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Archive for the ‘Strategic Decision Making’ Category

I hope you like the title of this post. It tees things up pretty well for what follows in this first of a two part series on cognitive biases.  The title demonstrates a Bias Blind Spot, suggesting the “I’m not biased, but THEY are”, and the famous “I knew it all along” type of thinking that accompanies the Hindsight Bias.  I had an opportunity to speak on behalf of Decision Lens at the recent Cambridge Health Institute conference on Portfolio Management last month, and one of the parts of my talk that stimulated the most interest and conversation, was the portion on cognitive biases and how structured decision making may help overcome them.  So, having had these conversations during and after the conference and given some thought to them in the context of portfolio decisions and project or product choices, I thought I would share a typical project selection type discussion that I have found myself in over the years and then break it down to look at what might be going on to help illustrate a few points.  Next post we’ll talk about possible remedies of structured and collaborative decision making and their potential to positively influence the process.  Enjoy.

Setting: A product or project Steering Committee meeting in a large company delivering  about 60% of its strategic planning goals from new product development (like many companies).

Committee Chair:  So we have a decision to make.  Which of these two product options are we going to pursue?  This is a critical strategic decision for us, and key to our ability to hit next year’s numbers.

VP R&D (Joe):  I think there’s not much to decide really, clearly WonderWidget is the superior option.  Acme consulting’s report said as much, and Maria, didn’t your team’s study find it to be preferred?

Director Market Research (Maria ): Well, I’m not sure how much value I put in Acme’s assessment, but yes, we did on the whole find WonderWidget the better choice. However, some groups preferred GreatGadget.  In fact, look at these numbers.  When I broke them out by age group against our target, an iron clad case can be made for GreatGadget.

VP R&D (Joe): OK, but c’mon. We’ve tried the GreatGadget approach before, and it has failed outright.  As I recall, the WonderWidget prototype shown to those groups didn’t even include our latest greatest improvements, isn’t that true Maria?

Director Market Research (Maria): Well, um…, I think…

VP R&D (Joe): I’m also not sure how we recruited those participants in the study, but they certainly didn’t seem representative of the typical savvy of most of our users, wouldn’t you agree Maria?

Director Market Research (Maria): I have some concerns about a couple of aspects of the study design, that may have contributed to what you observed.

VP Operations (Andre): Joe, there has to be considerable value in GreatGadget, it is right in our wheelhouse, it’s basically repurposing known technology!

VP R&D (Joe): I wouldn’t say that… Are you saying that because of the common interface?

VP Operations (Andre): We would have to be able to have GreatGadget commercially ready in a fraction of the time and cost!, 6 months max, and very little incremental investment based on our existing capabilities.

Director Market Research (Maria): We’ve had some quality complaints from product produced on that platform, though I do think it has some merit…

VP R&D (Joe): It may be faster and cheaper Andre, but Maria’s right, no one wants it.  We knew when we launched it that it may take a long time to work out the bugs.

VP Operations (Andre): Maria, those are minor problems, we can overcome those from my shop.  I’m 99% sure of it.

Director Market Research (Maria):  I didn’t say…

Committee Chair:  OK, I’ve been listening very objectively.  We have a track record of not always being very good at these decisions.  While we’ve been in a bit of a drought; we’re due for a win.  It sounds to me like we are reaching a general consensus that we should pursue GreatGadget.  So, how do we move forward?

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Now, let’s replay this conversation and take a little deeper look into what’s going on…

If you need to reference the biases discussed below, you can follow this link or those embedded in the discussion.

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Committee Chair:  So we have a decision to make.  Which of these two product options are we going to pursue?  This is a critical strategic decision for us, and key to our ability to hit next year’s numbers.

Comment >> Our committee chair leads out of the gate to trigger the Framing Effect, immediately limiting the options to two. Then throws in a dash of Focusing Effect, and Hyperbolic Discounting to drive the group to viewing the decision through the filter of next year’s number.

VP R&D (Joe):  I think there’s not much to decide really, clearly WonderWidget is the superior option.  Acme consulting’s report said as much, and Maria, didn’t your team’s study find it to be preferred?

Comment >> Joe responds with what could be Positive Outcome, Wishful Thinking and Optimism bias inferring a foregone conclusion about the route to successful decision. He then evokes the Interloper Effect about the objectivity of the consultants with no substantiation, and pursues the Confirmation Bias as he seeks corroboration for his position from Maria.

