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Second year first semester > Philosophy of science > Flashcards

Philosophy of science Flashcards

1
Q

Experts

A

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

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2
Q

Laypeople (novices):

A

Little information in the specified domain

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3
Q

The novice-experts problem

A

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

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4
Q

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

A

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

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5
Q

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

A

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

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6
Q

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

A

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

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7
Q

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

A

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

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8
Q

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

A

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

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9
Q

Illusion of understanding

A

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

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10
Q

Non-scientific practices

A

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

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11
Q

Pseudo-scientific practices

A

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

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12
Q

Science is a practice

A

Socially and institutionally organized
Aimed at producing knowledge about natural phenomena

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13
Q

Reproducible studies

A

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

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14
Q

Why replicate a study

A

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

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15
Q

Why do many results fail to replicate

A

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

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16
Q

Examples of social-institutional conditions that influence self-correcting

A

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

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17
Q

-

A

-

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18
Q

Three common features of scientific practice

A

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

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19
Q

Rationale for experimental control

A

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

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20
Q

By dividng participants randomly

A

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

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21
Q

Does randomization really solve the problem of variable control?

A

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

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22
Q

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

A

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

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23
Q

Whats data science

A

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

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24
Q

Any tool for data analysis does:

A

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

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25
Q

The bay model

A

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

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26
Q

Models

A

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

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27
Q

Representations

A

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

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28
Q

Models that exemplify

A

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

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29
Q

Why model at all

A

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)

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30
Q

Models of data

A

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

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31
Q

Raw data

A

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

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32
Q

Models of data (steps involved)

A

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

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33
Q

Scale models

A

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

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34
Q

Building a model

A

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

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35
Q

The solar system

A

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

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36
Q

Mechanistic models

A

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

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37
Q

Computer models

A

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

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38
Q

Thomas schellings checkerboard model of segregation (3 assumptions)

A

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

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39
Q

Idealized models

A

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.

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40
Q

Individual choices

A

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

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41
Q

Model

A

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

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42
Q

Building a model

A

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

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43
Q

Manipulating a model

A

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

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44
Q

Representation (model)

A

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

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45
Q

Robustness analysis

A

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

46
Q

Whats the point of robustness

A

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

47
Q

Rational

A

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

48
Q

Logic as consequence relation

A

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

49
Q

Do actual decision makers reason as economic models predict?

A

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

50
Q

Are humans irrational?

A

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

51
Q

Generalization vs prediction

A

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

52
Q

Inductive reasoning

A

A kind of risky reasoning

Conclusions from non-deductive arguments only follow probabilistically from premises

53
Q

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

A

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

54
Q

WHen is reasoning trustworthy

A

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

55
Q

Justifying induction non-deductively

A

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

56
Q

why cant it be proved non-deductively

A

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

57
Q

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

A
58
Q

The best way to make generalizations:

A

Nature is uniform: use induction; other method = success or failure

Nature is not uniform:
Use induction –> failure
Use some other method –> failure

59
Q

Abduction

A

inference to the best explanation

60
Q

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

A
61
Q

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

A

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

62
Q

What is best explanation

A

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

63
Q

What is probability (subjectivist interpretation)

A

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

64
Q

Degrees of belief

A

Level of confidence in the truth of a given hypothesis

Revealed by possible bets you would accept and reject

65
Q

Andrey kolmogorovs axioms of probability

A

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)

66
Q

Dutch book arguments: Basic idea

A

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

67
Q

Problems with subjectivism

A

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

68
Q

Frequency interpretation

A

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

69
Q

What is probability

A

Three possible answers:

  1. subjective
  2. Frequency
  3. Propensity
70
Q

What is a long sequence of trials

A

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

71
Q

Problem of single-case probabilities

A

Cannot assign probaabilities to one-off events

72
Q

Propensity interpretation

A

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

73
Q

Propensities can be understood as

A

Causal dispositions of a situation to produce certain outcomes

74
Q

Whats a causal disposition of a system

A

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

75
Q

What is statistics good for

A

Description
Estimation
Generalization
Hypothesis testing

76
Q

Null hypothesis significance testing

A

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

77
Q

Significance level

A

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

78
Q

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

A

No uncontroversial answer
Largely convention and background knowledge about phenomenon

79
Q

p-value:

A

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

80
Q

P value: Basic idea

A

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

81
Q

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

A

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

82
Q

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

A

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

83
Q

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

A

Type I error (false positive)
Erroneously rejecting the null hypothesis

Type II error (false negative)
Erroneously failing to reject null hypothesis

84
Q

NHST goals

A

Develop expectations assuming H0 is true

Check the likelihood of data given H0

Decide whether to reject H0

85
Q

Problems with NHST

A

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

86
Q

Bayes theorem

A

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)

87
Q

Advantage of Bayes Theorem

A

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

88
Q

Bayes Factor

A

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

89
Q

Problems for Bayesianism

A

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

90
Q

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

A

Science should be free from the influence of any social value

Good science involves only logical reasoning and evidence

91
Q

Social values

A

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

92
Q

Value free ideal

A

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

93
Q

illegitimate roles of values in science

A

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

94
Q

Legitimate roles of values in science

A

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

95
Q

If science is to deliver objective trustworthy knowledge then

A

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

96
Q

How can we make reliable causal inferences

A
97
Q

Understanding causes matters, causality allows us:

A

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

98
Q

Whats causation

A

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

99
Q

Temporal succession

A

C regularly comes before E

100
Q

Contiguity

A

C and E happen nearby in space

101
Q

Problems with regular association view

A

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

102
Q

Manipulability

A

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

103
Q

Inferences about causal relationships

A

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

104
Q

The principle of common cause

A

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

105
Q

The principle of common cuase (What does it bridge)

A

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

106
Q

Causal Markov condition (CMC) says:

A

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

107
Q

Scientific Revolution

A

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

108
Q

Thomas kuhn argues on paradigms:

A

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

109
Q

The no miracles argument

A

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

110
Q

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

A

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

111
Q

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

A

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

Second year first semester (5 decks)

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