Course Overview

The focus in this course is on the errors humans tend to make, and the approaches science methodology has given us (and we are still developing) to prevent or at least minimize those errors. Learn more »

    • OVERVIEW

      • What is the role of scientific expertise in a democracy? Where does the authority of science come from? Where does the authority of democratic decision-making come from?  
      • Topic 1 introduces the course, beginning with the question of when science or scientific reasoning is relevant. We begin by distinguishing facts and values, and using this distinction to consider the affordances and limitations of scientific inquiry and expertise for personal and political decision-making. These opening questions will return at the end of the semester, when we consider processes for effectively integrating epistocratic (expertise-based) and democratic input, in light of the capacities and limitations of scientific practice and ordinary citizens which will have been examined throughout the course.
    • EXAMPLES

      • Exemplary Quotes
        • “But the efficacy of wearing a bicycle helmet is a simple factual question that we should be able to get an answer for.”   
        • “It was embarrassing to discover how often my choice in the grocery store was determined by something irrelevant to the actual contents of the item.”      
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • Commonest difficulty is in distinguishing facts and values in fraught real-world contexts, when not reminded to do so. E.g., students' deference to scientists for questions is predicted by the ideological fraughtness of the topic, not whether it is a question of fact or value.  
        • “What could science possibly have to tell us about love!”     
        • "Science can't tell us anything about happiness, because people don't agree about what makes us happy, anyway."  
        • "I think democracy is always better, because people know what they want and everyone deserves to try to get what they want. Everyone has their own facts, and people should be allowed to pursue their vision of the world without interference from scientists."    
        • "I think we should always just defer to experts on everything, because experts know what's best for everyone and regular people don't have time to learn that much or think that hard anyway."    
        • "Shall I refuse my dinner because I do not fully understand the process of digestion?" - Oliver Heaviside
        • Mistaking "claim of fact" for "claim of value" due to lack of evidence or debate around the topic.
    • OVERVIEW

      • Science is based in the assumption that we all share a public reality. We will contrast theories of truth as correspondence vs truth as coherence, as well as underdetermination and social factors in science. Despite many limitations, science is effective primarily because it is self-correcting; that is, it involves a constant critique of the reliability and validity of our measures and the reality of the entities they seek to measure. Science assumes (or has come to believe in) an objective reality that we all share, which is at least in part knowable.   
    • EXAMPLES

      • Exemplary Quotes
        • “They seem to think that anybody’s opinion is as good as anybody else’s on this matter where there is only one reality out there. It may be hard to figure out, but it’s still there anyway.”
        • “Either the earth is going to warm by >4 degrees over the next 50 years because of human-added greenhouse gasses or not—whether or not the proponents on each side of the debate are biased! ‘Nature always bats last.’”
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • Commonest mistakes are: (a.) taking the logs of the science-raft for "ideals" rather than claims and (b.) not grasping just how science is self-correcting.
        • “Well, I just happen to think that if you punish people whenever they misread a word they will learn to read much faster—and most people agree with me. So...”
        • "Science is just another religion, no better and no worse than any other. They use textbooks as their scripture, and scientists are their priests. You should choose whichever authority feels most right to you or stick with the authority you were raised with, because there's no other way to choose between them."
    • OVERVIEW

      • It has been suggested that our trust in "a reality out there" is often strengthened by our actively interacting with the passively-percieved world (banging the table in front of us with our hand). Our “direct” experience with a scientist’s reality is expanding further: from the human senses to the human armed with instruments. The novel instruments that are now with us constantly (e.g. GPS, camera) allow us to interactively explore parts of the world that until recently were inaccessible or accessible only passively, through expensive technology or images made by scientists. This interactive experience of previously inaccessible aspects of the world now revealed by technology is broadening our sense of what counts as "real." We carry with us a growing range of interactive tools, these days primarily in the compact form of our smartphones. 
    • EXAMPLES

