Philosophy of science

Question 1

Experts

Answer 1

In a specified domain have a greater quantity of accurate information than most people do

Question 2

Laypeople (novices):

Answer 2

Little information in the specified domain

Question 3

The novice-experts problem

Answer 3

How should novices choose one putative expert as more credible or trustworthy than another

Question 4

Possible strategies to adress the novice experts problem (arguments presented)

Answer 4

Advantage: Information from putative experts is widespread and easily available
Problem: How can a novice make an accurate assessment of the putative experts arguments and technical language

Question 5

Possible strategies to adress the novice experts problem (agreement with other experts

Answer 5

Advantage: For any domain, there is typically more than one expert, and the great majority of experts agree on a certain view
Problem: There are many possible reasons why people in a field might agree, and such agreement doesn’t always signal that they are all correct

Question 6

Possible strategies to adress the novice experts problem (Appraisal by meta experts)

Answer 6

Advantage: Degree, prizes, work experience etc. Reflect publicly available certifications by other experts of ones expertise
Problem: Novices are not always in a position to assess the significance of ones credentials

Question 7

Possible strategies to adress the novice experts problem (conflicts of interest)

Answer 7

Advantage: sometimes, conflicts of interest are clear
Problem: In many contexts, novices cannot easily detect more subtle conflicts of interest

Question 8

Possible strategies to address the novice experts problem (past track-record)

Answer 8

Advantage: It seems easy to check how many times and in what situations a putative epert got it right
Problem: For complex phenomena, it may be beyond the novices capacity to check whether a putative expert got It right

Question 9

Illusion of understanding

Answer 9

People feel they understand complex phenomena with far greater precision, coherence and depth than they really do; they are subject to an illusion of explanatory depth

Question 10

Non-scientific practices

Answer 10

Do not aim at generating knowledge in the same way science does; their proponents try to create the false impression that they generate genuine trustworth knowledge

Question 11

Pseudo-scientific practices

Answer 11

Are not scientific, but their proponents try to create the false impression they generate genuine trustworthy knowledge

Question 12

Science is a practice

Answer 12

Socially and institutionally organized
Aimed at producing knowledge about natural phenomena

Question 13

Reproducible studies

Answer 13

Can be performed again
Produces the same or sufficiently similar ersults as the original study

Question 14

Why replicate a study

Answer 14

Limits the role of luck and error
E.g. False positives (type 1 error)
False negatives (type 2 error)

INcreases confidence a hypothesis is true (or false)
e.g. more evidence from different sources/labs

Helps science to self-correct

Question 15

Why do many results fail to replicate

Answer 15

Fraud
Questionable research practices (hacking - checking statistical significance of results before deciding whether to collect more data
Incentive structure and organization of science institutions

Question 16

Examples of social-institutional conditions that influence self-correcting

Answer 16

Open datasets
Replace null hypothesis significane testing
Reward replication work
Publish negative results
Diversity science

Question 17

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Answer 17

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Question 18

Three common features of scientific practice

Answer 18

1) publicly shared (oft mathematical) representations and techniques (hypothesis)
2)openness to criticism (Grounded in hypothesis)
3) empirical evidence

Question 19

Rationale for experimental control

Answer 19

Any measured change in the dependent variable is due only to the intervention

Question 20

By dividng participants randomly

Answer 20

You (supposedly) distribute participants with particular characteristics “equally” among the two groups. This would minimize the differences between the two groups with all known and unknown extraneous variables

Question 21

Does randomization really solve the problem of variable control?

Answer 21

Not really: Random group assignment does not guarantee that researchers selection of experimental groups does not distort experimental results

BUT

Random group assignment does not guarantee that extraneous variables do in fact vary equally across the two groups in any single experiment

Question 22

It is only over an indefinite series of repetitions of the random division that the variables Z will be equally distributed between the two groups
Repeat random division a lot and a lot of times and the frequency of education in one group will be about the same as the frequency of education in the other group
But

Answer 22

Researchers do not make random division of experimental participants indefinitely often, they do it once

Question 23

Whats data science

Answer 23

Use of computational, algorithmic, statistical and mathematical techniques to analyse and gain knowledge from the big data

