Monte Carlo Software

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

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

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

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

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

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

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

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

Rumors of this death are greatly exaggerated.  

Why is it that most of the high profile projects managed by the government in the UK all ultimately become beset by problems? A number of projects jump to mind – the Millennium Dome, Wembley Stadium and currently the NHS IT. All three have been plagued by developmental delays and financial mismanagement.

Recently, yet another worthy, but ambitious project has been announced – the North-South high speed rail line to connect London to Scotland. One wonders if the government undertakes detailed quantitative project risk analysis for its infrastructure initiatives?

A good example to highlight in this context is ENGCOMP, a Saskatchewan-based engineering consulting firm that has worked with the Canadian Department of National Defence (DND) to help define budgets for the fourth phase of construction of its Fleet Maintenance Facility at Canadian Forces Base Esquimalt in Victoria, British Columbia. Using @RISK, a Monte Carlo simulation tool, ENGCOMP helped the DND define and secure budget approval from the Federal Government’s Treasury Board. The consultancy firm was able to estimate the impact of the variability and uncertainties pertaining to risks, costs and scheduling. This assessment enabled it to estimate the project risk budget or the risk reserve and schedule contingency, which were both factored in when defining the total project cost of the infrastructure project.

The fact is, in the world of business, risk is inherent and unavoidable. Whilst one cannot completely control risk, one can certainly help reduce uncertainty, greatly increasing the chances of project success. For instance, a key finding of the project risk analysis conducted by ENGCOMP was that, taking into account all the risk and uncertainties on the project, there is an 85 per cent chance that the Fleet Maintenance Facility project will be completed in January 2014. A fairly positive result for the DND, given the scale and complexity of this project in question.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

When experts disagree, Cooke has pointed out, any attempt to impose agreement will "promote confusion between consensus and certainty."  In order to get around this problem, Aspinall points out in his article, the goal of risk analysis should be to "quantify uncertainty, not to remove it from the decision process."  His ongoing  risk assessment of volcanic activity on the island of Monserrat in the West Indies is the longest running application of Cooke's "expert elicitation" method.  For details about how the elicitation and the pooling of opinion works, I recommend taking a look at the January 2010 issue of Nature.  

Why attend?

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

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

Who should attend?

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

How much?

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

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

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

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

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

In the last two blogs I have challenged the idea that simulation results can be boiled down to a single statistic with any positive benefit. The context of a statistic is incredibly important, which is another reason why many statistics and charts/tables should be reported on, not simply one figure. And here’s a compelling reason why.

Consider two competing, similarly-sized projects, of which a company can only pursue one. Now let’s say this company would like to take on the project that has the “least risk”. If they are only familiar with generating the P90 for the total project cost they will be forced to select the project with the lowest P90. But what if the key drivers for exceeding the P90 are easier to mitigate in one project compared to the other? Perhaps the project with the lower P90 also has a higher P95 or P99 – this means the catastrophic failure is actually greater despite a lower P90 and is the mathematical equivalent of “when things go bad, they go really bad”. Not all P90s are created equally! Such an adverse outcome might sink a smaller company where a larger one could wear the loss. The context of the company running the analysis also impacts the context of the analysis itself.

So you can see not only do simulations generate results with which informed decisions can only be made if approached holistically, but if the language used is restrictive this outcome will never be achieved. Risk analyses are a necessary part of business because most of us wish to minimise the chance that something bad will happen, quite simply. Even if a manager tells you they “want the P90” what they are really asking is “tell me about the risk we’re facing”. The answer to this fundamental question is not found in a single figure taken from a simulation, but in a range of charts and tables which require correct interpretation.

More so, Monte Carlo simulation itself is only one piece of the risk and decision assessment pie. Decision modelling and optimisation, predictive modelling and statistical analyses should also form part of the quantitative approach to uncertainty. There is life beyond just risk simulation software, and I intend on exploring that in future blogs.

» Part 1
» Part 2

Rishi Prabhakar
Trainer/Consultant

Where I left off last time was lamenting the use of Monte Carlo simulation to create a single value (statistic etc.) from a model. It might still not be clear why this is anathema to me, so here goes.

