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| [[File:Topic Cover - 9.1 Heuristics.png|thumb]]
| | {{Cover|9.1 Heuristics}} |
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| Some of the heuristics biases that make our probability judgments go awry.
| | Humans make many decisions on a daily basis, often in the absence of complete information or under constraints of limited time and cognitive capacity. We present the mechanism and usefulness of cognitive shortcuts such as availability heuristic and representativeness heuristic, as well as the often unnoticed pitfalls that arise from using them for judgments and decision making. |
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| == The Lesson in Context == | | == The Lesson in Context == |
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| Humans make many decisions on a daily basis, often in the absence of complete information or under constraints of limited time and cognitive capacity. We use heuristics as useful shortcuts for quick decision making, which may introduce bias into our conclusions. The purpose of the lesson is not to cast doubt on our use of heuristics, but to recognize the limitations of quick human judgments and their consequences. This parallels [[2.1 Senses and Instrumentation]] and [[2.2 Systematic and Statistical Uncertainty]], where the limitations of instruments are discussed and quantified, without rejecting the validity and usefulness of instruments altogether. | | Humans make many decisions on a daily basis, often in the absence of complete information or under constraints of limited time and cognitive capacity. We use heuristics as useful shortcuts for quick decision making, which may introduce bias into our conclusions. The purpose of the lesson is not to cast doubt on our use of heuristics, but to recognize the limitations of quick human judgments and their consequences. This parallels [[2.1 Senses and Instrumentation]] and [[2.2 Systematic and Statistical Uncertainty]], where the limitations of instruments are discussed and quantified, without rejecting the validity and usefulness of instruments altogether. |
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| <!-- Expandable section relating this lesson to earlier lessons. --> | | <!-- Expandable section relating this lesson to other lessons. --> |
| {{Expand|Relation to Earlier Lessons| | | {{Expand|Relation to Other Lessons| |
| | '''Earlier Lessons''' |
| {{ContextLesson|2.1 Senses and Instrumentation}} | | {{ContextLesson|2.1 Senses and Instrumentation}} |
| {{ContextRelation|Senses and instrumentation are inherently imperfect, but imperfect tools can still be useful in obtaining partial knowledge. Similarly, heuristics are flawed, but they can make useful tools when time, knowledge, and mental resources are limited.}} | | {{ContextRelation|Senses and instrumentation are inherently imperfect, but imperfect tools can still be useful in obtaining partial knowledge. Similarly, heuristics are flawed, but they can make useful tools when time, knowledge, and mental resources are limited.}} |
| {{ContextLesson|2.2 Systematic and Statistical Uncertainty}} | | {{ContextLesson|2.2 Systematic and Statistical Uncertainty}} |
| {{ContextRelation|The use of heuristics can often introduce bias into our judgments — tendencies to make one decision more often than another, paralleling the idea of systematic uncertainty in instrumental measurements.}} | | {{ContextRelation|The use of heuristics can often introduce bias into our judgments — tendencies to make one decision more often than another, paralleling the idea of systematic uncertainty in instrumental measurements.}} |
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| <!-- Expandable section relating this lesson to later lessons. -->
| | '''Later Lessons''' |
| {{Expand|Relation to Later Lessons|
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| {{ContextLesson|9.2 Biases}} | | {{ContextLesson|9.2 Biases}} |
| {{ContextRelation|This lesson focuses on heuristics that affect our judgments of frequencies — how often things occur or likelihoods of events. The next lesson discusses biases in decision making that stem from a self-centered view of the world—an overemphasis on "me" and "now" and overusing defaults and assumptions.}} | | {{ContextRelation|This lesson focuses on heuristics that affect our judgments of frequencies — how often things occur or likelihoods of events. The next lesson discusses biases in decision making that stem from a self-centered view of the world—an overemphasis on "me" and "now" and overusing defaults and assumptions.}} |
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| {{ContextRelation|We single out confirmation bias into its own topic, as it permeates scientific and group decision making, affecting both our sense of the prevalence of events around us as well as the importance of "me" and "now".}} | | {{ContextRelation|We single out confirmation bias into its own topic, as it permeates scientific and group decision making, affecting both our sense of the prevalence of events around us as well as the importance of "me" and "now".}} |
| }} | | }} |
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| == Takeaways == | | == Takeaways == |
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| </tabber> | | </tabber> |
| <restricted>
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| == Useful Resources ==
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| <tabber>
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| |-|Lecture Video=
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| <br /><center><youtube>KPtvVh903h4</youtube></center><br />
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| |-|Discussion Slides=
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| {{LinkCard
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| |url=https://docs.google.com/presentation/d/1CoZTUBxEN6fSGRuAvgUKvxuymcJz4Y9JQWj0SowoQkA/
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| |title=Discussion Slides Template
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| |description=The discussion slides for this lesson.