Question 24

Any tool for data analysis does:

Answer 24

Makes assumptions (e.g. about the statistical structure of the data, about how to weigh different data etc.)
Based on algorithms
“trained” or “labelled” sample data to extract patterns or to make predictions

Question 25

The bay model

Answer 25

A model oft he san franciso bay: a downsized reconstruction of the bay in san Francisco 1000 times smaller than the actual bay Mimics the tides and currents of the actual bay Scientific model: used to learn not just about the model but about the actual bay John reber wanted to fill parts of the bay by building a dam The model showed this was not a good idea By manipulating and studying the model we can learn about the actual bay

Question 26

Models

Answer 26

Models represent a target system: models are about something else, namely their target How do models represent their target? By being relevantly similar, not by being identical The bay model replicated tides and currents but not the number of sailboats or the houses on the coast Models are incomplete and simpler versions of their target, theyre idealiizations The bay model has much faster tidal cycles Models are thus abstractions of their target

Question 27

Representations

Answer 27

These involve triadic relation between an agent or human representation and a represented world

Question 28

Models that exemplify

Answer 28

For a model to exemplify a (group of) target systems it must be a group member Fruit fly (Drosophila melanogaster) s a model organism in genetics and developmental biology Mice are model organisms in biology and medicine to model human diseases Fruit flies and mice are relevantly similar to their target system, in this case humans, but there are of course many differences

Question 29

Why model at all

Answer 29

When its impossible to perform experiments on the target (solar system) When its impractical to perform experiments on the actual target (The Bay model) When its immoral to perform experiments on the actual target (Using mice as a model organism to test vaccines for covid)

Question 30

Models of data

Answer 30

A model of data, or data model, is a regimented representation of some data set, often with the aim of highlighting whether or not the data count as evidence for a given hypothesis Data are any public records produced by observation, measurement or experiment

Question 31

Raw data

Answer 31

Video recordings of capupchin monkey behavior, observations of teh position of planets in the night sky, readings of a thermometer, participants answers on a questionnaire in a psychology experiment

Question 32

Models of data (steps involved)

Answer 32

1) Eliminating errors 2) Displaying measurements in a meaningful way 3) Extrapolating from those measurements to the expected data for measurements that weren’t actually taken

Question 33

Scale models

Answer 33

Concrete physical objects that serves a down-sized or enlarged representation of their target system

Question 34

Building a model

Answer 34

1) specification of the target system(s) 2) Construction of the model 3) analysis of the model

Question 35

The solar system

Answer 35

How is the model build in the documentary similar to its target system and how is it different Similar: in terms of the size of the planets and the distance between them Different: the composition of the planets, no atmosphere on earth, no satellites, no comets or debris

Question 36

Mechanistic models

Answer 36

Mechanistic models are representations of mechanisms Mechanisms are organized systems consisting of component parts and component operations that are organized spatially and temporally, so as to causally produce a phenomenon Mechanistic models represent the causal activities of organized component parts that produce some such phenomenon This illuminates how the target phenomenon works and how it depends on the orchestrated functioning of the mechanism that produces it

Question 37

Computer models

Answer 37

Computer models or simulations are programs run on a computer using algorithms to explore aspects or changes to a target system

Question 38

Thomas schellings checkerboard model of segregation (3 assumptions)

Answer 38

Assumption 1: Two sorts of agents Agents live in a two-dimensional grid Agents initially randomly distributed on the grid# Assumption 2: Agents have preferences for their neighbourhood Agent satisfied only if surrounded by at least t% (e.g. 30%) of agents like its self Assupmtion 3: Agents interact accordingly to a behavioural rule When an agent is not satisfied the agent moves to any vacant location on the grid

Question 39

Idealized models

Answer 39

Deliberately simplified or distorted representations Omitting, abstracting from certain known features of a target system/phenomenon Why? Make the model easy to construct, manipulate, analyse and run on a computer.

