System 1 and System 2 thinking, biases, and decision-making processes are complex. While we like to think we make rational decisions based on all available information, in reality we are prone to numerous cognitive biases that can negatively impact decision quality. Some key biases discussed include overconfidence, excessive optimism, confirmation bias, anchoring bias, sunk cost fallacy, loss aversion, and groupthink. Proper decision-making requires understanding these biases and implementing quality controls such as considering alternative options, dissenting views, and worst case scenarios to overcome natural human tendencies towards oversimplification and inertia. Collecting the right data, collaborating across silos, and focusing on what is truly important rather than just measurable are also important for improving
As thinking human beings and team leaders or architects we can benefit from knowing more about how we think, deliberate and decide. Most teams rely on trust, transparency, collaboration, and collective decision-making. “Thinking, Fast and Slow,” by Daniel Kahneman explains two systems that drive how we think. System 1 thinking is fast, intuitive, and emotional; System 2 is slow, deliberate, and logical.
In this presentation you learn how fast and slow thinking affects your reactions, behaviors, and decision-making. You’ll explore how several common development practices (with an emphasis on some agile practices), can amplify and exploit your thinking abilities and where they might lead you astray.
Fast thinking works pretty well in a well-known context. You save time when you don’t have to deliberate over details and nuances in order to make informed decisions. But fast thinking can lead to extremely poor decisions. You might jump to conclusions, be wildly optimistic, or greatly under-assess risks and rewards. You need to exploit both fast and slow thinking and be acutely aware of when fast thinking is tripping you up.
Presented at CodeMash 2015. By Joseph Ours
Joseph's presentation is based on the book "Thinking Fast and Slow" where Nobel Prize winner Daniel Kahneman introduces two mental systems, one that is fast and the other slow. Together they shape our impressions of the world around us and help us make choices. System 1 is largely unconscious and makes snap judgments based upon memories of similar events and our emotions. System 2 is painfully slow, and is the process by which we consciously check the facts and think carefully and rationally. System 2 is easily distracted. System 1 is wrong quite often. Real-world examples that demonstrate how the two systems work are that pro golfers will more accurately putt for par than they do for birdie regardless of distance and people will buy more cans of soup when there is a sign on the display that says “limit 12 per customer."
How to make better decisions thinking, fast and slow - jennifer vu huongJen Vuhuong
All decisions we make in our life are the battles between the 2 systems: Fast and slow system.
The presentation will talk about the characteristics and how to make the best use of the two systems.
Important concepts around how we all make decisions. This presentation introduces the work of Nobel prize winner Daniel Kahneman on Cognitive Biases, and helps you understand why we make errors in judgement, and how to look for signs you're about make one.
As thinking human beings and team leaders or architects we can benefit from knowing more about how we think, deliberate and decide. Most teams rely on trust, transparency, collaboration, and collective decision-making. “Thinking, Fast and Slow,” by Daniel Kahneman explains two systems that drive how we think. System 1 thinking is fast, intuitive, and emotional; System 2 is slow, deliberate, and logical.
In this presentation you learn how fast and slow thinking affects your reactions, behaviors, and decision-making. You’ll explore how several common development practices (with an emphasis on some agile practices), can amplify and exploit your thinking abilities and where they might lead you astray.
Fast thinking works pretty well in a well-known context. You save time when you don’t have to deliberate over details and nuances in order to make informed decisions. But fast thinking can lead to extremely poor decisions. You might jump to conclusions, be wildly optimistic, or greatly under-assess risks and rewards. You need to exploit both fast and slow thinking and be acutely aware of when fast thinking is tripping you up.
Presented at CodeMash 2015. By Joseph Ours
Joseph's presentation is based on the book "Thinking Fast and Slow" where Nobel Prize winner Daniel Kahneman introduces two mental systems, one that is fast and the other slow. Together they shape our impressions of the world around us and help us make choices. System 1 is largely unconscious and makes snap judgments based upon memories of similar events and our emotions. System 2 is painfully slow, and is the process by which we consciously check the facts and think carefully and rationally. System 2 is easily distracted. System 1 is wrong quite often. Real-world examples that demonstrate how the two systems work are that pro golfers will more accurately putt for par than they do for birdie regardless of distance and people will buy more cans of soup when there is a sign on the display that says “limit 12 per customer."
How to make better decisions thinking, fast and slow - jennifer vu huongJen Vuhuong
All decisions we make in our life are the battles between the 2 systems: Fast and slow system.
The presentation will talk about the characteristics and how to make the best use of the two systems.
Important concepts around how we all make decisions. This presentation introduces the work of Nobel prize winner Daniel Kahneman on Cognitive Biases, and helps you understand why we make errors in judgement, and how to look for signs you're about make one.
You're not so smart - Cognitive BiasesOdair Faléco
We think we are smart, but understanding Cognitive Biases shows how limited is our perception of reality and information around us.
On this presentation I expalin and bring some real examples of the most commom biases used in the market, web and UX.
There are many kinds of cognitive biases that influence individuals differently, but their common characteristic is that they lead to judgment and decision-making that deviates from rational objectivity.
People make many decisions. In decision-making scenarios people use rules of thumb (heuristics) to assist in decision-making. Often the heuristics lead to decisions contrary to the desired outcomes. This presentation outlines a set of cognitive biases common in decision making and how to prevent the biases or mitigate the consequences.
hinking, Fast and Slow is a best-selling[1] book published during 2011 by Nobel Memorial Prize in Economic Sciences laureate Daniel Kahneman. It was the 2012 winner of the National Academies Communication Award for best creative work that helps the public understanding of topics of behavioral science, engineering and medicine.[2]
The book summarizes research that Kahneman performed during decades, often in collaboration with Amos Tversky.[3][4] It covers all three phases of his career: his early work concerning cognitive biases, his work on prospect theory, and his later work on happiness.
The slide discusses about the different topics of the book.
Unconscious biases affect our perceptions, decisions, and interactions every day. How do we address biases if we don't know about them? In this talk, you will learn how to recognize and counter the biases that play a part in interviewing, meeting a new team member, and day-to-day interactions. You’ll also see common scenarios and how to address bias as it happens or after the fact. Together, we can make Asynchrony a more diverse and inclusive place to work.
Radical Candor: No BS, helping your team create better work.Digital Surgeons
Inspired by Google's Kim Scott, the Digital Surgeons team adapts Radical Candor to fit with their agile & innovative approach to designing the future of experiences.
Source: Candor, Inc.
http://www.radicalcandor.com/
Created for company team training on DiSC Personality Profiles. I took basic talking points and tried to make them visually interesting, personifying each of the four types with an animal and primary color scheme.
