June 15, 2009 by
Holly Bailey
Over the past three years I've been tracking an uptrend in risk analysis in what might seem an unlikely field, wildlife conservation. But on further thought, this makes perfect, straightforward sense. At-risk animal populations could use some analytical help sorting out the live-or-die questions.
The first benchmark occurred when the World Conservation Union began to use Palisade's Monte Carlo software to train field biologists concerned about disease control in the WCU's projects around the world. At the time I was told that "there are hundreds of conservation projects that need to account for the risk of disease."
Then just about the time I had come to the conclusion that I could head toward the seafood department in the supermarket primed for decision making under uncertainty and confident of my choices, I became aware that an economist friend of mine who studies fish populations was running not only statistical analysis of food fish populations but using the Monte Carlo features in Excel to forecast the results of various "harvesting" scenarios on the populations of my favorite fish, the Atlantic cod.
Today, I learned that a very rare marine mammal, the Hector's Dolphin, which inhabits micro territories off New Zealand, is the latest beneficiary of environmental risk analysis. Before a Hector's Dolphin ever comes into its watery world it faces some significant risks. The world's smallest dolphin--it's about five feet long--the Hector's Dolphin gives birth to large babies at the very slow rate of every two to three years. And once born, it, like most other dolphin species, risks entanglement in a commercial fishing net. Dr. Liz Slooten, University of Otago, has become an authority in modeling the effects of marine mammal bycatch on their populations and is now focusing her risk analyses on the endangered Hector's Dolphin. It may be the smallest dolphin with some of the smallest numbers, but it's no small fry to her.
Posted Friday, June 5, 2009 by
Holly Bailey
It must be that all the gloom in the financial sector is bringing out the gallows humor in me, because I had to laugh at this follow up on the "war games" stress testing in the British banking sector. First the Financial Times reported that risk analyses stress tests applied by regulators to British banks had accurately predicted the 2007 failure of several banks. Now the Telegraph has reported that after cracking down on lavish bonuses for bankers, the Financial Services Authority, the country's bank regulator,has paid out about $147 million in staff bonuses--which amounts to about a 40 percent hike in its payroll costs.
The Financial Services Authority defended the additional expense by saying that the heightened demands on the agency in 2008 required it to hire an additional staff 250 members--and 250 additional copies of whatever Monte Carlo software the agency uses?--and to pay for more highly skilled staff.
Posted Tuesday, June 2, 2009 by
Holly Bailey
In case you're one of the many people who believe that no one could have foreseen the banking crisis that began in the U.S. last year, the Financial Times reported just days ago that in 2004 the British banking system--specifically, the Financial Services Authority, the Bank of England, and the Treasury--did, in fact, foresee the failure of several British banks. They used risk simulation to conduct "war games" within the banking system. These war games were analogous to the recent stress tests to which U.S. banks were subjected, and they were intended to reveal the potential effects of sudden turmoil in the mortgage markets.
By revealing what banking authorities considered unacceptable risk exposure on the part of a few banks, including Northern Rock and HBOS, the 2004 risk analysis tests did fulfill their function, but bank regulators--who themselves were in did not believe they could force the banks to change their business practices. In 2007, as wholesale lending markets dried up, Northern Rock failed, and HBOS was rescued in a buyout by Lloyd's.
Posted Friday, May 29, 2009 by
Holly Bailey
As the market for neural network software has become more and more competitive, I've been intrigued to watch the proliferation of applications for this breed of statistical analysis. In a week that has produced news of neural networks put to use to diagnose epilepsy, pick stocks, protect children from internet pornography, and predict wind power came a particularly intriguing item that might not get the attention it deserves: Penn State information sciences professor Jim Jansen and his colleague Amanda Spink of the Queensland University used a neural net for a study of internet search engines and user satisfaction. Because click-throughs mean potential sales for businesses that rely on internet advertising, the study could send search engine developers scrambling to retool their engines.
Here was the question Jansen and Spink posed: what is it about the results a search engine produces that causes the person who receives them to click through on a particular result?
Here was how they went about answering that question: they obtained data on 7 million interactions from the search aggregator Dogpile and used neural networks software to classify the purpose of the search--e.g., information gathering, navigation help--used other statistical analysis methods to relate their classifications to the number of click-throughs. Here a click-through indicates that the user was satisfied enough with the result to pursue it further, and obviously a search engine that produces more click-through responses is more commercially desirable.
