1.2 Shared Reality and Modeling
From Sense & Sensibility & Science
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Science is grounded in belief in a common, shared reality with some degree of regularity.
Useful Links
- "It's only a model" Video
- Three Column Overview of the Week
- Lesson Slides (2023 Master)
- Website Page
Readings and Assignments
Lecture Video
Learning Goals
After this lesson, students should
- Understand that the self-correcting and ever-changing nature of science is a strength, not a weakness.
- Feel optimistic about the capacity of science to help solve problems for societal and personal decision-making.
- Understand the assumption of shared reality with regular patterns and the power of empirical evidence as a way to study this shared reality.
- Appreciate that scientific knowledge is built more like a raft than a pyramid.
- Understand the need for scientific modelling and how our knowledge of reality is necessarily expressed in terms of models.
| It's easy for students to get hung up on philosophical minutiae or edge cases of what really is reality, or does it even exist. Try to pull students back to the main goal of making practical decisions in our lives or in society by using science to understand the assumed shared reality. We only need things to be as real as the table in the middle of the room, so one can walk around it and avoid hurting oneself. |
Definitions
- Raft vs. Pyramid
- The Raft
- Every scientific claim is subject to question and reevaluation; we can use the rest of our scientific knowledge to question any one claim at a time, though we cannot question the entire edifice at once.
- The Pyramid
- Science builds on fixed foundations to ever higher levels of knowledge.
- The Raft
- Scientific Models
- Scientific modelling is a scientific activity, the aim of which is to make a particular part or feature of the world easier to understand, define, quantify, visualize, or simulate by referencing it to existing and usually commonly accepted knowledge. (via Wikipedia)
- Assumption of Reality
- Scientists assume an external reality, which is shared by and affects all people and has enough regularity to lend itself to induction. This external reality is what scientists seek to describe accurately.
- Empirical Evidence
- Science is based on appeal to empirical evidence, which is publicly accessible on the assumption of reality (although may require special instruments and/or expertise to acquire).
- Evaluation of Models
- The extent to which a scientific model is as useful tool for describing some real external thing. There are several features that determine the usefulness of a model.
- Ability to explain past observations.
- Ability to explain future observations.
- Simplicity or ease of use.
- Refutability and the ability to characterize our confidence in the model for a given problem.
- The extent to which a scientific model is as useful tool for describing some real external thing. There are several features that determine the usefulness of a model.
- Science vs. Decree
- Science gains its authority from its self-questioning character, not from the concentrated power of individuals.
PlayPosit Definitions
These are additional definitions that Johann provides during the PlayPosit.
| We should figure out what we want to do about this. Do we want to keep them as integrated definitions/learning goals? Do we want to scratch them from the PlayPosit? Do we want to do something else? |
- Realism vs. Idealism
- Realism
- We all inhabit a common reality, which has a structure that exists independently of what people think and say about it (except insofar as reality is comprised of, or is causally affected by, thoughts, theories, and other symbols).
- The structure of the world is regular, such that the patterns we observe are likely to hold in new contexts.
- Idealism
- The physical world is dependent on the conscious activity of humans. Also called phenomenalism.
- Realism
- Scientific Realism vs. Anti-realism
- Scientific Realism
- Science aims to provide a true description of the world, which is assumed to exist in a mind-independent fashion, and it often succeeds (or at least is approximates the truth).
- Scientific Anti-realism
- Anti-realist theories of science differ from one another. Among the views defended:
- Scientific theories can never "reach beyond" experience in what they say.
- Perhaps scientific theories can make claims that reach further, but we can't ever expect to get claims of that kind right.
- The objects of scientific study themselves do not exist in a truly mind-independent fashion.
- Anti-realist theories of science differ from one another. Among the views defended:
- Scientific Realism
- Types of Metrics
- Conventionalist Metrics
- When an individual scientist or the scientific community at large define some metric to be correct by convention.
- Operationalist Metrics
- When the truth of a metric is taken to consist of the operations involved in proving or applying it.
- Realist Metrics
- When the truth of a metric isn't based on human choices, but instead on some real phenomena in the world at large.
- Conventionalist Metrics
Examples
- When scientists first developed thermometers, several different substances were used. The problem was, these substances had different rates of expansion, yielding different ways to quantify "temperature." For example, water, alcohol, and mercury expand at different rates: if you set up thermometers with "0 degrees" equalized, each of the substances will hit "100 degrees" at a different temperature. How, then, do we know which kind of thermometer to use? Is the temperature "really" 100 degrees when a mercury thermometer says so, or when a water thermometer says so?
- Whose reality is more representative of the “true” colors of the world? ours or that of the mantis shrimp?
- The operational answer is that we can't compare these two because both are correct in their own way.
- The realist answer is that neither animal can fully see the full spectrum and through science we can try to understand it.
- The point is that the mantis shrimp sees more of the real world than we do but neither has a perfect representation of the world.
- Spherical cows.
Common Misconceptions
Science always changes its mind. One day drinking wine is good for you, the next day it isn't anymore. Why should we trust anything scientists say?