Director Market Research (Maria ): Well, I’m not sure how much value I put in Acme’s assessment, but yes, we did on the whole find WonderWidget the better choice. However, some groups preferred GreatGadget.  In fact, look at these numbers.  When I broke them out by age group against our target, an iron clad case can be made for GreatGadget.

Comment >> Maria counters Joe’s Interloper Effect with a dose of Ingroup Bias rewarding her group for their superior research efforts, she then seems to have an episode of the Framing Effect as she begins to parse the data in ways to support an argument that runs contrary to Joe’s position.

VP R&D (Joe): OK, but c’mon. We’ve tried the GreatGadget approach before, and it has failed outright.  As I recall, the WonderWidget prototype shown to those groups didn’t even include our latest greatest improvements, isn’t that true Maria?

Comment >> Whoa!  Maria hits Joe right in his Semmelweis Reflex as he responds to reject the new evidence that contradicts his position, he reels and strikes back with a combination of the Subjective Validation, The Primacy Effect, and Negativity Bias as he doesn’t substantiate the outright failure, gives the initial failure more emphasis than the current research, and gives more weight to the negative aspects of the previous effort, than any positives.  He then slips in an uppercut that tags Maria right in the Suggestibility Bias.

Director Market Research (Maria): Well, um…, I think…

Comment >> Maria is now suffering from some combination of False Memory, and Cryptomnesia as she fights her confusion to sort facts from suggestions and is likely moving down the path to some form of Information Bias to try to shore up the data to make the case when the data is either unavailable or irrelevant to the influence driven argument.

VP R&D (Joe): I’m also not sure how we recruited those participants in the study, but they certainly didn’t seem representative of the typical savvy of most of our users, wouldn’t you agree Maria?

Comment >> Joe doubles down on triggering Maria’s Suggestibility Bias with his Fundamental Attribution Error about the participants in the study.

Director Market Research (Maria): I have some concerns about a couple of aspects of the study design, that may have contributed to what you observed.

Comment >> Maria hints at the fact that she may be concerned about a variety biases, like the Hawthorne Effect, Herd Instinct, Expectation Bias, or  Selection Biases to which studies may be prone.

VP Operations (Andre): Joe, there has to be considerable value in GreatGadget, it is right in our wheelhouse, it’s basically repurposing known technology!

Comment>> Andre is new to the party, and comes with a BYOB (Bring Your Own Bias) of Status Quo, and the Mere Exposure Effect.

VP R&D (Joe): I wouldn’t say that… Are you saying that because of the common interface?

VP Operations (Andre): We would have to be able to have GreatGadget commercially ready in a fraction of the time and cost!, 6 months max, and very little incremental investment based on our existing capabilities.

Comment>> Andre goes on to fall victim to the Planning Fallacy, by likely underestimating the time and cost required to undertake this similar but entirely new effort.  He is likely in the throes of the Overconfidence Bias.

Director Market Research (Maria): We’ve had some quality complaints from product produced on that platform, though I do think it has some merit…

VP R&D (Joe): It may be faster and cheaper Andre, but Maria’s right, no one wants it.  We knew when we launched it that it may take a long time to work out the bugs.

Comment>> Joe makes a huge leap, and by way of the Authority Bias he attributes expertise to Maria and exaggerates her position through a bit of Egocentric Bias, and the Availability Cascade bias (a.k.a; if you say it enough it is true).  Then he pulls out the Hindsight Bias!

VP Operations (Andre): Maria, those are minor problems, we can overcome those from my shop.  I’m 99% sure of it.

Comment>> ???… Overconfidence Bias run amuck.

Director Market Research (Maria):  I didn’t say…

Comment>> Sorry Maria, looks like this train is leaving the station…

Committee Chair:  OK, I’ve been listening very objectively.  We have a track record of not always being very good at these decisions. While we’ve been in a bit of a drought; we’re due for a win.  It sounds to me like we are reaching a general consensus that we should pursue GreatGadget.  So, how do we move forward?

Comment>> Lastly, this is a mix of Bias Blindspot (believing you are not biased), Outcome Bias (judging the result rather than the quality of the decision at the time, i.e. knowing what you knew then), and the Gambler’s Fallacy that biases one to think that a series of losses must be leading to a win, while the odds in the meantime remain exactly the same… all with a False Consensus Effect cherry on top.