Posted Friday, May 22, 2009 by
Holly Bailey
Let's move on from yesterday's blog on the Efficient Frontier, formulated half a century ago by Harry Markowitz, to the New Frontier postulated by investment advisor Richard Machaud. Michaud is the author of Efficient Asset Management:A Practical Guide to Stock Portfolio Optimization and Asset Allocation (Oxford University Press, 2008), among other works, and now heads up New Frontier Advisors, an institutional research and investment advisory company.
Michaud's New Frontier adds further sophistication to Markowitz's ideas about optimizing investment diversification to balance risk and return by introducing resampling to the optimization process. Resampling is a method from statistical analysis that compensates for possible error by analyzing a dataset from which a subset has been portioned off and replacing values in the initial analysis with randomly sampled values from the subset.
More specifically about the New Frontier technique, Michaud adds resampling capability to Monte Carlo simulation. According to one commentator, this "allows managers to assign a greater range of probabilities to various outcomes. The goal is to produce a more realistic portfolio based on a more realistic frontier."
New Frontier now markets proprietary Monte Carlo software with a built-in resampling function to its institutional clients, and my own in-house experts tell me that resampling functionality is available in some commercial Monte Carlo Excel software as well.
New Frontier now markets proprietary Monte Carlo software with a built-in resampling function to its institutional clients, and my own in-house experts tell me that resampling functionality is available in some commercial Monte Carlo Excel software as well.
Posted Thursday, May 21, 2009 by
Holly Bailey
In my comments over the months since the economic sucker punch landed, I have been reiterating that Monte Carlo simulation is not to blame for the faulty risk assessment that brought down the derivatives markets. The assumptions that went into those risk simulation models were the source of the trouble, and that's too bad, because many versions of Monte Carlo software are flexible enough to accommodate all kinds of probability functions and timelines.
Today I came across a lucid article from IndexUniverse.com detailing just one of the ways Monte Carlo simulation can be tuned to the combined unfolding of time and risk. Tomorrow, I'll look specifically at that variation of risk analysis, but first, today, a little background.
Since Harry Markowitz won the Nobel Prize in Economics in 1990, the Efficient Frontier has been the line in the sand under which portfolio managers wiggle their toes. The efficient frontier is a major component of his Modern Portfolio Theory, which brought him the big prize. In the 1950s Markowitz was researching the idea of the present value of investments in order to optimize the return across collection or portfolio of these, and he realized that the element that was missing from ideas about present value was risk. This insight led, eventually, to his prescriptions for diversifying investments to maximize the return and minimize the risk across an entire portfolio.
Portfolio diversification is now gospel among financial planners. But gospel doesn't mean all investment advisors treat or even produce the same Monte Carlo Excel models of portfolio risk in the same way. Tomorrow, one investment advisory firm's approach to Monte Carlo and the Efficient Frontier.
Posted Thursday, May 14, 2009 by
Holly Bailey
In his book Secret Knowledge painter David Hockney advanced the idea that the Renaissance masters had worked from 3-D images of their subjects that were created by curved mirrors. Disputing this theory provided David G. Stork his entry into the computer analysis of art. Now Chief Scientist at Ricoh Innovations, Stork uses a combination of optics and high-powered computing to get inside a painting and create a virtual world from which it was drawn. Among the more famous subjects on which he has trained his "computer vision" are Vermeer's "Girl with a Pearl Earring," Jan Van Eyck's "Portrait of a Cardinal," and Veláquez's "Las Meninas." Many of this wide-ranging physicist's thirty-some patents involve neural network technology, and his publications center on statistical analysis of images, machine learning, and neural network optimization. His computer analysis of paintings focus--sorry, but there's no way to skirt this pun--on the sources and behavior of light in the two-dimensional painting surface and use what the computer "learns" to simulate the fully dimensional world from which the painting was made. Stork posits, for instance, the real-world version of the daylight streaming through the window of the tavern in Caravaggio's "Calling of Saint Matthew" was actually the glow of a lantern.
Posted Tuesday, May 12, 2009 by
Holly Bailey
"Formal quantitative assessments of uncertainty can mark a truly significant step forward in enhancing regulatory analysis under Presidential Executive Orders." This stuffy-sounding statement appeared today in a otherwise trenchant Huffington Post column by Harvard environmental economist Robert Stavins. Dr. Stavin's target in today's piece is the so-called RIA--the Regulatory Impact Analysis required by Presidential Order for any proposed new piece of federal regulation.