You're not so smart - Cognitive BiasesOdair Faléco
We think we are smart, but understanding Cognitive Biases shows how limited is our perception of reality and information around us.
On this presentation I expalin and bring some real examples of the most commom biases used in the market, web and UX.
There are many kinds of cognitive biases that influence individuals differently, but their common characteristic is that they lead to judgment and decision-making that deviates from rational objectivity.
People make many decisions. In decision-making scenarios people use rules of thumb (heuristics) to assist in decision-making. Often the heuristics lead to decisions contrary to the desired outcomes. This presentation outlines a set of cognitive biases common in decision making and how to prevent the biases or mitigate the consequences.
hinking, Fast and Slow is a best-selling[1] book published during 2011 by Nobel Memorial Prize in Economic Sciences laureate Daniel Kahneman. It was the 2012 winner of the National Academies Communication Award for best creative work that helps the public understanding of topics of behavioral science, engineering and medicine.[2]
The book summarizes research that Kahneman performed during decades, often in collaboration with Amos Tversky.[3][4] It covers all three phases of his career: his early work concerning cognitive biases, his work on prospect theory, and his later work on happiness.
The slide discusses about the different topics of the book.
Unconscious biases affect our perceptions, decisions, and interactions every day. How do we address biases if we don't know about them? In this talk, you will learn how to recognize and counter the biases that play a part in interviewing, meeting a new team member, and day-to-day interactions. You’ll also see common scenarios and how to address bias as it happens or after the fact. Together, we can make Asynchrony a more diverse and inclusive place to work.
Radical Candor: No BS, helping your team create better work.Digital Surgeons
Inspired by Google's Kim Scott, the Digital Surgeons team adapts Radical Candor to fit with their agile & innovative approach to designing the future of experiences.
Source: Candor, Inc.
http://www.radicalcandor.com/
Created for company team training on DiSC Personality Profiles. I took basic talking points and tried to make them visually interesting, personifying each of the four types with an animal and primary color scheme.
J1 2015 "Thinking Fast and Slow with Software Development"Daniel Bryant
In the international bestseller Thinking, Fast and Slow, Daniel Kahneman explains how we, as human beings, think and reason and, perhaps surprisingly, how our thought processes are often fundamentally flawed and biased. This session explores the ideas presented in the book in the context of professional software development. Along this journey, the presentation also shares techniques, processes, and models that can help overcome some of the identified limitations of our decision-making abilities. Topics discussed include the “availability heuristic,” which can lead developers to choose the “latest and greatest” technology without proper evaluation; “optimistic bias,” which can blind architects so they can’t see the “unknown unknowns” within a project; and more.
SC 2015: Thinking Fast and Slow with Software DevelopmentDaniel Bryant
In the international bestseller ‘Thinking, Fast and Slow’, Daniel Kahneman explains how we as human beings think and reason, and perhaps surprisingly how our thought processes are often fundamentally flawed and biased. This talk explores the ideas presented in the book in the context of professional software development. As software developers we all like to think that we are highly logical, and make only rational choices, but after reading the book I’m not so sure. Here I’ll share my thinking on thinking. Topics that will be discussed include; the ‘Availability Heuristic’, which can lead developers to choose the ‘latest and greatest’ technology without proper evaluation; ‘Optimistic Bias’ which can blind architects from the ‘unknown unknowns’ within a project; and more!
A summary of research dealing with two concepts from prospect theory: loss aversion and the endowment effect by Dr. Russell James III, University of Georgia
J1 2015 "Building a Microservice Ecosystem: Some Assembly Still Required"Daniel Bryant
Microservice platforms are finally becoming a reality: Mesos, Kubernetes, and a whole bunch of PaaS-style offerings are available, but the reality is that these platforms still don’t provide everything you need in order to build a fully functional microservice ecosystem. Come to this session to learn about the essential deployment, orchestration, and glue components that often have to be self-assembled. The presentation begins by looking at deployment techniques and tools and examines where to test (QA, staging, or production), how to test (integration and contracts), and how to separate deployment and release. It then discusses orchestration, configuration, and service discovery. Finally it looks at essential glue such as logging, monitoring, and alerting.
Hello Everyone,
A big thank you for all the interest in this study guide. It was originally created as a fun introduction that took the Cognitive Bias wiki and tried to make it easier to memorize.
However, the authors of the wiki article have expressed some concern over the accuracy of certain entries. The document was taken down until that could be corrected.
But, people started asking that I release a new version with a warning. In response, a new "Beta version" of the document has been uploaded with a very strong warning label up front and improved citations. I make it clear that all the text is based on an evolving wiki page and that some of the cognitive biases in there might be incorrect wiki entries. My hope is that this will continue to get people interested in pitching in to help fix the Cognitive Bias wiki pages. :) When the wiki is in a good place, I will take the document out of Beta, and will remove the warning label.
If you are a cognitive expert, join “Operation Fix The Cognitive Bias Wiki!” Add your suggestion to the conversation here: http://en.wikipedia.org/wiki/Talk:List_of_cognitive_biases
Thanks for your interest!
Eric
P.S. . The images have been updated for better remixing and sharing rights. Rather than using permission based images, now all the images are public domain or free non-commercial use by anyone.
Before deciding on a course of action, prudent managers evaluate the situation confronting them. Unfortunately, some managers are cautious to a fault – taking costly steps to defend against unlikely outcomes. Others are overconfident – underestimating the range of potential outcomes. And still, others are highly impressionable – allowing memorable events in the past to dictate their view of what might be possible now.
These are just three of the well-documented psychological traps that afflict most managers at some point, assert authors John S. Hammond, Ralph L. Keeney, and Howard Raiffa in their 1998 article. Still, more pitfalls distort reasoning ability or cater to our own biases. Examples of the latter include the tendencies to stick with the status quo, to look for evidence confirming one’s preferences, and to throw good money after bad because it’s hard to admit making a mistake.
Luckily, techniques exist to overcome each one of these problems. For instance, since the way a problem is posed can influence how you think about it, try to reframe the question in various ways and ask yourself how your thinking might change for each version. Even if we can’t eradicate the distortions ingrained in the way our minds work, we can build tests like this into our decision-making processes to improve the quality of the choices we make.
There’s heaps of fascinating research about the many behavioral biases we are all subject to as individuals.
