7 Deadly Sins – Illusory Correlation

Or ‘magical thinking’ as Massimo Piattelli-Palmarini calls it.  This is about making positive correlations even though the supporting data is weak.  Sometimes we notice only data that supports our hypothesis and ignore data that doesn’t.

An example of magical thinking goes like this. We come across a few people who exhibit a certain symptom and also a certain illness, and we associate that symptom with the illness, such that if we see that symptom, then we decide that the illness is also present.

You see someone with red spots, and you diagnose measles.

We forget that sometimes the same symptom appears for a different illnes.  Or the illness is present without that symptom.

7 Deadly Sins – Overconfidence

Massimo Piatelli-Palmarini writes in his deliciously written book “Inevitable Illusions” about the 7 deadly sins of our cognitive illusions.

His first sin is overconfidence. This is where we feel certain about our knowledge of something, but our knowledge does not really warrant such confidence.

He describes experiments where subjects are asked to answer questions and then rate how confident they are about each answer.  Experiments show that our confidence leads our knowledge.

We think we know something more than we really know.

The results of the experiments also bring about something sobering: we are most overconfident in areas we are more knowledgeable about.  That is, the difference between the level of our overconfidence and knowledge in these areas is bigger than the difference between our level of overconfidence and knowledge in other areas - hence we tend to make mistakes of overconfidence in our areas of expertise.

On Issues Versus Risks

Whenever you find yourself in an introductory presentation on risk management, you can expect to hear a question like: “What’s the difference between an issue and a risk?” The expected answer seems to be always: “A risk is something that may or may happen, while an issue is something that has already happened.” 

Correct enough, but this description falls short of conveying any relationship between the two.

Here’s one I coined, I like, and plan to use and re-use: “Issues are the risks you failed to manage, now come to haunt you.”

The sentence makes clear that many of the issues that you face could have been mitigated if only you had done proper risk management.  The assertion is not always true of course.  Some issues just come from unpredictable circumstances, and no risk management is that perfect.  So surely,  there are exceptions, but the strong assertion of the sentence emphasises just that – that exceptions are the exception.

I believe I originally picked up this relationship from Bill Duncan.  A few years ago he quoted someone he knew who said that in a good risk management process, all the issues that arise will have been previously identified in the risk register.  So it’s not my original idea, but I like the “now come to haunt you” bit, which is mine.

Pinpointing the Risk

"It is important to correctly identify the cause from the risk", said the presenter of a risk management process overview.

 

I hadn't given much thought about the distinction between the two, and simply implicitly assumed that I know which is which.  But when I tried to articulate how to distinguish between the a cause and a risk, I felt stuck.  After all, they all seemed to be a chain of event/consequence.

 

Ignoring for the meantime that each event E can be a consequence of any number of events, and that E itself can cause any number of consequence, it is clear that from one point of view, an event E2 can be a consequence of an event E1.  Similarly event E3 can be a consequence of event E2.  So a specific event is both a cause and a consequence.

 

For example, let us suppose we are concerned about the risks our property is facing.

 

Risk: Fire

Cause: Faulty electrical wiring

Consequence: House burns down

 

In this case, we put "Fire" as a risk in our risk register.

 

But what about "Faulty electrical wiring"?  Isn't it a risk as well?

 

Risk: Faulty electrical wiring

Cause: substandard workmanship

Consequence: Fire, leading to house burning down.

 

So should Faulty electrical wiring then be in the risk register?

 

Kik Piney reminded me that it is essential to be clear first about the objectives when going about identifying risks.  Having just studied ISO 31000:2009, I am aware of the relationship between objectives and risk, but for some reason I left it out.  (I am not too sure about being clear first about objectives before going about identifying risks, because sometimes noticing potential areas where things can go wrong will actually help you know what your objectives are).

 

Now suppose we have decided that our objective is "to protect our property".  In this case, it is clear that the risk is fire:

 

Objective: Protect property

Risk: Fire

Risk: Repossession

Risk: loss of property due to plane falling on property

Risk: loss of property due to earthquake

 

"Faulty electrical wiring" is not a risk. Either the property has faulty wiring or it does not.

 

If the objective instead is 'Acquire a problem-free property', then 'faulty electrical wiring' is a risk.  The property we are considering to acquire 'may or may not' have this characteristic. 

 

Final point: always relate risks to objectives.  Nothing new here. Just a reminder.

Project Success

Bill Duncan comments on the definition of project success (link) and touches on the different dimensions beyond merely completing the project 'on time'.  His thoughts sparked a few thoughts as well.