Stavins's concern is that current methods of evaluating proposed environmental regulations do not attempt to account in a meaningful way for uncertainty, especially uncertainty over time. His solution to this inadequacy is Monte Carlo simulation (which has over the past year been lambasted for its use in the financial sector for its own inadequacies--but never mind, anyone reading this is likely to understand that a risk analysis model is only as good as its inputs).
Posted Thursday, May 7, 2009 by
Holly Bailey
At the height of panic--and consternation--over turmoil in the financial sector at the turn of the year, many an accusatory finger was pointed at the risk analysis models the finance industry used to establish the value of various types of debts. Often the particular charge was that the simulations produced by Monte Carlo software lacked not only precision but even the capacity for precision. At the time, I responded in this blog that a risk assessment model is only as reliable as the probabilities it is build on. Now some folks in financial planning firms whose customers experienced the unhappy results of models created with their companies' proprietary Monte Carlo software are becoming believers. They are revisiting the probability distributions that generated those risk simulations, and in comments to the press are citing the need for distributions with fatter tails in order to account for randomness over longer time periods, in order to foresee a Black Swan event. This is no doubt due to the sudden prominence of author Nasim Nicholas Taleb, whose recent fame is due to his timely second-guessing of the markets.
That people who work in finance and investing should start listening to a critic only after coming to grief is not surprising. What I do find surprising, however, is the planners who mention to reporters that they have been limited to the standard bell curve distribution or that their software doesn't provide for sensitivity analyses. Any number of commercially available Monte Carlo software packages have been offering a fairly wide choice of distributions, along with sensitivity analyses for quite a while. Fatter tails aren't hard to come by, so why can't planners seem to find them?
Posted Friday, May 1, 2009 by
Holly Bailey
In my last blog I mentioned there has been a dramatic upswing in the use of risk analysis and Monte Carlo software in clinical trials for new drugs. A new unpublished paper by Todd Clark of VOI Consulting makes clear some of the reasons more people in the pharmaceutical industry are turning to operational risk software to guide them in setting up trials.
First of all, a clinical trial is probably not one trial but a process involving a series of trials, each of which takes a number of years and millions of dollars to complete. This process takes place before the company even presents the drug to the FDA for approval. Then, as the U.S. Government Accountability Office, points out, the FDA eventually approves only 1 in 10,000 compounds a safe and effective. No wonder--again according to the GAO--"the number of new drugs being produced has generally declined while research and development expenses have been steadily increasing."
Although there are enormous profits to be made if a drug developed for a large number of patients is approved, there are great sums of money to be lost and many tricky decisions to be evaluated along the way to successful product strategies. As Clark points out, even the planning of a single clinical trial is itself fraught with uncertainty: How many subjects? What kind of subjects? What kinds of physicians? Where to hold the trials? And the answer to each of these questions is in turn a balancing out of numerous variables.
So there's plenty of risk to go around. But potentially plenty of reward. Just made for risk assessment with Monte Carlo.
Posted Wednesday, April 29, 2009 by
Holly Bailey
Over the past couple of years I have heard with increasing frequency from customers in the pharmaceutical industry who use Palisade's Monte Carlo software in the development of new drugs, especially in testing. In clinical trials, risk analysis can help developers extrapolate results from a limited group of patients to predict outcomes for a much larger population. Results from these predictive models have turning up in medical publications and online summaries every day.Now a recent commentary on the manufacturing sector of the industry has identified Lean Six Sigma teamed with Monte Carlo simulation as a "new industry paradigm." The business of manufacturing pharmaceuticals has historically made big profits and emerged unscathed from economic downturns. Decision makers in this sector haven't had to worry about Six-Sigma style operations management. But all of that is changing.
Posted Sunday, April 19, 2009 by
Holly Bailey
It was bound to happen. Online communities such as FaceBook and Twitter, which are themselves commercial animals, are being mined by all kinds of enterprises from ad agencies to credit card companies for the commercially valuable data they can yield. A recent opinion piece in the Manchester Evening News rounds up a fair number of potential uses of this socially generated data and tries to sort out the good from the not so good, and the bad from the truly ugly.
According to the Evening News's Paul Taylor, businesses are using social networking sites for everything from checking out individual job applicants to statistical analysis of myriad purchasing decisions with neural network technology. On of the worrisome scenarios he highlights is the probable effect of upcoming legislation by Parliament that would require law enforcement agencies to keep records of web traffic. Another is the move by Google to obtain customers' permission to let Google use cell phone software to keep track of their whereabouts and apply its operation research magic to turn the information it acquires this way into marketable fact. But he balances these possibilities with other brighter ones--such as helping doctors do better risk assessment in creating treatment plans.
