Expect the best,

plan for the worst and

prepare to be surprised

Denis Waitley (American motivational Speaker and Author of self-help books. b.1933)

Think about it!

p.s. Having thought about this quote a bit further, I’m not sure I totally agree with the first proposition. This would work for people who cannot deal with failures or shy away from challenges. Not sure what I would change the first statement to. Perhaps something along the lines of “expect the obvious”, as this suggests accepting that the law of averages does apply, so on average you’ll be correct. The rest follows quite nicely from there.

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In most if not all circumstances we tend to look at uncertainly as a forward thinking process. It usually, and quite naturally, applies to the likelihood of events taking place in the future. In the project management world, uncertainty is ascribed to the likelihood of positive or negative events occurring and thus, in the context of risk management, to the impact these will have on the project’s ability to meet its objectives.

The literature is full of excellent reference material dealing with our innate inability to properly understand, let along manage, probabilities and (to a larger extent) probability distributions.

If you are a project manager you will (excuse the pun –>) probably agree if I say that a significant amount of brain power goes into managing aspects associated with uncertainty. Two books I have read recently, Uncertain Science…Uncertain World by Henry N. Pollack and The Arrow of Time by Peter Coveney, made me more aware of the fact that whereas I am more inquisitive and conscientious about the prospects of events taking place in the future, I am not as diligent in confirming the prospects of events that took place in the past.

Confused? Let me explain.

It is not unusual for decisions about the course of action to be taken in the future to be based on events that took place in the past. In the project lingo it is referred to as Historical Records. This could include important reference information that could include project schedules, project budgets, issues and risks of comparable projects, and other formal information produced in the course of past projects. Having these historical records is a recommended source of past wisdom upon which present and future assessments can be made.

If you are a project manager who is lucky enough to have access to the Organizational Process Assets (including closed projects’ records and lessons learned) you might conclude that given a similar set of objectives, using the same resources and applying the same technology, all you’ve got to do is just copy and past the old plans into a revised and well formatted template and hey presto, you are good to go! Right?

Well, not quite! The reason for this is that you have to factor in the uncertainties that shaped the actual outcome of the project you are attempting to replicate. You should ask yourself the following questions:

Given that the past project was scheduled to go for X months but ended up taking Y months, what circumstances led to this discrepancy and how likely are these circumstances to have an impact on the current project?

The past project has had a number of risks that have materialised requiring active risk management. Is the likelihood of these risks occuring in the current project the same, or have underlying conditions changed to the point that the risk mitigation strategy for them should be completely different?

The above is fairly self-explanatory so I won’t take it any further. The bottom line is that learning from the past require further scrutiny of the past uncertainty and its re-calibration to the current reality. This makes the apparent simplicity in which we can utilize past data seem slightly more complicated – and indeed it is. The past can be used as a guide but this is where it ends as the lessons learned in the past should be re-examined with fresh, current and relevant view of future uncertainties and their application to the project at hand.

Think about it.

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Even the most devout religious people I know (and there are a few of them still out there) know that an active task of praying is not something you include in your project task list.

It is therefore surprising that occasionally when querying project resources on their assessment of completing their task on time they respond with “fingers crossed we will“.

When confronted with such a response I lose my bearing for few minutes as I’m not quite sure how this needs to be interpreted.

Does it mean that most likely we’re on track and finish on time?

Does it mean we urgently require divine intervention in order to finish on time?

Or, more likely, does this mean that no way in the world we will finish this task on time?

In today’s multi-cultural world, finding a common prayer denomination is a problem. Which one of the multitude of faiths and religions should I base my project baseline on? Faced with this dilemma my natural inclination is to take all prayers out of my project plan. Individuals on my projects are free to use this as a perosnal risk mitigation activity but the consequences (or lack of) of such activity cannot get factored into any activity’s success rate.

So please, please, please – when I ask you for your assessment of reality, please ground your comments on physical progress and the real obstacles holding you up from completing your task on time.

Think about it!

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From reading other Project Management related blogs I know, with a high level of certainty, that my experiences are unique as not very often am I fortunate enough to “implement” project management in the ways the founding fathers have intended.

I am often reminded of this unfortunate fact when exercising basic concepts of risk management.