A simulation is not a number. It’s not one possible (future) outcome – that’s a scenario. Monte Carlo simulation is a methodology for understanding one’s exposure to outcomes not situated close to the central tendency of the process/project in question. Note the plural “outcomes”. Risk analysis, when done properly, should let you know essentially all possible outcomes and how likely they are for your model. Output from a simulation can include a plot of means (over time), or P5s, or P95s, or the mean ± one standard deviation or any number of statistics. But that’s not plotting a simulation! Let’s not give a minimalist graph too much credit.

Such statements also perpetuate the idea that simulation is only used for creating means (or other centrally tending statistics) and ignores the wealth of information available. Risk simulation software exists to help you do risk analysis which must include not only several statistics but also sensitivity information. It is all too easy to turn a risk assessment into a hunt for a regularly asked for percentile (such as the P90) and there ends the task. I see this a lot, especially in project cost estimation where the pressure both from management and regulatory bodies is to accurately estimate some large percentile. Once found there is usually scant further risk analysis.

Nothing good ensues. When risk analyses are run “to get ‘the’ number” they become simply another box to tick in a process and ultimately any benefits (perceived or actual) will be forgotten and lost to the ages. The notion of context is also lost. No single number by itself really means anything, or at least shouldn’t mean anything to a decision maker. I have often heard phrases like “the model returned/gave $1.2m” followed by an audience nodding in agreement. Huh? Which statistic are you talking about there, and how about reporting a few other numbers around it to place that $1.2m somewhere meaningful?

In the next installment I will look further into this issue of context and hopefully prove the necessity of an holistic approach to understanding and reporting simulation results.

» Part 1

Rishi Prabhakar
Trainer/Consultant

I read an interesting article on the causes of the Global Financial Crisis by John B. Taylor. Although the topic is interesting enough already, especially for a member of a risk analysis-specialising company, something else caught my eye. I have observed in training workshops, onsite consulting and now academic papers a phenomenon regarding probabilistic modelling. Many of those using the methods don’t understand what they should actually be getting from the methodology. There is an intellectual leap from the deterministic to the probabilistic that sometimes does not get made. This limits the usefulness of Monte Carlo simulation, and the value of performing such statistical analyses.

Back to the article which spurred me to write this blog in the first place. Or rather, the graph. Yes a single graph of housing starts vs. time (and its brief description) leapt out at me. One of the lines on the graph was claimed to show model simulations of housing starts using the actual interest rate, compared to the interest rate ‘predicted’ by the Taylor Rule and a third line showing actual data.

So what’s the problem?

The problem is that simulation techniques should not be used to create a single value. The single ‘simulation’ line implies a single modelled/returned value for each time period. This is deterministic modelling. There may be a particular scenario that has been modelled, but it certainly isn’t a simulation that is being represented by that single line. Simulations produce thousands of data, observed values and their associated percentiles as well central moments (mean, variance etc.). Not just one value (sorry Value at Risk – that includes you too) that can be plotted as a single line. I would guess that if a simulation were run as I understand the term then the line in the chart was probably constructed using the simulated means. But I shouldn’t be guessing.

This is far from the only time I’ve seen simulation results reduced to a single entity. I have heard from clients in the past “the simulation gave $X” with little to no context around it, and this is supposed to both mean something to me and to their customers and help to make better decisions under uncertainty…

In the next blog I will explore this idea further and discuss the sorts of results that should be gleaned from a simulation. In particular, why narrowing simulation results down to a single number is counterproductive to healthy business practices.


Rishi Prabhakar
Trainer/Consultant

The latest Business in Britain survey from Lloyds TSB Commercial shows that the UK's commercial enterprises are regaining confidence.  The six monthly report charts the performance of 1,732 UK companies and their views on prospects for the coming year. Its most recent business confidence shows that expectations for both sales and orders have started to recover. The balance of firms anticipating an upturn in sales has climbed to 21% - from just 1% six months ago.   And hopes for orders are also looking brighter. The balance expecting order levels to rise over the coming six months has climbed to 23%, from just 6% in the last survey.

But companies planning major new business drives for 2010 would do well to follow the example of Thales UK, which uses @RISK  to enable it to assess commercial feasibility of potential new business wins. @RISK's in-depth risk analysis ensures the leading provider of mission-critical electronic information systems for aerospace, defence and security markets around the world, is fully informed when making business-critical decisions.