- When scientists make any claim, they make it always with some level of uncertainty, leaving open the possibility that they may be wrong. Any claim is subject to scrutiny and may be overturned or amended by new evidence. The ever changing and improving nature of scientific knowledge is a strength, not a weakness.
Taking the logs of the science-raft for "ideals" rather than claims. 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...
- The central characteristic of a scientific theory is that it is falsifiable. Every scientific claim is subject to question and reevaluation; we can use the rest of our scientific knowledge to question any one claim at a time, though we cannot question the entire edifice at once.
- Finite computation power means that it is necessary to simplify our models for simulation. The simplification has to depend on the question one is trying to answer with the simulation. It is infeasible to "simulate everything" also because even the best model involves simplifications and idealizations.
So what if our model of reality is "wrong" if it makes our lives more harmonious? And maybe we can just agree to disagree.
- If it's a belief that affects a decision and its likely outcome than at some point your misconception is going to catch up with you.
Context
In this lesson, we lay the philosophical groundwork for future topics by establishing a common set of assumptions and attitudes in science, namely, that the world is full of regular patterns that can be studied empirically, and that scientific knowledge is constantly evolving in light of new evidence.
Before
- 1.1 Introduction and When Is Science Relevant
- Decision making relies on knowing the effects of each decision in the real world. Collective decision making thus relies on a collective understanding of the shared reality through the scientific method.
After
- 2.2 Systematic and Statistical Uncertainty
- Our understanding of the shared reality is never perfect, but is always improving. When it comes to measured quantities, it is possible and necessary to quantify the inaccuracy or imprecision in our description of the shared reality.
- 3.1 Probabilistic Reasoning
- Since every claim of fact is to some degree uncertain, each claim should be associated with a level of confidence, or a probability that it is correct.
- 5.2 Scientific Optimism
- Although the first step of a scientific attitude is to admit one's ignorance or the uncertainty in one's knowledge, it is still possible to make progress by successive iterative improvements.
- 6.1 Correlation and Causation
- One important aspect of the shared reality is cause and effect, which is studied in a series of future lessons on causation.
- 8.1 Orders of Understanding
- When constructing a model of a complex system, we need to abstract out the most important aspects of the system in relation to the question at hand. This requires understanding (or hypothesizing) the order of importance of various aspects of the system, so that only the top one(s) are considered.
- 13.2 Deliberative Polling
- A type of event that helps the public make better group decisions after being informed on the relevant (shared) facts about an issue.
Recommended Outline
Before Class
- Review PlayPosit and discussion questions and ask faculty, Gabriel, or Emlen any questions you have.
During Class
- (5 min) Introduce the lesson and go over the plan for the day. Make sure people have groups, spokespeople, etc.
- (25 min) Go through the university modelling activity. Spend 3 minutes introducing the activity and going through the two examples. Spend 5 minutes in small groups creating and drawing the models. Spend 3 minutes (30 sec per group) presenting each group's prompt and model. Spend the remaining time (15 min) going through the discussion questions as a class.
- (15 min) Intromission/extramission discussion.
- (33 min) For each of the discussion questions, spend 7 min to discuss in small groups, and then 4 min to discuss as a whole class, with short GSI commentary. Adjust the duration of each question as necessary.
- (2 min) Remind students to prepare for 2.1 Senses and Instrumentation, specifically, to download a spectrogram app.
After Class
Make sure the students have a spectrograph app downloaded on their phone for the next class.
Lesson Content
University Modelling Activity
Our university is a large and complex system with many interacting parts. Each group will pick (or be assigned) one of the following scenarios, for which you will construct a model of (a part of) the university. We use the word "model" here very loosely. It can be a schematic drawing, a map, a mathematical description, or anything else that represents the essence of the university to help answer the problem at hand.
| The students don't have to actually answer the question. They just need to determine a model that would in principle help them answer it. |
| Really encourage the students to draw a schematic of their model and write down enough on the page that they could figure out what they were originally trying to say. |
Examples
- Want to know how water flows/collects after a (rare) rainfall. Use a topographical map. Make sure you don't have local minima/pools.
- Illness spread. We model the students as having a certain chance of having the illness and also of having some chance of spreading the illness to each person they come into contact with. Also care about how many unique people each student comes into contact with.
| Don't dwell on these examples. The point is that a very different picture can be drawn depending on the purpose of the model. One should distill the essence of the system when coming up with a model. |
Scenarios
- You run into a visiting student on Sproul Plaza that wants to know directions to the dinosaur in VLSB. What's a model of the university that helps them get there?
- You are trying to spread a rumor in the university by word of mouth. You want to know how many times the rumor is retold before it reaches everyone. What's a model of the university that answers this question?
- You want to know how different types of knowledge come into the university and propagate within it. For both academic knowledge and pop culture knowledge, what's a model of the university that explains how each spreads?
- You're an administrator trying to balance the university's budget. What's a model that helps you do this?
- There is an acute shortage of classroom space on campus. You want to create more spaces for instruction. What's a model of the university that helps you do this?
