σ L6
L6σ Why People Make Bad Decisions The Role of Cognitive Biases Scott Leek Sigma Consulting Resources, LLC American Society for Quality Denver Section October 16, 2012 © 2012 Sigma Consulting Resources, LLC 1 Cognitive Biases & Decision Making L6σ Topics • Cognitive Biases and decision making • Review of Common Biases and Mitigation Strategies Hindsight Bias Confirmation Bias Anchoring and Adjustment Heuristic Availability Heuristic Representativeness Heuristic o o o Insensitivity to Sample Size Insensitivity to Prior Probability Conjunction Fallacy • Decision Quality Control Checklist © 2012 Sigma Consulting Resources, LLC 2 L6σ Exercise Decision Making Identify at least 3 decisions you have made, or been involved in making, that turned out to be wrong or “not so good.” The decisions can be recent or in the past, large or small. © 2012 Sigma Consulting Resources, LLC 3 L6σ Cognitive Bias Definition • “…a replicable pattern in perceptual distortion, inaccurate judgment, illogical interpretation, or what is broadly called irrationality” • Arise from multiple confounded sources Information-processing shortcuts (e.g., availability heuristic) Mental noise (wrong way on a one-way street) Limited information processing capacity (e.g., Bayesian probabilities) Emotional or moral motivations (e.g., just-world hypothesis) Social influence (e.g., groupthink) © 2012 Sigma Consulting Resources, LLC 4 L6σ Exercise Test Decision Making By a show of hands how people… Identified three or more examples of “not so good decisions?” Identified two or more examples of “not so good decisions?” Identified one or more examples of “not so good decisions?” If you were unable to identify an example you may be suffering from the… © 2012 Sigma Consulting Resources, LLC 5 L6σ Cognitive Biases Hindsight Bias © 2012 Sigma Consulting Resources, LLC 6 L6σ Hindsight Bias Definition In hindsight… • “…the consistent exaggeration of what could have been anticipated in foresight” (“I knew it all along” or “creeping determinism”) • “…the inclination to see events that have already occurred as being more predictable than they were before they took place” © 2012 Sigma Consulting Resources, LLC 7 L6σ Hindsight Bias Problems (“So What”) • Failure to learn from the outcome of events, not being surprised by anomalous outcomes (if we are unable to acknowledge when our predictions are wrong, they will never be right) • Influences attributions of blame after unforeseen catastrophic events • People tend to misremember (memory distortion) their predictions in order to exaggerate in hindsight what they knew in foresight • Causes people to rely too heavily on knowledge of the outcomes of historical events, leading to accepting sufficient, though not necessary explanations too easily (“tried it, didn’t work” turns out there was an interaction) © 2012 Sigma Consulting Resources, LLC 8 L6σ Hindsight Bias Preventing or Mitigating • Awareness is not enough to mitigate • Use of the scientific method or derivative like Plan-Do-Study-Act (PDSA) • Recording predictions prior to events (a priori) like process changes, experiments, et cetera and reviewing those predictions after the events (a posteriori), formally updating current knowledge • Focus on why outcomes occur, not just if the predictions are correct, try to explain alternative or anomalous outcomes • Reward people based on logic of judgment, not just outcomes (Hogarth, e.g., control charts and Type I errors, testing with true/ false give a reason) © 2012 Sigma Consulting Resources, LLC 9 L6σ Cognitive Biases Confirmation Bias © 2012 Sigma Consulting Resources, LLC 10 L6σ Confirmation Bias Case Study A sales manager believes that a new marketing method will increase the sales call success rate. An experiment was designed to test the effectiveness of the new method. The experiment was run for one week when 480 sales calls were made. The new method was randomly assigned to sales calls and the number of sales made was recorded. The brochure resulted in 270 sales. Treatment # Sale Made # No Sale Made New Method 270 90 Old Method 90 30 Conclusions? © 2012 Sigma Consulting Resources, LLC 11 L6σ Confirmation Bias