Thales operates in a highly competitive environment, with technologically advanced countries presenting tough opposition when it tenders for contracts. It must continually develop highly sophisticated equipment that is robust and failsafe to meet the stringent demands of its customers. Bringing products of this calibre to market is costly in terms of time and resource, so for every competitive new business opportunity, Thales must be confident that it has a reasonable chance of success.

Using Monte Carlo analysis to show all potential scenarios and the likelihood that each will occur, @RISK enables Thales to calculate the competitiveness of complex markets, measure probabilities for project costs, quantify rate of return, and even account for the effects of cumulative business, thereby providing decision-makers with the most complete picture possible.  From this risk analysis, Thales can make an informed decision on the commercial viability of the potential new business offered.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

Palisade Training services show you how to apply @RISK and the DecisionTools Suite to real-life problems, maximizing your software investment. All seminars include free step-by-step books and multimedia training CDs that include dozens of example models.

North America

• March 1-2, 2010, Houston, TX
  Monte Carlo Simulation for the Oil and Gas Industry using @RISK
• 3-4 March 2010, Dayton/Versailles, OH
  Decision-Making and Quantitative Risk Analysis using @RISK
• 10-11 March 2010, Jersey City, NJ
  Decision-Making and Quantitative Risk Analysis using @RISK
• 23-24 March 2010, Denver, CO
  Decision-Making and Quantitative Risk Analysis using @RISK
• 23-25 March 2010, Denver, CO
  Decision-Making & Quantitative Risk Analysis using the DecisionTools Suite

Brazil

• 3-5 de março 2010,Rio de Janeiro
  Seminário de Avaliação de Riscos na Indústria Petrolífera e de Gás (3 Días)
• 17-19 de março 2010, São Paulo, Brasil
  Avaliação do Risco para Usuários Iniciantes/Intermediários (em português)

Latin-America

• 8 al 10 de marzo del 2010,Medellín, Colombia
  Risk in Supply Chain (en español)
• 15 al 17 de marzo del 2010,Lima, Peru
  Evaluación de Riesgo para Usuarios Principiantes/Intermedios (en español)
• 18 al 19 de marzo del 2010,Lima, Peru
  Administración de Riesgos en Proyectos con @RISK para Project (en español)

Asia-Pacific

• 10-11 March 2010, Melbourne
  Decision-Making and Quantitative Risk Analysis using @RISK

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

In Part 2 of this series I finished by asking what should be done with historical data, now that we have decided that storing it is probably a good idea. I won’t keep you waiting any longer.

Auditing and calibration of the model at both the micro and macro level. It’s as important as any other element of risk or statistical analysis, or indeed the model building itself. At the distribution level historical data helps to both parameterise the distributions and in fact select them in the first place. As a minimum a few data points will help you to understand possible central tendencies and variability for your risks, and also generate a list of feasible distributions to choose from. With a reasonable number of observations @RISK for Excel can be used to fit distributions to the data taking care of both distribution selection and parameterisation simultaneously. Only five data points are technically needed, but a reasonable fit will require either more than that or other holistic information to achieve validity.

At the macro level total project cost estimates are often ignored from the portfolio perspective. Commonly high percentiles are reported from such models to use in a ‘contingency’ calculation, such as the P90 or P95. Whilst a high percentile, the P90 (say) should still be exceeded 10% of the time! If your projects never go over this percentile then either there are some major mitigating factors not included in the model or the volatility is being consistently overstated. Likewise, the P10 for total cost (these ‘good’ percentiles are rarely if ever reported or considered in project cost estimation work) should be bettered in roughly 10% of projects. If this is not the case then the upside risk has been overstated. This may be due to misconceptions about the positive skewing present in most cost/delay risks or mistakes made in the parameterisation of the risks where the estimate (“most likely” etc.) is actually the “best case” or close to it, rather than a central tendency of the process over time. There could also be other possibilities.

No matter how you look at it, the collection and intelligent use of historical data is integral to effective and useful risk analysis and management, and critical to achieving valid Monte Carlo simulation results. If you aren’t currently recording everything you can get your hands on start right now!

 

» Part 1
» Part 2

Rishi Prabhakar
Trainer/Consultant

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

In my last blog I mentioned a ‘fact’ about data that came up during a recent public training course (Decision-Making and Quantitative Risk Analysis). This fact stuns me every time I think about it, and certainly floored me the first time I encountered it. So many companies just don’t have it.

