Tag Archives: probability

Probability -v- luck. Should we give up our day-job?

Based on a successful day at the races, 5 winners and one place from 8 bets, this article looks at the balance between luck and process in achieving the result.  Our conclusion is that you should not confuse luck with skill. Good processes will help build success, persistence will generate more opportunities for you to be lucky, and skill or capability will shift the odds in your favour, but randomness rules!

To quote Coleman Cox: I am a great believer in Luck. The harder I work, the more of it I seem to have.

Click to download the PDF.

For more papers on risk and probability see: https://mosaicprojects.com.au/PMKI-SCH-045.php#Process1

Complex Decision Making Explained

Complex decision making is a vital project management skill; required not only by the project manager but also by the project’s sponsor and client / customer among others.

Some of the key areas involving complex decisions include risk management, many aspects of planning (particularly optimising choices) and dealing effectively with issues and problems in a range of areas from scope and quality to cost and performance.

There is an underlaying assumption in project management (derived from traditional scientific management) that decisions will be based on a rational assessment of the situation to optimise outcomes. Unfortunately this is not true! As complexity increases assuming a ‘rational decision making paradigm’ becomes increasingly unrealistic. Human decision makers become ‘predictably irrational’.

Understanding the built in biases and ‘predictable irrational’ decision making processes used by people confronted with complex decisions can help managers requiring optimised decisions to craft strategies to minimise suboptimal outcomes. But where can busy project managers access this information?

I have just finished reading the most amazing paper on the subject that canvases the whole spectrum from risk aversion to behavioural economics in a practical, easy to read format; and it is free!

Behavioural economics and complex decision making: implications for the Australian tax and transfer system has been written by Andrew Reeson and Simon Dunsttall of the Australian national science agency, CSIRO. The report was commissioned by the ‘Henry Review’ into the Australian taxation system and is published on their web site. Whilst you can safely skip the last section which focuses on applying the knowledge to our tax system. The preceding 7 sections are focused on how people make complex decisions in any sphere and are just as relevant to complex project decisions as to complex investment and taxation decisions.

You can download this free resource from the review panel’s website: download the paper (a copy is also on the Mosaic web site on the assumption the Government site is temporary and will close once the Henry Review has reported: download from Mosaic).

If you find the report useful and you don’t live in Australia, you can buy the next Australian you meet a beer; it was his or her taxes that paid for this amazingly useful report. I know I will be keeping my copy handy for a very long time to come.

PMI COS Seminar

This week, I will be presenting live from Australia the final session of the Fall PMI College Of Scheduling (COS) Wednesday Webinar Series: Scheduling in the Age of Complexity. This hour-long event will provide key insights for better scheduling from a personal level: What is the role of the scheduler and what is our future?

The PMI-COS Fall series is designed to bring highlights from the 6th Annual Scheduling Conference held in Boston, MA earlier this year.  Archived presentations are available at http://www.pmicos.org/ondemandlearning.asp if you find them of interest, why not sign up for the College?

The Featured Presentation:   Scheduling in the Age of Complexity

Scheduling was developed as a computer based modelling process at a time when ‘command and control’ was the dominant management paradigm. The mathematical precision of the early scheduling calculations were somehow translated into certain project outcomes. Today, the certainties are no longer so apparent. Most projects run late and uncertainty and complexity are starting to take center stage.

This paper identifies the key elements in Complexity Theory to suggest the real role of a schedule in ‘the age of complexity’. It concludes by recommending a way to re-establish the role of the scheduler in the successful delivery of projects in the 21st Century.

DATE:  Wednesday, December 2, 2009
TIME:   5:00pm EST (US Eastern Daylight Savings Time); Doors open at 4:45pm

LOCATION: http://pmi.acrobat.com/r31077016/

There is no dial-in telephone option for the presentation. All voice will be through the classroom platform.

The Probability of Chance

I have just returned from a trip to Singapore where I was facilitating a workshop to set up the initial risk register and risk management plan for a $1 billion project to deliver one package in a multi billion oil development. The beginning of November is also the Spring Racing Carnival in my home state featuring the Melbourne Cup – the race that literally stops the nation. The combination of these two events and many hours sitting in aeroplanes started me thinking about the difference between project risk and the more widely understood actuarial risks managed by insurance companies and the like.

I have already posted on some of the challenges faced by project risk managers dealing with a single occurrence, the project, using theories based on constrained probability distributions in large populations (see: A Long Tail); and written a number of papers on risk management, see: http://www.mosaicprojects.com.au/Resources_Papers.html#Risk. This post looks at the challenges from a different perspective, how people in project teams perceive and understand probability.

The Singapore workshop started with the consideration of range statements for two sets of parameters, the likely impact of a risk event and the probability of it occurring. The outcomes were quite straightforward:

  • >$20 million was seen as a very high impact risk through to <$500,000 for a very low impact risk.
  • >70% probability was seen as a very high probability through to <5% for a very low probability.

The valuation of a ‘very high impact’ was based on a percentage of the project’s anticipated profit. Interestingly, the project manager for the overall project (some $20 billion investment) thought the monetary values were on the high side but accepted the views of the engineering company I was working with.

The focus of this post is on the difficulty of assessing probability based on limited data for a one off event such as a project. The following simple scenario illustrates the problem:

There are 3 sealed envelopes – one contains $100.

As a starting point, most people would agree there is a 33.33% chance any one of the envelopes will contain the money.

If we open one envelope and it is empty, there is now a 50:50 chance either of the remaining envelops has the money. One does, one does not.

Now to make the situation interesting…….

I give you one envelope and keep two for myself.

