38 terms

Philosophy Of Science

1. Natural sciences (chemistry, physics, biology)
2. Humanities (history, art, linguistics, philosophy, religious studies)
3. Social sciences (Sociology, olitical science, economics, psychology, anthropology)
Natural sciences
- mostly interested in universals and regularities,

- less interested in individual events, instead focusing on zooming- out towards mass-events.

Producing simplified knowledge by generalization is the aim.
-Allows concise knowledge:
1Laws of nature
2Scientific theories
3Mathematical models. Laws were the highest grade of scientific knowledge
instead of zooming- out like natural science does, humanities zooms in and is convinced that every single event is important instead of studying a system of events. They sometimes do recognize certain periods like 'Renaissance" but then they zoom - in past these categories. States that true creation follows no rules
Social sciences
Much younger form of science, started with debates on how to study societies ( late 19th century) . Social science is both attracted to natural science and humanities. Diversity even within single disciplines; psychology with both quantitative and qualitative methods of research.
Nomothetic approach
Two ways to describe the world (and to conceptualize the differences between natural sciences and humanities:
- Generalize;
explain outcomes as following from a general rule or pattern


Identifying generalities, ultimate aim is to put certain observations in a general framework. Details from the particular will be lost (weakness)
Idiographic approach
Two ways to describe the world (and to conceptualize the differences between natural sciences and humanities:
- Specify;
opposite of nomothetic approach. understand the meaning of unique, contingent, and often SUBJECTIVE outcomes.


understanding the uniqueness of an event and focusing on the particular. Understanding each individual outcome. Seeing the single details instead of the bigger picture.
Theory of knowledge
Three forms of Knowledge
1. Knowledge by acquaintance;
2. 'How to knowledge';
3. Propositional knowledge
Knowledge by acquaintance
this knowledge is never expressed and never written down, it corresponds with expectations. E.g you visit a friend who lives in a city, so you assume this person will know the city/ or I know the hague
'How to' knowledge
I know how to ride a bike but I do not know all the ins and outs of a bicycle
Propositional knowledge
Mostly focused on this form of knowledge by philosophers. =>Knowledge of facts which is important in science , in logical reasoning and in arguments.
Everyone can claim many items of propositional knowledge.
• Knowledge of my bodily sensations ( I know that I feel pain in my knee)
• Knowledge that I currently perceive about objects (I know that there is a door over there)
• Knowledge of objects that I do not see right now (I know I parked my bike at the station)
• Knowledge of pasts events that I have experienced(I know that I had coffee for breakfast)
• Knowledge of past events outside my experience (I know dinosaurs became extinct 65 million years ago)
• Knowledge of future events (I know that night will fall. Knowledge of induction, we use patterns in the past to predict that night will fall)
• Knowledge of non-perceivable facts (knowledge about something which you cannot perceive)
• Knowledge about mathematical facts ( I know that 1+1=2)
• Knowledge about conceptual relations (I know that bachelors are unmarried men)
What is knowledge?
Knowledge is the JTB account:
- Justified True Belief.
-The JTB account tells us what conditions must be fulfilled for A knows that P (A is a person and P is a proposition).
- The JTB account tells us how to recognize knowledge.
-Knowledge is property of a person in contrast with truth which has nothing to do with people.
The JTB account of knowledge
A knows that P only if:
1. P is true
2. A beliefs P
3. A is justified in believing P (there must be good ground for holding a belief. This principle excludes luck and keeps the real knowledge)
If all conditions hold than A knows that P, if one fails, it is not knowledge.
• Mary knows that it is raining only if:
1. It is raining
2. Mary believes that it is raining (knowledge is a mental state)
3. Mary is justified in believing that it is raining (people use umbrellas and the street is wet)
A statement is true, and only true if there is A FACT corresponding to it. (Correspondence theory of truth).
Bertrand Russel on truth
• Minds do not create truths, the mind has beliefs but the concept of truth belongs not to the mind but to the world, therefore it does not depend on which person asserts P. Truth is not the achievement of a person whereas knowledge is.
• Whether P is true depends on:
1. Content of P
2. Structure of the world
• If P is true than P has always been true, therefore truth must be formulated precise (e.g; "I am going to Amsterdam tomorrow" is not always true. "I am going to Amsterdam the 13th of March 2015" will always be true).
• Assessments of truth can change over time ( e.g. "the world was flat" was seen as truth long ago, nowadays "the world is round" is the truth and has always been true).
⇒ Important: difference between truth and belief: truth depends on the facts and is not influenced by person, date, time or any other. Belief is a personal achievement and is part of the concept knowledge which is a property of a person. !!!
reminder nomothetic approach
consisting of identifying regularities, formulating laws to describe these regularities, used by natural sciences mainly. Induction used as a technique for detecting regularities and formulating laws.
• Concise and simple
• Universal in scope
• Mathematical equations (comparisons)
• They do not only say what is the case but also what must be the case
• Laws are descriptions of necessary relations between entities or properties
Formal models
• Universal generalizations: "all A's are B's".
• Conditional statements: "if C, then D".
⇒ Extension of the nomothetic approach to other sciences (proving that there is an substantial universality or regularity underlying human affairs)