Question 40

Individual choices

Answer 40

Can lead (under specific conditions) to significant unintended consequences for larger groups

Question 41

Model

Answer 41

Idealized representation of something compicated with the goal of making it more simple/tracable or understandable

Question 42

Building a model

Answer 42

Figuring out what should be included in the model and how given certain aims is an opportunity to learn about the model

Question 43

Manipulating a model

Answer 43

Figuring out how the model changes if you intervene on it in some way is an opportunity to learn about the odel

Question 44

Representation (model)

Answer 44

Meant to stand in for their target systems Different features of model more or less similar to certain features of target system

Question 45

Robustness analysis

Answer 45

Build slightly different models of the same target Manipulate the models in comparable ways Compare models results

Question 46

Whats the point of robustness

Answer 46

Assess sensitivity of a model to changes in its basic structure identify model features responsible for certain results Evaluate with similarities and idealizations matter to learning about the world

Question 47

Rational

Answer 47

To be rational is to reason in accordance with principles of reasoning that are grounded in logic

Question 48

Logic as consequence relation

Answer 48

Let X a set of sentences and c any sentence, then c is a logical consequence of X just in case there is no situation in which everything in X is true but c is untrue Counterexamples: Is an exeption to a proposed general rule or hypothesis

Question 49

Do actual decision makers reason as economic models predict?

Answer 49

Economic models are idealized There is more than one system of logic Deviations from economic mmodels may indicate the logic assumed by economists is inadequate to represent human rationality

Question 50

Are humans irrational?

Answer 50

Human deviations from economic models and from deductive logic need not be symptoms of irrationality

Question 51

Generalization vs prediction

Answer 51

Generalization: o1 o2 and on have each been observed with property P. Threfore all Os have property P Prediction: O1,O2,...On each have been observed with property P. Therefore the next observed On+1 will have propety P

Question 52

Inductive reasoning

Answer 52

A kind of risky reasoning Conclusions from non-deductive arguments only follow probabilistically from premises

Question 53

How can we acquire (non lucky, trustworthy) knowledge from a risky form of reasoning

Answer 53

justifying induction deductively: If the conclusion of an inductive argument followed deductively from its premises, the falsehood of the conclusion would contradict the truth of the premises But the falsehood of the conclusion of an inductive argument does not contradict the truth of its premises therefore, the conclusion of an inductive argument cannot follow deductively

Question 54

WHen is reasoning trustworthy

Answer 54

A form of reasoning is trustworthy or reliable if it yields true conclusions most of the time

Question 55

Justifying induction non-deductively

Answer 55

Inductive argument 1; inductive argument 2; inductive argument n have all been reliable in the past Therefore, inductive argument n+1 will be reliable in the future This argument is itself reliable only if we assume that nature is uniform

Question 56

why cant it be proved non-deductively

Answer 56

In trying to show non-deductively that its true that nature is uniform, you presuppose the reliability of inductive reasoning But its exactly the reliability of inductive reasoning we want to establish

Question 57

Induction cannot be shown to be reliable via a deductive argument This violates the risky character of induction Induction cannot be shown to be reliable via a non-deductive argument This involves circular reasoning

Question 58

The best way to make generalizations:

Answer 58

Nature is uniform: use induction; other method = success or failure Nature is not uniform: Use induction --> failure Use some other method --> failure

Question 59

Abduction

Answer 59

inference to the best explanation

Question 60

How can we acquire (non-lucky) knowledge from (a risky form of reasoning, which is guided by) explanatory considerations?

Question 61

What candidate hypotheses should be considered in relation to agny given set of observations

Answer 61

If abduction is to be reliable, the, at least typically, the set of candidate hypotheses should contain true hypotheses

Question 62

What is best explanation

Answer 62

If abduction is to be reliable, then it should be clear and agreed upon what explanatory virtues like simplicity, fruitfulness and unifying power mean

Question 63

What is probability (subjectivist interpretation)

Answer 63

The probability of an outcome is an individuals subjective, rational degree of belief that the outcome will obtain

Question 64

Degrees of belief

Answer 64

Level of confidence in the truth of a given hypothesis Revealed by possible bets you would accept and reject

Question 65

Andrey kolmogorovs axioms of probability

Answer 65

Axiom 1: All probabilities are numbers between 0 and 1 Axiom 2: if a proposition is certainly true, then it has a probability of 1. If certainly false, then it has prob. 0 Axiom 3: If h and h* are exclusive alternatives (they cannot both be true at the same time), then P(h or h*) = P(h) + P(h*)