Success can be defined in several dimensions.  The more success criteria defined, the greater the chance that they will conflict with each other.  Invariably, there will be 'success criteria creep.'  Some ranking of success criteria may be required. Perhaps a ranking system may be of use to rank the success criteria according to importance in order to provide guidance whenever conflicts arise.  For example, while it may be deemed important to achieve each major milestone according to schedule, is that more important than completing the whole project on time?  And is completing the project on time more important than meeting a specified project cost?

Other questions to help rank the success criteria might include:

• What are the consequences of not meeting this success criteria?
• Are we prepared to spend more in order to meet this success criteria (otherwise is just a nice-to-have?)  If so, how much? 
• Is it acceptable to fail to meet a success criteria in order to achieve another criteria?

 

Giving this a little more thought, I find a relationship between project requirements and success criteria: why did we define success this way and not that way? The answer lies in the requirements.  We defined this as a success criteria because it is important.  It is important because <project requirement>.  A simple example: success critera A: the stadium is ready for use by March 11, 2011.  Why?  Because a large event is going to use it on March 25, 2011.  Failing to make the stadium available by March 11 means a failure to hold the event.

In Praise of Bibliographies

One of the best things about books are their bibliographies.  Sometimes a book's bibliography is worth more than the book itself.  If it is any good, the bibliography arms you with a map, or a mini-library, to other treasures about the subject you are reading about.   

When you are reading the very first book you have read about a subject, the bibliography is often a map to a new world. If it's a good bibliography, it leads you to treasure. But sometimes it can lead you to a rubbish pile. 

I always skim through the bibliographies of each book I read, marking down titles that may interest me next. 

It’s still vivid in my mind the occasion when I first came across Patrick Henry Winston’s “Lisp”. I remember exactly where I was.  I found a copy at our school library. I was in university, somewhat new to programming, but already infected by an intense interest in computer science, a subject I only had recently discovered. 

I had already read perhaps a dozen Pascal and Fortran books before picking up “Lisp”, but Winston’s was the most wonderfully strange computer programming book I had come across then (and still since). 

First was the very strange programming language (Lisp was not like normal procedural languages).  Then the domain was very new to me (it was the first book I read about Artificial Intelligence).  But it was also because Winston had a quirky writing style.  Looking back later, and understanding his academic interest in how humans learn, I’m sure this style was deliberately designed.

His book imprinted in my mind the names of dozens of computer scientists in the AI field. Names like Marvin Minsky, Douglas Lenat, Elaine Rich, and others whose very unusual sounding names (to me back then) felt like they were not normal humans, but members of a different breed, a strange breed, an alien breed. I swore to read all of them.   

Not all bibliographies are good (and by bibliography I include ‘References’). But, off the top of my head, authors who are great at compiling bibliographies include Andrew S. Tanenbaum, C. J. Date, Jeffrey D. Ullman, Douglas Comer.

Sometimes I forget how I learned about a book. I estimate that of all the books I’ve read or intend to read, the vast majority were inspired from a bibliography of some book I read.

So I thought it might be a fun exercise to list down some key books that have influenced me, write down their bibliographies (I’ll limit it to books only), and see how they are connected.

This is a tedious exercise so it will happen slowly, and when time permits.

Example of a Decision Tree

A simple example of using a decision tree to help us with decision-making.

A couple renting an apartment and is wondering whether they should sign a 1-year contract on the rent.  If they sign a contract, their rent is guaranteed not to increase during the 1-year period. If they don’t sign a contract, their rent will increase by about $20 after 6 months.

This seems like a simple decision.  But there is a drawback to signing the contract. If the couple decides to terminate the contract before the end of 1 year, they are liable to pay up to 25 weeks worth of rent to the landlord, unless the landlord is able to get someone else to rent the place earlier. 

The couple intends to buy their own home if the right opportunity comes, so there is a chance that they would need to terminate any contract they sign.

Supposing the initial rent is $900 per month, what is the couple’s best option?

Let us choose the simplest situation first.  Let’s assume there is zero chance that the couple will terminate the contract.  So the decision tree looks like this:

The tree says that the option to sign a lease contract will result in a total 1-year rent of $10,800 ($900 * 12 months), while not signing a lease contract will result in a total 1-year rent of $11,800 ($900 * 6 months + $900 * 1.2 * 6 months).

But what happens if the couple finds their dream house and moves out of the house after 8 months?