These include remarkable optical distortions and the way we miss the obvious when we are concentrating on something else. We have a tendency to overestimate ourselves - most famously 90% of drivers assess themselves as above average in ability. We have an attachment to what we already own - how come we won’t buy concert tickets from scalpers at an inflated price, and simultaneously won’t sell tickets we own at face value? We also tend to overweigh risks, even against the chance of regret rather than actual loss.
It’s no surprise then that group decisions are even more flawed.
So how can we overcome biased decision making?
Here are some biases that we often see, followed by some techniques we use to overcome them. We have found that by applying these techniques companies can make better decisions, which in turn increases their resource reallocation and creates more profitable growth.
What is unconscious bias and why does it exist? We all have hidden biases, so it's important to learn what yours are and how to ensure they aren't affecting your business decisions, as well as what organizations can do to prevent these biases from affecting their ability to innovate and remain competitive!
Key videos in the presentation:
https://www.youtube.com/watch?v=NW5s_-Nl3JE
https://www.youtube.com/watch?v=Ahg6qcgoay4
Breaking biases: probably the best strategy to improve your player recruitmentPeter Minkjan
Contrary to popular belief, the key to better scouting isn't just about adopting specific tools or hiring more skilled scouts; it's about recognizing and overcoming biases when assessing player qualities.
As humans, we naturally have biases. While they can provide shortcuts to understand the world, biases can also lead to errors in decision-making. Actively acknowledging and overcoming biases improves the accuracy of player selection, increasing the chances of making better recruiting decisions.
Common biases in player recruitment
We've identified ten specific biases that frequently emerge within scouting teams.
Each bias represents a specific cognitive tendency or pattern of thinking that can lead to errors in judgment or decision-making.
Critical Thinking Getting To The Right Decision For Cil 2010Rebecca Jones
Session C202, Rebecca Jones (Dysart & Jones Associates) & Deb Wallace (Harvard Business School), look at the basics of critical thinking, the difference this productive dialogue has on decision-making & how HBS Baker Library uses this approach.
Capital biasReducing human error in capital decision-makingTawnaDelatorrejs
Capital bias
Reducing human error in capital decision-making
A report by the
Center for Integrated Research
Deloitte’s Capital Efficiency practice helps organizations make better and faster decisions by
assisting them in improving the quality of their capital allocation decisions to enhance robustness,
efficiency, and return on investment.
Capital bias
The balancing act | 2
Choreographing the optimism bias, expert bias,
and narrow framing | 3
Mitigating biases in planning: The US Navy | 7
Prioritization: Leveling the playing field | 9
Stripping away your own organization’s biases | 11
Endnotes | 12
CONTENTS
Reducing human error in capital decision-making
1
A look at the S&P 500 suggests just how dif-ficult it can be to consistently drive positive results. Take one measure, return on in-
vested capital (ROIC). In a Deloitte study, neither
the amount of capital expenditures (as a percentage
of revenue) nor the growth in capital expenditure
demonstrated any kind of meaningful correlation
with ROIC.1 Regardless of industry, individual com-
panies can often have a difficult time maintaining
high and steady returns on their investments year
over year.
Given such uncertainty in capital allocation re-
sults, it may not be surprising that more than 60
percent of finance executives say they are not con-
fident in their organization’s ability to optimally al-
locate capital.2 After all, many companies are bal-
ancing competing priorities, diverse stakeholder
interests, and a complex variety of proposals that
can make capital allocation decisions even more dif-
ficult to execute in practice.
Why is this? On paper it seems practical enough
for everyone throughout the organization to be on
the same page. In an ideal world, a company estab-
lishes the goals and priorities; then, from senior
managers to frontline employees, everyone is ex-
pected to act in a manner that supports these man-
dates.
However, behavioral science, and possibly your
own experience, suggest it’s likely not always that
simple. Individuals at any level of an organization
may be overly optimistic about certain courses of
action, rely too much on specific pieces of informa-
tion (and people), or simply interpret the objective
through too narrow a lens (that may even run coun-
ter to other views on how to achieve these goals).
Within the behavioral science field, these are
referred to as cognitive biases and they exist in
many endeavors, not just capital planning. These
same biases can explain why we are too optimistic
about our retirement portfolios, can rely solely on
the opinions of experts in matters of health, and
narrowly frame our car buying decisions based on
a single attribute, such as fuel efficiency—ignoring
safety features, price, and aesthetic design. In the
language of the behavioral sciences, these translate
into the optimism bias, expert bias, and narrow
framing, respectively.
Though these biases, an ...
The next stages of your journey to agile performance managementDavid Perks
In a transition from traditional performance management to agile performance management, there are people capabilities that need to be strengthened. This is because everybody leads in an agile environment, and usually leadership development training has not been available wholesale throughout the organisation. You don't need the capabilities in order to begin, but you do need the capabilities in order to master an agile culture and foster an agile performance management mindset among your people.
Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
Kseniya Leshchenko: Shared development support service model as the way to ma...Lviv Startup Club
Kseniya Leshchenko: Shared development support service model as the way to make small projects with small budgets profitable for the company (UA)
Kyiv PMDay 2024 Summer
Website – www.pmday.org
Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
Event Report - SAP Sapphire 2024 Orlando - lots of innovation and old challengesHolger Mueller
Holger Mueller of Constellation Research shares his key takeaways from SAP's Sapphire confernece, held in Orlando, June 3rd till 5th 2024, in the Orange Convention Center.
Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
Understanding User Needs and Satisfying ThemAggregage
https://www.productmanagementtoday.com/frs/26903918/understanding-user-needs-and-satisfying-them
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
In this webinar, we won't focus on the research methods for discovering user-needs. We will focus on synthesis of the needs we discover, communication and alignment tools, and how we operationalize addressing those needs.
Industry expert Scott Sehlhorst will:
• Introduce a taxonomy for user goals with real world examples
• Present the Onion Diagram, a tool for contextualizing task-level goals
• Illustrate how customer journey maps capture activity-level and task-level goals
• Demonstrate the best approach to selection and prioritization of user-goals to address
• Highlight the crucial benchmarks, observable changes, in ensuring fulfillment of customer needs
Company Valuation webinar series - Tuesday, 4 June 2024FelixPerez547899
This session provided an update as to the latest valuation data in the UK and then delved into a discussion on the upcoming election and the impacts on valuation. We finished, as always with a Q&A
In the Adani-Hindenburg case, what is SEBI investigating.pptxAdani case
Adani SEBI investigation revealed that the latter had sought information from five foreign jurisdictions concerning the holdings of the firm’s foreign portfolio investors (FPIs) in relation to the alleged violations of the MPS Regulations. Nevertheless, the economic interest of the twelve FPIs based in tax haven jurisdictions still needs to be determined. The Adani Group firms classed these FPIs as public shareholders. According to Hindenburg, FPIs were used to get around regulatory standards.