Definition • A tendency for people to favor information that confirms existing beliefs or theories (paradigm, Kuhn) • Ambiguous evidence is interpreted as supporting existing beliefs or theories • Fail to search for disconfirming evidence • Typically falls into three categories of bias: Search for information Interpretation Memory (hindsight bias) • In light of the confirmation bias the oft quoted “I’ll believe it when I see it” might better be stated “I’ll see it when I believe it.” (see Thomas Kuhn, The Structure of Scientific Revolutions © 2012 Sigma Consulting Resources, LLC 12 L6σ Confirmation Bias Problems (“So What”) • Overconfidence in decision-making based on ignoring or not seeking all relevant data • Leads to flawed causal models which in turn influences what we observe, leading to flawed causal models in what can be a selfreinforcing loop (Senge’s “Reflexive Loop”) • Leads to the “We have made the decision now find the data to support it…” scenario • Plays a role in “Groupthink” © 2012 Sigma Consulting Resources, LLC 13 L6σ Confirmation Bias Problems (“So What”) I take actions based on my beliefs I adopt beliefs about the world The “Reflexive Loop” (our beliefs affect what data we select next time) I draw conclusions I make assumptions based on the meanings I added I add meanings I select data from what I observe Observable data and experiences From the “Fifth Discipline Field Book” by Peter Senge © 2012 Sigma Consulting Resources, LLC 14 L6σ Confirmation Bias Preventing or Mitigating • Beware of asking (or being asked) to “prove” something. When the objective is to “prove,” that will be the bias • Build into questions, data collection and analysis a search for disconfirming information (use all quadrants of the 2X2 table) • Adopt the opposing or contrary point of view or position, in a group allow someone to play “devils advocate” • Use of the scientific method or derivative like Plan-Do-Study-Act (PDSA) © 2012 Sigma Consulting Resources, LLC 15 L6σ Cognitive Biases Anchoring and Adjustment © 2012 Sigma Consulting Resources, LLC 16 Anchoring and Adjustment L6σ Case Study An engineer was asked to prepare a budget for completing engineering projects over the next year. The engineer obtained the budget for the previous year and after a brief analysis prepared a budget similar to the previous year’s budget but 5% higher. What was the basis for the budget (goal)? © 2012 Sigma Consulting Resources, LLC 17 Anchoring and Adjustment L6σ Definition • In the process of making estimations…”people start with an implicitly suggested reference point (anchor) and make adjustments to it to reach their estimate,” even if the anchor is irrelevant • In an early study (Tversky and Kahneman) spun a roulette wheel in front of a group of experimental subjects. The result was 65. Subjects were asked to record this result. They were then asked to estimate the percentage of African nations that were members of the United Nations. The process was repeated with a second group of subjects, but the result from the roulette wheel was 10 • The median estimates for the two groups were significantly different with the group shown the 65 having a median estimate of 45% and the group shown the 10 having a median estimate of 25% © 2012 Sigma Consulting Resources, LLC 18 Anchoring and Adjustment L6σ Problems (“So What”) • May lead to frustration and failure to accomplish goals and objectives because the goal was not realistic or attainable • May lead to the waste of underachievement, much more could have been accomplished if the goal was set higher • Application and implications for process improvement teams or functional teams with measureable goals © 2012 Sigma Consulting Resources, LLC 19 Anchoring and Adjustment L6σ Preventing or Mitigating • Anchors analogous to “last years budget” will always influence estimates but can be balanced by an exploration of the causal factors influencing the estimate (outcomes) • Use of models like the SMART (Specific, Measureable, Attainable, Relevant, Time-bound) criteria when creating goals • Can have profound implications when negotiating © 2012 Sigma Consulting Resources, LLC 20 L6σ Cognitive