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Explore the Rational Choice Model for decision-making & common biases affecting choices in strategic management. Learn to identify, analyze, and overcome biases for effective decision-making.
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Mastering Strategic ManagementChapter 10.3 to EndDecision Making and Bias
Sound Familiar?Rational Choice Model SOLVE • S - State the problem in precise language • O - Outline your usual response • L - List your alternatives and their consequences • V - Vitalize the concept by a) selecting best alternative, b) formulate plan of action, & c) implement plan • E - Evaluate the success of your choice
Rational Choice Model Only problem with this model is that most people indicate that it is rarely the way they make decisions… Several concern when applying model within org complex • Rational decision-making assumes that options are clear and that a single best solution exists • Many strategic decisions are not presented in obvious ways • Also assumes no time or cost constraints • And, that accurate information is available
Making Choices: Rational vs Organizational Reality Goals Rational: Clear, compatible, agreed upon Reality: Ambiguous, conflicting, lack agreement Processing Information Rational: People can process all information Reality: People process only limited information Evaluation Timing Rational: Choices evaluated simultaneously Reality: Choices evaluated sequentially Standards Rational: Evaluate against absolute standards Reality: Evaluate against implicit favourite Info Quality Rational: People rely on factual information Reality: Quality of information limited Decision Objective Rational: Maximization-optimal Reality: Satisfying-“good enough”
Limitations Rational Decision-Making In reality, decision making is not rational because there are limits on our ability to collect and process information. Because of these limitations, Nobel Prize-winner Herbert Simon argued that we can learn more by examining scenarios where individuals deviate from the ideal. These decision biases provide clues to why individuals such as CEOs make decisions that in retrospect often seem very illogical— especially when they lead to actions that damage the firm and its performance.
How about this one? Knowledge-based Decision Making • Identify the Mega Question • Background Materials • Informing the Issue • Identifying the Choices • Analyzing the Choices • Determining Areas of Con census • Identify Action, Intent and Accountability (Handout)
Analytic Conceptual Directive Behavioural Decision-Making Styles High Tolerance for Ambiguity Low Rational Intuitive Way of Thinking
Intuition! A ball and a bat together cost $1.10. The bat cost a dollar more than the ball. How much does the ball cost? Over 50% of Harvard and M.I.T. students got this wrong. Why? They did not bother to check. They relied on their intuition that happened to deceive them.
Anchoring & Adjustments When individuals react to arbitrary or irrelevant numbers when setting financial or other numerical targets. Might be tempting to compare your post-grad starting salary with wages earned pre-grad, or compare to siblings, friends, parents, & others with different majors Instead, research average starting salary for graduates with your background, experience, & other relevant characteristics This bias can undermine firm performance when executives make decisions about potential ROI by comparing to previous deals rather than based on a realistic & careful study of potential choices
Example • A Roulette wheel, marked 0 to 100 • But, only stops on 65 or 10! • Question - Is the % of African nations among UN members larger or small than the number you just ‘spun’? • Same effect (without wheel) if you ask whether Ghandi was older than 114 when he died? How old was he? • Interestingly, answers are higher than if you ask whether he was older than 35 when he died? How old was he? Why?
Adjusting…. While anchoring has some effect, people often try to adjust for anchors they know are wrong by adjusting But, they will only adjust until the point where they no longer sense that they have to adjust Which suggests that they will adjust until reaching the edge of the unknown quantity or area of unknown Zone of Probable Solution… You Adjusting….. Adjust more! Overcome, by going a little further than you think is necessary!
Availability Bias The availability bias occurs when more readily available information is incorrectly assessed to also be more likely • Do you think that the odds of dying in a auto accident are high or lower than dying from stomach cancer? • Perhaps your are influenced by media coverage, which tends to report accidents, but not deaths by stomach cancer, which kills more than 100x more people! • This bias affects companies which focus on readily available information such as their own performance figures, while failing to collect meaningful data on their competitors or industry trends
Availability Bias • Directly affected by the number of examples we can quickly come up with, & • Especially ease at which we come up with them! • Interestingly, once we push harder (i.e. to come up with more examples), it quickly becomes harder! Let’s try! • Think of the last few times you rode your bike? • Now, try to identify the last 12 times you rode your bike? • First few were (probably) easy! But then harder… • Odd result, people who do come up with 12 (rather than a few) are less confident…
Assuming you’re a Couple... What % of the cleaning do you do? Bet if I asked both of you, it adds to more than 100%. Why? Easier to Remember what I did!