Premium MEAN Stack Development Solutions for Modern BusinessesSynapseIndia
Stay ahead of the curve with our premium MEAN Stack Development Solutions. Our expert developers utilize MongoDB, Express.js, AngularJS, and Node.js to create modern and responsive web applications. Trust us for cutting-edge solutions that drive your business growth and success.
Know more: https://www.synapseindia.com/technology/mean-stack-development-company.html
8. Action oriented biases
drive us to take action less thoughtfully than we should
Overconfidence. Excessive optimism.
Overestimating our skill level relative The tendency for people to be
to others. We overestimate our ability overoptimistic about the outcome of
to affect future outcomes, take credit planned actions.
for past outcomes, and neglect to role
of chance.
Interest biases
arise in the presence of conflicting incentives
Misalligned individual incentives. Inappropriate attachments.
Incentives to adopt views or to seek Emotional attachment of individuals to
outcomes favorable to their unit or people or element of the business,
themselves, at the expense of the creating a misalignment of interests.
overall interest of the company.
Misaligned perception of corporate
goals. Disagreements about the
relative weigth of objectives pursued
by the organization
9. Pattern-recognition biases
lead us to recognize patterns even where there are none.
Confirmation bias. Power of storytelling
The over-weighting of evidence The tendency to remember and to
consistent with a favored belief, believe more easily a set of facts
underweighting of evidence against a when they are part of a coherent
favored belief. story.
Management by example Champion bias
Generalizing based on examples that The tendency to evaluate a plan or
are particulary recent or memorable. proposal based on the track record of
the person presenting it, more than
False analogies the facts supporting it.
Relying on comparisons with
situations that are not directly
comparable.
10. Stability biases
create a tendency toward inertia in the presence of uncertainty.
Anchoring and insufficient
Sunk-cost fallacy.
adjustments.
Paying attention to historical costs that
Rooting oneself to an initial value,
are not recoverable when considering
leading to insufficient adjustments of
future courses of action.
subsequent estimates.
Loss aversion
The tendency to feel losses more
acutely than gains of the same
Status quo bias.
amount, making us more risk-averse
Preference for the status quo in the
than a rational calculation would
absence of pressure to change it
suggest.
Social biases
arise from the preference for harmony over conflict.
Groupthink Sunflower management
Striving for consensus at the cost of a Tendency for groups to align with the
realistic appraisal of alternative views of their leaders, wheter
courses of action. expressed or assumed.
12. Ask yourself
#1 #2
CHECK FOR SELF-INTERESTED CHECK FOR THE AFFECT
BIASES HEURISTIC
Is there any reason to suspect the Has the team fallen in love with it´s
team making the recommendation of proposal?
errors motivated by self-interest?
Rigorously apply all the quality
Review the proposal with extra care,
controls on the checklist.
especially for overoptimism.
#3
CHECK FOR GROUPTHINK
Were there dissenting opinions within
the team? Were they explored
adequatley?
Solicit dissenting views, discreetly if
necessary.
13. Ask the recommenders
#4 #5
CHECK FOR SALIENCY BIAS CHECK FOR CONFIRMATION BIAS
Could the dignosis be overly Are credible alternatives included along with
influenced by a analogy to a the recommendation?
memorable success? Request additional options.
Ask for more analogies, and rigorously
analyze their similarity to the current
situation.
#7
CHECK FOR ANCHORING BIAS
Do you know where the numbers came
#6 from? Can there be ..unsubstantiated
CHECK FOR AVAILABILITY BIAS
numbers? ..extrapolation from history? .. a
If you had to make this decision again
motivation to use a certain anchor?
in a year´s time, what information
Reanchor with new analysis generated by
would you want, and can you get
other models or benchmarks.
more of it now?
Use checklist of the data needed for
each kind of decision.
14. Ask the recommenders
#8 #9
CHECK FOR HALO EFFECT CHECK FOR SUNK-COST FALLACY,
Is the team assuming that a person or a ENDOWMENT EFFECT
approach that was successful in one area Are the recommenders overly attached to a
will be as successful in another? history of past decisions?
Eliminate false inferences Consider the issue as if you were a new
CEO.
15. Ask about the proposal
# 10 # 11
CHECK FOR OVERCONFIDENCE,
CHECK FOR DISASTER NEGLECT
PLANNING FALLACY, OPTIMISTIC
Is the worst case bad enough?
BIASES, COMPETITOR NEGLECT
Is the base case overly optimistic? Have the team conduct a pre-mortem:
Imagine that the worst has happened,
Have the team build a case taking an
and develop a story about the causes.
outside view; use war games.
# 12
CHECK FOR LOSS AVERSION
Is the recommending team overly
cautious?
Realign incentives to share
responsibility for the risk or to remove
risk.
16. The best way to ruin a decision making process
is letting the boss speak first.
17. Decision making is a team sport. Operating
inside silos is deadly. Collaborate.
18. We need a better recording device.
Today we are getting better and better at measuring what´s
easy to measure - not what´s important.
Our ability to make a decision can never be better than the
current picture I have of the situation
We need to understand what data we need in order to make BY: MATT BLAZE ON FLICKR.COM
IMAGE
good decisions and then measure it evidence based.
23. lesson in
a tweet
ed
ssǝɔ oɹ d ƃ uıʞɐɯ uoısıɔǝ p i as
reb
Wea
How most people think it
How it really is...
is...
24. If everone agrees only one
person has been thinking
Gary Klein Daniel Kahneman
Editor's Notes
Dangerous biases can creep into every strategic choice. Here ’ s a presentation on how to find them—before they lead you and your management team astray. Thanks to a slew of popular new books, many executives today realize how biases can distort reasoning in business. Confirmation bias, for instance, leads people to ignore evidence that contradicts their preconceived notions. Anchoring causes them to weigh one piece of information too heavily in making decisions; loss aversion makes them too cautious. In our experience, however, awareness of the effects of biases has done little to improve the quality of business decisions at either the individual or the organizational level. Though there may now be far more talk of biases among managers, talk alone will not eliminate them. But it is possible to take steps to counteract them. A recent McKinsey study of more than 1,000 major business investments showed that when organizations worked at reducing the effect of bias in their decision-making processes, they achieved returns up to seven percentage points higher. This is a straightforward way to detect bias and minimizeits effects in the most common kind of decision that executives make: reviewing a recommendation from someone else and determining whether to accept it, reject it, or pass it on to the next level. For most executives, these reviews seem simpleenough. First, they need to quickly grasp the relevant acts (getting them from people who know more about the details than they do). Second, they need to figure out if the people making the recommendation are intentionally clouding the facts in some way. And finally, they need to apply their own experience, knowledge, and reasoning to decide whether the recommendation is right. However, this process is fraught at every stage with the potential for distortions in judgment that result from cognitive biases. Executives can ’ t do much about their own biases, as we shall see. But given the proper tools, they can recognize and neutralize those of their teams. Over time, by using these tools, they will build decision processes that reduce the effect of biases in their organizations. And in doing so, they ’ ll help upgrade the quality of decisions their organizations make.
