Module 1 Lectures

Question 1

What are descriptive statements?

Answer 1

They give an account of how the world actually is, without evaluating it

ex: Russia is at war with Ukraine

Question 2

What are normative statements?

Answer 2

Express how the world ought to be and/or to express an evaluation if it is good or bad

ex: Russia should not have started the war with Ukraine

Question 3

What are scientists reasoning towards in general?

Answer 3

Scientists aim to build models that capture worldly phenomena

Question 4

What is a model?

Answer 4

A model is a representation of a worldly target system

ex: a street map of eindhoven is a model of the road infrastructure

Question 5

Do models accurately display the world?

Answer 5

They try to accurately display certain aspects of the world but ignore other parts of the world.

Every model has a specific goal

Question 6

What is an argument?

Answer 6

A connected series of statements (premises) intended to give reason for another statement (conclusion)

Question 7

Which 2 types of arguments exist?

Answer 7

Deductive and inductive arguments

Question 8

What are deductive arguments and their characteristics?

Answer 8

Arguments in which the premises are intended to guarantee the conclusion. If premises are true then the conclusion must be true

All A’s are B
Vaios is A
Vaios is B

The strongest arguments

Scientifically often unattainable, except math-based science

Question 9

What does it mean if an deductive argument is valid?

Answer 9

The logical form of the argument is such that if the premises are true, the conclusion must be true

Does not specify if premises are true or false, only on the structure of the argument

ex. statistics

Question 10

What does it mean if an deductive argument is sound?

Answer 10

Valid argument that have true premises and therefore true conclusion

Question 11

What are inductive arguments?

Answer 11

Argument in which the truth of the premises doesn’t guarantee the truth of the conclusion. The premises make the conclusion probable (occur with some probability)

Weaker than deductive but most common in science

Range in probability but always less than 1

Often draw conclusions on larger group with small sample

Question 12

What are the three reasoning strategies in science?

Answer 12

Hypothesis-driven approach (HD): test a theory

Data-driven approach (DD): observe reality

Application-oriented approach (AO): build towards an application

Question 13

What are the 5 steps in the hypothesis-driven approach?

Answer 13

1. Look at the real-world
2. Create a model based on the hypothesis from the real world
3. Create predictions generated from the model
4. Check if the predictions from the model agree with observations (data) made in the real-world
5. Make a conclusion based on the agreement of data and prediction to see if the model fits the real world

Question 14

Which step is deduction in a hypothesis-driven approach?

Answer 14

The step of using the model to get predictions is deduction

If model (M) then prediction (P)

Question 15

What are the different parts of the model in the hypothesis-driven approach?

Answer 15

Hypothesis (H)

Auxiliary assumptions (AA): assumptions connected to the hypothesis which are known DO NOT fit the world (ex. no friction)

Initial conditions (IC): the initial state of the system (ex. starting variables)

Question 16

What are the characteristics the predictions (P) need to have?

Answer 16

Sufficiently suprising: it should not be too easy for P to be true if H is false

Sufficiently precise: P should be precise enough to test H

A single prediction: only a single prediction should follow the hypothesis. More predictions make it easier to pass tests.

Question 17

What is the inductive step in the hypothesis driven approach?

Answer 17

Step 5 where it is checked if the model fits the real world according to if the real-world data and predictions agree

Question 18

How does positive and negative evidence look like in the inductive step of the hypothesis driven approach?

Answer 18

Positive evidence:
If M, then P*
P*, so M (with prob)
Future evidence can still disprove it

Negative evidence:
If (H & AA & IC), then P*
Not P*, so (not H or not AA or not IC)
Stronger than positive evidence
*initial conditions only for dynamic model

Question 19

What are the steps of analyzing the deductive step in the hypothesis-driven approach?

Answer 19

Step 1: identify H, AA, and IC associated with model

Step 2: identify P

Step 3: Evaluate if plausible that if (H & AA & IC), P follows

Step 4: Evaluate surprisingness, precision, and singularity of P

Question 20

What are the steps of analyzing the inductive step in the hypothesis-driven approach?

Answer 20

Decision tree:

Does data and prediction agree?
No: there is evidene that the model fails to fit the real world objects

Yes:
Are there plausible alternative models?
No: there is evidence that the model fits the real world objects

yes: The data are inconclusive regarding the fit between model and real world

Question 21

What is the objective of the hypothesis driven scheme?

Is this objective always achieved?

Answer 21

Objective:
standard of how to do hypothesis driven research

Much research does not achieve this due to unfeasibility, sloppiness, faulty argumentation, biases, and more

Question 22

What are the steps in the data-driven approach?

Answer 22

1. look at real world
2. Get data from the real world
3. create a model based on the data from the real world which agrees with eachother
4. Check if the model fits the real world according to the agreement of the model and the data.

Question 23

What is the deductive step in the data driven approach?

Answer 23

There is none

Question 24

What is the inductive step in the data driven approach?

Answer 24

Step 4 which checks if the model fits the real world by seeing the agreement of the data and the model.

D, so M (with some prob)

Question 25

Where does the probability of M depend on in the inductive step of the data driven approach?

Answer 25

formula: D, so M (with some prob)

Reasons:
The existence of other plausible models/hypotheses consistent with D

Quality, amount, conditions, and deviations of observations

Plausibility of auxiliary assumptions (AA)

Question 26

What are the steps to evaluate the data driven approach?

Answer 26

1. identify D
2. identify M
3. evaluate the three points determining the probability of M (plausible other models, observations, AA)

Question 27

What are the steps and stages in the application-oriented approach?

Answer 27

Problem definition:
0. define the design specification for the application

Model stage
1. Create a model
2. Make model according to design specs
3. Get model data and look if the data matches the design specs

Artifact stage:
4. Use analogical reasoning to go from the model to the actual artifact
5. Make the artifact according to the design specs
6. Get artifact data and look if the data matches the design specs

At step 3 and 6, the approach can go back to adjust the design specs.

Question 28

How do you evaluate the application oriented approach (globally)?

Answer 28

1. Evaluate design specs
2. Evaluate the model stage and artifact stage according to hypothesis driven approach
3. Evaluate the analogy between the model and artifact

Question 29

What are the evaluation metrics of the design specs?

Answer 29

SMART

Specific
Measurable
Acceptable
Realistic
Time-related

Question 30

What are the steps to evaluate the model stage of the application-oriented approach?

Answer 30

Evaluate the model -> prediction -> data as in hypothesis driven approach

Question 31

What are the steps to evaluate the analogical argument between the model and artifact stage of the application-oriented approach?

Is it inductive or deductive?

Answer 31

The model and artifact ought to be sufficiently similar in the relevant respects

Inductive formula:
M (model) has properties f, g, h. It meets z (design specs)
A (artifact) will have properties f, g, h
Then, A will meet z (with prob)

Question 32

On what characteristics does the confidence in the interference depend in the analogical in evaluation for the application oriented approach?

Answer 32

Inductive formula:
M (model) has properties f, g, h. It meets z (design specs)
A (artifact) will have properties f, g, h
Then, A will meet z (with prob)

characteristics:
The relevance of the similarities of f, g, h
The number of relevant similarities
The degree of similarity
The relevance, number, and degree of dissimilarities
The existence of other, independent models yielding the same conclusion

Question 33

What are the steps to evaluate the artifact stage of the application-oriented approach?

Answer 33

1. Evaluate the artifact-> prediction -> data as in hypothesis driven approach
2. Evaluate the fit of the artifact to the design specs

Question 34

What is the qualitative and quantitative version of the data driven approach?

Answer 34

Qualitative: Historical texts
Quantitative: statistics