Exponential Decline

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.

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). Read more of Dr. Strauss' bio here.

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

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.

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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

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.

In a recent report The Innovation Imperative in Manufacturing - How the United States Can restore its edge, produced jointly by The Boston Consulting Group, the National Association of Manufacturers and the Manufacturing Institute, The United States ranked #8 out of 110 countries in innovation leadership.     

The Top Ten List (overall)

1. Singapore
2. South Korea
3. Switzerland
4. Iceland
5. Ireland
6. Hong Kong
7. Finland
8. United States
9. Japan
10. Sweden

The first half of this report painted a pretty bleak picture for the US.  Among the depressing  statements:
“The United States is losing its distinction as an innovation leader and may be under-investing in the future.”

 “The United States is disadvantaged in several key areas, including workforce quality and economic, immigration and infrastructure policies.”
Some companies are even “moving R & D centers abroad to capitalize on leading-edge talent and lower cost scientists and engineers or to better meet local market needs . . .”

This information is difficult to swallow, but it clearly makes the point:

It’s time for change.

In the article, they actually used the quote “innovate or die!”

Even without the current economic crisis, we know we have issues and challenges at many levels, and if this report helps revitalize our companies and government into action, then all for it.

The second half the report does a nice job detailing what they considered to be the Four Key Factors for Success, which they felt are:

• Idea generation
• Structured Processes
• Leadership
• Skilled Workers

 
The research feels the US Government plays a vital part in encouraging innovation, and their role is to boost Company payback on innovation through consistent programs, like supporting innovation activities through government-funded laboratories and research labs. Tax credits are also a common way, but this deemed to be more of “thank you” then a motivator, and is often inconsistent from year to year.

Although they stated that some recommendations were beyond the scope of the report, they suggest the US make concrete improvement in six areas:

1. Strengthen the workforce
2. Lead by example
3. Make innovation easier
4. Maintain a strong Manufacturing base
5. Improve the pay back
6. Be Consistent

Although, I feel there is a lot of good information to be learned from the report, one should keep in mind that the bulk of the information was gathered through a NAM Survey of Corporate members with only ~1000 respondents and a series of 30  one hour interviews with “Senior Executives.” It’s truly hard to know how representative the sample was to the actual population.

To reiterate, we know that innovation, quality and jobs in the US have been on the decline for past years. It’s time to act, Adopt an innovation,  product development,  or quality program such as Design for Six Sigma (DFSS), Design for Lean Six Sigma (DFLSS), Critical Parameter Management, (CPM)or whatever you want to call it, deploy it and stick to it! As I have may have mentioned before, many companies I have worked with who indicated successful deployment really never got off the ground floor.
 

You can read the entire report at www.nam.org/~/media/AboutUs/ManufacturingInstitute/innovationreport.ashx

In assigning blame for what they are now calling "the credit crunch," the news media have been pointing vaguely in the direction of risk assessment and the models produced by Monte Carlo simulation.  But with the exception of Joe Nocera's excellent piece focusing on value-at-risk in the New York Times, I had not seen a clear explanation of the factors feeding into the exponential decline of the credit markets until I came across a policy paper from the Association of Chartered Certified Accountants, "Climbing out of the Credit Crunch."

This paper provides an excellent, plain-English account of the interplay of the many factors that brought the current turmoil in the financial sector--a number of these factors the ACCA identifies are psychological and attitudinal.  Its discussion of risk identification and management focuses not on risk analysis models but on the underlying assumptions--a common "garbage in, garbage out" observation--and a passive, unquestioning reliance on these models.  This leads the ACCA to one of what it identifies as a major risk management failure: "a very clear disconnect between incentives to senior staff" and [highly sophisticated] risk management functions."

The point is that risk management is so crucial to financial performance that executives who keep a close, critical eye on the tools and techniques they are using to assess risk should be rewarded for that vigilance. 

Warren Buffet did not have a good year.  This fact about 2008 was detailed last week in a New York Times article and in a letter from Mr. Buffet to stockholders of his Berkshire Hathaway company.  This letter offers plenty of plain-spoken criticism of the risk assessment failures in the financial sector to go around--'beware of geeks bearing formulas." 

But he reserves his harshest criticisms for derivatives--"weapons of mass destruction.".  He not only cites the hazards of using risk analysis models to value these complexly structured investment products, but he points out something I haven't seen mentioned before: derivatives create a "web of mutual dependence" among financial institutions that lingers for years.

About this web of dependence, he says, “Participants seeking to dodge troubles face the same problem as someone seeking to avoid venereal disease,” he wrote. “It’s not just whom you sleep with, but also whom they are sleeping with.”

I recommend Warren Buffet's letter if only for its Olympian view of the exponential decline in the credit markets and his list of the intriguing entertainments he has planned for his shareholders at their annual meeting this May.  His bad year makes for some good reading.