Take for instance the following scenario. As I go about doing my PM ‘things’ I happen to come across a ‘Risk’. Having identified the risk I advise the appropriate interested parties of the consequences, should this risk eventuate, with distinct hope that these relevant stakeholders will work with me to devise an appropriate risk mitigation plan. So far so good.

What, frequently, occurs is that I am asked whether I have registered (i.e. documented) the ‘Risk’ in the ‘Risk Register’ and upon hearing the affirmative response those interested parties are merrily going on their merry way, knowing that now that the risk has been ‘registered’ things will be just fine.

Now, I know this never happens to you, so you don’t have to endure the humiliation and frustration associated with chasing people up with the hope that they will actually assist you to find an actual, yes – tangible – implement-able – realistic – risk mitigation plan.

Which leads  me to the ‘meaning of life ‘ question: Why is it that for some people the mere input of a cursory line in a risk register is seen as the end rather than the start of the process? Is it because for them the risk register is just an artefact whose sole purpose is to demonstrate that you’ve given the problem some thought and that if disaster strikes they’d be able to say that it is not their fault – after all they did mention it in the risk register?

Sigh,

Think about it!

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One of the scheduling tools at the project manager’s disposal is the Critical Path Method, the outcome of which would be:

1. A list of all activities required to complete the project
2. The time (duration) that each activity will take
3. The dependencies between the activities.

With the above, the longest path of planned activities can be identified, and the “critical” activities (i.e those activities whose combined duration dictate how long the duration is planned to be) are identified.

There are a number of methodological issues with the Critical Path Method, one of which is that the view used to identified the critical path and the tasks it includes is a static view, correct at a point in time, and does not take into account the likelihood of any task on the project schedule taking longer (or shorter) than initially planned.

Let’s look at the following example:

Simply viewing at this schedule, without taking into account other supporting information could be misleading. One issue that could substantially affect the accuracy of this view is the availability (or lack) of resources (see elaboration on  this issue in my earlier post “The Critical Path Does not Tell You Everything You Need to Know About Your Project Constraints“). Another factor that needs to be examined is the level of comfort, or the likelihood of achieving the planned duration.

Having examined the above schedule, would you change your view of this project given the following additional information?

The table above introduces further information regarding the Optimistic, Pessimistic and Most Likely duration for each of these tasks. It clearly demonstrates that although tasks 2 – 5 share a Most Likely Duration, the level of risk associated with each is gradually increasing, from 5 days in task1 to 9 days in task 5.

Utilizing the above added information to better understand the risk dimension associated with the schedule requires the execution of a sensitivity analysis.

Sensitivity Analysis is defined as a “Simulation analysis in which key quantitative assumptions and computations (underlying a decision, estimate, or project) are changed systematically to assess their effect on the final outcome. Employed commonly in evaluation of the overall risk or in identification of critical factors, it attempts to predict alternative outcomes of the same course of action. In comparison, contingency analysis uses qualitative assumptions to paint different scenarios. Also called what-if analysis“.

In other words, a greater insight into the risks hidden behind the project schedule can be derived by adjusting the various tasks’ durations (within their PERT parameters) and confirming, for each such variation, whether a task is included or excluded from the critical path. Performing such adjustments a large number of times would result in an indication of how many times, or in what percentage of times, did any particular task appear on the critical path. The larger the frequency, the greater the need to examine the task and put in place the mechanism to ensure that the task does not negatively deviate from its scheduled duration.

Just to wrap this up, let’s look at the schedule from two additional, extreme perspectives:

The following is a Critical Path view of the project schedule should all tasks durations happen to match the best case duration estimates:

Next is the view of the project schedule should all tasks durations happen to match the worst case duration estimates:

Performing Sensitivity Analysis on the above project would result in a result similar to the one outlined below:

What the above diagram suggests is that when running a simulation with a large number of samples (lets say 1,000 times), then Task1 will appear on the Critical Path 100%, Task4 and Task5 – 39% of the times, etc.

Wouldn’t this be useful to know?

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A 2007 article by Young Hoon Kwak and Lisa Ingall, titled “Exploring Monte Carlo Simulation Applications for Project Management”*, examines the Monte Carlo Simulation method and its uses in the field of Project Management.

Apart from being a good reference document, where a brief history of this technique is being discussed and explained, this article provides a good review of various studies published around the benefits as well as the potential complexities associated with implementing this technique in real life situations.