Availability & RiskGreat days to be selling insurance… Assessing risk, Which is higher danger? 2xStroke vs accidental death 20xTornadoes vs asthma 52x Death by lightning vs botulism 18x Accidental death vs Death by disease 4x Death by accident vrs dying from diabetes
Escalation of Commitment The idea of “throwing good money after bad” illustrates the bias of escalation of commitment – when individuals continue on a failing course of action even after it becomes clear that this may be a poor path to follow. Sunk costs are Sunk!! Regularly seen at Casinos when individuals, on a losing streak, think the next spin is increasingly likely to win
Principal / Agent Problem • You work for owners /shareholders • You are managing a big project that appears to be heading for disaster… • No one knows yet & you may be able to ‘double up’ and still win! (i.e. invest even more $$ in a dubious project) Do you do it?
Fundamental Attribution Error • Fundamental attribution error occurs when good outcomes are attributed to personal characteristics but undesirable outcomes are attributed to external circumstances. • Teachers often note when student does well on test, it’s attributed to intelligence & hard work! But when student performs poorly, test was hard! In a similar vein, some CEOs quick to take credit when firm performs well, but often attribute poor performance to external factors such as the state of the economy
Halo Effect • Hitler loved dogs and little children… • Halo effect, inclines us to match our view of all the qualities of a person to our judgment of one attribute that is particularly significant • Story of Facebook or Google • “I knew the market was going to crash in 2008”
Hindsight Bias • Tendency to overestimate our ability to have predicted an outcome that could not possibly have been predicted • Bias also occurs when mistakes seem obvious after they have already occurred - “Monday morning quarterback” • When gearing up to go camping, a father says that he just knows someone is going to forget something. It turns out that his son forgot his fishing rod. "I was sure it would happen," says the father. • Three friends decide to bet on a horse race. One of them breaks from the other 2 & chooses a horse with very low winning odds, saying that he has a good feeling about that horse. The long shot ends up winning, prompting him to claim he’d been certain of outcome.
Kodak The decline of photography firms such as Kodak resulting from increasing popularity of digital cameras may seem obvious in retrospect But, it is easy to overlook poor quality of early digital technology & dismiss any notion that Kodak had good reasons not to view this new technology as a significant threat to film, when digital cameras were first introduced
The Black Swan • Flawed stores of our past that shape our world views • We continuously try and make sense of the world • But the stories we invent to explain • Are simple • Concrete rather than abstract • Assign a larger role to talent, stupidity and intentions than blind luck • Focus on a few striking events that did happen rather than on countless other events that failed to happen
Where were you when your first heard about 911? • Flashbulb memories • The day after the Space Shuttle Columbia blew up, Prof asked 1st year students to write down where they were when they first heard the news • Kept the pages (in student’s own hand writing) • Asked students again, 5 years later • Interestingly, many students had changed their location, and when presented with the evidence, in their own handwriting, even denied it was their handwriting! • False Memories…
Base-Rate Neglect(Or, When You Hear Hoofbeats, Don’t Expect a Zebra) Mark is a thin man from Germany with glasses who likes to listen to Mozart. Which is more likely? A) Mark is a truck driver? B) He is a professor of literature in Frankfurt? • How many truck drivers are there in Germany? • How many Literary Profs are there in Frankfurt? Advice – always try to examine the most common or typical result in considering a course of action. Should I invest in this new company (data – only 20% of new companies survive for 5 years…) Should I run away and join the circus? (Data 83% of circus performers rate themselves ‘very happy’)
Correlation & Causality • Lead to problems when individuals make inaccurate attributions about the causes of events. • Three things are necessary to determine cause—or why one element affects another • correlation • temporal order • ruling out other potential causes • Also includes tendency to misread randomness
Correlation & Causality • For example, understanding marketing’s impact on sales involves • correlation (do sales increase after more marketing), • temporal order (does marketing spending occur before sales increase), and • ruling out other potential causes (sales increase from better products, more employees, a recession, a competitor went bankrupt, etc.) • The first two items can be tracked easily, but the 3rd is almost impossible to isolate because there are always so many changing factors. • ceteris paribus (all things being equal or constant) is almost never true
Causal Direction… • I collected a bunch of data on male baldness, including age of patient & degree of hair loss • Statistically - THERE IS A CORRELATION!! • But, which direction? • Is the hair loss making these guys old, or is age contributing to hair loss? SO, WHAT’S CAUSING WHAT?