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| }}
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| <br />
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| |-|Handouts and Activities=
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| {{LinkCard
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| |url=https://github.com/sensesensibilityscience/datascience/blob/master/base_rate2.ipynb
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| |title=Bayes' Rule Visualizer
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| |description=An interactive tool for visualizing and calculating quantities with Bayes' rule.}}
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| {{LinkCard
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| |url=https://datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https://github.com/sensesensibilityscience/datascience&urlpath=tree/datascience/base_rate2.ipynb&branch=master
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| |title=Bayes' Rule Visualizer (Datahub Link)
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| |description='''Special link for use at UC Berkeley only.''' An interactive tool for visualizing and calculating quantities with Bayes' rule.}}
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| <br />
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| |-|Readings and Assignments=
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| {{LinkCardInternal
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| |url=:File:Validity What Follows from What - Priest.pdf
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| |title=Validity: What Follows From What?
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| |description=Excerpt from Logic: A Very Short Introduction. By Graham Priest.}}
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| {{LinkCardInternal
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| |url=:File:Judgment Under Uncertainty- Heuristics and Biases - Tversky, Kahneman.pdf
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| |title=Judgment Under Uncertainty: Heuristics and Biases
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| |description=Biases in judgements reveal some heuristics of thinking under uncertainty. By Kahneman and Tversky.}}
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| <br />
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| </tabber>
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| == Recommended Outline ==
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| === Before Class ===
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| {{BoxCaution|This class in particular has ''a lot'' of content. It's also very easy to confuse the students if not explained very carefully. Make sure to take extra time reviewing this lesson.}}
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| === During Class ===
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| {| class="wikitable" style="margin-left: 0px; margin-right: auto;"
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| |5 Minutes
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| |Ask your students the [[#Jessie|Jessie]] question.
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| |25 Minutes
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| |Go through the [[#KOALA-{{#time:y}}|KOALA-{{#time:y}}]] example and exercise. Note that this has many sub-steps and is worth reviewing how you'll present it.
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| |6 Minutes
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| |Guide the students through the [[#Jessie Again|Jessie Again]] prompt and let them work through the problem.
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| |17 Minutes
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| |Have the students answer the [[#Conjunction Fallacy|conjunction fallacy problem]] and present the corresponding examples.
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| |16 Minutes
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| |Have the students answer the [[#Availability Heuristic|availability heuristic problem]] and present the corresponding examples.
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| |-
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| |11 Minutes
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| |Have the students discuss [[#Bounded Rationality|bounded rationality]] in small groups.
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| |}
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| == Lesson Content ==
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| === Jessie ===
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| You're sitting in your room cramming for an exam when your roommate decides to throw an impromptu party and invites people from all over the university. Realizing that you're not going to get any work done, you decide to make the most of it and start mingling with the crowd. In the process, you strike up a conversation with someone named Jessie who starts droning ''on and on'' about rockets. They go on for so long that you start to lose interest and begin thinking about what sort of person Jessie really is.
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| Here's some helpful numbers:
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| * One in four students study engineering at your college.
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| * Half of all engineering majors like rockets.
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| * A sixth of all non-engineering majors like rockets.
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| Which is more likely?
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| <ol style="list-style-type:lower-alpha">
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| <li>Jessie is an engineering major.<li>
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| <li>Jessie is a non-engineering major.</li>
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| <li>{{Correct|It's equally likely that Jessie's an engineering or non-engineering major.}}</li>
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| </ol>
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| {{BoxWarning|title=Don't Reveal The Answer Yet!|It's actually 50/50 odds that Jessie is an engineering major. This is explained later [[#Jessie Again|Jessie Again]].}}
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| === KOALA-{{#time:y}} ===
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| We introduce and practice Bayes' rule in this activity. It makes substantial use of the Bayes' rule visualizer.