Most scientific economic models of human decisions assume people to behave rationally. Most psychological theory does not. Ritchard Thaler named the discrepancy between ekonomists ’ and psychologists ’ view of peoples rationality ” Econs and humans ” as if the economists and psychologists were studying different species. By incorporating psychological knowledge and economic reasoning Kahneman proved that humans, contrary to popular belief, are very prone to biased thinking and often display inability to make accurat statistical judgements. It seems a fast way of thinking, altough it might have had evolutionary benefits, sometimes tricks us into misjudgements by overriding our more rational and slower way of thinking. The Vitruvian man on the picture is one of Leonardo da Vinci's drawings from 1492. The drawing is named after the Roman architect Vitruvius who influenced the renaissance and it has exactly the proportions he described. The image is a perfect example of Leonardo's interest in proportions and he joined the Pythagorean tradition which described the human existence as the soul, represented of the circle and the material part represented of the quadrant. The human body is representing the perfect match between soul and materia which are mirrored by the well weighted proportions.
An economic idea that the people in the economy make choices based on their rational outlook, available information and past experiences. We have two systems that we use whe we make decisions. System 1 & 2. Read more: http://www.investopedia.com/terms/r/rationaltheoryofexpectations.asp#ixzz2KXWrZGUe
First have a look at the photo. You don ’ t have to think long to realise that the person is angry. In fact you don ’ t have to think at all. The ability to distinguish friend from enemy, danger from pleasure etc. has been an important part of human evolution. It must happen automatically. The imediate realization that the woman is angry is a product of the first of the two ways our brain processes information. We call it ” fast thinking ” or system 1. System 1 is an active process. It helps us react instinctively and has thus been an important aspect of the human evolution. System 1 evluates everything that goes on, inside and outside of our bodies, and does so more or less automatically and with little effort. System 1 tries to work out an answer to a problem or draw conclusions as quickly and efortlessly as possible, which is very benefitial in everyday life. If every activity required full koncentration and mental effort it would be exhausting. System 1 is thus very beneficial in many aspects but when it comes to complex judgement, system 1 is a risky thought process. In intuitive, or System One, thinking, impressions, associations, feelings, intentions, and preparationsfor action flow effortlessly. System One produces a constant representation of the world around us and allows us to do things like walk, avoid obstacles, and contemplate something else all at the same time. We ’ re usually in this mode when we brush our teeth, banter with friends, or play tennis. We ’ re not consciously focusing on how to do those things; we just do them. Our visual system and associative memory (both important aspects of System One) are designed to produce a single coherent interpretation of what is going on around us. That sense making is highly sensitive to context. Consider the word “ bank. ” For most people reading HBR, it would signify a financial institution. But if the same readers encountered this word in Field & Stream, they would probably understand it differently. Context is complicated: In addition to visual cues, memories, and associations, it comprises goals, anxieties, and other inputs. As System One makes sense of those inputs and develops a narrative, it suppresses alternative stories. Because System One is so good at making up contextual stories and we ’ re not aware of its operations, it can lead us astray. The stories it creates are generally accurate, but there are exceptions. Cognitive biases are one major, well-documented example. An insidious feature of cognitive failures is that we have no way of knowing that they ’ re happening: We almost never catch ourselves in the act of making intuitive errors. Experience doesn ’ t help us recognize them.
Now, try to solve this problem. This takes som complex mental operations. In fact, research have shown, that when performing this type of operations we actually get tense, our pupils dialate and our pulse might even raise, which is not the case with system 1. Instead this kind of operations is the second way in which our brain process information – we call it system 2 or ” slow thinking ” . System 2 is the kind of efferful deliberate mental activities we use to analyse komplex problems. It ’ s sort of a lazy system but has an extraordinary capacity whenever activiated. Although complex problems or decisions can only be solved through system 2 there is no guarantee that system 2 will do so. In fact, our mind works according to a least effort principle. System 2 demands effort. It ’ s a lazy system. Because of that our system 1 will try to do the job even when the problem demands rationality and analyzis. Here ’ s how it might work…
A simple problem to be solved. The problem was presented to a bunch of students at MIT, Harvard and Princeton. All smart kids and one would guess it would be a piece of cake. But in fact… More than 50% of students at Harvard, MIT, Princeton answered 10¢! It ’ s not that they lack the ability to solve the problem. They were all smart kids at Ivy league schools. They simply relied on their intuitive solution. The one that springs to mind. It ’ s an operation of System 1. By actively analyse the problem and resist ” jumping to conclusion ” we are able to activate the thought processes of System 2. The brains ability to perform complex operations is remarkable as seen in the performance of skilled chessplayers. But that kind of advanced mental operations is effortful which makes us prone to simplifications. Therefore, when performing advanced decision making, we must constantly be aware of our intutive tendencies. The effortful nature of system 2 make us prone to intuitivity even though we might not think so. When we think of our selves we identify with the consciouss, reasoning self. The intuitive answer (eg. 10¢) tend to even pop up in the heads of those who get it right (eg. 5¢). The difference appears to lie in the extent that people are able to resist the intuition and activate system 2. In other words, it ’ s a matter of inhibiting the fast thinking and to start thinking more slowly. In the case of the students, it was not that they weren ’ t smart enough. Their system 1 simply jumped to conclusion and System 2 is simply too lazy to monitor and inhibit the intuitive respons of system 1. The persistence of system 1 makes humans very prone to biased thinking. Just think about all the situations that must be assessed and all the decisions that must be made, more or less instantly, in the everyday workplace? How much effort is put into complete analysis and how much is influenced by system 1?