The article points out that one of the limitations of using this technique as being project managers’ discomfort with statistical approaches, as well as lack of thorough understanding of the method (see also my earlier post discussing  this issue in “Some Risk Management Related Thoughts“.

Discussing the process for utilizing Monte Carlo Simulation in the context of Time Management, the article suggests the following steps are commonly used:

  1. Utilize subject matter expertise to assign a probability distribution function of duration to each task or group of tasks in the project network;
  2. Possibly use Three-Point-Estimates to simplify this process, where an expert knowledge is used to supply the most-likely, worst-case and best-case durations for each task or group of tasks;
  3. Fit the above estimates to a duration probability distribution (such as Normal, Beta, Triangular, etc.) for the task;
  4. Execute the simulation and use the results to formulate expected completion date and required schedule reserve for the project.

Outlining the advantages of utilizing Monte Carlo Simulation applications in Project Management, the article points out that its primary advantage is in being an “extremely useful tool when trying to understand and quantify the potential effects of uncertainty in projects“. Clearly, not utilizing this technique, project managers lack a powerful tool that can result in not meeting the project’s schedule and cost targets. Better quantification of the necessary schedule and cost reserves can substantially reduce such risks.

The article highlights the importance of having access to expert knowledge and prior experience and detailed data from previous projects in order to mitigate the inherent issues of estimates uncertainly which would ultimately affect the quality of the simulation results. This is correct not just with respect to the three-point-estimates but also with respect to choosing the correct probability distributions with which to model these estimates.

An interesting point is raised when referring to an earlier study published  by Grave R. (2001. “Open and Closed: The Monte Carlo Model,” PM Network, vol. 15, no. 2, pp. 48-52) which discusses the merits of using different types of probability distributions for project task duration estimates. Grave suggests the use of open-ended distribution (the lognormal distribution) instead of using closed-ended distributions (such as the triangular distribution) when performing the Monte Carlo Simulations.

His logic is as follows: A closed-ended distribution (e.g. triangular distribution) does not consider the possibility  of a task duration completing BEFORE the best-case or AFTER the worst-case duration estimate. However, in real life projects, due to various constraints, it is possible for a task to complete before or after the best or worst scenarios.

When an open-ended distribution is used, the possibility for exceeding the upper limit of the task duration is recognized, thus making the simulation more realistic.

The article touches (although very briefly) on one of the aspects used within the context of Monte Carlo Simulation which is the Criticality Index (I will endeavor to provide a more detailed discussion about  this feature in a future post). In a nutshell, the Criticality Index is a reflection of the rate at which the task appears on the critical path of the project through the simulation iterations.

Overall an interesting article. If you are already using Monte Carlo Simulation as part of your portfolio of project tools, this article will encourage you to keep doing so. And if you’re not – you’ll need to ask yourself – WHY?

*see in http://home.gwu.edu/~kwak/Monte_Carlo_Kwak_Ingall.pdf

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To ensure the successful completion of a project, it is of utmost importance for the project manager to find ways to handle uncertainties that can pose potential risks for a project. Risk management is an iterative process. Risks can relate to any aspect of the project – be it the cost, schedule, or quality. The key to managing risks is to identify them early on in the project and develop an appropriate risk response plan.

To develop a Risk Response Plan, you need to quantify the impact of risks on the project. This process is known as quantitative risk analysis wherein risks are categorized as high or low priority risks depending on the quantum of their impact on the project. The Project Management Body of Knowledge (PMBOK) advocates the use of Monte Carlo analysis for performing quantitative risk analysis.

What is Monte Carlo Analysis?

Monte Carlo analysis involves determining the impact of the identified risks by running simulations to identify the range of possible outcomes for a number of scenarios. A random sampling is performed by using uncertain risk variable inputs to generate the range of outcomes with a confidence measure for each outcome. This is typically done by establishing a mathematical model and then running simulations using this model to estimate the impact of project risks. This technique helps in forecasting the likely outcome of an event and thereby helps in making informed project decisions.

While managing a project, you would have faced numerous situations where you have a list of potential risks for the project, but you have no clue of their possible impact on the project. To solve this problem, you can consider the worst-case scenario by summing up the maximum expected values for all the variables. Similarly, you can calculate the best-case scenario. You can now use the Monte Carlo analysis and run simulations to generate the most likely outcome for the event. In most situations, you will come across a bell-shaped normal distribution pattern for the possible outcomes.

