5.2 Scientific Optimism

We introduce what may be called the "gas pedal of scientific progress"—a can-do spirit as a psychological trick to help one stick to a problem long enough to solve it. We motivate students with a relentless sense of optimism about their own ability to solve difficult problems, as well as demonstrate the practical importance of iterative progress.

The Lesson in Context

This lesson teaches students that one's optimistic and persistent attitude towards scientific problem solving is just as important as understanding the philosophical underpinnings of the scientific method. Throughout the semester, we teach students how science or human reasoning can go awry, and it is important to balance this healthy skepticism with the optimism that iterative progress is still possible in problems big and small. Students will experience this hands-on in an activity in which they have to solve various puzzles that build upon each other.

Earlier Lessons

1.2 Shared Reality and Modeling

• Knowing that our perception and measurement of external reality are inevitably imperfect, it is still possible to collectively make iterative progress towards improving our understanding of the shared reality.

3.2 Calibration of Credence Levels

• Scientific predictions are inevitably imprecise, but the precision (and accuracy) can be numerically estimated (credence level) and iteratively improved over time.
• Persistance and a "can-do" attitude in problem solving can be developed by harboring a growth mindset and recognizing the value of iterative progress.

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Later Lessons

8.1 Orders of Understanding

• Understanding a complex system fully can seem intractable. Often, a first step in understanding is to make a first-order description of the system. One can then make incremental improvements by tackling second- or third-order effects.

10.1 Confirmation Bias

• One often mistakes scientific progress as a series of correct ideas confirmed by experiments. In reality, experiments are often designed to falsify a given idea, and only a small number of ideas survive. The rejection of ideas by experimentation is itself a form of incremental scientific progress, rather than failure.

Takeaways

Learning GoalsDefinitionsExamplesCommon MisconceptionsExpanded Learning Goals

After this lesson, students should

1. Appreciate how the "can-do" spirit of inquiry (and inventive experimental techniques) counter-balances the difficulties of discovery/innovation.
2. Appreciate that iterative work on a problem is the norm in science and the most productive approach (and in some cases the only way of being productive), even when it looks like it's not getting anywhere. Persisting on difficult problems will eventually pay off with interesting insights.
3. Recognize that an optimistic view of the tractability of a problem and/or one's ability to solve it eventually can in itself affect one's actual capacity to solve the problem.
4. Feel optimistic about the possibility of "enlarging the pie" in societal problems, rather than resorting to playing a "zero-sum game."

Scientific Optimism

The can-do attitude of problem solving that pushes one to persist in working iteratively on a problem.

Scientific optimism does not refer to the belief that science is always right, or that it can solve all the world's problems, or that one is inherently intellectually superior to others.

Iterative Progress

The practice of checking how an idea/solution/policy is playing out, and adjusting it in light of new evidence, often repeatedly or in frequent small steps.

Meno

"I do not insist that my argument is right in all respects, but I would contend as far as I can, in both word and deed, that we will be better people, braver and less idle, if we believe one must search for the things one does not know, rather than if we believe it is not possible to find out what we do not know and that we must not search for it."

- Plato, in the voice of Socrates

Cosmic Distance Ladder

Over time cosmologists have been able to measure distances to farther and farther objects. Several centuries ago the best that could be done was having approximate distances to the moon and other planets. But, by gradually building on each others techniques, we now know the distances of the farthest objects in the observable universe. They can start by using parallax to get estimates based on how the relative locations of stars shift in the night sky as the Earth rotates around the sun. Then, by comparing the luminosity of ever brighter (and rarer) objects of consistent known brightness, astronomers have been able to create a series of standard candles that make up a "cosmic distance ladder" reaching all the way to the edges of the known universe. Astronomers have now developed several independent cosmic distance ladders. But, the classic one begins with using nearer and farther Cepheid variables then eventually type 1A supernova to measure the most distant objects.

Poincaré Conjecture

After proving the longstanding Poincaré Conjecture and being offered the prestigious Fields Medal, Grigori Perelman rejected the prize, stating that his work merely built upon his predecessor Richard Hamilton's. Even though it is the final triumph that is publicised and celebrated, it is the countless hours of incremental work that lays the foundation for that triumph.

Katalin Kariko

The daughter of a butcher in Hungary, decided she wanted to be a scientist even though she'd never met one. She spent her entire career studying mRNA, convinced it could be used to make vaccines. As grant after grant was rejected, and the University of Pennsylvania rejected her tenure, Dr. Kariko nevertheless persisted in her project. Recently, in her 60s, she and her colleagues made the breakthrough that led to the mRNA vaccine for Covid-19. Scientists are now hopeful that this breakthrough may lead to other vaccines for a wide variety of major diseases, including malaria, cancer, and AIDS.

Kati Kariko Helped Shield the World From the Coronavirus

New York Times profile of mRNA researcher Katalin Karikó.

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.”

“But NASA sees a goldmine in the abundance of carbon dioxide in the Martian atmosphere, which it hopes astronauts will be able to harvest in space with relative ease. The agency envisions the resulting technologies will see a range of products created in space, and even power microbial bioreactors.”

Source

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.

Even tiny improvements can be quite substantial in changing people's lives. Additionally, even though progress may be slow or invisible (only in certain communities, etc.), its cumulative effect can be enormous.

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.

We may not know exactly what dark matter is. But, scientists have managed to substantially expand the list of things it isn't. This is still progress and could ultimately give us real insight about the nature of dark matter.

After this lesson, students should

1. Attitudes
   1. Appreciate how the "can-do" spirit of inquiry (and inventive experimental techniques) counter-balances the difficulties of discovery/innovation.
   2. Appreciate that iterative work on a problem is the norm in science and the most productive approach (and in some cases the only way of being productive), even when it looks like it's not getting anywhere.
   3. Persist on difficult problems (scientific and non-scientific) due to optimism that iterative work will eventually pay off with interesting insights into the problem.
   4. Recognise that an optimistic view of the tractability of a problem and/or one's ability to solve it eventually can in itself affect one's capacity to solve the problem.
2. Concept Acquisition
   1. Scientific Optimism: An attitude of optimism that persistence and iteration on a difficult scientific problem will eventually pay off with interesting insights into your problem.
   2. Skeptical/Gatekeeping Function: Science is in the business of rigorously testing claims against experience, rather than merely accepting them.
   3. Discovery/Innovation Function: Science is in the business of generating new theories for how to explain the world. This is both difficult (requires resources, uncertain success) and important (need to make decisions, wouldn't have anything to "gatekeep" if new scientific ideas weren't being generated).
   4. Omnivorous Science: Constantly learning new techniques, exposure to a variety of hypotheses & theories, interdisciplinary discussion, etc. Important to progress because there are payoffs for learning novel experimental/technological/theoretical techniques and questions/problems from many domains of science, even beyond the one that one starts from.
3. Concept Application
   1. Explain the interplay and importance of the skeptical/gatekeeping aspect of science versus discovery/innovation.
   2. Explain how "Omnivorous science" can be important to progress.
   3. Critique impatience with the gradual nature of scientific progress by appealing to science's iterative nature.
   4. Critique cases of policy failures due to lack of an appropriately iterative/persistent approach to the problem.
   5. Generate examples of iterative processes for solving non-scientific problems.

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