- You want to know where the outdoor wifi signal is best on campus. What's a model of the university that helps you do this?
Discussion Questions
As a whole class, have the groups briefly share their prompts and the models they came up with. Then, still as a class, answer the following questions.
- Was there one model that addressed all of the prompts? Would it have been meaningful/useful to construct such a sophisticated model?
- How did you choose what goes in a model and what doesn't? In other words, what assumptions or simplifications did you make?
- For each of the models, what were its limitations? Is there any way you can improve the model by adding more detail to address that prompt better?
- Does adding more detail always improve the model? Why or why not?
- Is there some underlying truth that your model is getting at? What is it? How would you know?
- Are any of the models you described really what the university is?
| GSIs ask students to write the group members' names on the paper and then collect all the sheets at the end of the section. We will use these models students have drawn in 8.3 Plenary. |
Extramission vs Intromission
Have two models of seeing.
- Extramission theory, where some sort of beam that detects features of objects comes out of our eyes.
- Intromission theory, where light is emitted by or reflects off of objects and comes into our eyes.
There's lots of cases where the predictions the models make are nearly identical. But, there are cases that extramission doesn't explain that intromission does (such as a pinhole camera).
Vision Discussion Questions
- Imagine yourself as a fifth century B.C.E. philosopher. You have very limited understanding of the world. These two models are presented to you and make similar predictions. Are the two models just as good as each other?
Absent different predictions, there are still reasons to use one model instead of another. - One of your rival philosophers tells you that in all the cases he's studied the two theories seem identical. So, it doesn't matter which theory we choose. Do you agree with this statement? Why or why not?
- Assuming that you don't agree, is there some argument through which you can tell the two models apart?
The only way to tell the two models apart is to validate them in the real world. You can do this with experiments on a pinhole camera, etc. - Given that the intromission model seems to better represent reality than the extramission model, the extramission model is now useless. Do you agree or disagree? Why?
In certain cases like ray tracing, the extramission model is good enough and also computationally simpler than intromission. - In you experience, do you know of models that have been supplanted by other models but where the original model still remains in some way useful? What are they?
Lots of possible examples. Newtonian gravity replaced by general relativity. Shared-electron (covalent) model of chemical bonding being replaced by quantum mechanics. Improved models of ganglion receptive fields explaining different optical illusions. - Is intromission how we see?
It better approximates reality than extramission. But, it's still a model. Better models may still be out there waiting to be developed.
Discussion Questions
- Come up with ways in which a society can reach an agreement on facts about reality, e.g. whether human activity has caused climate change, or whether a particular drug/vaccine is effective. Write down as many as you can.
Since there's a shared reality, facts can be determined by empirical observation. This means doing experiments on the real world, making logical conclusions, and sharing those results with the public. Other people can replicate these experiments to see if they get the same results. The public also need to be educated on these results and engage in public dialogue through forums (e.g. deliberative polling). Towards the end of the course, we will host a deliberative polling event during class and introduce other ways (e.g. Denver Bullet Study) to make group decisions that involve facts as well as values. - "Science changes its mind all the time, from how the heavenly bodies go to which foods are good for you. What scientists call a fact today will probably be overturned in the future anyway. Why should we believe any of it?" Using the raft as a metaphor for science (as opposed to a pyramid), how would you respond to this criticism?
Our scientific understanding of reality is never complete, but science is always self-correcting. Every "change in opinion" is the replacement of an old understanding by a more accurate, precise, and/or complete one informed by new evidence, just as rotten logs in a raft are replaced by new ones, one by one. The self-correcting and ever improving nature of science is its strength, not its weakness. There may be some wrong turns, but overall science advances by developing increasingly thorough, accurate, and complete representations of our shared reality. - We have mental representations of all kinds of entities that we have never observed with our own naked senses, like microbes, the rings around Jupiter, and (for most of us) the continent of Antarctica. Why do we believe that these are just as real as directly observed entities like kittens and mangoes? Stretch question: What about even more inaccessible entities like black holes, electrons, or personality traits?
Microbes are observable through a microscope. Rings around Jupiter are observable through a good telescope. Antarctica is observable by taking a boat there. Even if we haven't done these observations ourselves, we trust that there are qualified people who have carefully done them. These observations have been repeated by different people over the years with the same results, and if someone is still unconvinced, they can do it themselves. Additionally, we describe these things not as real in and of themselves. Instead we think of them as models that capture some aspects of the real entities and have predictive power. Stretch question: These entities have been postulated by scientists and found to explain a lot of the data we do see. Supposing them to exist allows us to make predictions which have been born out by observation. Even when we have not seen the raw data ourselves, we can understand how scientists in general work towards understanding and try to avoid error, and trust their epistemic authority on the basis of the demonstrated effectiveness of scientific methods. Try not to let students get carried away by unlikely hypotheticals such as "what if everyone has been lying to you". Beyond reasonable doubt, such entities as microbes are as real as the table in the middle of the room that one should avoid walking into.