7 Good Logic, Bad Logic, and Everything in Between
Week 7:
TL;DR: , do your homework
readings (links) & lectures ～ assignments due ～ live session agenda
TL;DR: , do your homework
readings (links) & lectures ～ assignments due ～ live session agenda
Figure 22: Fun stuff from https://yourlogicalfallacyis.com
Figure 23: Week 7 “reading” time estimated at 250 words per minute
Readings
Lectures
- ↓ 7.1 Introduction to Fallacies, Assumptions, and Conventions
- ↓ 7.3 DEAD Conventions
- ↓ 7.4 Similarities Are More Interesting Than Differences
- ↓ 7.5 Introduction to Demonstrating Fallacies
- ↓ 7.6.0.1 Demonstrating Fallacies
- ↓ 7.6.1 Other Fallacies
- ↓ 7.6.2 Bad Decisions Are Human
- ↓ 7.7 IBM Internet of Things
- ↓ 7.8 Rules of Inference
- ↓ 7.9 Example of Rules of Inference
- ↓ 7.9.1 Two Limits of Inferential Rules
- ↓ 7.10 Week 7 Wrap-Up
- ↓ 7.11 Video
7.1 Introduction to Fallacies, Assumptions, and Conventions ↑
| Players | Slug | Excerpt |
|---|---|---|
| λ θ | tenacious hold | many of these fallacies and assumptions and conventions that we hold dear are actually incredibly tempting, and we want to hold onto them. They take the form of what we call “really deep assumptions,” and they have a tenacious hold on individuals and organizations. |
| χ ω | infer consequences | The only way to move them around is to figure out how to infer from data science the consequences for the fallacies and assumptions and conventions that are holding people and organizations back. |
7.3 DEAD Conventions ↑
| Players | Slug | Excerpt |
|---|---|---|
| α λ | dead conventions | A routine, something that’s expected, maybe a psychological schema or a way of doing things that’s just the way we do things because, after all, that’s the way we do things |
| α β | without question | every moderately complex social system, whether we’re talking about a country, a society, a business, a family, if you kind of step back from it, is just filled with conventions that nobody ever really questions. |
| α ξ | recognition | No one could even remember why we started doing it that way. And actually, now, we don’t even think about it. It’s just actually a convention. And we don’t even recognize that it’s there. In fact, the only way we might recognize that it was even there as a convention is if something happened that made it impossible to fulfill. |
| λ ρ | extraterrestrial consultant | imagine an extraterrestrial creature from a matriarchal society lands in my house and watches what we do. That person is going to probably see a convention. Or it may be more likely, a McKinsey consultant, whose job it is to re-engineer our domestic production systems for maximum efficiency at home, might come in and do a time study and watch what we do and then hand us a huge expensive report that proposes that I do both of these jobs. |
| α τ | on the shelf | think about how hard it would be to actually change that behavior. It might take us weeks or months. In fact, we might not even do it even though we’ve decided it’s a convention and we want to change it. That McKinsey report might end up on the shelf. |
| ε ρ | muscle memory | conventions really are like muscle memory in the mind. You do them without even knowing that you’re doing them unless you’re from the outside. |
| ο β | cannon | What about those two guys who are standing at the front of the cannon in this very formal pose with their kind of rods? What actually are they doing there? |
| ρ δ | horses | people who later became the kind of stewards of the cannon earlier were the people who used to lead horses around. |
| ο β | OSX | even though the convention, the deep belief in Apple, everyone knew it to be true, that Apple needed a proprietary operating system was the logic of their business, Steve Jobs just turned that upside down and blew it up. And he built something called OS 10, or what’s now known as System 10, built around the BSD Unix core. And interestingly, this accounts for at least part of Apple’s extraordinary success going forward. |
| β ρ | bad tv | 15 years ago, I used to go to the movies. I never watched TV. And if you worked in Hollywood, it was certainly the case that the serious people– the really good actors, the really good writers, the serious directors– they wanted to work in movies. And TV was kind of for second-tier people. It wasn’t that interesting. It was about making money but not about making really excellent content. |
| ο φ | good tv | HBO sort of turned that on its head and decided that it was going to produce a very different kind of content, a very expensive kind of content. A content that was actually demanding more from its audience than probably any television network or television show had ever done in the past. Content that actually required people to think and to remember and to struggle with complicated plot lines and really complex ethical arguments. |
| υ φ | paradigm shift | HBO truly broke a set of conventions about content, the television industry, the movie industry. And in doing so, it didn’t just innovate for itself. It actually revolutionized the entire content industry in a very significant way. |
| σ γ | sticky conventions | it’s almost never the case that a convention goes away easily. And it’s almost never the case that really compelling data, in and of itself, by itself, can wipe a convention off the face of traditional organizations. |
7.4 Similarities Are More Interesting Than Differences ↑
| Players | Slug | Excerpt |
|---|---|---|
| ν σ | the more things change | sometimes most interesting is not what looks different or not what changes, which is where we’re attracted to as human beings, but instead, to focus on what people assume will remain the same, what won’t change. |