      • Exemplary Quotes
        • "It sure helped public health and medicine once we realized there were things affecting our health that were just a little too small to see. I wonder if we could have figured that out without the invention of the microscope. I guess we might have just thought there were more invisible entities out there."
        • “It didn’t occur to anybody that there was a such a clear periodic pattern in the populations of those wolves and rabbits until somebody just started writing down every sighting—we’re pretty bad at estimating and remembering times between occasional events.”
        • “It’s amazing to first see a slow motion picture of a violin string making a note—wouldn’t it be great if our eyes and brains were fast enough to do this?”
        • “Do you think that modern technology offering us more different vantage points fundamentally changes our position on any practical questions?   For example, does it make a difference that in relatively recent decades we have gotten used to seeing the earth as a whole from space?”
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • “We can't really know anything about other galaxies, because we can only see them through fancy instruments and we can never know if the instruments are telling us the truth.”
    • OVERVIEW

      • We examine sources of statistical uncertainty/error (which can be averaged down) and systematic uncertainty (which can’t). We also connect these concepts to related terminology (jargon) from other fields: precision vs. accuracy, variance vs. bias, and reproducibility vs. validity. 
    • EXAMPLES

      • Exemplary Quotes
        • "It won't do us any good to average lots and lots of test subjects' heights together if our tape measure got shrunk in the wash!"
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • Difficulty distinguishing degrees of statistical error or uncertainty
          • "We can't say one instrument has more statistical uncertainty than the other, because everything has statistical uncertainty."
        • Failing to Notice Possibility of Statistical Error
          • "I know that everyone likes chocolate, because I asked three of my friends and all of them said they like chocolate."   
          • "Scientists ran a randomized controlled study with 30 people and four conditions, and the results were statistically significant, so we know the drug works." 
        • Failing to Notice Possibility of Systematic Error
          • “There’s no way that X [fill in with name of candidate] could win the election!  Everyone that I know is voting for Y [fill in with name of other candidate].”
          • "We surveyed over a hundred thousand people, so our study is definitely an accurate picture of how Americans think about science."
          • "We asked students to share if they were sexually active with a show of hands, and almost no one raised their hand. So we know that it's rare at our school."
    • OVERVIEW

      • “Scientific optimism” is a rarely-discussed feature of the culture of science, a kind of psychological trick/technique to keep focused on a problem much longer than the usual attention span. Scientists adopt a can-do attitude, and convince themselves that the problem is solvable. This is an antidote and a contrast to almost all of the other skeptical, self-doubting aspects of scientific culture. With this “scientific optimism,” scientists can successfully take on problems that take years or even decades to solve, with hundreds of steps and iterations involved in developing techniques, inventing technologies, collecting and analyzing data.
      • There is a history of problems becoming solvable once the news goes round that another group somewhere in the world has solved it. Belief that a problem is solvable makes it worth sticking to it long enough to solve it. Scientific optimism can be seen as an intentional self-delusion that a problem is solvable. (In the end, of course, this scientific optimism must be weighed against the cost of working on a problem that turns out not to be solvable given our current capabilities/knowledge, but nevertheless it has proven useful in overcoming the human tendency to give up too soon.)  
      • One consequence of scientific optimism is that one approaches group problem-solving with an eye to enlarging the pie of resources, rather than fighting over scraps in a game assumed to be zero-sum.
    • EXAMPLES

      • Exemplary Quotes
        • “I think we’re getting frustrated too quickly, and giving up too easily on each possible approach.     Imagine that we had just heard that the other team had gotten this to work—we would be wracking our brains for weeks trying to figure out how they did it, not just the hour-and-a-half we just tried.  This is a really hard problem, and we have to expect that it’s going to take a while to get some approaches to solving it.”
        • “I know she seems a little overly optimistic, but when I talked to her over lunch I realized that she is just trying to develop a “can-do” spirit so that we will all have the chance to try to solve the problem.”
        • “We’re capable, we know all the people we need to figure this out, and we’ve solved comparably difficult problems before... so one way or the other we’re going to find out how to make this work.”
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • “Many people have tried to solve this problem of increasing illiteracy and failed, so we shouldn’t throw more good money after bad — some problems are just intractable.”
        • "Scientists have been trying to figure out what dark matter is for decades, and we still basically have no idea. We'll probably never know, so it's not worth working on."  
    • OVERVIEW