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| {{LinkCard
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| |url=https://github.com/sensesensibilityscience/datascience/blob/master/base_rate2.ipynb
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| |title=Bayes' Rule Visualizer
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| |description=An interactive tool for visualizing and calculating quantities with Bayes' rule.}}
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| {{LinkCard
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| |url=https://datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https://github.com/sensesensibilityscience/datascience&urlpath=tree/datascience/base_rate2.ipynb&branch=master
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| |title=Bayes' Rule Visualizer (Datahub Link)
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| |description='''Special link for use at UC Berkeley only.''' An interactive tool for visualizing and calculating quantities with Bayes' rule.}}
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| {{BoxCaution|This is one of the more confusing topics, so it is worth spending more time on it.}}
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| ==== Instructions ====
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| {| class="wikitable" style="margin-left: 0px; margin-right: auto;"
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| |2 Minutes
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| |Have the students answer the [[#KOALA-{{#time:y}} Warm-up Question|warm-up question]].
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| |1 Minute
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| |Pull up the notebook and project it in front of the class.
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| |-
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| |1 Minute
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| |Explain what the notebook's colors mean using the legend in the demo.
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| |2 Minutes
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| |Using the notebook as a visual aid, remind the students what prior probability is and that it's the same thing as "base rate," "incidence rate," and "positivity rate."
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| |2 Minutes
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| |Remind the students of what true/false positive/negative rates mean. Drag the rates sliders around to demonstrate the following.
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| : <math>\begin{align}\text{(true positive rate)} &= 1 - \text{(false negative rate)} \\
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| \text{(true negative rate)} &= 1 - \text{(false positive rate)}\end{align}</math>
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| |4 Minutes
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| |Click on the "Positive" button to highlight the true and false positives. Use this to explain the middle line of the Bayes rule formula below. Then explain the last line step by step as well.
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| : <math>\begin{align}
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| &\ \ \quad\text{(probability that a positive is a true positive)} \\
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| &= \frac{(\#\text{ true positives})}{(\#\text{ true positives})+(\#\text{ false positives})} \\
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| &= \frac{\text{(base rate)}\times\text{(true positive rate)}}{\text{(base rate)}\times\text{(true positive rate)}+(1-\text{base rate})\times\text{(false positive rate)}}
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| \end{align}</math>
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| |-
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| |8 Minutes
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| |Use the demo to work out the [[#KOALA-{{#time:y}} Example|KOALA-{{#time:y}} example]].
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| |5 Minutes
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| |Have the students do the [[#KOALA-{{#time:y}} Discussion Question|discussion]] in small groups.
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| |}
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| ==== KOALA-{{#time:y}} Warm-up Question ====
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| Suppose there is an epidemic of KOALA-{{#time:y}} breaking out among koalas in New South Wales, which about 1% of the koalas have contracted. A test for KOALA-{{#time:y}} was developed, whose false positive rate is 9% and false negative rate is 10%. If a particular koala has tested positive of KOALA-{{#time:y}}, what is the actual probability that it really has KOALA-{{#time:y}}?
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| <ol style="list-style-type:lower-alpha">
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| <li>{{Correct|Between 0-10%}}</li>
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| <li>Between 10-25%</li>
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| <li>Between 25-50%</li>
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| <li>Between 50-75%</li>
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| <li>Between 75-90%</li>
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| <li>Between 90-100%</li>
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| </ol>
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| {{BoxWarning|title=Don't Reveal The Answer Yet!|This will be worked out in detail below. The correct answer is "a" (9.2%).}}
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| ==== KOALA-{{#time:y}} Example ====
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| [[File:KOALA-21.png|thumb|Visualization of the calculations for KOALA-{{#time:y}}.]]
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| It's now time to reveal the answer to the [[#KOALA-{{#time:y}} Warm-up Question|warm-up question]]. As shown in the figure, there is a large population with a relatively small base rate of infection. So if you were doing a mass surveillance testing of koalas then most of the ones that test as positive actually don't have the disease at all.