| η ν | drawn to change | you’re drawn to what’s changing. The technology is changing. The vacuum cleaners, the computers or the early computers that the Chiffon scientist is using, the chemicals– new chemical science. |
| ρ ι | gender norm | What’s really interesting is the assumption about what will remain the same, and it’s so obvious. What you see is that the role of women is going to be exactly the same in the future as it is today– in other words, 1952. That’s the assumption about what remains the same. Everything else changes, but the woman’s role doesn’t change. |
| ω π | packed logic | Nobody was able to unpack the obvious logic that if housework got a lot easier and if women were ashamed in relationships because the drain was clogged, then pretty soon, those women were not going to want to be in that setting anymore and they were going to look for something more meaningful to do with their lives. |
| κ φ | fighting chance | imagine for a moment that you were going back in 1952 and you were trying to explain to someone through the non-contextual collection of data that women’s role in the family was going to change. You wouldn’t get very far at all with that. In fact, the convention will win out. On the other hand, if you own the context– if you own the context and bring the data to it, you have a fighting chance of changing people’s minds. |
7.5 Introduction to Demonstrating Fallacies ↑
| Players | Slug | Excerpt |
|---|---|---|
| γ μ | nutshell convention | assumptions that are buried so deep within the paradigm or so deep within the way an organization thinks about itself that even getting people to recognize that they are assumptions is actually a real challenge. |
| φ η | tempting fallacies | those assumptions and the problems they cause actually get compounded by what people do with them by reasoning from assumptions. This is what I like to call “tempting fallacies of argumentation.” |
| χ δ | illusions | The human brain just wants to draw inferences and conclusions that kind of seem right but aren’t actually justifiable. Think of optical illusions that we all know are illusions but we still fall for them. |
| η γ | not a bias | It’s a little different from cognitive biases because these aren’t just information processing problems, like biases are. They’re actually reasoning problems. They’re reasoning processes that are just faulty, and it’s different to change them. |
| λ φ | tough odds | there are so many logical fallacies, an infinite number of ways to get a reasoning process wrong. There are actually very few ways to get it right. |
| χ μ | haha | maybe we’d laugh at them, although I’ll tell you, most of the time, it tends to be nervous laughter because we know we’ve fallen prey ourselves to these fallacies, even though we should know better |
| β φ | transformational impact | maybe these are the tempting fallacies of argumentation where bringing data science to the table could have the most transformational impact, where we could really start to change things. |
| ε τ | BRIC | India is a BRIC country. So if the BRIC countries are a great place to invest, as we’re being told, then India must be a great place to invest. That’s the fallacy. People make an inference about the nature of a specific case or a specific individual within a group of cases from aggregated statistics that are collected about the group to which that individual belongs. |
| ο θ | ecological fallacy | The aggregated statistics wash out a lot of the variation. But if you believe in the category and you believe in the statistics that attach to that category, you’re much more likely to believe that that statistic or that characteristic attaches to any individual that’s gotten pulled out of that category and talked about specifically. |
| ν η | innoculation | how can people try to counteract this fallacy and make sure it doesn’t infect decision making? |
7.6.0.1 Demonstrating Fallacies ↑
| Players | Slug | Excerpt |
|---|---|---|
| α σ | bias != fallacy | If bias is about how you interact with a piece of information, a fallacy of argumentation is how you reason from A to B or from A to C and how we often do that incorrectly. |
7.6.0.2 ↑
| Players | Slug | Excerpt |
|---|---|---|
| β δ | NA | NA |
7.6.0.4 ↑
| Players | Slug | Excerpt |
|---|---|---|
| ι ξ | NA | NA |
7.6.0.5 ↑
| Players | Slug | Excerpt |
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| γ ο | NA | NA |
7.6.0.7 ↑
| Players | Slug | Excerpt |
|---|---|---|
| τ β | NA | NA |
7.6.1 Other Fallacies ↑
| Players | Slug | Excerpt |
|---|---|---|
| φ ν | NA | NA |
7.6.2 Bad Decisions Are Human ↑
| Players | Slug | Excerpt |
|---|---|---|
| λ ω | NA | NA |
7.7 IBM Internet of Things ↑
| Players | Slug | Excerpt |
|---|---|---|
| ν π | NA | NA |
7.8 Rules of Inference ↑
| Players | Slug | Excerpt |
|---|---|---|
| ζ η | NA | NA |
7.9 Example of Rules of Inference ↑
| Players | Slug | Excerpt |
|---|---|---|
| γ α | NA | NA |
7.9.1 Two Limits of Inferential Rules ↑
| Players | Slug | Excerpt |
|---|---|---|
| α φ | NA | NA |
7.10 Week 7 Wrap-Up ↑
| Players | Slug | Excerpt |
|---|---|---|
| τ φ | NA | NA |
Bibliography
Creswell, John W. 2009c. “Chapter 7: Research Questions & Hypotheses.” In Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Thousand Oaks, Calif. : Sage Publications, c2009. http://www.study.net//r_mat.asp?crs_id=30124014&mat_id=50272420.
Creswell, John W. 2009d. “Chapter 8: Quantitative Methods.” In Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Thousand Oaks, Calif. : Sage Publications, c2009. http://www.study.net//r_mat.asp?crs_id=30124014&mat_id=50272422.
Ward, Tony, Samuel Clack, and Brian D Haig. 2016. “The Abductive Theory of Method: Scientific Inquiry and Clinical Practice.” Behaviour Change 33 (4): 212–31. doi:10.1017/bec.2017.1.