A simple problem to be solved. The problem was presented to a bunch of students at MIT, Harvard and Princeton. All smart kids and one would guess it would be a piece of cake. But in fact… More than 50% of students at Harvard, MIT, Princeton answered 10¢! It ’ s not that they lack the ability to solve the problem. They were all smart kids at Ivy league schools. They simply relied on their intuitive solution. The one that springs to mind. It ’ s an operation of System 1. By actively analyse the problem and resist ” jumping to conclusion ” we are able to activate the thought processes of System 2. The brains ability to perform complex operations is remarkable as seen in the performance of skilled chessplayers. But that kind of advanced mental operations is effortful which makes us prone to simplifications. Therefore, when performing advanced decision making, we must constantly be aware of our intutive tendencies. The effortful nature of system 2 make us prone to intuitivity even though we might not think so. When we think of our selves we identify with the consciouss, reasoning self. The intuitive answer (eg. 10¢) tend to even pop up in the heads of those who get it right (eg. 5¢). The difference appears to lie in the extent that people are able to resist the intuition and activate system 2. In other words, it ’ s a matter of inhibiting the fast thinking and to start thinking more slowly. In the case of the students, it was not that they weren ’ t smart enough. Their system 1 simply jumped to conclusion and System 2 is simply too lazy to monitor and inhibit the intuitive respons of system 1. The persistence of system 1 makes humans very prone to biased thinking. Just think about all the situations that must be assessed and all the decisions that must be made, more or less instantly, in the everyday workplace? How much effort is put into complete analysis and how much is influenced by system 1?
Through years of research, Kahneman showed that peoples ability to statistical judegement is biased in several ways. For one people are not adequately sensitive to sample size. We intuitively believe that the laws of large numbers applies to small numbers as well. Our system 1 wants to put together coherence, find the easy route, which makes us see patterns where there really is no patterns and attribute causality to random events. For instance, consider the three sequences above and imaging they are the sex of six babies born in sequence in a hospital. Are these sequences equally likely? In fact they are, given that the six events are independet of each other and the number of boys and girls who were born in the hospital in the last few hours has no effect on the sex of the next baby. The problem here is that our tendency to see patterns och coherence makes the first two sequences appear as regularities and not randomness. BGBBGB appears to be the only random sequence and given the information above, randomness is expected, regularity is not. We don ’ t expect regularity as a result of a random process and therefor we reject the first two sequences, eventhough any combination of Bs ang Gs is as likely as any other. (OBS! tree boys and tree girls are more likely than 6 girls but only if order is disregarded. A specifik sequence is as likely as any other). This is also the case with ” the hot hand ” in basketball. The ” fact ” that a player occasionally sinks three or four baskets and thus has acquired a hot hand is generellay accepted by players, coaches and fans. The ” hot hand ” is though a an illusion of pattern. In order to evaluate a pattern or randomeness in events you need to have a lot of data. So how does this apply to us? Well, given the knowledge of ” the law of small numbers ” , ask yourself the following questions: How many good years should you wait before concluding that an investment adviser is skilled? How many successful acquisitions should be needed for a board of directors to believe that the CEO has extraordinary flair for such details? How many reqruitments is needed to conclude that the recruitment method is satisfactory? Are we perhaps far too willing to reject the beief that much of what we see in life is random?
Through years of research, Kahneman showed that peoples ability to statistical judegement is biased in several ways. For one people are not adequately sensitive to sample size. We intuitively believe that the laws of large numbers applies to small numbers as well. Our system 1 wants to put together coherence, find the easy route, which makes us see patterns where there really is no patterns and attribute causality to random events. For instance, consider the three sequences above and imaging they are the sex of six babies born in sequence in a hospital. Are these sequences equally likely? In fact they are, given that the six events are independet of each other and the number of boys and girls who were born in the hospital in the last few hours has no effect on the sex of the next baby. The problem here is that our tendency to see patterns och coherence makes the first two sequences appear as regularities and not randomness. BGBBGB appears to be the only random sequence and given the information above, randomness is expected, regularity is not. We don ’ t expect regularity as a result of a random process and therefor we reject the first two sequences, eventhough any combination of Bs ang Gs is as likely as any other. (OBS! tree boys and tree girls are more likely than 6 girls but only if order is disregarded. A specifik sequence is as likely as any other). This is also the case with ” the hot hand ” in basketball. The ” fact ” that a player occasionally sinks three or four baskets and thus has acquired a hot hand is generellay accepted by players, coaches and fans. The ” hot hand ” is though a an illusion of pattern. In order to evaluate a pattern or randomeness in events you need to have a lot of data. So how does this apply to us? Well, given the knowledge of ” the law of small numbers ” , ask yourself the following questions: How many good years should you wait before concluding that an investment adviser is skilled? How many successful acquisitions should be needed for a board of directors to believe that the CEO has extraordinary flair for such details? How many reqruitments is needed to conclude that the recruitment method is satisfactory? Are we perhaps far too willing to reject the beief that much of what we see in life is random?
When the first two questions are prompted to people the estimate of the second question will be much higher than if the first question was never prompted – as in the lower square. It ’ s called an anchoring effect and is a well known fenomena in experimental psychological research. It occurs when people consider a particular value (144 years) for a quantity before estimating that quantity. The estimate then stays closer to that value than if the same estimate is performed without the initial value. The value (144) is the anchor. Anchoring is not just a laboratory curiosity; it can be just as strong in the real world. In one field study, real-estate agents were given an oportunity to assess the value of a house that was actually on the market. Before visiting the house they studied a comprehensive booklet of information that included asked price. Half the agents saw an asking price that was manipulated (the anchor) – significantly higher than the real price. The other half saw the real asking price. They then visited the house to assess its value. They were also asked about what factors influenced their decision, Even though none of the agents believed to be influenced by the presented asking price, result showed that the agents who saw the high asking price put a substantially higher value on the house. 3 former…
To help executives vet decisions, we have developed a tool, based on a 12-question checklist, that is intended to unearth defects in thinking—in other words, the cognitive biases of the teams making recommendations. The questions fall into three categories: questions the decision makers should ask themselves, questions they should use to challenge the people proposing a course of action, and questions aimed at evaluating the proposal. It ’ s important to note that, because you can ’ t recognize your own biases, the individuals using this quality screen should be completely independent from the teams making the recommendations.
