Harvard students comment on "Uncertain Risks"

1. Which part(s) of the discussion do you expect would be most illuminating for non-quantitatively-inclined readers?

For non-quantitatively inclined readers, the discussion around risk and uncertainty can be made more illuminating by focusing on relatable scenarios and everyday decision-making contexts. Instead of delving deep into numerical calculations and statistical models, emphasizing the practical implications of uncertainty and how it affects personal decision-making could resonate more with such readers.

2. Suggest another framing of the issues discussed that would be more effective.

Another framing could involve using analogies or anecdotes that simplify complex concepts. For instance, likening uncertainty to navigating through foggy weather, where you can't see what's ahead clearly, but you still have to make decisions based on limited information. This analogy could help convey the essence of uncertainty without delving into technicalities. Additionally, using real-life examples, such as deciding whether to wear a seatbelt while driving or choosing between different healthcare options, could make the discussion more relatable and accessible to a broader audience.

I would assume the most challenging aspect of the article for students unfamiliar with quantitative prediction in general is likely the section discussing the detailed calculations of risk and uncertainty related to COVID-19. Here, the article introduces concepts like fractions, percentages, and the factors affecting these calculations, such as numerator, denominator, and uncertainty range. This involves mathematical reasoning and understanding statistical measures that can be quite complex for those not comfortable in quantitative methods. Understanding how these calculations relate to real-world risk assessment, particularly in a public health context, requires a grasp of both statistical concepts and their application to everyday decisions and policy-making.

I thought the discussion about applying the risk and uncertainty to COVID-19 would probably be illuminating to non-quantitatively inclined readers since it uses the framework on an issue that has affected everyone. This portion of the essay provided concrete examples such as calculating the risk of deaths and the uncertainty around the risk estimate, which would allow people to specifically grasp the interplay between risk and uncertainty in a situation that is easily explained/applies to many. The explanations of certain activities as being high risk/low risk also helped contextualize the concept in a way that deals with everyday tasks.

The part of the essay which could be most illuminating for non-quantitatively inclined readers is the discussion of COVID at the end. Because we all experienced COVID, especially the first few weeks when uncertainty about the lethality of the virus was at its highest, I feel like the distinction between uncertainty and risk is intuitive. When the virus began spreading globally we were only somewhat certain about the risk of death. Nobody knew the long term effects of the virus, the sample size of infected individuals was small, and the data was new and sometimes difficult to verify. Despite this, a ballpark estimate of the risk of death was still achievable. We knew that the death rate was not 100%, or even 10%. As the pandemic went on, uncertainty declined rapidly and people were able to take a slight sigh of relief as more information was available to the pubic. When you think about it, the risk of death from the original COVID-19 virus never changed over time, only our certainty of the figure.

The essay's discussion of deer hunting and Russian Roulette is likely the most illuminating component to non-quantitatively inclined readers given that it takes a relatively uncomplicated situation and explains the interplay between risk and uncertainty in either scenario. Each example serves to emphasize the importance of estimating odds in related situations, more common in daily life. Furthermore, the comments made by Dan Gilbert regarding humans' outlook on likelihood further contextualize the differences between risk and uncertainty as well as its manifestations in subsequent human action.

The parts of the essay that give actual, quantitative examples about each scenario would likely be the most helpful for non-quantitatively-inclined readers. Simply telling someone what the difference between risk and uncertainty is not very useful in a vacuum, but having one control "variable" (i.e., the level of risk) helps to illustrate it greatly. That is to say, showing how the moon landing has high risk because the astronaughts might die in any number of ways, and how a rare disease would have low risk if it doesn't kill often, but both have high uncertainty due to the large unknowns of a moon landing or the disease's erratic behavior, provides extremely helpful contrast for demonstrating the difference between the terms.

In the essay, the part that was most illuminating was explaining that uncertainty is inherent in risk assessment, and it's not just about whether something will happen or won't happen, but also about the degree of certainty regarding the outcome. In thinking about COVID-19, the uncertainty associated with the pandemic relates to some of the other notable events mentioned in the essay. Some of those examples used were Russian Roulette, lying on a couch, and the Apollo 11 mission, which helped illustrate the different levels of risk and uncertainty people encounter in their lives.