Question 66

Dutch book arguments: Basic idea

Answer 66

If your degrees of belief do not conform to the rules of probability, there are possible betting situations where you are guaranteed to lose money (you fall prey of a dutch book) You do not want to lose money Therefore, your degrees of belief should respect the rules of probability

Question 67

Problems with subjectivism

Answer 67

If the only constraint on your degrees of belief is they cohere with the axioms of probability, then you may have very odd "rational" degrees of belief Betting behaviour doesnt seem to generally be a good guide about what one (should) believe

Question 68

Frequency interpretation

Answer 68

The probability of an outcome is the frequency with which the outcome occurs in a long sequence of trials

Question 69

What is probability

Answer 69

Three possible answers: 1. subjective 2. Frequency 3. Propensity

Question 70

What is a long sequence of trials

Answer 70

A long sequence of similar trials: Similar to an experiment repeated over and over again to produce an infinite series of observations about the value of a variable of interest

Question 71

Problem of single-case probabilities

Answer 71

Cannot assign probaabilities to one-off events

Question 72

Propensity interpretation

Answer 72

The probability of an outcome is a propensity inherent in the physical conditions producing the outcome

Question 73

Propensities can be understood as

Answer 73

Causal dispositions of a situation to produce certain outcomes

Question 74

Whats a causal disposition of a system

Answer 74

The systems tendency to behave in a certain way under certain circumstances

Question 75

What is statistics good for

Answer 75

Description Estimation Generalization Hypothesis testing

Question 76

Null hypothesis significance testing

Answer 76

1) formulate a null hypothesis E.g. this treatment is not effective There is no correlation between these variables This person has NO special ability etc 2) develop expectations in the form of probability distributions for possible outcomes given the truth of hypothesis 3) gather data/observations and evaluate to what degree observed data violate expectations 4) draw an inference from this comparison

Question 77

Significance level

Answer 77

Decision about how improbable, given the truth of the null, an observed result must be to warrant rejecting null

Question 78

How surprising/improbable should an outcome be to be considered significant

Answer 78

No uncontroversial answer Largely convention and background knowledge about phenomenon

Question 79

p-value:

Answer 79

Probablity of obtaining test results at least as extreme as the results actually observed under the assumption that the null hypothesis is true

Question 80

P value: Basic idea

Answer 80

An index of how incompatible observed data are with a statistical hypothesis The smaller the p-value, the more surprising data are given the null

Question 81

Higher significance level (i.e. a lower probability for statistical significance)

Answer 81

reduces chance of type I, but increases chance of type II error

Question 82

lower significance level (i.e. a higher probability for statistical significance)

Answer 82

reduces chance of type II, but raises chance of type I

Question 83

Choice of significance level determines the degree to which one should be willing to accept diffirent kinds of errors

Answer 83

Type I error (false positive) Erroneously rejecting the null hypothesis Type II error (false negative) Erroneously failing to reject null hypothesis

Question 84

NHST goals

Answer 84

Develop expectations assuming H0 is true Check the likelihood of data given H0 Decide whether to reject H0

Question 85

Problems with NHST

Answer 85

1) silent on whats true (we want to know what hypotheses are true... but p values and denying H0 does not say how likely a hypothesis is 2) Silent on priors We have a f? (remember, NHST only says what we cannot deny) airly good idea of how the world works before testing NHST cannot account for that 3) Null good, what now? If we fail to deny H0 what should we believe

Question 86

Bayes theorem

Answer 86

1) formulate competing hypothesis 2) assign a prior probability to each one 3) Gather data 4) Evaluate the degree to which the data (dis)contfirms the hypothesis 5) Update probabilities of the hypotheses (inductively)

Question 87

Advantage of Bayes Theorem

Answer 87

1) Allows us to account for our previous knowledge of the world 2) Allows us to check how much the data confirms or disconfirms a hypothesis

Question 88

Bayes Factor

Answer 88

The typical approach is to calculate the posterior probability for all hypotheses But we can instead measure the ratio to which observation (dis)confirms each hypothesis