Is there any reason to suspect motivated errors, or errors driven by the self-interest of the recommending team? Decision makers should never directly ask the people making the proposal this. After all, it ’ s nearly impossible to do so without appearing to question their diligence and even their integrity, and that conversation cannot end well. The issue here is not just intentional deception. People do sometimes lie deliberately, of course, butself-deception and rationalization are more common problems. Research has shown that professionals who sincerely believe that their decisions are “ not for sale ” (such as physicians) are still biased in the direction of their own interests. Bob, for instance, should recognize that lowering prices to respond to competitive pressures will have a material impact on the commissions of his sales team (especially if bonuses are based on revenues, not margins). Devesh should wonder whether the team recommending the acquisition would expect to run the acquired company and therefore might be influenced by “ empire building ” motives. Of course, a preference for a particular outcome is built into every recommendation. Decision makers need to assess not whether there ’ s a risk of motivated error but whether it is significant. A proposal from a set of individuals who stand to gain more than usual from the outcome—either in financial terms or, more frequently, in terms of organizational power, reputation, or career options—needs especially careful quality control. Reviewers also should watch out for pernicious sets of options that includeonly one realistic alternative—the one that the recommending team prefers. In such cases, decision makers will have to pay even more attention to the remaining questions on this checklist, particularly those covering optimistic biases. 2. Have the people making the recommendation fallen in love with it? All of us are subject to the affect heuristic: When evaluating something we like, we tend to minimize its risks and costs and exaggerate its benefits; when assessing something we dislike, we do the opposite. Executives often observe this phenomenon in decisions with a strong emotional component, such as those concerning employees, brands, or locations. This question is also best left unspoken but is usually easy to answer. It is likely that Devesh will easily sense whether the members of the deal team have maintained a neutral perspective regarding the acquisition. If they have become emotional about it, the remedy, again, is to examine with extra thoroughness all the components of the recommendation and all the biases that may have affected the people making it. 3. Were there dissenting opinions within the recommending team? If so, were they explored adequately? In many corporate cultures, a team presenting a recommendation to a higher echelon will claim to be unanimous. The unanimity is sometimes genuine, but it could be sham unity imposed by the team ’ s leader or a case of groupthink—the tendency of groups to minimize conflict by converging on a decision because it appears to be gathering support.Groupthink is especially likely if there is little diversity of background and viewpoint within a team. Regardless of its cause, an absence of dissent in a team addressing a complex problem should sound an alarm. In the long run, a senior executive should strive to create a climate where substantive disagreements are seen as a productive part of the decision process (and resolved objectively), rather than as a sign of conflict between individuals (and suppressed). In the short run, if faced with a recommendation in which dissent clearly was stifled, a decision maker has few options. Because asking another group of people to generate additional options is often impractical, the best choice may be to discreetly solicit dissenting views from members of the recommending team, perhaps through private meetings. And the opinions of those who braved the pressure for conformity in the decision-making process deserve special attention.
Could the diagnosis of the situation be overly influenced by salient analogies? Many recommendations r efer to a past success story, which the decision maker is encouraged to repeat by approving the proposal. The business development team advocating the acquisition to Devesh took this approach, using the example of a recent successful deal it had completed More informally, a decision maker can simply prompt the team to use a broader set of comparisons. You could ask for descriptions of five recent deals, other than the recently acquired company, that were somewhat similar to the one being considered. 5. Have credible alternatives been considered? In a good decision process, other alternatives are fully evaluated in an objective and fact-based way. Yet when trying to solve a problem, both individuals and groups are prone to generating one plausible hypothesis and then seeking only evidence that supports it. A good practice is to insist that people submit at least one or two alternatives to the main recommendation and explain their pros and cons. A decision maker should ask: What alternatives did you consider? At what stage were they discarded? Did you actively look for information that would disprove your main hypothesis or only for the confirming evidence described in your final recommendation? Some proposals feature a perfunctory list of “ risks and mitigating actions ” or a set of implausible alternatives that make the recommendation look appealing by contrast. The challenge is to encourage a genuine admission of uncertainty and a sincere recognition of multiple options. 6. If you had to make this decision again in a year, what information would you want, and can you get more of it now? One challenge executives face when reviewing a recommendation is the WYSIATI assumption: What you see is all there is. Because our intuitive mind constructs a coherent narrative based on the evidence we have, making up for holes in it, we tend to overlook what is missing. Devesh, for instance, found the acquisition proposal compelling until he realized he had not seen a legal due diligence on the target company ’ s patent portfolio—perhaps not a major issue if the acquisition were being made primarily to gain new customers but a critical question when the goal was to extend the product line.To force yourself to examine the adequacy of the data, Harvard Business School professor Max Bazerman suggests asking the question above. In many cases, data are unavailable. But in some cases, useful information will be uncovered. Checklists that specify what information is relevant to a certain type of decision are also helpful ncourage his sales team to evaluate 7. Do you know where the numbers came from? A focused examination of the key numbers underlying the proposal will help decision makers see through any anchoring bias. Questions to ask include: Which numbers in this plan are facts and which are estimates? Were these estimates developed by adjusting from another number? Who put the first number on the table? Three different types of anchoring bias are common in business decisions. In the classic case, initial estimates, which are often best guesses, are used, and their accuracy is not challenged. The team makinghe proposal to Lisa, for instance, used a guesstimate on an important cost component of the capital investment project. Finally, some anchors are clearly deliberate, such as when a buyer sets a low floor in a price negotiation. The trap of anchors is that people always believe they can disregard them, but in fact they cannot. Judges who are asked to roll a set of dice before making a (fortunately simulated) sentencing decision will of course deny that the dice influenced them, but analysis of their decisions shows that they did. When a recommendation appears to be anchored by an initial reference and the number in question has a material impact, the decision maker should require the team behind the proposal to adjust its estimates after some reanchoring. If Lisa discovers that the investment budget she was asked to approve was derived from the costing of an earlier project, she can reanchor the team with a number she arrives at in a completely different way, such as a linear model based on investment projects carried out in other divisions, or competitive benchmarks. The aim is neither to arrive directly at a different numbernor to slavishly “ copy and paste ” the practices of benchmarked competitors, but to force the team to consider its assumptions in another light.