As a starting point you have a 33.33% chance of having the money and I have a 66.66% chance – the odds in my favour are 2 envelopes to your 1 envelope

Now I open one of my envelopes and we see it is empty. What does this do to the probabilities?

One perspective says there is now a 50:50 chance the money is in your envelope and 50:50 it is in my envelop – we know it has to be in one or the other and it has not moved.

On the other hand nothing has changed the original starting scenario – the odds in my favour were 2:1 and at least one of my envelopes had to be empty so on this basis is there still twice the probability my remaining envelop has the money compared to yours…… we have done nothing to improve your chances, you still only have one out of the three original envelopes!

Which scenario best represents the situation and why??

Now to make the situation even more interesting….

If I was to offer you $40 for your envelop would taking the money be a good or a bad bet???

If the scenario suggesting a 50:50 chance is true, the Expected Monetary Value (EMV) of your envelope is $100 x 50% = $50

If nothing has changed the starting scenario the EMV of the envelope is $100 x 33.33% = $33.33.

Which option is correct????

Peter de Jager posed a similar question to the PMI Melbourne chapter and favours the 2:1 option remaining true, many of the chapter disagreed.

Any thoughts would be appreciated.

Mathematical Modelling of Project Estimates

I have just finished reading a very interesting paper by Dr. Pavel Barseghyan; Problem of the Mathematical Theory of Human work the paper is available from the PM World Today web site.

Dr. Barseghyan’s key message is the unreliability of historical data to predict future project outcomes using simple regression analysis. This is similar to the core argument I raised in my paper Scheduling in the Age of Complexity presented to the PMI College of Scheduling conference in Boston earlier this year. Historical data is all we have but cannot be relied on due to the complexity of the relationships between the various project ‘actors’. As a practitioner, I was looking at the problem from an ‘observed’ perspective it’s fascinating to see rigorous statistical analysis obtaining similar outcomes.

A counterpoint to Dr. Barseghyan’s second argument that improved analysis will yield more correct results is the work of N.N. Taleb particularly in his book ‘The Black Swan’. Taleb’s arguments go a long way towards explaining much of the GFC – models based on historical data cannot predict unknown futures. For more on this argument see: http://www.edge.org/3rd_culture/taleb08/taleb08_index.html 

Personally I feel both of these lines of reasoning need to be joined in the practice of modern project management. We need the best possible predictors of likely future outcomes based on effective modelling (as argued by Dr. Barseghyan). But we also need to be aware that the best predictions cannot control the future and adopt prudent, effective and simple risk management processes that recognise each project is a unique journey into the unknown.

I would certainly recommend reading Dr. Barseghyan’s paper.

A Long Tail

One of the key books that started my interest in risk, uncertainty and ultimately complexity theory was Against the gods: The remarkable story of risk written by Peter L Bernstein, and published in 1996 when Peter was aged 77! This book explained much of the history behind the development of risk management in a way that I could understand and is a recommended read for anyone involved in managing projects. Despite his success, Peter Bernstein never retired, and at the time of his death last month, aged 90, he was working on another book on risk. As authors go, a very long and distinguished career.


The limitations of the risk framework built in the 18th century and so clearly described in Bernstein’s book have been defined and expanded in recent times in The Black Swan by N.N. Taleb (another recommended read). Taleb’s ideas are discussed in my post Risky Business.

The major failing of traditional risk models is the issue of ‘boundaries’. Rules of probability such as The law of large numbers work if the population is bounded. The problem with project data is that there are no limits to many aspects of project risk. Consider the following:

  • You plot the distribution and average the weight of 1000 adult males. Adding another person, even if he is the heaviest person in the world only makes a small difference to the average. No one weighs a ton! The results are normal (Gaussian-Poisson) and theories such as the Law of Large Numbers and Least Squares (Standard Deviation) apply.
  • You plot the distribution and average the net wealth of 1000 people. Adding Bill Gates to the group causes a quantum change in the values. Unlike weight, wealth can be unlimited. Gaussian-Poisson theories do not apply!

Most texts and discussion on risk assume reasonable/predictable limits. Managing variables with no known range of results is rarely discussed and many project variables are in this category. For more on this see Scheduling in the Age of complexity.

Fortunately our colleague, David Hillson’s latest book Managing risk in projects will be published by Gower on 11 August 2009. This book is part of the Gower Foundations in Project Management series, and will provide a concise description of current best practice in project risk management while also introducing the latest developments, to enable project managers, project sponsors and others responsible for managing risk in projects to do so effectively. I would suggest another ‘must read’ if you are interested in project management.

More later….

Risky Business

I came across the following whilst finishing my paper Scheduling in the Age of Complexity.

Statistical and applied probabilistic knowledge is the core of knowledge; statistics is what tells you if something is true, false, or merely anecdotal; it is the “logic of science”; it is the instrument of risk-taking; it is the applied tools of epistemology; you can’t be a modern intellectual and not think probabilistically—but… let’s not be suckers. The problem is much more complicated than it seems to the casual, mechanistic user who picked it up in graduate school. Statistics can fool you. In fact it is fooling your government right now. It can even bankrupt the system (let’s face it: use of probabilistic methods for the estimation of risks did just blow up the banking system).

The quote is from Nassim Nicholas Taleb, author of the Black Swan and Distinguished Professor of Risk Engineering at New York University’s Polytechnic Institute.

Read his full essay, THE FOURTH QUADRANT: A MAP OF THE LIMITS OF STATISTICS, at http://www.edge.org/3rd_culture/taleb08/taleb08_index.html and you will start to understand the current financial crisis.