I expect the following parts of the discussion to be the most illuminating for non-quantitatively-inclined readers. First, the contrasting examples given of high risk/low uncertainty (Russian roulette), low risk/low uncertainty (lying on the couch), high risk/high uncertainty (Apollo 11), and low risk/high uncertainty ( a new disease that behaves erratically), illustrate how the concepts of risk and uncertainty work together clearly. I think another part of the discussion that provides further useful information to readers is how the COVID-19 mortality risk is put into perspective by comparing it to the overall annual death rate in the US, and to past pandemics. This helps readers understand the true risk level of the pandemic, and does not raise as much of an emotional reaction to the statistics of death tolls presented, although we should still be wary of them. Understanding how we have gotten used to a low-risk world and the way in which people misunderstand uncertainty is crucial to non-quantitively-inclined readers. 

The part of the discussion that was most illuminating for me (a non-quantitatively-inclined reader) was the clear way uncertainty and risk - and the differences between them - were explained. Using examples like the Apollo missions (high risk AND high uncertainty) and russian roulette (high risk, low uncertainty) really put things into perspective. Therefore, the high uncertainty nature of the COVID-19 projections became much more understandable in this framework.

Hello,

I found this reading to be thought-provoking. Many people in society will spew random statistics they saw or heard on the news without considering the factors that may influence this data. This is what non-quantitative thinkers will look at; due to this, they do not grasp the true complexity of the situation and what it takes to resolve such an event. For the Russian roulette analogy, many people may think, "Yes, there is a 17% chance of a round being fired, causing almost certain death," but those are not the true odds. I say this because the essay mentioned that revolvers misfire or malfunction, which can affect 1 in 6 chances of a live round going off. This is uncertainty because we are, in a sense, in the dark about the millions of factors that could have affected the weapon, person's hand movement, etc., which means without this information, we will never know the true odds of death in any given Russian roulette round. 

Best,

Joey Cano

For non-quantitatively-inclined readers, I would expect the introduction to be the most illuminating. The analogy to Russian Roulette and the deer hunting were helpful for setting up the conversation on risk and uncertainty. By bringing up this example throughout the post, the article provides a relatable way to understand these complex concepts. Using the Apollo 11 voyage as an example for high-risk and high-uncertainty was another helpful analogy made for non-quantitatively-inclined readers. Using COVID-19 was also another good example that grounded the reader in the concepts of data and uncertainty.

I think that the discussion on the uncertainty around the risk of COVID would be very helpful for non-quantitatively-inclined readers. When you are estimating something like risk, you have an average estimate of the risk and some degree of uncertainty around your estimate. This article’s discussion on COVID risk does a great job of illustrating how with few data points, we can still find an estimate of risk but we will have a larger uncertainty. This is because we did not know enough about the virus (early in the pandemic) to be certain if our best estimate of risk is close to the true value. As we see more cases of COVID, we can say with more certainty what the actual risk of the virus is since it is far less likely that we are just seeing a non-reflective sample of how dangerous COVID is. Knowing this concept is important in statistics since all estimates have some degree of uncertainty and understanding that uncertainty can help a lot in interpreting the statistical values we see in the world.

Readers who are not as familiar with quantitative concepts probably would find the section distinguishing "risk" and "uncertainty" especially helpful and insightful. This part of the discussion demonstrates how risks can be quantified, whereas uncertainties often cannot. Using accessible scenarios, such as playing Russian Roulette versus encountering unpredictable challenges in hunting, would help readers clarify the implications of each in real-world decision-making. Understanding this distinction can profoundly impact how individuals and organizations prepare for future challenges, emphasizing the importance of flexibility and resilience when handling unknowns.