      • In casual conversations, we use the word “causation” in many ways. Sometimes when we say “x causes y,” we’re pointing out who are what or who is responsible for y. Sometimes we’re pointing out something about the causal mechanism or process that leads to y. In this class, the focus is on yet a third reason we use causal language: to identify the “levers” in the world that we can push or pull to bring things about. If we want to generate good policies, for example, it’s important to know what some intervention will bring about, and it’s this sense of causation that is more relevant here. Moreover, science often proceeds by identifying causal relationships in the sense defined below well before the mechanism by which it does so is understood. It is therefore valuable to have a definition of causation that captures this aspect of scientific advance.
      • In this class, we will examine causal relationships using variables, interventions,and randomized controlled trials.
    • EXAMPLES

      • Exemplary Quotes
        • “Let’s think causally here.  There are lots of words and concepts that we’re getting confused by here, but let’s remember that right now all we care about is what is causing what.”
        • “What if causation goes the other way, or there’s a common cause?  We’re getting all upset about the violence on television causing the violence in the streets because they seem to go up and down together in prevalence, but how do we know that it isn’t the other way around, or that they aren’t both being caused by some third factor.   Maybe we can look at the timing of one with respect to the other?  Or could we possibly control one of the factors by itself and see what happens?”
        • “Even if we don’t know how, this seems to work.  I know it seems crazy that you can fix this educational problem of delayed reading simply by feeding cereal to the kids every morning, but this was a pretty impressive randomized controlled trial so it’s hard to come up with another explanation.”
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • Misconceptions of Induction
          • "Aspirin is no better than a placebo. I used to give my sister bread pills when she asked me to get her aspirin, and she always said how much better they made her feel and never noticed the difference."
          • "Our analyses of 833 diverse middle schoolers found that most of them learned better with hands-on activities. But we can't conclude anything about children who weren't in our study, because we didn't collect any data about them."
          • "Eighty percent of people who took the drug got better. But the drug didn't really work, because a quarter of people who took the placebo got better even though they didn't take the drug, and twenty percent of people who took the drug didn't get any better at all."  
          • "RCTs cannot give sufficient evidence for causation that salt causes heart disease, because there might be other factors at play."
        • Misconceptions of Control Condition
          • "We should give the experimental drug to all eight hundred people in a study, instead of giving it to just half of them, because we want to maximize our sample size."
          • "If you give a drug to 500 people with a disease, and 80% of them get better, then we know the drug works."
        • Misconceptions of Randomization
    • OVERVIEW  

      • In the previous class we began our discussion of causality, distinguishing it from mere association and considering ideal kinds of evidence for causality, when we can run randomized controlled trials. However, in many cases, it is not possible to run RCTs to test causal hypotheses, for ethical or practical reasons. In this class, we consider other forms of evidence for causality, which cannot individually be as conclusive as RCTs but together can still present compelling evidence for causal theories.
    • EXAMPLES  

      • Exemplary Quotes
        • “Ok, I agree that ‘correlation doesn’t prove causation’ in general, but in a case like this where we have lots of other kinds of evidence it sure gives us a pretty strong guess about causation.”
        • “There is an answer to this causal question.  Just because we can’t ethically do a randomized controlled study with these patients, it doesn’t mean that we can’t make progress establishing the causal link between these treatment options and the outcome.   After all, we have pretty good evidence that the energy from the sun is caused by nuclear fusion and we haven’t done any randomized controlled experiments!”
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
    • OVERVIEW

      • It is often important to distinguish claims of singular causation, where A caused B, from claims of general causation, where variable X tends to affect variable Y. RCTs can only provide evidence of general causation, which might inform our understanding of particular instances of singular causation but cannot allow us to conclude causality with certainty. Both general and singular causation are subjects of scientific investigation. For example, whether Zika causes microencephalitis is a question of general causation, while whether an asteroid caused the mass extinction of the dinosaurs is a question of singular causation. We also distinguish productive vs. dependent causation, and its implications for responsibility in legal and moral dilemmas like the Trolley Problem.
      • Defining causal relationships using "variables" and "interventions." How we say that this particular thing caused that particular thing. Connections with the Trolley Problem and legal responsibility. Causal relations in observational sciences (e.g., paleontology, cosmology) where experiments are not generally possible. 
    • EXAMPLES

    • OVERVIEW

      • What does a scientist mean by “signal” and “noise”? We humans are always hunting for signal in noise; that is, we are looking for regularities, causal relationships and communications (the signal) amidst various distractions, both random and intentional (the noise). Scientists have developed a variety of ways to do this, including “filters" both technological and conceptual. 
    • EXAMPLES