Let us try to understand this with the help of an example. Suppose you are managing a project involving creation of an eLearning module. The creation of the eLearning module comprises of three tasks: writing content, creating graphics, and integrating the multimedia elements. Based on prior experience or other expert knowledge, you determine the best case, most-likely, and worst-case estimates for each of these activities as given below:

Tasks Best-case estimate Most likely estimate Worst-case estimate
Writing content 4 days 6 days 8 days
Creating graphics 5 days 7 days 9 days
Multimedia integration 2 days 4 days 6 days
Total duration 11 days 17 days 23 days

The Monte Carlo simulation randomly selects the input values for the different tasks to generate the possible outcomes. Let us assume that the simulation is run 500 times. From the above table, we can see that the project can be completed anywhere between 11 to 23 days. When the Monte Carlo simulation runs are performed, we can analyse the percentage of times each duration outcome between 11 and 23 is obtained. The following table depicts the outcome of a possible Monte Carlo simulation:

Total Project Duration Number of times the simulation result was less than or equal to the Total Project Duration Percentage of simulation runs where the result was less than or equal to the Total Project Duration
11 5 1%
12 20 4%
13 75 15%
14 90 18%
15 125 25%
16 140 28%
17 165 33%
18 275 55%
19 440 88%
20 475 95%
21 490 98%
22 495 99%
23 500 100%

This can be shown graphically in the following manner:

What the above table and chart suggest is, for example, that the likelihood of completing the project in 17 days or less is 33%. Similarly, the likelihood of completing the project in 19 days or less is 88%, etc. Note the importance of verifying the possibility of completing the project in 17 days, as this, according to the Most Likely estimates, was the time you would expect the project to take. Given the above analysis, it looks much more likely that the project will end up taking anywhere between 19 – 20 days.

Benefits of Using Monte Carlo Analysis

Whenever you face a complex estimation or forecasting situation that involves a high degree of complexity and uncertainty, it is best advised to use the Monte Carlo simulation to analyze the likelihood of meeting your objectives, given your project risk factors, as determined by your schedule risk profile. It is very effective as it is based on evaluation of data numerically and there is no guesswork involved. The key benefits of using the Monte Carlo analysis are listed below:

  • It is an easy method for arriving at the likely outcome for an uncertain event and an associated confidence limit for the outcome. The only pre-requisites are that you should identify the range limits and the correlation with other variables.
  • It is a useful technique for easing decision-making based on numerical data to back your decision.
  • Monte Carlo simulations are typically useful while analyzing cost and schedule. With the help of the Monte Carlo analysis, you can add the cost and schedule risk event to your forecasting model with a greater level of confidence.
  • You can also use the Monte Carlo analysis to find the likelihood of meeting your project milestones and intermediate goals.

Now that you are aware of the Monte Carlo analysis and its benefits, let us look at the steps that need to be performed while analysing data using the Monte Carlo simulation.

Monte Carlo Analysis: Steps

The series of steps followed in the Monte Carlo analysis are listed below:

  1. Identify the key project risk variables.
  2. Identify the range limits for these project variables.
  3. Specify probability weights for this range of values.
  4. Establish the relationships for the correlated variables.
  5. Perform simulation runs based on the identified variables and the correlations.
  6. Statistically analyze the results of the simulation run.

Each of the above listed steps of the Monte Carlo simulation is detailed below:

  1. Identification of the key project risk variables: A risk variable is a parameter which is critical to the success of the project and a slight variation in its outcome might have a negative impact on the project. The project risk variables are typically isolated using the sensitivity and uncertainty analysis.

    Sensitivity analysis is used for determining the most critical variables in a project. To identify the most critical variables in the project, all the variables are subjected to a fixed deviation and the outcome is analysed. The variables that have the greatest impact on the outcome of the project are isolated as the key project risk variables. However, sensitivity analysis in itself might give some misleading results as it does not take into consideration the realistic nature of the projected change on a specific variable. Therefore it is important to perform uncertainty analysis in conjunction with the sensitivity analysis.

    Uncertainty analysis involves establishing the suitability of a result and it helps in verifying the fitness or validity of a particular variable. A project variable causing high impact on the overall project might be insignificant if the probability of its occurrence is extremely low. Therefore it is important to perform uncertainty analysis.