Biases Availability Heuristic © 2012 Sigma Consulting Resources, LLC 21 L6σ Availability Heuristic Case Study Structure A Structure B XXXXXXXX XXXXXXXX XXXXXXXX XX XX XX XX XX XX XX XX XX A path in a structure is a line that connects an element in the top row to an element in the bottom row and passes through one and only one element in each row. In which structure (A or B) are there more paths? How many? © 2012 Sigma Consulting Resources, LLC The number of paths in each structure is the same 83 = 29 = 512 22 L6σ Availability Heuristic Case Study • In a study (Tversky and Kahneman) 85% of respondents found more paths in Structure A than in Structure B • The bias towards Structure A is explained by the eight columns which make the paths more distinctive and available © 2012 Sigma Consulting Resources, LLC 23 L6σ Availability Heuristic Definition • “[A] mental shortcut that uses the ease with which examples come to mind to make judgments about the probability of events. The availability heuristic operates on the notion that ‘if you can think of it, it must be important’” • How many words start with the letter “k?” How many words have the third letter of “k?” • The heuristic can be beneficial, but the frequency that events come to mind are usually not accurate reflections of their actual probability in reality © 2012 Sigma Consulting Resources, LLC 24 L6σ Availability Heuristic Problems (“So What”) • If the available instances or associations reasonably represent the circumstances there is not a problem, otherwise correct conclusions and decisions are more a matter of good fortune • Customers and stakeholders are often surveyed about their experience's and perceptions regarding a product or service, the responses can often be biased by the availability heuristic © 2012 Sigma Consulting Resources, LLC 25 L6σ Availability Heuristic Preventing or Mitigating • Prior to decisions check the data used to make the decision, was the most available data used? If so, is there bias? • Is the data used to make the decision representative? • Is base rate data available? © 2012 Sigma Consulting Resources, LLC 26 L6σ Cognitive Biases Representativeness Heuristic © 2012 Sigma Consulting Resources, LLC 27 Representativeness Heuristic L6σ Definition • Used when making judgments about the probability of events often because of its ease of computation • Representativeness is "the degree to which [an event] (i) is similar in essential characteristics to its parent population, and (ii) reflects the salient features of the process by which it is generated” © 2012 Sigma Consulting Resources, LLC 28 Representativeness Heuristic L6σ Problems (“So What”) • Just because something is more representative does not mean it is more likely (base rate vs. case rate data) • People overestimate their ability to predict the likelihood of an event • Rooted in three types of biases Insensitivity to sample size Insensitivity to prior probabilities Conjunction fallacy © 2012 Sigma Consulting Resources, LLC 29 Insensitivity to Sample Size L6σ Case Study A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50% of all babies are girls. However, the exact percentage varies from day to day. Sometimes it may be higher than 50%, sometimes lower. For a period of 1 year, each hospital recorded the days on which more than 60% of the babies born were girls. Which hospital do you think recorded more such days? A. The larger hospital B. The smaller hospital C. About the same (that is, within 5% of each other) © 2012 Sigma Consulting Resources, LLC 30 Insensitivity to Sample Size L6σ Case Study • In a study (Tversky and Kahneman) 56% of respondents select option C, and 22% selected options A and B respectively • According to sampling theory the larger hospital is much more likely to report a ratio close to 50% on a given day compared to the smaller hospital © 2012 Sigma Consulting Resources, LLC 31 Insensitivity to Sample Size L6σ Definition • Tendency to expect different sized groups of samples to be equally representative of a process or population • Insensitivity to, or lack of knowledge of the role sampling error plays in sample statistics © 2012 Sigma Consulting