Data, that is. Historical data from completed projects, sometimes billion-dollar projects, is simply not collected especially in resources and infrastructure cost estimation. Instead every risk is re-estimated from scratch in every new project based entirely upon an estimator’s recollections or guesses. This is not a suggestion that estimators don’t know what they’re talking about, rather that the benefits of adding historical data to the analysis far outweigh the cost of gathering the information in the first place.

I first worked in the banking sector, hence my surprise to learn of this lack of data storage in certain areas of risk analysis. Project cost estimation, especially in resources and infrastructure – I’m talking to you. In financial circles there are literally millions of data points collected daily across the entire organisation. Gathering data (and then analysing it for some benefit) is simply ‘what we do’, and this process isn’t challenged. Some of the data is quite ‘small’, such as the number of seconds a particular caller was kept on hold before being answered, and others are quite ‘big’, such as multi-million dollar losses due to fraudulent activities. Regardless, it’s all kept in the knowledge that information is power – in this case the power to make intelligent decisions in the future.

How can you judge the efficacy of an estimation process (workshops etc.) if you don’t track the final observed outcomes specifically to make such a judgment? Well, you can’t. And that leaves your company’s risk and decision assessment process in limbo. Without measurement there can be no process improvement or corporate learning. Are you ‘passing’ or ‘failing’ with your use of Monte Carlo simulation via risk analysis software?

Generally the observed outcomes for risks in models will be near the estimated value, and this is to be expected. However the main role of risk analysis is to adjudge exposure to the unexpected. Far too many cost estimation models have very little volatility in their line items. I am very curious to know just how often the realised value of a given line item is outside the range of “possible values” as defined in the model. And what about the total project costs overall? This hints at and leads to the big question which is what could/should be done with such data if it were to be recorded?

I shall address these questions in the next blog. I know you’re excited to find out!

Rishi Prabhakar
Trainer/Consultant

An anonymous comment in the Vail (Colorado) Daily News about the dangers of overanalyzing a decision reminded me that, while the benefits of risk analysis have been much vaunted, the costs of decision evaluation have not been clearly defined.  Sure, it's pretty easy to come up with a figure for a DFSS training effort or a budget for an entire risk management department. But what about the statistical analysis process itself?  

Well, there's staff time or your own time (which is worth something), Monte Carlo software, some portion of your computing costs,data acquisition, and on and on. Many variables. But the kind of costs I'm thinking of are the kind you rack up while you're analyzing, say, option valuation, and not doing something else.  These are opportunity costs.  They are what really limit how thoroughgoing your risk analysis becomes, which layer you drill down to--and they are very difficult to quantify.

How do you calculate whether the time you're spending in risk assessment is cost-effective? It's a problem of operations risk.  So I suppose you could enumerate all the other activities that would consume the same amount of time and model their paybacks.  But that would cost you more time in statistical analysis. . . . and you would be left in a positive feedback loop.

In the days ahead I'll be talking to risk management and operations research folks to find out how they decide how much analysis is just the right amount--not too much, and not too little.   I'll be surprised if I turn up any computational approaches--but who knows?  

Palisade Training services show you how to apply @RISK and the DecisionTools Suite to real-life problems, maximizing your software investment. All seminars include free step-by-step books and multimedia training CDs that include dozens of example models.

North America

• 9-11 February 2010, Ottawa, ON
  Decision-Making & Quantitative Risk Analysis using the DecisionTools Suite
• 16-17 February 2010, Cleveland, OH
  Decision-Making and Quantitative Risk Analysis using @RISK
• 23-24 February 2010, Orlando, FL
  Decision-Making and Quantitative Risk Analysis using @RISK

Europe

• 8-10 Feb 2010, London (Central)
  @RISK and the DecisionTools Suite: An Intensive 2.5 Day Course

Latin-America

• 10 al 12 de febrero del 2010,Santa Cruz, Bolivia
  Evaluación de Riesgo para Usuarios Principiantes/Intermedios (en español)
• 24 al 26 de febrero del 2010,Distrito Federal, México
  Evaluación de Riesgo para Usuarios Principiantes/Intermedios (en español)