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| ==== KOALA-{{#time:y}} Discussion Question ====
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| Have students work in small groups to work out this problem, following the KOALA-{{#time:y}} example above. Give assistance where needed. They may now use the notebook as well.
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| <ol start=1><li>Based on a daily case count of 73,000 (November 2021) and a 14-day recovery period, it can be Fermi estimated that the prevalence of Covid-19 is 0.3%. For the commonly used PCR test, the true positive rate (sensitivity) is 98%, and the true negative rate (specificity) is 80%. If you do one PCR test and get a positive result, what are the actual odds that you have Covid?</li></ol>
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| <!-- {{BoxAnswer|The students are free to work this out using the notebook. But, they can also do it explicitly. [[File:COVID PCR Test Calculation 1.png|frame]]}} -->
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| {{BoxAnswer|The students are free to work this out using the notebook. But, they can also do it explicitly.
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| : <math>\frac{0.003\times0.98}{0.003\times0.98+(1-0.003)\times(1-0.8)}=0.0145=1.45\%</math>}}
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| <ol start=2><li>Ask the students if this sounds right to them. The reason it shouldn't is that we don't actually just give COVID tests out to the general public. People normally take PCR tests once they have some symptoms. Therefore we shouldn't use the COVID rates amongst the general population as our base rate. Instead, we should use the fraction of those who ''suspect'' they have COVID (due to symptoms or close contact) that actually do have it.</li>
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| <li>Suppose one third of those suspecting they have COVID do really have it. Using this as our base rate, what is the probability that you have COVID if your PCR test comes back positive?</li></ol>
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| {{BoxAnswer|<math>\frac{0.33\times0.98}{0.33\times0.98+(1-0.33)\times(1-0.8)}=0.707=70.7\%</math>}}
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| === Jessie Again ===
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| Now that we have some practice working out Bayesian probabilities with diseases, it's time to try and figure out who Jessie ''really'' is. Recall our "disease" formula.
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| : <math>\begin{align}
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| &\ \ \quad\text{(probability that a positive is a true positive)} \\
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| &= \frac{(\#\text{ true positives})}{(\#\text{ true positives})+(\#\text{ false positives})} \\
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| &= \frac{\text{(base rate)}\times\text{(true positive rate)}}{\text{(base rate)}\times\text{(true positive rate)}+(1-\text{base rate})\times\text{(false positive rate)}}
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| \end{align}</math>
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| <!-- [[File:Bayes Rule Definition - Three Lines.png|600px]] -->
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| {{BoxTip|We're using the term "test" here very broadly. For example, our [[#Jessie|original conversation]] with Jessie counts! In that case, we were using Jessie's interest in rocketry as a "test" for whether or not they're an engineering major. If Jessie is interested in rockets and is also an engineering major then we have a true positive. But if Jessie isn't an engineering major then it's a false positive.}}
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| {{BoxCaution|We have a "positive" result because we know Jessie is interested in rocketry.}}
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| {{BoxAnswer|title=Solution|The first item gives the "Base rate of positivity." The second item is the "True positive rate of the test" and the third item is the "False positive rate of the test." Plugging all these numbers in, we get a 50% chance that Jessie is an engineering major.}}
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| Here are the values from the original problem. Given this information, what is the actual probability that Jessie is an engineering major? The students are free to work this out either directly or with the notebook.
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| * One in four students study engineering at your college.
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| * Half of all engineering majors like rockets.
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| * A sixth of all non-engineering majors like rockets.
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| Which is more likely?
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| <ol style="list-style-type:lower-alpha">
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| <li>Jessie is an engineering major.<li>
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| <li>Jessie is a non-engineering major.</li>
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| <li>{{Correct|It's equally likely that Jessie's an engineering or non-engineering major.}}</li>
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| </ol>
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| === Conjunction Fallacy ===
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| Very quickly poll your students with the following question.