Resources, LLC 32 Insensitivity to Prior Probability L6σ Case Study • A study (Tversky and Kahneman) involved telling one group of participants that a provided description of a person came from a group of 70 engineers and 30 lawyers and then asking them to assess the probability that the described person was an engineer (or lawyer). • A second group was told that the description came from a group of 30 engineers and 70 lawyers and asked to assess the same probability. • The experiment was repeated with variations in descriptions. © 2012 Sigma Consulting Resources, LLC 33 Insensitivity to Prior Probability L6σ Case Study • Tversky and Kahneman found a strong tendency for participants to disregard the “base rate” (frequency of occurrence) information preferring to rely on the descriptive information • When considered the base rate probabilities were not adjusted appropriately (Bayesian probabilities) given the additional information © 2012 Sigma Consulting Resources, LLC 34 Insensitivity to Prior Probability L6σ Definition • Tendency to ignore or improperly weight base rate probabilities • Improperly weighting additional information when discounting base rate probabilities • A related bias is the Conjunction Fallacy which states that the conjunction of two events cannot be more likely than the occurrence of either event alone (“Linda” study) © 2012 Sigma Consulting Resources, LLC 35 L6σ Cognitive Biases Now What? © 2012 Sigma Consulting Resources, LLC 36 L6σ Cognitive Biases Now What? Kahneman, Lovallo, and Sibony have proposed a Decision Quality Control Checklist involving three phases of assessment Preliminary © 2012 Sigma Consulting Resources, LLC Challenge 37 Evaluation Decision Quality Control Checklist Preliminary 1. Check for Self-interested Biases (overoptimistic) 2. Check for the Affect Heuristic (in love with the solution) 3. Check for Groupthink (dissenting opinions explored) © 2012 Sigma Consulting Resources, LLC 38 L6σ Decision Quality Control Checklist L6σ Challenge 4. Check for Representativeness Bias* 5. Check for the Confirmation Bias (credible alternatives) 6. Check for Availability Bias (imagine perfect information) 7. Check for Anchoring Bias (where did the numbers come from) 8. Check for Halo Effect (assumption success will be transferable) 9. Check for Sunk-Cost Fallacy (overly attached to history) *Kahneman et al refer to this as the Saliency Bias © 2012 Sigma Consulting Resources, LLC 39 Decision Quality Control Checklist Evaluation 10. Check for Optimistic Biases (game it) 11. Check for Disaster Neglect (worst case bad enough) 12. Check for Loss Aversion (overly cautious) © 2012 Sigma Consulting Resources, LLC 40 L6σ L6σ Cognitive Biases Summary • Cognitive Biases and decision making • Review of Common Biases and Mitigation Strategies Hindsight Bias Confirmation Bias Anchoring and Adjustment Heuristic Availability Heuristic Representativeness Heuristic o o o Insensitivity to Sample Size Insensitivity to Prior Probability Conjunction Fallacy • Decision Quality Control Checklist © 2012 Sigma Consulting Resources, LLC 41 L6σ Cognitive Biases Questions © 2012 Sigma Consulting Resources, LLC 42 Design of Experiments (DOE) L6σ References Kahneman, D., Lovallo, D., Sibony, O., “The Big Idea: Before You Make That Big Decision…”, Harvard Business Review June 2011, Harvard Business Publishing. Lovitt, M. R., “Pragmatic Knowledge and Its Application to Quality,” 1992 ASQC Quality Congress Transactions, ASQ (formerly ASQC), Milwaukee, WI 1992. Lovitt, M. R., “Using Quality Tools and Methods to Reduce Bias in Judgment,” Quality Engineering 8(1), 93-116 (1995-96), Marcel Dekker, Inc. 1995. Moen, Ronald D., Nolan, Thomas W., Provost, Lloyd P., (1991): Improving Quality Through Planned Experimentation, McGraw-Hill, New York. Other References Bazerman, M. A., Judgment in Managerial Decision Making, John Wiley and Sons, New York, 1990. Hograth, R., Judgment and Choice, John Wiley and Sons, New York, 1987. Kahneman, D., Slovic, P., and Tversky, A., Judgment Under Uncertainty: Heuristics and Biases, Cambridge University Press, Cambridge, 1982. © 2012 Sigma Consulting Resources, LLC 43
© Copyright 2024