My sister, who has an advanced degree in business, has been telling me for years that there is no problem, large or small, that can't be ameliorated by throwing money at it.  This is something she has in common with Bill and Melinda Gates, malaria being the case in point.

It is a deadly and obstinate disease.  All through the twentieth century science made steady advances--and several scientists won Nobel prizes for these advances--and it has been eradicated in wealthy countries like the U.S.  But it is still a grim presence in many less-developed countries, where it kills more than a million children a year.  As was recently mentioned in the Wall Street Journal, the Gates Foundation has spent a lot of money attempting to address the causes and prevention of this stubborn disease in poorer countries.

In the first annual letter reporting the activities of the Gates Foundation, Bill Gates makes special note of the fact that because there are many weapons against malaria, none of which is a total solution, the foundation brought in a specialist in Monte Carlo simulation to analyze the best way to combine the tools. "This modeling work," he says, "which will show where we can eliminate malaria and where we can just reduce the disease burden, is a wonderful use of advanced mathematics to save lives, and if it goes as well as I expect, we will apply it to other diseases. "

He is not predicting an exponential decline in malaria.  But evidently there is no problem, large or small, that can't be ameliorated by throwing a little Microsoft Excel statistics and Monte Carlo software at it.

This week saw Palisade sponsoring the first SMI-organized conference on Financial Modelling in the Oil and Gas Industry, in London (UK). Palisade’s Michael Rees spoke on the use of @RISK, PrecisionTree, Evolver and RISKOptimizer within the industry. The talk included examples of the use of the software to conduct reserves estimation, model exponential decline and production forecasting, model prices, costs and investments and to generate an integrated risk-based decision evaluation process. Other examples included using the software to help make decisions concerning exploration and production, to implement real options valuation, and to optimize production schedules using Evolver’s and RISKOptimizer’s genetic algorithm optimization capabilities.

Palisade recently also offered the first European regional training seminar dedicated solely to Oil and Gas applications. This was held in Oslo, Norway in late October. Due to the success of this event, others are likely to be held in 2009 – watch this space!

DMUU Training Team

The risk analysis model below examines the familiar production forecasting model for oil and gas wells, the exponential decline curve. The standard equation, q = qie-at (3.3), can be used with random variables for both qi (the initial production rate, sometimes called IP) and a (the constant decline rate). Here the model has an additional parameter, t (time), which makes the output (Rate, STB/YR) more complicated than the volumetric reserves output.

No longer do we just want a distribution of numbers for output. Instead we want a distribution of forecasts or graphs.  The worksheet has two input cells, IP and Decline, and a column of outputs for the Rate of production in STB/YR over 15 years.

After simulation you can generate a summary graph like that shown in the model. This graph shows uncertainty over the 15 year period. The shaded region represents one standard deviation on each side of the mean. The dotted curves represent the 5th and 95th percentiles. Thus, between these dotted curves is a 90% confidence interval. We can think of the band as being made up of numerous decline curves, each of which resulted from choices of qi and a.

» @RISK Example model: Band.xls

This example was taken from Decisions Involving Uncertainty: An @RISK Tutorial for the Petroleum Industry by James Murtha, published by Palisade Corporation, where a detailed, step-by-step explanation can be found.

When considering decision-making under uncertainty, one may need to evaluate which of several decision possibilities to select, and then conduct a detailed risk analysis of that decision. Palisade Corporation’s Decision support software (such as the PrecisionTree decision tree software and the @RISK Monte Carlo simulation software) can be used in a wide range of decision analysis contexts to support the selection and detailed analysis in these situations. In addition, one may need to calibrate models or explore and analyse existing data sets, and the statistics software StatTools can facilitate certain forms of statistical analysis that may not be possible when using Excel statistics functionality. (Palisade’s DecisionTools Suite also contains other software products, including RISKOptimizer and Evolver to deal with optimisation problems, the neural network software NeuralTools, and TopRank to support model auditing and sensitivity analysis).

Frequent applications of the DecisionTools Suite include cost estimation (project cost estimation, construction cost estimation, cost budgeting and contingency planning), discounted cash flow analysis and financial forecasting, risk registers (event risk modelling and operational risk), options valuation and real options analysis, Six Sigma analysis, product strategy, environmental risk analysis, veterinary risk assessments, operations management, retirement planning and so on. Indeed, the range and flexibility of the DecisionTools Suite means that the number of applications is vast, and really only limited by a user’s ability to appropriately formulate their own situation in a way that is suitable for quantitative analysis.

Whilst the software can be used essentially in applications in all industries and functions, the oil and gas sector is a very active one. The increasing cost of discovery and recovery of oil from more remote and hostile environments means that an effective resource allocation and a rigorous decision evaluation are key to business success in these contexts. Decision tree software and Monte Carlo simulation software are therefore widely used in exploration and production (e.g. seismic testing decisions, reserves estimation, and production forecasting using exponential decline curves or other methods) and for other aspects of risk assessment for large projects.