4 laws in humanities and social sciences
• Political science: Iron law of oligarchy
all organizations come eventually to be dominated by a small elite
• Cognitive psychology: Evolution of basic colour terms:
as any given language evolve in time, they acquire basic colour. (All languages start with the colours black and white, then red, then green or yellow etc.)
• Linguistics : Zipf's law
a frequency of using a word: the most frequent word is being used twice as much as the second frequent word etc.)
• International relations : demographic peace theory
democracies do not go to war with one another
Bottom up, from the particular to the general.
Making a leap from some observed cases to a generalization. It goes beyond observations performed. --> ampliative, unlike deduction

!!!Ampliative: an argument that goes beyond the premises

x1 is an A and a B
x2 is an A and a B
x3 is an A and a B
All As are Bs
- Goes from general to particular;

Top down, from a general claim to a specific/ particular claim. "all swans are white (the general) , this is a swan (the particular) and therefore it must be white.
All As are Bs
x1 is an A
x1 is a B
arguments and other logical schema's, that goes beyond the premises (Greek: ample = enough, more than enough)
A proposition upon which an argument is based or from which a conclusion is drawn
David Hume (1711-1776)
Hume's problem of induction:
How can we justify the conclusion of an inductive interference?
There are only two possible sources of justification:
1. Induction- but this runs into circularity
2. Deduction- but this is too narrow to justify ampliative conclusions (so one has to perform all observations possible to be sure of a regularity)
So, both sources are ineffective, - His conclusion; we have to accept that human will always use induction.
Pragmatic Warrant
• We cannot be sure that the world contains any universal regularities. Induction is at least as quick as any strategy to detect these regularities and it is therefore never wrong to look for regularities like induction does. This is the pragmatic justification for using induction
Nomothetic approach:
seeks causes and formulates explanations, natural sciences
Idiographic approach
mistrusting concepts of cause and explanations. Prefers interpretation and understanding. Typical to the humanities.
reducing something difficult to something general and simple
an answer to a "why" question, an account that gives us understanding of why things are as they are

-Explanandum: that which is to be explained
- Explanans: that which does the explaining
Knowledge and explanation:
if we know all facts it would not be complete.
•Lack of: awareness of connections between the facts.