      • Exemplary Quotes
        • “It’s really hard to see the effect since there are so many other issues going on that act as noise, but there really appears to be a remarkable correlation between a young child’s ability to defer gratification and later successes in life.”
        • “The problem is that nowadays we are inundated with stories about every scary crime that happens anywhere in the world, so this “noise" confuses us and we can’t see the striking “signal” that crime in our country has gone down dramatically in the past three decades.”
        • "Any signal can count as noise, just like any noise can be considered as signal; it depends on what you're trying to see."
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
    • OVERVIEW

      • Humans are so good at finding signals in noise that sometimes they do so even when there is no signal. Many techniques of science and much of statistics is aimed at avoiding fooling yourself this way. A further problem is that we often aren’t aware of how much noise we have searched through, when we believe we have found a signal—the “Look Elsewhere Effect." For example, we tend to think coincidences are meaningful. Statistics was invented primarily to deal with the problem of distinguishing real signal from noise fluctuations that look like signal.
    • EXAMPLES

      • Exemplary Quotes
        • "I know it seems super meaningful that we ran into each other in Australia, when neither of us live in Australia, but I guess the chances of running into someone you know at some point, if you travel a lot and know a lot of people, are pretty high."
        • "There are many many cases of people making insanely correct predictions, so many that some people are convinced clairvoyance is real. But there are many more cases of people making totally wrong predictions. So it's probably just noise; with enough predictions, someone will be correct by luck."
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
    • OVERVIEW

      • It is often necessary to make decisions or judgments under conditions of uncertainty. When this happens, two kinds of errors are possible; we might think that something is present when it is not (a Type 1, or false positive, error), or we might think that something is absent when it is present (a Type 2, or false negative error). In different contexts, these two types of errors may come with different costs. When one kind of error is worse than the other, it is prudent to err on the side of making the less bad error. Sometimes it even makes sense to make one kind of error quite a lot in order to avoid making the other kind of error. For example, even though the large majority of tumors are benign, it makes sense to get tumors biopsied because if you do have a cancerous tumor and assume it is benign (a false negative), it can kill you.
    • EXAMPLES  

      • Exemplary Quotes
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • "We don't have to prepare for the hurricane they're forecasting might hit, because most of the time they say a hurricane might come, it turns out to be not that bad."
    • OVERVIEW

      • An important element of the culture of science is the use of “tentative” propositions, often quantified. These can be as confident as 99.99999%—you would bet your life on it—but it would still be understood to be held as a proposition which could be wrong. This makes it psychologically easier for a scientist to be open to being wrong—and to look actively for ways they might have gotten it wrong. This cultural understanding of the importance of recognizing and reporting one's credence level leads to insistence on including error bars on graphs: a data point is completely meaningless without an error bar.  
    • EXAMPLES

      • Exemplary Quotes
        • "I'm 95% confident that this battery is not going to explode. But more than even a 1% chance of our robot exploding would lead is too risky, so we shouldn't use this battery until we are more confident it won't explode."
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • "Dr. Ryan, are you absolutely certain? We can't authorize spending hundreds of millions of dollars sending a fleet to Patagonia unless we're completely certain about the outcome."
    • OVERVIEW

      • Most people’s estimates of their confidence is wrong in characteristic ways: high confidence tends to be over-estimated and low confidence tends to be under-estimated. This can be trained to be closer to an accurate calibration, most effectively with repeated, unambiguous, and immediate feedback. One problem that arises from poor calibration is that juries often use witness’ confidence to gauge the likelihood that they are correct, but this often yields poor results due to the poor calibration of the witnesses. 
    • EXAMPLES

      • Exemplary Quotes
        • "Weather forecasters often seem wrong, but they only give probabilities, and their probabilities are really well calibrated. So we should trust weather forecasters, but remember that a 90% chance of rain also means a 10% chance of no rain."
        • "Being well calibrated does not require always predicting the correct outcome but requires being able to predict how often one will be wrong."
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • Some confusion between confidence, calibration, and accuracy.
        • The temptation to rely on confidence over calibration is sometimes hard to resist.
        • "He seems super confident, and she said she was only 85% sure, so we should trust him over her."
    • OVERVIEW