Can you see a halo effect? This effect is at work when we see a story as simpler and more emotionally coherent than it really is. As Phil Rosenzweig shows in the book The Halo Effect, it causes us to attribute the successes and failures of firms to the personalities of their leaders. Companies deemed “ excellent ” are frequently circled by halos. Once an expert brands them in this way, people tend to assume that all their practices must be exemplary. In making its case for its capital investment Does the team making the recommendation have specific information regarding the other company ’ s decision, or is the team making assumptions based on the company ’ s overall reputation? If the investment was indeed a success, how much of that success is attributable to chance events such as lucky timing?9. Are the people making the recommendation overly attached to past decisions? Companies do not start from scratch every day. Their history, and what they learn from it, matter. But history leads us astray when we evaluate options in reference to a past starting point instead of the future. The most visible consequence is the sunk-cost fallacy: When considering new investments, we should disregard past expenditures that don ’ t affect future costs or revenues, but we don ’ t. Note that Lisa ’ s team was evaluating a capacity improvement in a product line that was struggling financially—partly because it was subscale, the team argued. Lisa should ask the team to look at this investment the way an incoming CEO might: If I personally hadn ’ t decided to build the plant in the first place, would I invest in adding capacity? ustomer segments in which the company has a competitive ad
Is the base case overly optimistic? Most recommendations contain forecasts, which are notoriously prone to excessive optimism. One contributing factor is overconfidence, which could, say, lead Devesh ’ s team to underestimate the challenge of integrating the acquired company and capturing synergies. Groups with a successful track record are more prone to this bias than others, so Devesh should be especially careful if the business development team has been on a winning streak. Another factor frequently at work here is the planning fallacy. The planning fallacy arises from “ inside view ” thinking, which focuses exclusively on the case at hand and ignores the history of similar projects. This is like trying to divine the future of a company by considering only its plans and the obstacles it anticipates. An “ outside view ” of forecasting, in contrast, is statistical in nature and mainly uses the generalizable aspects of a broad set of problems to make predictions. Lisa should keep this in mind when reviewing her team ’ s proposal. When drawing up a timeline for the completion of the proposed plant, did the team use a top-down (outside-view) comparison with similar projects, or did it estimate the time required for each step and add it up—a bottom- up (inside-view) approach that is likely to result in underestimates? A third factor is the failure to anticipate how competitors will respond to a decision. For instance, inproposing price cuts, All these biases are exacerbated in most organizations by the inevitable interplay (and frequent confusion) between forecasts and estimates on the one hand, and plans or targets on the other. Forecasts should be accurate, whereas targets should be ambitious. The two sets of numbers should not be confused by senior leadership. Correcting for optimistic biases is difficult, and asking teams to revise their estimates will not suffice. The decision maker must take the lead by adopting an outside view, as opposed to the inside view of the people making proposals.Several techniques help promote an outside view. The use of “ war games ” is a powerful antidote to the lack of thinking about competitors ’ reactions to proposed moves. 11. Is the worst case bad enough? Many companies, when making important decisions, ask strategy teams to propose a range of scenarios, or at least a best and a worst case. Unfortunately, the worst case is rarely bad enough. A decision maker should ask: Where did the worst case come from? How sensitive is it to our competitors ’ responses? What could happen that we have not thought of? The acquisition proposal Devesh is reviewing hinges on the target ’ s sales forecast, and like most sales forecasts in due diligence reports, it follows a steep, straight, upward line. Devesh may ask his team to prepare a range of scenarios reflecting the merger ’ s risks, but the team is likely to miss risks it has not experienced yet. A useful technique in such situations is the “ premortem, ” pioneered by psychologist Gary Klein. Participants project themselves into the future, imagine the worst has already happened, and make up a story about how it happened. Devesh ’ s team could consider such scenarios as the departure of key executives who do not fit into the acquiring company ’ s culture, technical problems with the target ’ s key product lines, and insufficient resources for integration. It would then be able to consider whether to mitigate those risks or reassess the proposal. 12. Is the recommending team overly cautious? On the flip side, excessive conservatism is a source of less visible but serious chronic underperformance in organizations. Many executives complain that their teams ’ plans aren ’ t creative or ambitious enough. This issue is hard to address for two reasons. First and most important, the people making recommendations are subject to loss aversion: When they contemplate risky decisions, their wish to avoid losses is stronger than their desire for gains. No individual or team wants to be responsible for a failed project. Second, the fact that very few companies make explicitchoices about what level of risk they will assume only exacerbates individual managers ’ loss aversion. When launching new ventures, many companies tackle this problem by creating separate organizational units with different objectives and budgets. But dealing with excessive conservatism in “ ordinary ” operations remains a challenge. Implementing Quality Control Over Decisions These 12 questions should be helpful to anyone who relies substantially on others ’ evaluations to make a final decision. But there ’ s a time and place to ask them, and there are ways to make them part and parcel of your organization ’ s decision-making processes. When to use the checklist. This approach is not designed for routine decisions that an executive formally rubber-stamps. The sweet spot for quality control is decisions that are both important and recurring, and so justify a formal process. Approving an R&D project, deciding on a large capital expenditure, and making a midsize acquisition of a company are all examples of “ quality controllable ” decisions. Who should conduct the review. As we mentioned earlier, the very idea of quality control also assumes a real separation between the decision makerand the team making the recommendation. In many instances an executive will overtly or covertly influence a team ’ s proposal, perhaps by picking team members whose opinions are already known, making his or her preferences clear in advance, or signaling opinions during the recommendation phase. If that is the case, the decision maker becomes a de facto member of the recommendation team and can no longer judge the quality of the proposal because his or her own biases have influenced it. A clear and common sign that this has happened is overlap between the decision and action stages. If, at the time of a decision, steps have already been taken to implement it, the executive making the final call has probably communicated a preference for the outcome being recommended. Enforcing discipline. Last, executives need to be prepared to be systematic—something that not all corporate cultures welcome. As Atul Gawande points out in The Checklist Manifesto, because each item on a checklist tends to seem sensible and unsurprising, it is tempting to use checklists partially and selectively. Doctors who adopted the World Health Organization ’ s Surgical Safety Checklist knew that measures as simple as checking the patient ’ s medication allergies made sense. But only by going through the checklist completely, systematically, and routinely did they achieve results—a spectacular reduction in complications and mortality. Using checklists is a matter of discipline, not genius. Partial adherence may be a recipe for total failure. Costs and benefits. Is applying quality control to decisions a good investment of effort? Timepressed executives do not want to delay action, and few corporations are prepared to devote special resources to a quality control exercise. But in the end, Bob, Lisa, and Devesh all did, and averted serious problems as a result. Bob resisted the temptation to implement the price cut his team was clamoring for at the risk of destroying profitability and triggering a price war. Instead, he challenged the team to propose an alternative, and eventually successful, marketing plan. Lisa refused to approve an investment that, as she discovered, aimed to justify and prop up earlier sunk-cost investments in the same business. Her team later proposed an investment in a new technology that would leapfrog the competition. Finally, Devesh signed off on the deal his team was proposing, but not before additional due diligence had uncovered issues that led to a significant reduction in the acquisition price. The real challenge for executives who want to implement decision quality control is not time or cost. It is the need to build awareness that even highly experienced, superbly competent, and wellintentioned managers are fallible. Organizations need to realize that a disciplined decision-making process, not individual genius, is the key to a sound strategy. And they will have to create a culture of open debate in which such processes can flourish
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