Question 89

Problems for Bayesianism

Answer 89

1) How do we define priors (there are often no objective criteria to define priors in hypotheses Solution: We do have a lot of background knowledge that constrains our common priors Variability in priors isnt always bad Makes it transparent how disagreements arise and how to settle disputes 2) Bayesianism not always the right approach

Question 90

Should science be free from all social values, if it is to delive objective, trustworthy knowledge

Answer 90

Science should be free from the influence of any social value Good science involves only logical reasoning and evidence

Question 91

Social values

Answer 91

Things, relationships or states that are (believed by some community to be) good

Question 92

Value free ideal

Answer 92

Suggests that science can produce objective knowledge to the extent it is free from values

Question 93

illegitimate roles of values in science

Answer 93

Endorsing a scientific theory not because of evidence but because we want it to be true Manipulating results to support a particular hypothesis and get published Exclusion of members of certain groups from scientific societies and institutions

Question 94

Legitimate roles of values in science

Answer 94

Choice of research questions Choice of how much evidence is needed before accepting or rejecting a hypothesis

Question 95

If science is to deliver objective trustworthy knowledge then

Answer 95

Scientists judgements and methods should be critically and openly assessed in light of diverse bodies of data, competing interpretations and alternative hypotheses --> an intersubjective process

Question 96

How can we make reliable causal inferences

Question 97

Understanding causes matters, causality allows us:

Answer 97

To intervene and predict, so that we can stop bad things happening and make more good things happen To explain why or how things happen

Question 98

Whats causation

Answer 98

Regular association Difference-making and manipulability Energy transference Dispositions/tendencies

Question 99

Temporal succession

Answer 99

C regularly comes before E

Question 100

Contiguity

Answer 100

C and E happen nearby in space

Question 101

Problems with regular association view

Answer 101

1) some variables are causally related, but not (spatially or temporally) contiguous 2) Causal relationships are asymmetric (C causes E but E does not cause C) 3) Some variables are conditionally dependent/associated but not causally related

Question 102

Manipulability

Answer 102

Two variables C and E are causally related when, if the value of C changed, the value of E would change too Basic idea: If C and E are merely associated or correlated, then intervening on C will not change the value of E

Question 103

Inferences about causal relationships

Answer 103

You always need assumptions that connect what can be observed to the underlying causal structure that generates the observations

Question 104

The principle of common cause

Answer 104

An observed dependece between two variables X and Y is indicative of either X causing Y, Y causing X, or the existence of a common cause Z

Question 105

The principle of common cuase (What does it bridge)

Answer 105

Observed patterns of conditional dependence and independence in a set of variables with causal relationships between the variables

Question 106

Causal Markov condition (CMC) says:

Answer 106

that each variable in a graph is independent of every other variable (exept its effects) conditional on all of its direct causes

Question 107

Scientific Revolution

Answer 107

A radical change of a reigning scientific paradigm being overturned in favor of a new paradigm Change in: out image of reality How data is collected analyzed interpreted Which logic/methods are accepted

Question 108

Thomas kuhn argues on paradigms:

Answer 108

It is impossible to compare different paradigms Khun suggests paradigm shifts are like gestalt switches where someones perspective changes from one thing to another If hes right: scientific revolutions prevent science from proceeding in a straight line

Question 109

The no miracles argument

Answer 109

1) our best scientific theories are massively predictively successful, and facilitate incredible technological innovation 2) the best explanation forr these successes is that our best theories are true

Question 110

What should we conclude about scientific progress, if we pay more attention to the history of science

Answer 110

A pessimistic induction: 1) There have been many empirically successful theories in the history of science that have subsequently been rejected as false 2) our current best theories are no different in kind from those theories that were rejected By induction we have reason to bleieve our current best theoreis will be rejected as false -> current theories not true

Question 111

What should we conclude about scientific progress, if we pay more attention to the history ofscience (Conclusions)

Answer 111

History and sociology of science indicate its simplistic and inaccurate to say tha tscientific knowledge accumulates linearly over time science proceds more erratically Controversial to say that because of its practical successes science makes progess delivering us an increasingly accurate image of the world