      • A useful “jargon” of the scientists is to speak of orders of understanding or orders of explanation. “A zeroth order explanation/cause” or “a first order explanation/cause,” is a major cause/factor, as opposed to “a second order” or “third order explanation/cause," which would be real causes with smaller effect sizes. This is useful because explanations for how things/actors in the world behave can often be parsed into a primary explanation, a secondary less important cause, a third order, still less significant cause, etc.
    • EXAMPLES

      • Exemplary Quotes
        • “Ok, at first glance this dramatic increase in breast cancer in Korea seems like an intractably complicated problem, but maybe there is one aspect like diet or environmental change that is the primary driver.  If we can identify it first, then we can look for the next most important cause.”
        • “It turned out that the prices of these stocks was to first order being determined by the buy/sell orders of just a few major pension funds.   After that, the second order effect was the automatic buying and selling from the index funds.   In fact, the small investors that we thought were important barely affected the prices of these stocks at all—a third or fourth order effect at best.” 
        • “It used to be that the annual population of predator bears was the first order determinant of the annual salmon population, but nowadays the bears are a second order effect, and the fishing industry is the primary determinant.”
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
    • OVERVIEW

      • Physicists train their students in doing “Fermi problems,” back-of-the-envelope estimates of quantities that arise in physics problems and in life. This is useful as an approach to performing “sanity checks” of claims in the world and of your own ideas, beliefs, and inventions. Checking numbers with quick Fermi estimates may be even more important in a world in which it is difficult to evaluate the credibility of numbers available by Googling. 
    • EXAMPLES

      • Exemplary Quotes
        • “He’s suggesting that the Federal budget deficit is due to the money we spend on job training programs.  But that’s ridiculous!   Even if every single person out of work -- let’s imagine that it is 10% of the working-age population (say 10 million people out of work) -- went to a job training program that cost as much as a year of college at a good university (say, $40,000), that would cost 400 billion dollars.   Hmm... well that’s not quite as small as I expected, but it’s still not trillions of dollars, and furthermore I am sure we aren’t spending that much on each person for job training. Let see, can I estimate that cost per person in some more realistic way than using college costs...”
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • "I don't know how many pianos there are in Chicago so I can' t estimate how many piano tuners are currently working there."
        • "Keyla asked me how many firecrackers were shot last night. I tried googling it, but couldn't find out. I guess we'll never know now, will we?"
    • OVERVIEW

      • Here we will explore the most widespread heuristics and biases which psychologists of judgment and decision-making have discovered in everyday reasoning: the availability heuristic, representativeness heuristic, and anchoring heuristic, and biases like optimism bias, hindsight bias, and status quo bias. Many examples are drawn from Daniel Kahneman's book, Thinking Fast and Slow.
    • EXAMPLES

      • Exemplary Quotes
        • Representativeness Heruistic
          • "He's a great speaker for a mathematician, and mathematicians are not usually good speakers. Maybe he's done some theater, too. But most mathematicians have not done theater, so it's also possible he's just really good at public speaking."
        • Availability Heuristic
          • "When asked whether lightning or sharks are responsible for the most human deaths, most tend to answer sharks since sharks are often portrayed in fiction or documentaries as violent animals when  in reality only 19 shark attacks are recorded each year in the United States versus 51 for lightning strikes."
        • Anchoring Heuristic
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • Representativeness Heuristic
          • "He's a great speaker for a mathematician, and mathematicians are not usually good speakers. People who do theater are good speakers. So he must be a mathematician who does drama."
        • Availability Heuristic
        • Anchoring Heuristic
          • "My strategy to buy souvenir goods when on holiday trips is to ask the vendor for the price and negotiate my way down 30% from the initial etiquette price"
    • OVERVIEW

      • Science has a particularly bad track record when it comes to studies of human sub-populations for the purpose of setting policy—particularly when groups in power study groups out of power. We should be aware of this, and wary of misusing science in such a way as to perpetuate injustice.
    • EXAMPLES

      • Exemplary Quotes
        • "The data describe in The Bell Curve shows that Black students perform more poorly on IQ tests than White students. But historically, tests like that have been used to justify existing power structures and racial oppression, so maybe we should think about that more carefully before we interpret it to mean that White students are smarter. The IQ tests were written by Whites, for students who had grown up in similar environments. Maybe there are cultural biases. And hang on, there's a lot of vocabulary on those tests; that requires education, and we know that there are systemic racial inequalities in the education system. That by itself could explain the difference."
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
    • OVERVIEW