Causal Direction… • I collected a bunch of data on the temperature in pot & number of kernels that pop per sec. • Statistically - THERE IS A CORRELATION!! • But, which direction? Is the heat making the kernels pop or are the popping kernels heating up the pot? SO, WHAT’S CAUSING WHAT?
Causal Direction… • They’ve been building a lot of houses near me over the last 10 years, and I believe that crime is increasing too… • So, I collected a bunch of crime data, & pop density • Statistically - THERE IS A CORRELATION!! But, which direction? Is increasing pop. density leading to more crime? or does the increasing crime rate attracting more people?
Which is more Probable? • That there will be a massive flood somewhere in North America next year, in which more than 1000 people die OR • That there will be a major earthquake in BC sometime next year, causing a flood in which more than 1000 people die Let’s try again! I have a standard 6-sided dice, with 4 green sides and 2 red sides. Rolling it 20 times, what sequence is most probable within 20 rolls? 1 - RGRRR 2 - GRGRG 3 - GRRRR
7 - Sampling BiasIf women have 1.53 children and men have 1.38 children, does this mean that women have more children than men?
Sampling Bias • Misunderstandings about sampling may occur when individuals draw broad conclusions from a small sets of observations instead of reliable sources of information derived from large, randomly drawn samples • Or, asking the wrong people - bias samples (i.e. near Hospitals, ask people about health issues; survey every shopper from 9-noon, M-F) • Or no sample - Many CEOs have been known to make major financial decisions based on their own instincts rather than on careful number crunching
Dice • Roll the dice until you get the same number twice in a roll (3s or 6s…) • Now, what do you think the odds are of the next roll also being the same number? • How about a 4th identical number? [No dice, flip a coin] No matter what you “feel”… what are the real odds?
Law of Small Numbers… • A study of USA kidney cancer in 3,141 counties of USA reveals a remarkable pattern! • Kidney cancer is lowest in mostly rural, sparsely populated and located in traditionally Republican states • Your thoughts? • You probably started to think about links & causes… Maybe discarded Republican, and focused on rural… • But, also turns out that kidney cancer is highest in mostly rural, sparsely populated and located in traditionally Republican states… • Turns out extremes (high or low), are easier to find in small samples…
My Magic Vase! • In this giant vase, I placed 1000 marbles with numbers 1 to 1000 printed on them • So the ‘average’ is 500 • If you pick 1 marble, what is the odds that you will pick the average – the one labeled 500? Be ‘near’ 500? • What if you could pick 2 marbles? Have your odds of (the average) being nearer 500 improved? 3 marbles?
The Archer Might help to think of an archer, sometimes high, sometimes low… but on average somewhere near the middle…
Overconfidence • Overconfidence bias occurs when individuals are more confident in their abilities to predict an event than logic suggests is actually possible • For example, two-thirds of lawyers in civil cases believe their side will emerge victorious. • But as the famed Yankees player/manager Yogi Berra once noted, “It’s hard to make predictions, especially about the future.” • Such overconfidence is common in CEOs that have had success in the past and who often rely on their own intuition rather than on hard data and market research.
Regression to the Mean • Pilot Training – Yelling is effective. Every time I yell at a rookie pilot for a bad landing, the next one is better. Every time I praise a pilot for a good landing, the next one is worse. Ergo, Yelling works & Praise doesn’t! • On average, why are tall people’s kids shorter than they are, and short people’s kids taller? [PS if it wasn’t this way, what would we look like in a couple of centuries?] • Why do we tend to notice these abnormalities or exceptions anyways?
