Your SlideShare is downloading. ×
0
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Neuroscience
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Neuroscience

330

Published on

What the PMBOK Doesn’t Teach You: How Cognitive Science Can Improve Your Practice of Project Management …

What the PMBOK Doesn’t Teach You: How Cognitive Science Can Improve Your Practice of Project Management

Project management expertise only comes about after years of practice. What are you supposed to do in the mean time? We will review recent experiments in neuroscience, behavioral economics, and cognitive psychology with a view toward how they can be applied to the practice of project management.

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
330
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
5
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • My name is Michael Christenson, and this afternoon I’ll be talking about how recent work in neuroscience, behavioral economics, and cognitive psychology can be applied to the practice of project management.
  • My first project produced dismal results - my estimates were off by a factor of 5. That provided the impetus for this presentation. What can I do to mitigate novice errors? It turns out there are a number of things. But first, let’s start with an anecdote.
  • Daniel Kahneman received the Nobel Prize in Economics in 2002. His book, “Thinking, Fast and Slow” was one of the books consulted in the development of this presentation.
  • Around 1963, Daniel Kahneman…convinced the Israeli Ministry of Education that they needed a curriculum to teach decision-making in high schools. A team was assembled, which included Seymour Fox, dean of the Hebrew University’s School of Education, an expert in curriculum development.
  • After a year of good progress, he asked everyone to estimate how much longer it would take to finish the textbook. Their estimates were narrowly centered around 2 years, plus or minus ½ year.Then he asked Dean Fox if he could bring to mind other teams that had developed a curriculum from scratch, and he said he could think of quite a few in general, and several in detail.Kahneman asked how long, on average, did it take for them to complete their projects.
  • He was disturbed to find 40% didn’t even finish, and Fox couldn’t think of any group that finished in less than seven years or more than ten. Grasping at straws, Kahneman asked how the current group compared, in terms of skills and resources… and Fox said “We’re below average, but not by much.” Until Kahneman had prompted him, there was no connection in Fox’s mind between his knowledge of history and his forecast for the future, which was within the 2 year range of the rest of the group
  • Kahneman said he learned three lessons from this: First, there are two profoundly different approaches to forecasting, which he came to call the inside and outside view. Secondly, estimates can fall prey to a variety of planning fallacies – in this case, a best-case scenario rather than a realistic assessment. And thirdly, irrational perseverance: the folly displayed in failing to abandon the project. When confronted with reality, “we gave up rationality rather than give up on the enterprise.”
  • But first, a brief view of your brain.
  • Homo sapiens was anatomically recognizable approximately 200,000 years ago. Saber-tooth cats died out about 9,000 years ago. We have been cat food for about 190,000 of the last 200,000 years. These unique environmental pressures have led to us being particularly good at certain traits, and less good at others. I will jump ahead and give you a quick preview of some of the things we will be covering later in the presentation: A Baysian analysis of the underlying distribution of saber-tooth tigers in the neighborhood was not a trait that was selected for survival.
  • Here is a list of what the brain does well – recognizing immediate threats in the neighborhood. What it does too well – recognizing patterns, even if they don’t exist, which is the clustering illusion, which we will return to later, and Heuristics, which are experience-based strategies using readily accessible, though loosely applicable, information for problem solving, learning, and discovery. What the brain does not so well – statistics.
  • Wait – cross modal what? Apparently, our brain is very consistent at combining information from different sensory modalities. If you get nothing else from this presentation, you now know that you’re an expert in Cross modal synaesthetic abstraction.
  • One of the texts I used in the development of this presentation was Thinking, Fast and Slow by Nobel Prize winner Daniel Kahneman. He posits two systems, cleverly named System 1 and System 2, which explain a number of anomalieswe will be covering shortly. In short, System 1 is intuitive, and System 2 does the heavy lifting, but only at gun-point. If there is any other way out, other than thinking, System 2 will take it. This results in System 2 accepting answers from System 1 that don’t answer the exact question being asked, but one that is similar (and easier); and System 2 tends not to check System 1’s work if it is vaguely plausible.
  • In other words, your brain lies to you. In fact, there are predictable ways that your brain lies to you, from so called optical illusions to cognitive biases. Cognitive biases are widespread, and effect decision-making, estimating probabilities, attributional (social), and memory – all necessary for successful project management.
  • The classic Müller-Lyer Illusion demonstrates two important points: even though you know the lines are of equal length, you will still see the top one as shorter.
  • It also shows that, once you recognize the trap, the arrows pointing outward and inward, you can be alerted to the need to pull out your ruler in the future if you come across it again.
  • This is an example of System 1 jumping in with answer, and demonstrates that System 2 is lazy; it won’t necessarily check, if System 1 comes up with a quick and plausible answer.
  • Good resolutions are useless attempts to interfere with scientific laws.
