The first group of questions are based on Hunt Allcott and Matthew Gentzkow, 2017, Social Media and Fake News in the 2016 Election, Journal of Economic Perspectives, 31(2): 211-236.
- What is fake news?
- Give a recent example of fake news. How do we know it is fake?
- Have technologically developments made fake news easier or harder to produce? Justify your conclusion.
- Who is least likely to be fooled by fake news? Use the results from Table 1 to support your conclusions.
- To some extent, fake news is encouraged by confirmation bias. What is confirmation bias? How does confirmation bias help us understand why false ideas can persist.
- Here is a very recent item about a Turkish lawmaker smashing a cellphone in parliament, as he objects to a law that would ban propagating misleading information. https://www.bbc.com/news/av/world-europe-63249301 What does this tell us about the difficulty of regulating the truth of information?
Bayes rule.
- You are afraid you have caught tuberculosis, which hits 0.01% of the population, so you decide to get tested. If you do have tuberculosis, there is a 99.9% chance that the test will return a result of “positive”. If you don’t have tuberculosis, there is a 0.2% chance that it will show up positive – a “false positive”.
- If your test shows a positive result, what is the probability that you have tuberculosis?
- If your test shows a negative result, what is the probability that you do not have tuberculosis?
- How accurate are PCR tests for COVID19? How accurate are antigen (“at home”) tests for COVID19? If you test positive with a PCR test, what is the probability that you have COVID19? And with an antigen test? [You will need to look up some information on this, and may need to add some guesstimates of your own.] Show any workings.