      • Distinguishing pathological science, pseudo-science, fraudulent science, poorly-done science, good science that happens to get the wrong answer (which should happen statistically for 1 in 20 papers that give a 0.05 confidence level result). What do practicing scientists do when they try to judge a paper in a field or sub-field outside their immediate area of expertise? 
    • EXAMPLES

      • Exemplary Quotes
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • "I took a shot of apple cider vinegar for a Month and just overall felt like it started me on the right track in the morning." https://spoonuniversity.com/lifestyle/i-took-a-shot-of-apple-cider-vinegar-for-a-month-and-i-won-t-stop
    • OVERVIEW

      • This class explores confirmation bias in the search for and assessment of evidence. In particular, we consider the ways that people tend to seek out and think about evidence in such a way as to reinforce their existing opinions, rather than testing them against new information or alternative views.
    • EXAMPLES

      • Exemplary Quotes
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
    • OVERVIEW

      • Science is not a single “scientific method” (as often taught in school), but better characterized as an ever-evolving collection of tricks and techniques to compensate for our mental (and, occasionally, physical) failings and build on our strengths—and, in particular, to help us avoid fooling ourselves. These techniques must constantly be re-invented, as we develop new ways to study and explain the world. In the last few decades we have entered a period in which most scientific analyses are complicated enough to require significant debugging before a result is clear. This has exposed another way we sometimes fool ourselves: the tendency to look for bugs and problems with a measurement only when the result surprises us. Where previously we recognized the need for “double blind” experimentation for medical studies, now some fields of science have started introducing blind analysis, where the results are not seen during the development and debugging of the analysis—and there is a commitment to publish the results, however they turn out, when the analysis is “un-blinded” and the results interpreted. 
    • EXAMPLES

      • Exemplary Quotes
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • Confusion between blind analysis and double blind experiments is common. These concepts are related but distinct.
    • OVERVIEW

      • Sometimes groups of people reach better conclusions than people working independently, and sometimes they reach worse conclusions. There are features of group reasoning that can help, and features of group reasoning that can hurt. Here we explore how to avoid the pitfalls of group reasoning and to maximize the benefits.
    • EXAMPLES

      • Exemplary Quotes
        • "We can get a pretty good estimate of the weight of this turkey by asking everyone in the family to write their guess privately on a piece of paper, and then averaging the answers."
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • "We can be pretty confident we are right, because all five members of our family agree that vaccines cause autism. Since all five of us think so, we must be right."
    • OVERVIEW

      • Often people think that science is necessarily reductionist, but in fact we can observe many patterns that are emergent, i.e., visible only at higher levels of organization. That is, some phenomena are only describable in terms of higher-level, nonreductionist patterns. In emergent phenomena, complex patterns (like organisms with emotions) can emerge from surprisingly simple sets of rules (like natural selection). Humans often mistake emergent phenomena as either magically inexplicable or intentionally planned by some conductor/choreographer/director. This is especially likely if one is not aware that causal explanations can depend on emergence. The internet in general, and social media in particular, are relatively untested domains in which new sets of rules (algorithms that choose what to show, likes, etc.) are being tried out. These new sets of rules give rise to unintended emergent phenomena, such as the propagation of misinformation. In the case of social media, it seems to grow conspiracy theories by connecting people with similar views and exacerbating confirmation bias. At the same time, emergent phenomena of this new social world online may seem so choreographed that they give rise to new conspiracy theories. These two patterns may exacerbate the historically documented tendency of people to believe in false conspiracy theories, through interpreting surprising emergent patterns as deliberate and communicating with others who agree. On the other hand, it is also possible that the digital revolution makes actual conspiracies easier, as the internet facilitates communication and therefore coordination across distances.
    • EXAMPLES