  • Procrastination is the second issue I wanted to address today. An experiment was run at MIT that struck me as having direct applicability to the Agile vs. waterfall methodologies. Regular interval papers were of higher quality than papers due at the end of class or due when determined by the students.
  • The study was done to determine three things: Are people willing to self-impose meaningful deadlines to overcome procrastination? Are self-imposed deadlines effective in improving performance? And When self-imposing deadlines, do people set them optimally, for maximum performance enhancement? (Yes to the first two, no to the third).
  • The third topic we’re going to look at is prognostication, which started this whole thing.
  • Hofstadter’s Law is a nice little recursive formulation.
  • There are a number of areas of expertise that are covered in the PMBOK. Chapter 6 of the 3rd edition covers Project Time Management, (6.4.2 Activity Duration Estimating) and recommends “Expert Judgment” as a useful tool for activity duration estimation and that if this expertise is not available, the duration estimates will be more uncertain and risky.
  • Subjects were tasked to give estimates for software tasks in a controlled manner, in 3 groups with various “anchoring” methods being used.  The only difference between the groups was the expectation statement by the manager before estimation.Group 1 (control – no explicit anchor given)“I’d like to give an estimate for this project myself, but I admit I have noexperience estimating. We’ll wait for your calculations for an estimate.”Group 2 ( ’2 months’ condition)“I admit I have no experience with software projects, but I guess thiswill take about 2 months to finish. I may be wrong of course, we’llwait for your calculations for a better estimate.”Group 3 (’20 months’ condition)“I admit I have no experience with software projects, but I guess thiswill take about 20 months to finish. I may be wrong of course, we’llwait for your calculations for a better estimate.”Jorge Aranda Master Thesis, 2005, Graduate Department of Computer Science University of Toronto
  • The planning fallacy in general is the tendency to underestimate how long it will take to complete a task while simultaneously overestimating the benefits
  • Other cognitive biases can interfere with planning, including those listed here.
  • PERT (Program Evaluation and Review Technique) an event-orientated technique to quantify knowledge about uncertainties in projects for which little or no previous experience exists. It still relies on subjective estimates prone to human error and variability arising from unforeseen events. But it’s a step in the right direction.
  • A few
  • To maximize predictive accuracy, final decisions should be left to formulas. 60% of studies show significantly better accuracy for algorithms. Statistical algorithms outdo humans in noisy environments for 2 reasons: they are more likely to detect weakly valid cues and more likely to maintain a modest level of accuracy by using such cues consistently.
  • Regress toward the mean
  • 34. Reference-Class Forecasting, similar to the flat-rate labor guides for car repairs, can be developed and utilized. Kahneman found that human judgment is generally overly optimistic. People tend to take an “inside view,” and concluded that disregard of distributional information is perhaps the major source of error in forecasting. Reference class forecasting for a specific project involves the following three steps:Identify a reference class of past, similar projects.Establish a probability distribution for the selected reference class for the parameter that is being forecast.Compare the specific project with the reference class distribution, in order to establish the most likely outcome for the specific project.In 2005, the American Planning Association (APA) endorsed reference class forecasting and recommended that planners should never rely solely on conventional forecasting techniques:“APA encourages planners to use reference class forecasting in addition to traditional methods as a way to improve accuracy. The reference class forecasting method is beneficial for non-routine projects ... Planners should never rely solely on civil engineering technology as a way to generate project forecasts”
  • Conclusions: An inconsistency is built into the design of our minds. Your brain is predictably irrational. The Human Mind is not bound to reality, but to frames.However, given an awareness of these design flaws, measures can be taken to compensate for them. When you see lines with fins pointing in different directions, you will henceforth recognize the situation. That said, cognitive illusions are generally more difficult to recognize than perceptual illusions.
  • The PMBOK recommends developing expertise, which is somewhat difficult. Expertise in a domain is not a single skill but rather a large collection of mini-skills. As any lab rat can tell you, these are the 4 things you need to learn the maze: Whether professionals have a chance to develop intuitive expertise depends essentially on the quality & speed of feedback, as well as sufficient opportunity for feedback. Project management tends to not feature any of these requirements. Every project is unique, so you never get to run the same maze twice. You may make a decision at the beginning of the project, the impact of which isn’t felt for a year or more. And of course, there’s very little time to reflect on much of anything.
  • It’s not completely dismal. Herbert Simon’s definition of intuition: The situation has provided a cue; this cue has given the expert access to information stored in memory, and the information provides the answer. Intuition is nothing more and nothing less than recognition, and that can be learned. Procrastination can be mitigated by adopting agile, or at least setting arbitrary deadlines. To maximize predictive accuracy, final decisions should be left to formulas.
  • So it seems that there possibly were things in the general area of cognitive science that might be useful in the practice of project management. I consulted a number of books, all of which followed a rather rigid form: Paradoxical Imagery or Controversial Statement [colon] Snarky secondary clause that is three times longer than the title before the colon; along with an optional mention of my website and/or other books .
  • Transcript

