Sense & Sensibility & Science
Every day we make decisions as individuals, as voters, and as members of our various communities. We make decisions as students and parents and policy makers. The problem is, we don’t do it so well.
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 »
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- When is science relevant? The many uses of a scientific approach.
- Science is grounded in belief in a common, shared reality with some degree of regularity.
- Science uses both our direct senses and a variety of instruments to extend our ability to observe phenomena. We trust our instruments for the same reasons we trust our senses; interactive exploration and comparison.
- This topic explores the sources of error and uncertainty in data.
- Without scientific optimism, the idea that science is necessarily iterative and if we as scientists keep looking we will eventually gain insights, scientists would have discovered far less than they have.
- An introduction to the scientific approach to determining causal relationships.
- Building on Correlation and Causation, we examine how to collect evidence for causality in more difficult cases.
- Distinguishing singular causation (A caused B) from general causation (X tends to cause Y).
- The challenges of finding the information we want amidst messy data.
- We often find mistake noise for signal; how do we minimize these mistakes, given that they are not always easy to tell apart?
- Considering the relative costs of each possible mistake helps us make better decisions under conditions of uncertainty, when we cannot eliminate the possibility of a mistake either way.
- Using meta-judgments of the likelihood that your best judgment is right— how confident you are—enables decisions that take uncertainty into account.
- It is important to check the calibration of credence levels; that is, how good one's judgments are about how likely each of one's claims is to be right.
- Because each event and/or phenomenon has many causal factors, it is often important to distinguish which factors affect it the most and which factors play a smaller role.
- Estimating quantities based on what we know.
- Some of the psychological biases that make our probability judgments go awry.
- The capacity for science to be misused to reinforce existing power structures.
- How to catch bad science.
- Our tendency to preserve our existing or preferred beliefs, even against the evidence.
- Blind analysis, the practice of deciding how we will analyze data before finding out if the analysis we have chosen supports our hypothesis, counteracts confirmation bias.
- Explore ways that groups fall short of their optimal reasoning ability. There are better and worse ways to aggregate a group’s knowledge.
- Many phenomena in science are emergent, i.e., visible only at higher levels of organization. This tends to occur when large numbers of elements interact, e.g. as in individuals on social media. The rise of conspiracy theories and polarization via confirmation bias on social media is an important case of a recent emergent phenomenon.
- How should (and shouldn't) values/emotions/goals/desires and conflicts of interest properly be woven together with science's hyper-rational elements in decision making processes?
- 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.
- Another approach to getting groups of people to come together to make decisions, in a process where the integration of facts and values is scaffolded.
- A third approach to integrating facts and values under conditions of uncertainty about what the future will be like.
- Students design their own decision-making processes, utilizing their favorite aspects of the processes we have discussed.