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Sound familiar?  In my next post I’ll talk about how to use a structured approach to decision making to help neutralize some of these effects and increase the chances that the group makes the best decision possible with the information available.

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Being a strategist, I have a fascination with chess, or at least the ideas of chess. It conjures images of fierce competition, intellectual rigor, intense strategic thinking and steely eyed focus.  It can also be surprisingly dramatic and controversial, with colorful characters.  I really want to be able to play well.  I can beat my six year old son fairly handily, his brain is not yet fully developed, so this should give you some insight into my skill level. I have several iPhone apps that are fun and instructive and provide a useful distraction and brain exercise to combat the monotony of tarmacs and airports.  I like to see me and my iPhone as my personal rendition of the legendary competition between undisputed World Chess Champion Garry Kasparov and IBM’s Deep Blue Chess computer, except in my melodrama the opponents both have less capacity and the skill gap is greater and skewed to the machine. My level of pure frustration with chess is very much on par with what my friends who play golf describe as their emotional relationship to that sport.  One good move or a flash of insight keeps me coming back to the chessboard like a good approach shot does them to the fairway.

Garry Kasparov is considered by many (especially in the post Fischer era) to be the greatest chess player in the world.  He wrote an interesting book called ”How Life Imitates Chess”, which in the end is very much a book about… decision making.

Some of my own progress as a chess novice has been stunted by analysis paralysis.  Determining options can be daunting, choosing which to pursue can be even more so.   After only three moves the number of possible positions on the board can be well over 60,000.  So how do we decide?  If this were a purely analytical process based on logic and analysis, it seems that when Garry Kasparov faced IBM’s Deep Blue computer in 1996 and 1997 that the ability of the computer to win these matches should have been a foregone conclusion as it is when I compete against my iPhone.  Yet Kasparov won the 1996 match 4-2, he lost the 1997 rematch narrowly 2-1/2 – 3-1/2.  He offered to play a third match during an appearance on Larry King Live with a number of conditions, including a willingness to concede Deep Blue as world champion if it won the match.  IBM chose not to take him up on the offer despite the computer’s ability to calculate 200,000,000 positions on the board per second!

There are four basic chess values that Deep Blue must consider before deciding on a move. They are material, position, King safety and tempo.

Material is easy. The rule of thumb is that if a pawn is considered to be worth a value of 1, pieces (knights and bishops) are worth 3 each, a rook is worth 5, and the Queen 9. The King, of course, is beyond value, since his loss means the end of the game. This varies slightly in certain situations — retaining the Bishop pair in the end game generally increases their value beyond 6, for example – but the laws of material are fairly constant.

Position is more complex. In the old days, it was thought that control of the center was all that mattered. Nearly all grandmaster games before the 20th century began with Pawn to King 4 or Pawn to Queen 4. Control of the center is still important, but certain grandmasters in this century found some effective “hypermodern” openings that delay development of the center, with the idea that the opponent will overextend his position and leave himself vulnerable for attack.

The simplest way to understand position is by looking at your pieces and counting the number of safe squares that they can attack. The more squares they control, the stronger the position. Thus, a seemingly quiet pawn move can be very strong if it opens many new squares for a more powerful piece behind it.

The defensive aspect of position is the safety of the King. This is self-explanatory. A computer must assign a value to the safety of the King’s position in order to know how to make a purely defensive move.

Tempo is related to position but focuses on the race to develop control of the board. A player is said to “lose a tempo” if he dillydallies while the opponent is making more productive advances.

The programmers have defined how Deep Blue’s program evaluates these factors. The computer then searches through all the legal moves and chooses the one that yields the highest value.

I don’t know about you, but this process is beyond my computational capacity in any meaningful and constrained time frame.

So let’s think about this.  It’s been estimated that through a process of elimination and prioritization of high potential moves, that human chess masters consider approximately three dozen serious options or so before making a move versus two hundred million per second by Deep Blue.  Then, we apply a mix of analysis, judgment, preference, creativity, experience, intuition and a dash of guts to form a decision cocktail and make a choice (or sometimes take a gamble), often resulting in a very good outcomes.

So, How do Garry Kasparovs work?


So is this yet another tale of man versus machine, like the folklore of John Henry versus the steam hammer? Is it better to have nearly limitless computational capacity with limited experience and intuition, or vast experience and intuition and less computational capacity? It seems to me that a Deep Blue Kasparov would be invincible. Maybe man versus machine is the wrong question?

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