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| Andy is a junior at Berkeley. His favorite book is Howard Zinn's "People's History of the United States." He's passionate about politics, and he regularly attends local protests. He is most likely to be:
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| <ol style="list-style-type:lower-alpha">
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| <li>{{Correct|A computer science major.}}</li>
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| <li>A computer science and also a political science major (double major).</li>
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| <li>A computer science major who is a member of the Berkeley College Democrats.</li>
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| <li>A computer science and also a political science major (double major) who is a member of the Berkeley College Democrats.</li>
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| </ol>
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| {{BoxAnswer|Andy is most likely a computer science major. This is because all the other categories are ''also'' built on the assumption that he's a computer science major. <math>A</math> is always more likely than <math>A</math> and <math>B</math>. We call people's tendency to neglect this fact the "conjunction fallacy."}}
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| ==== Conjunction Fallacy Examples ====
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| Conjunction fallacy is often made due to our use of the representativeness heuristic—how representative something is of a category or outcome is used as a proxy. We use it to evaluate how likely the category membership or outcome is without taking base rates into account. Present one or more of the following examples.
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| {{LinkCardInternal
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| |url=:File:Stolwijk and Vis - 2021 - Politicians, the Representativeness Heuristic and .pdf
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| |title=Politicians, the Representativeness Heuristic, and Decision-Making Biases
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| |description=Source for the examples.}}
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| # One group of Dutch local politicians is asked how likely they think their municipality will make the headlines of all major newspapers next year, while another group is asked how likely they think their municipality will make the headlines next year ''due to a terrorist attack on King's Day''. (For context, the terrorist attack on King's Day in 2009, in which a car drove into a crowd at the royal parade, killing 8, is a salient event to the Dutch public.) Do you expect the first group or the second group to rate their event to be more likely? Which is actually more likely?
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| # One group of participants assesses the likelihood of an earthquake hitting California next year and causing a massive flood, while the other group assesses the likelihood of a massive flood somewhere in North America next year. Do you expect the first group or the second group to rate their event to be more likely?
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| {{BoxCaution|These questions are posed to different people, so individual participants are not confronted with this seemingly obvious logical fallacy. In quizzes and exams, we ask the students to recognise which heuristic is at play in a given scenario, or to state what the expected experimental result will be.}}
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| === Availability Heuristic ===
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| Ask your students the following classic example.
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| In the English language, are there more words that have "K" as the first letter OR as the third letter?
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| <ol style="list-style-type:lower-alpha">
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| <li>More words with "K" as the first letter.</li>
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| <li>{{Correct|More words with "K" as the third letter.}}</li>
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| </ol>
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| {{BoxAnswer|title=Explanation|It's difficult to answer this accurately because it's much easier to think of words with the first letter K than the third letter, just because of how we retrieve words from memory.}}
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| Now present and discuss one or more of the following examples.
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| <ol start=1><li>Is it more likely that one dies due to a shark attack or due to vending machine attacks?</li></ol>
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| {{BoxAnswer|It's the latter, but shark attacks are more common in the news.}}
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| <ol start=2>
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| <li>Repeated vivid stories about a type of events in the media inflate people's perception of the rates or likelihood of such events.</li><ol>
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| <li>Vivid descriptions of crimes committed by immigrants skew public perception about immigration as a threat to public safety.</li>
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| <li>Mass murders and terrorist attacks provide more salient memories, compared to domestic homicides, but are actually less common. The public, however, is typically more concerned with terrorist attacks than domestic homicides.</li>
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| <li>Ask students if they can think of any other events which are commonly reported in the news but probably not actually very common, and/or events which are very common but rarely reported and therefore likely to be underestimated.</li>
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| </ol><li>"Gratitude journals" encourage one to record only the positives of one's daily life, providing an abundance of examples of good days compared to bad days. We can use availability heuristic to our advantage to improve our mood.</li>
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| </ol>
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| === Bounded Rationality ===
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| Ask your students if they agree with the following quote. Have them discuss in small groups and then bring everyone back and get the answers for the class as a whole.
| | {{#restricted:{{Private:9.1 Heuristics}}}} |
| : "''Cognitive heuristics are a hindrance to rational reasoning. They lead to poor judgment and harmful cognitive biases in decision making, and we should strive to avoid them.''"</restricted>{{NavCard|prev=8.2 Fermi Problems|next=9.2 Biases}}
| | {{NavCard|chapter=Lesson plans|text=All lesson plans|prev=8.2 Fermi Problems|next=9.2 Biases}} |
| [[Category:Lesson plans]] | | [[Category:Lesson plans]] |