    • 1. What the PMBOK Doesn’t Teach You: How Cognitive Science Can ImproveYour Practice of Project Management
    • 2. Abysmal Estimates 30 25 20 Dropped Actual 15 4th Est 3rd Est 2nd Est 10 1st Est 5 0 App Notif Return Auto-pop Other Wrong 2@1 Fringe Yr. X of YRe-iterative projectionsfor task completionfor a simple project
    • 3. Daniel Kahneman In 2002, Kahneman received the Nobel Memorial Prize in Economics, despite being a research psychologist, for his work in Prospect theory. In 2011, he made the Bloomberg 50 most influential people in global finance. Currently a senior scholar and faculty member emeritus at Princeton Universitys Department of Psychology and Woodrow Wilson School of Public and International Affairs.
    • 4. Judgment and Decision- Making Curriculum
    • 5. Team Member Estimates 32.5 21.5 Years Remaining 10.5 0 TM 1 TM 2 TM 3 TM 4 TM 5 TM 6 TM 7
    • 6. Historical Data121086 Actual Est.420 PJT 1 PJT 2 PJT 3 PJT 4 PJT 5 PJT 6 PJT 7 (3 didn’t finish)
    • 7. Three Lessons:1. There are two profoundly different approaches to forecasting - the inside view and the outside view.2. Estimates can fall prey to a variety of planning fallacies – in this case, a best-case scenario rather than a realistic assessment.3. “Irrational perseverance”: the folly displayed in failing to abandon the project. When confronted with reality, “we gave up rationality rather than give up on the enterprise.”
    • 8. Three Topics Prevarication Prognostication Procrastination
    • 9. Three TopicsPREVARICATION: YOUR BRAIN ISLYING TO YOU
    • 10. Three TopicsPREVARICATION: YOUR BRAIN ISLYING TO YOUPROCRASTINATION: BASICALLY ALAZY HUNK OF MEAT
    • 11. Three TopicsPREVARICATION: YOUR BRAIN ISLYING TO YOUPROCRASTINATION: BASICALLY ALAZY HUNK OF MEATPROGNOSTICATION: BEST LEFT TOTHE ALGORITHMS
    • 12. Chapter IITHIS IS YOUR BRAIN
    • 13. What the brain does well• Fight or Flight Decisions• Cross Modal Synaesthetic AbstractionWhat the brain does too well• Pattern recognition• Heuristics (A machine for jumping to conclusions)What the brain does not do well at all• Multi-tasking• Statistics
    • 14. Cross Modal Synaesthetic Abstraction Vilayanur Subramanian “Rama” Ramachandran,neuro-scientist, tells his audience that these are Martian alphabets and asks them which one they think is called kiki and which one booba. 98% of the audience point out the smooth shape as Booba and the sharp one as Kiki.
    • 15. Two Systems, Fast and SlowSystem 1 System 2Answer to 2 + 2 = ? Focus attentionDetect that one object is closer than Compare two items for overall valueanotherDetect hostility in a voice Check the validity of a complex logical argumentOrient to sudden loud sound Solve a math problemRead words on large billboards Tell someone your phone numberDrive a car on an empty road Pick one voice out of a noisy roomAutomatic, subconscious CostlyIntuitive, heuristic Thinks its in charge
    • 16. Chapter VPREVARICATION
    • 17. Müller-Lyer Illusion
    • 18. Müller-Lyer Illusion
    • 19. A bat and ball cost $1.10.The bat costs one dollar more than the ball.How much does the ball cost? $.10 + $1.10 ≠ $1.10
    • 20. Good resolutions are useless attempts to interfere with scientific laws. Their origin is pure vanity. Their result is absolutely nil. -Oscar WildeChapter VIPROCRASTINATION
    • 21. ProcrastinationClass A: All papers due on last day of classClass B: Students allowed to set deadlinesClass C: Papers due at regular intervals
    • 22. Ariely & Wertenbroch, 2002People have self-control problems, they recognize them, and they try tocontrol them by self-imposing costly deadlines. These deadlines helppeople control procrastination, but they are not as effective as someexternally imposed deadlines in improving task performance.
    • 23. Chapter VIIPROGNOSTICATION
    • 24. PrognosticationHofstadters Law: It always takes longer thanyou expect, even when you take into accountHofstadters Law.— Douglas Hofstadter, Gödel, Escher, Bach:An Eternal Golden Braid