      • Exemplars
        • Perhaps we are most familiar with this from the example of some objects feeling hot and some cold, which is a collective effect of the average motion of all the atoms or molecules making up the object, not of each individual atom or molecule.
        • A great example of this sort of emergent phenomenon can be seen in Conway’s Game of Life (https://en.wikipedia.org/wiki/Conway%27sGameof_Life). For the SSS course we would like to act out a Game of Life activity with students playing the roles of the cells (perhaps in a football field, perhaps online).
        • Genes are "selected" by natural selection pressures that make genes which improve survival and reproduction more common through, literally, survival and reproduction, while genes which undermine survival or reproduction are less common because they are part of an organism that dies sooner and repoduces less. Although a gene may be described in reductionist terms of its molecular makeup and structure, its function in the organism must be described at the level of the whole organism in order to show why selection pressures push for or against it. Thus, a full explanation for why a gene is one way and not another (molecularly) must refer to its effect on the organism as a whole.
        • The spiral shape of a hurricane or storm is an emergent phenomenon from the movements and temperatures of the gases and liquid droplets that make it up.
        • "Consider, for example, a tornado. At any moment, a tornado depends for its existence on dust and debris, and ultimately on whatever micro-entities compose it; and its properties and behaviors likewise depend, one way or another, on the properties and interacting behaviors of its fundamental components. Yet the tornado’s identity does not depend on any specific composing micro-entity or configuration, and its features and behaviors appear to differ in kind from those of its most basic constituents, as is reflected in the fact that one can have a rather good understanding of how tornadoes work while being entirely ignorant of particle physics." - Stanford Encyclopedia of Philosophy,"Emergent properties," Timothy O'Connor.
      • Cautionary Quotes: Mistakes, Misconceptions, and Misunderstandings
        • "My Facebook wall is covered with articles about the protests, so Mark Zuckerburg must want people riled up."
        • "Everyone I know voted for Graham, but he lost the election. It must have been rigged."
        • "Neuroscientists can't learn anything from psychologists, because they're actually looking at the brain, which is the cause of all human behavior."
        • "Someday scientists will know exactly what happiness is, because we'll be able to see it in the brain. Then philosophers and psychologists will be out of a job."
        • "If you can't explain it in terms of the movement of particles, you haven't explained it at all."
        • "If Twitter is exacerbating polarization, it must serve Jack Dorsey's ends in some way."
        • "If the world were really round, there wouldn't be so many people at these Flat Earther conventions."
    • OVERVIEW

      • Reason by itself, without the arational elements of values, goals, priorities, principles, preferences, fears, desires, and ambitions, does not yield decisions: Decision-making requires weaving the rational with all of these arational elements that get humans to approach problems in the first place. Consequently, we must look for, study, and develop principled approaches to coordinating all these elements appropriately in our decision-making processes. Without such scaffolds, the rationality will frequently be what gets neglected. In the following classes, we explore some of the techniques that have been used to scaffold this kind of principled decision-making. None of these existing approaches accomplishes everything we would like. Nonetheless, they offer examples of techniques that we can recombine creatively with further new ideas and approaches to allow us to make better decisions in groups, appropriately applying rationality to achieve the complex goals of the relevant communities. We begin by exploring the desiderata that optimal decison-making processes should fulfill.
    • EXAMPLES

    • OVERVIEW
      • The Denver Bullet Study offers one approach to integrating facts and values in a controversial real-world problem, drawing facts from a set of experts, gauging the values of different stakeholders, and bringing these together for a final decision.
    • OVERVIEW

      • In the two classes of this week, we try out an approach to random-sample-representative decision-making, using a panel of experts to answer questions generated by small deliberative groups, each with a moderator. We also have a presentation from the professor who developed this technique, describing its use around the world. One example topic we have used is Fracking for Natural Gas.  
    • EXAMPLES

    • OVERVIEW

      • Here, we explore scenario planning, a technique for systematically considering possible futures. This is valuable for planning because we often do not know exactly what the future will look like, and need to plan for multiple contingencies.
    • EXAMPLES

      • Exemplary Quotes
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
    • OVERVIEW
      • Students design their own decision-making processes, utilizing their favorite aspects of the processes we have discussed.
      • Adversarial vs. Inquisitorial Modes of Truth-Seeking 
    • EXAMPLES
      • Exemplary Quotes
      • Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
        • Students often confuse "desiderata," or goals of decision-making processes, with the processes designed to achieve those desiderata. E.g., asked to come up with desiderata, they suggest "use a representative sample," which is a process which achieves the desiderata, "allow representatives of all relevant groups to have a voice," "ensure that our solution will work for everybody," or "avoid systematic bias."