The parts of the discussion that I expect would be the most illuminating for non-quantitatively inclined readers would be the introduction with analogies and the description of human perception of risk. The initial introduction, comparing the certainty of outcomes in hunting and Russian Roulette, can help non-quantitative readers grasp the fundamental differences between risk and uncertainty because analogies provide a relatable way to understand complex concepts. The section discussing how humans typically perceive risk, categorizing it into "it will happen," "it won't happen," and "it might happen," can resonate with non-quantitative readers because it calls awareness to something that they do without thinking.

I found this essay to be extremely accessible to those non-quantitatively-inclined, simply because hardly any numbers/math are mentioned -- when they are, they're (usually) representing a probability (essentially a well-know fraction with which most are familiar). On the difference between risk and uncertainty, the Deer Hunter "one shot" example versus the Russian Roulette game was a perfect example used to highlight such differences. The reason for this is that both examples fall on the opposite ends of an uncertainty spectrum, so their differences are easier to recognize. Its fairly straightforward to understand you risk an almost 1/6 chance of death from Russian roulette, and most people can understand how the uncertainty of this event is practically zero. This is simply because most people understand that if you shoot yourself in the head with a loaded gun, you'll most likely die. On the other hand, in the case of deer hunting and killing a deer with one shot, most people understand that there are higher levels of uncertainty associated with each risk. This is because there are more factors (weather, area, skill of hunter, rifle type, etc.) involved when it comes to hunting, rather than simply holding a gun to you head and pulling the trigger.

For non-quantitatively inclined readers, the discussion on how fear and uncertainty shape perceptions of risk, especially in the context of COVID-19, would be particularly enlightening. It delves into how the novelty and invisibility of the virus, coupled with initial data scarcity, led to heightened anxiety and exaggerated perceptions of risk. By highlighting the emotional reactions triggered by the pandemic and their impact on public responses and policy decisions, it provides valuable insights into the complexities of risk assessment. This segment underscores the importance of clear communication about known risks and uncertainties, as well as the need for transparency in decision-making processes, to navigate uncertain situations more effectively. Ultimately, it offers a pathway towards fostering resilience and informed decision-making in the face of uncertainty.

In order to better understand a high-risk high reward situation, you can change the framing to one of a roller coaster simulation. Imagine A new type­ of roller coaster gets introduce­d at an amusement park. It promises safe­ty and excitement. Be­ing the first of its kind, predicting safety is difficult. Ride­s can break down sometimes. Without past e­xamples of this specific ride, calculating risks prove­s challenging. This lack of information makes trying the ne­w ride uncertain regarding pote­ntial risk. Trying a new rollercoaster and being an early reviewer can be fun, but it may also come with alot of dangers

I believe that the distinction between risk and uncertainty will be the most illuminating for the quantitatively non-inclined; especially the example of a (essentially) 0 uncertainty game with known risk odds. Obviously, the example of Russian Roulette in The Deer Hunter is a grizzly way of conveying such truths, but given the statistical principles of the game are very transparent and easy to conceptualize, its use allows the reader to focus on the math behind risk as opposed to spending time understanding the specific mechanism of a hypothetical example.

As a non-quantitatively inclined reader, I found the example from The Deer Hunter to be the most illuminating. Although I had no prior knowledge of the movie, these simple and easy-to-understand examples helped me comprehend the difference between risk and uncertainty without a high understanding of statistics or data science. Simple examples like this make the essay more accessible to a broader audience that does not need to be an expert in the topic to understand the message the writer is trying to send.

1. I believe the parts that were most illuminating for non-quantitatively-inclined readers was the comparison between the low-risk low-uncertainty situations in contrast to the high-risk in general (both high uncertainty and low). But, for this example, let's keep it at high-risk low-uncertainty. People are generally good at making judgements on things that are obvious, so, when presented with the option of lying on a couch, versus participating in a round of Russian Roulette, then people would be highly inclined to take the couch option, even though they know the uncertainty of Russian Roulette. Even without knowing exactly the "odds" as the essay mentioned, non-quantitatively-inclined people will be good at measuring risk without the need to pay too much detail for uncertainty.