    • 25. Areas of Expertise
    • 26. How Expectations Mess Up Project Estimates• control – no explicit anchoringmean – 8.3median – 7standard deviation – 4.4• ‘2 months’ conditionmean – 6.8 monthsmedian – 6 monthsstandard deviation – 3.7• ‘20 month’ conditionmean – 17.4median – 16standard deviation – 5.6
    • 27. Planning Fallacy• The tendency to underestimate how long it will take to complete a task, the costs, and the risks involved, while simultaneously overestimating the benefits
    • 28. Planning Fallacy• Self-serving bias: taking credit for when things went to schedule, while blaming delays on outside influences• Discounting multiple high impact (though individually unlikely) risks• Underestimating Overhead: failure to take into consideration vacations, illness, staff changes, meetings• Insufficient consideration of distributional information about outcomes
    • 29. TE = (O + 4M + P) ÷ 6 Expected time (TE ) Optimistic Time (O) Most likely Time (M) Pessimistic Time (P)
    • 30. Chapter IXSUGGESTIONS
    • 31. Algorithms
    • 32. Regress your prediction1. Determine the baseline2. Come up with your intuitive prediction that matches your impression of the evidence3. Estimate the correlation between the evidence &4. If the correlation is .30, move 30% of the distance from the average toward your prediction
    • 33. Reference-Class Forecasting
    • 34. Chapter XCONCLUSIONS
    • 35. • Repeated patterns of events• Feedback between a decision and it’s outcome• The ability to link decisions to their outcomes• Time to reflect on those outcomes
    • 36. Predictably Irrational, Revised and Thinking, Expanded Edition: The Fast and Slow Hidden Forces That Shape by Daniel Kahneman Our Decisions by Dan ArielyModels.Behaving. Badly: The Black Swan: SecondWhy Confusing Illusion with Edition: The Impact of theReality Can Lead to Disaster, Highly Improbable: With aon Wall Street and in Life by new section: "OnEmanuel Derman Robustness and Fragility“ by Nassim Nicholas Taleb Committee on Military and You Are Not So Smart: Why Intelligence Methodology You Have Too Many Friends for Emergent on Facebook, Why Your Neurophysiological and Memory Is Mostly Cognitive/Neural Research Fiction, and 46 Other Ways in the Next Two Decades: Youre Deluding Yourself by Emerging Cognitive David McRaney Neuroscience and Related Technologies
    • 37. Cognitive Bias RapI want to talk to you all about cognitive neuroscience Because your brains not good at data conversion,on your brain you cant hang too much reliance theres a little thing called "loss aversion"it lies, ignores, forgets and cheats which means you over-value what you already haveits nothing but a lazy hunk of meat and wont trade it for something thats half as badA neuron is a cell in your head People think that they know youout from which the dendrites spread but you dont know them the same way toolike the branches of a tree You can see that thats not right --but instead of roots its got an axon or three its the illusion of asymmetric insightthrough which flows electricitywhat happens next is anybodys guess & one that I think is particularly ominousits a big old neuropsychopharmacological mess goes by the name of “The Just World Hypothesis” or, “people get what they deserve”Now, dont get pissed and act all pious - to believe that takes a lot of nerveeveryones got some cognitive bias & ignores the supreme importance of luckyou might have heard about the halo effect (says the guy who was killed by a pick-up truck)if someones good at cookingyou think theyre also good at sex The minds not bound to reality but to frames change the frame and change the gameor when the echo chamber repeats the same tire tirade keep an eye on your mind & the tricks it likes to playuntil false becomes truth, & dont think its real, all the things it has to saythats the availability cascade,if you make a pattern out of the blooming confusionthats not really there, thats the clustering illusion

    ×