•Lack of: knowledge of which facts are accidental and which facts have been determined

•Lack of: ability to intervene effectively in the world
Pseudo explanation
the claims you use to explain something are not true. Claims in an explanation must be true, otherwise, pseudo-explanation
Deductive- nomological model (DN model)

-Introduced by Hempel in 1965
- it is intended to capture the form of any deterministic scientific explanation of an individual event, such as the expansion of a particular metal bar when heated

- explanation is an deductive argument

- Hempel suggests that a scientific law can be explained by deducing it from premises including at least one other law

- An explanation needs to include at least one law; it needs to contain a valid deductive argument from true premises, and a particular fact;

- a deterministic event explanation is always a sound, law-involving deductive argument

• Objections DN model
- Objection 1:
Cause explains effect and not the effect explains the cause. The height of the flag explains the length of the shadow. Not the length of the shadow explains the height of the flag. Yet the DN model allows both

- Objection 2:
Only relevant information should explain an outcome but the DN model allows irrelevant information explain. It will therefore not always give understanding of the explanandem.

- a cause cannot be explained by its own effects
Inductive - statistical model (IS model)

Extension of DN model by Hempel 1956. So called probabilistic situations where the probability of an outcome is between 0% and 100%.

- law-involving argument giving good reason to expect that the explanandum event occurred (Hempel)
- explanation is an inductive argument

• Example explanation by IS model:
Why has plant P died?

- Any plant sprayed with X has a probability of 80% of dying
- Plant P was sprayed with X
- Therefore plant P had a probability of 80% of dying

• But: the IS model is blocking things; too restrictive.

In conclusion:
- explanations should track causes, not only state enough conditions for occurance.
- we need a new, more causal model of explanation: to explain X is to trace the causal processes and interactions that produced E.
INUS account of cause
John L. Mackie
• A cause of effect X is an INUS condition:
an Insufficient but Necessary part of an Unnecessary but Sufficient condition for the occurrence of X

• Suppose event E can be caused by either route 1 (factors A+B) or route 2 (factors C + D).
To produce E, route 2 is sufficient...but unnecessary, since we can produce E also by route 1. However, if we choose to produce E by route 2, then C is necessary...but insufficient on its own to produce E
To falsify
to prove that a hypotheses is not true
the act of falsifying
the hypotheses creates the possibility for falsification
Karl Popper 1902-1994
Concerned about:
- The ways to test scientific hypotheses
- How can you allow creativity in science?
- How does science differ from non-science?

In science, we need to test hypotheses;
⇒ How to test a hypotheses
• Hypotheses itself too general and theoretical therefore check the predictions (observational claims).
• Explanation: hypotheses (H) entails observational claim (O). Checking whether (O) is true or false, this tells us something about (H). Two outcomes:
1. (O) is true
observing that (O) is right does not say that (H) is true. It is still possible that (H) is false since something else could have caused (O).
2. (O) is false
observing that (O) is false gives certainty that (H) is false.
• Popper sees an important asymmetry between these outcomes!
• Example:
you see a white swan and say: the hypotheses is right : NO
you see a black swan and say: the hypotheses is wrong: YES
• Logically invalid deductive argument :
(H) entails (O)
Hence, (H)
• Logically valid deductive argument:
(H) entails (O)
Not (O)
Hence, not (H)

• Poppers rejection of induction: by rejecting confirmation, Popper rejected induction . Since Popper argued that Hume's problem was unsolvable, he stamp out induction.
⇒ Popper had two possible results of the hypotheses test:
1. Hypotheses is falsified and scientist must abandon it.
2. Hypotheses is not falsified and survives, but receives no confirmation.

Therefore science consist, according to Popper, not of true or highly confirmed findings, but of a set of not yet falsified hypotheses. In other words; not the accumulation of truths, but the elimination of errors.
⇒ Origin of hypotheses:
scientists create hypotheses freely, any source of inspiration- from dreams to observations.
⇒ Demarcation of science:
Since falsification is the scientific method, it also divides science and non- science. Practioners of the discipline must be able to say what would falsify their hypotheses. Astronomy is a pseudo-science since their claims are unfalsifiable.
⇒ Critique of falsification: 4
1. Some valuable scientific hypotheses are not falsifiable.
2. It may eliminate examples of good science.
3. Falsification may be less of importance to scientific practice since people can improve after the hypotheses is falsified.
4. Refutation is less straightforward than popper assumed