Falling somewhere in the space between sinister and beneficial is the use of social networking data for marketing. About the same time as Paul Taylor's opinion piece was published, a marketer's blog for the auto industry laid out the conceptual framework of a strategy based on online communities that it has trademarked as "Social Influence Marketing." A component of any campaign as essential, it claims, as direct marketing and branding.
At the moment, all of this should mean more to you if you are young, because, at the moment, the young are the people who are most attracted to social networks. And they are the ones who will immediately see the utility of network data for marketing and product strategies. But if, as they say, youth is only a state of mind, it won't be long before the rest of us catch up and catch on as the social network and its exploitation evolve.
Posted Thursday, April 16, 2009 by
Holly Bailey
The demand for computing speed is relentless, and both the hardware and software industries have been looking to parallel computing to accelerate application performance. Parallel computing, which is based on the observation that two computers harnessed to components of the same task can accomplish that task faster than a single larger computer with equivalent power. Many hands make light work.
The "multicore" was just the latest development in a succession of innovations in parallel computing. It is a chip that houses more than one CPU and functions like so many computers working on the same problem. It was expected to be a generalized performance solution for "embarrassingly parallel" operations--those which can be easily separated into sub-tasks, like genetic algorithm optimization and operations management programs for risk assessment.
Last year, Microsoft and Intel made joint grants to two universities totaling $20 million to further the use of their new multicore computer chips. The two companies have asked researchers at the University of Illinois at Urbana-Champaign and University of California, Berkeley, to develop software to exploit the potential of the new chips. Although at the time of the Microsoft-Intel grants, the companies spoke of glitzy consumer applications like personal health monitors and personal assistants on cell phones, one of the most important destinations for the multicores was large processing centers that manage data for marketing and financial concerns.
Time will reveal the fate of the multicore chip--but probably not quickly--and in the meantime, necessity may well turn out to be the mother of raw speed.
Posted Tuesday, April 14, 2009 by
Holly Bailey
Last month I noticed a slight uptick in press coverage of computer-vs.-brain stories and thought I had neatly disposed of that topic with my blog on wetware vs. software. Not so. The uptick has become an upswing--though not yet quite an obsession. On one side of campus, biologists are patiently explaining to the lay person why the AI version of a neural network can never approach the complexity and capacity of the biological model for that set of algorithms, and several buildings away computer scientists are optimistically predicting a "brain on a chip."
For the moment, the brain-on-a-chip crowd, exemplified by the consortium of European scientists attempting to map and then simulate the workings of biological neurons and synapses, has practical goals in mind. Smaller, higher capacity formats for computation will add speed and layers of complexity to now commonplace analytical processes in business and industry such as statistical analysis, genetic algorithm optimization, and simulations for risk assessment. In the long view, they are tantalized by the prospect that computers could take over the job of thinking from humans.
For the beauty-of-the-biological-brain crowd, represented this week by neuroscientists Sam Wang and Sandra Aamodt in the New York Times, the best use of computer science is to give science a scale on which to measure the information processing capacity of the human brain. They tell us, for instance, that the human brain is so compact it can store about a third of all the archived information on the entire Internet. Accordingly, it would take a whopping amount of parallel processing for computers to begin to stand in for a single synapse.
I'm not sure why the idea of the brain as a computer or the computer as a brain is so compelling, but it is clear that it will be a long time--think geological time--before there is a meeting of the minds.
Posted Thursday, April 9, 2009 by
Holly Bailey
For at least the past decade the specter of global climate change has been an elephant on the public's horizon, growing larger every year as it draws a little bit closer. Now that it is close enough to be simulated--as I reported in my last blog-- and perceived as a real elephant, a number of scientists and engineers who work in environmental risk analysis and operations management have begun to size up what that elephant will look and feel like when it's standing in front of us.
As two recent news items point out, these practical people are trying to anticipate the practical issues the elephant will drag in with it. And this is a matter of computational mathematics. Reporting on the research of a team from Lethbridge University in Alberta, Canada, and the University of New South Wales in Canberra, Australia, The International Journal of Mathematics in Operational Research examines the question of how to figure out where the people who are likely to be displaced by rising sea levels and desertification should go. They have developed an decision evaluation algorithm to optimize relocation strategies.
The elephant nods sympathetically and asks if the professor and his human relatives know about operational risk software.
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