  2. Identification of the range limits for the project variables: This process involves defining the maximum and minimum values for each identified project risk variable. If you have historical data available with you, this can be an easier task. You simply need to organize the available data in the form of a frequency distribution by grouping the number of occurrences at consecutive value intervals. In situations where you do not have exhaustive historical data, you need to rely on expert judgement to determine the most likely values.
  3. Specification of probability weights for the established range of values: The next step involves allocating the probability of occurrence for the project risk variable. To do so, multi-value probability distributions are deployed. Some commonly used probability distributions for analyzing risks are normal distribution, uniform distribution, triangular distribution, and step distribution. The normal, uniform, and triangular distributions are even distributions and establish the probability symmetrically within the defined range with varying concentration towards the centre. Various types of commonly used probability distributions are depicted in the diagrams below:



  4. Establishing the relationships for the correlated variables: The next step involves defining the correlation between the project risk variables. Correlation is the relationship between two or more variables wherein a change in one variable induces a simultaneous change in the other. In the Monte Carlo simulation, input values for the project risk variables are randomly selected to execute the simulation runs. Therefore, if certain risk variable inputs are generated that violate the correlation between the variables, the output is likely to be off the expected value. It is therefore very important to establish the correlation between variables and then accordingly apply constraints to the simulation runs to ensure that the random selection of the inputs does not violate the defined correlation. This is done by specifying a correlation coefficient that defines the relationship between two or more variables. When the simulation rounds are performed by the computer, the specification of a correlation coefficient ensures that the relationship specified is adhered to without any violations.
  5. Performing Simulation Runs: The next step involves performing simulation runs. This is typically done using a simulation software and ideally 500 – 1000 simulation runs constitute a good sample size. While executing the simulation runs, random values of risk variables are selected with the specified probability distribution and correlations.
  6. Statistical Analysis of the Simulation Results: Each simulation run represents the probability of occurrence of a risk event. A cumulative probability distribution of all the simulation runs is plotted and it can be used to interpret the probability for the result of the project being above or below a specific value. This cumulative probability distribution can be used to assess the overall project risk.

Summary

Monte Carlo simulation is a valuable technique for analyzing risks, specifically those related to cost and schedule. The fact that it is based on numeric data gathered by running multiple simulations adds even greater value to this technique. It also helps in removing any kind of project bias regarding the selection of alternatives while planning for risks. While running the Monte Carlo simulation, it is advisable to seek active participation of the key project decision-makers and stakeholders, specifically while agreeing on the range values of the project risk variables and the probability distribution patterns to be used. This will go a long way in building stakeholder confidence in your overall risk-handling capability for the project. Moreover, this serves as a good opportunity to make them aware of the entire risk management planning being done for the project.

Though there are numerous benefits of the Monte Carlo simulation, the reliability of the outputs depends on the accuracy of the range values and the correlation patterns, if any, that you have specified during the simulation. Therefore, you should practice extreme caution while identifying the correlations and specifying the range values. Else, the entire effort will go waste and you will not get accurate results.

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imageIn part 1 of this article I raised a number of risk related observations, particularly around the validity of Murphy’s Law as well as the reality behind the Law of Averages.

Another series of Scientific American articles (sorry but I’m a real Scientific American fan), titled “Why Our Brains Do Not Intuitively Grasp Probabilities” and How Randomness Rules Our World and Why We Cannot See It describes the concept of “Folk Numeracy” which is “our natural tendency to misperceive and miscalculate probabilities, to think anecdotally, instead of statistically, and to focus on and remember short-term trends and small-number runs”. In a nutshell, we are evolutionarily evolved to clearly notice short term trends but are predisposed to forget or ignore long term trends. The author of these articles goes on to suggest that our intuition has evolved in a manner which enables us to utilize this capability in the context of social interactions and social relationships (which means that our intuition does play an important role in our ability to form alliances and identify social path that could be of some usefulness to us)  we are nevertheless ill equipped to use this capability when it comes to probabilistic problems.

In “Knowing Your Chances” (Scientific American Mind – April/May 2009), the authors make a reference to an early book published in 1938 by the English writer H. G. Wells, who predicted in his “World Brain” that statistical thinking would become an indispensable trait, similar to reading and writing. This prediction, however, has not materialized and the authors of the article make the observation that “At the beginning of the 21st century, nearly everyone living in an industrial society has been taught reading and writing but not statistical thinking – how to understand information about risks and uncertainties in our technological world.  That lack of understanding is shared by many physicians, journalists and politicians…and as a result, spread misconceptions to the public.”

So what does it all mean?

We are all naturally pre-disposed to a certain level of Risk Attitude. Risk Attitude (as defined by David Hillson & Ruth Murray-Webster) is a “chosen state of mind with regard to those uncertainties that could have a positive or negative effect on objectives, or more simply a chosen response to perception of  significant uncertainty”.

Josh Nankivel, based on a podcast by Cornelius Fichtner (which I thoroughly enjoyed while preparing for my PMP) gives a good summary of the commonly referenced Risk Attitudes (a complete copy of which is given below):

  1. Risk Seeker – enjoys and seeks uncertainty in search of greater opportunities, can be overly optimistic and not take possible negative consequences seriously.
  2. Risk Averse – uncomfortable with uncertainty, doesn’t like risk
  3. Risk Tolerant – reasonably comfortable with uncertainty, but usually sticks head in the sand and ignores them
  4. Risk Neutral – analyzes risks and weighs negative/positive possible outcomes and probabilities objectively.

Josh makes the observation, which I tend to agree with, that most project managers will tend to be Risk Tolerant. They will conduct basic Risk Identification process early in the piece but then rely on their gut-feel and ‘lets hope for the best’ approach when faced with reality. Josh goes on to suggest that the Risk Neutral is the goal and he is probably (excuse the pan) correct. The problem, as indicated above, is that for most of us this will require conscious effort and elaborate attention to details we are not naturally inclined to adopt.

Formal adherence to Risk Management processes can cut through the complexity and the PMBOK is certainly a good place to start as it refers to the basic tools and techniques required to ensure you manage your risks adequately.

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imageI’ve had some interesting professional challenges lately, all of which can be traced back to issues associated with risk management. This is not surprising. In my view, the biggest challenge in any project is properly managing risks. It’s not that all other areas of project management are a walk in the park. It’s more around the fact that when it comes to identifying and managing risks some tend to be swayed by subjective arguments, wishful thinking and gut feel.

Most people subscribe to the reality of Murphy’s Law, namely that “if something can go wrong, it will”. Despite the common wisdom hidden in this simple, yet powerful, statement, some people tend to dismiss it on the grounds that statistically speaking our chances of hitting a bad run are equal to our chances of hitting a good run. So no reason for overwhelming concern as the Law of Averages will sort things out.

This notion is not quite correct, as demonstrated in an article published in the April 1997 edition of Scientific American under the heading of “The Science of Murphy’s Law”. The article’s conclusion is that “life’s little annoyances are not as random as they seem; the awful truth is that the universe is against you‘. So in that respect, when we say that “if something can go wrong, it will”, we actually mean it. Not that things will go wrong 100% of the time, but there are good chances that they will go wrong over 50% of the time.

Which, puts in question the Law of Averages. Well, things are not quite straightforward there either. Another Scientific American article (this time from April 1988, titled: “Repealing the Law of Averages”) tackles the common wisdom, according to which, when tossing a fair coin and maintaining a running count of how many times each side turns up, then after a large number of tossing in the air, we will get a relatively even number of heads and tails. This assertion is mathematically correct but only in VERY large numbers (can you count to infinite?). In real life situations, where the sample group is limited, the Law of Averages cannot be invoked, at least not as a serious planning tool.

To be continued…

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imageFreakonomics has published an article about the psychologist Barry Schwartz’s book “The Paradox of Choice: Why More Is Less“. The book makes the argument that (apart from economists) most people would find too much choice a bad thing and would rather having less choice than more.

I referred in an earlier post (“Rational decision making process? Not really!“) to a publication by CSIRO that dispels some ‘conventional wisdom’ perceptions. One of the findings of that study was that when faced with too much complexity people will tend to prefer making no decision at all.

I know that one cannot simply infer from the above arguments to situations encountered in a highly professional environment, as it could be argued that decision making processes at executive levels will most likely be carried out by individuals trained in the art of making decisions, in which case it could be expected that they will be less prone to fall into the traps outlined above. Having said that, as project managers we need to be aware of these basic human tendencies and realize that in most cases, too much information is bad information and that if we want to minimize the load on decision makers’ time we’d better present to them a small and concise set of choices from which they could possible choose.

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