Philosophy midterm

1. Natural science

2. Humanities

3. Social science

- oldest intellectual pursuit

- quantitative based

- empiricism - all knowledge is ultimately derived from observation

- interested in regularities

- individual events are applied to classes of events

- physical universe is uniform

→comprehensive, powerful knowledge

- mathematisation, abstraction, idealization

- “arts” were ways of doing things without thinking

- became a science ca. 15th century (Humanism, Renaissance)

- studied world:

• historical human actors

• actions that carry intention+ meaning

• texts, artworks, artifacts with meaning

- historical particularities - each event is unique

- distrust generalization and idealization

- interpretation, empathy (reconstruction), hermeneutics (text interpretation)

- studied world:

• human actors and institutions

• behavior

• rationality

• ritual, cultures

- uses both concepts from humanities and natural science

• identifies/describes regularities

• formulate generalizations and laws

• explain observations through generalizations and laws

• typical for natural sciences

• seeks causes and formulates explanations

• understanding the meaning of contextual, unique and often subjective results

• mistrusts concepts of cause and explanation

• prefers interpretation and understanding

• scientific theories

• mathematical models + equations

• relations between physical quantities

• seen as highest degree of scientific knowledge

• also outside of science

• few terms that explain more phenomena → simplicity

• “simpler” images of world

• most developed

• iconic role in society

• social & historical power

• delegates reflection to others

• science is based on facts

• facts are claims about the world that can be established through careful use of senses

• reasoning takes us from factual basis to laws and theories

• the resulting knowledge is securely established and objective

• doing observations

• formulating theories

• theories explain and predict observations

• observations test theories and help decide between theories

1. Facts are directly given to careful,, unprejudiced observers via senses

2. Facts are prior to and independent of theory

3. Facts constitute a firm and reliable foundation for scientific knowledge

-observations as subjective, passive, fallible

- against the common sense view: what you see is not the same as what I see

→ it depends on knowledge and experience

→ observation statements may differ

→ facts are not unproblematically + directly given to observers

- scientists disagree about observations

- background theory & technological advances needed

- sometimes observations are fallible because of theory or technology

→ theories are subject to revision

Theory-laden observations

- facts do not precede theory

- our experiences often depend on theories we already hold

→ we don’t know which facts to look at if we don’t have a theory (we do observations that help answer our theory)

- make observations more objective

• arranging the observable situation in such a way that the observation statement does not rely on subjective/cultural/ perspective influences

(active + public but still fallible)

• consistency (do it same way every time)

• repeatability (someone else can do it too)

• compatibility with a good theory

practical interventions that isolate the process under investigation by eliminating other influences

- compatible with a good theory

- routine, objective procedures

- don’t rely on fine subjective interpretation

- consistent and repeatable outcomes

- eliminating spurious influences is difficult (need to know a lot about them and how to eliminate)

- can be faulty if knowledge informing them is faulty

- outmoted by new technology

- rejected because of advancing understanding which shows experimental setup is inadequate

- irrelevant because of advances in theory

- setup does not succeed in isolating process under investigation

- measurement methods used that are insensitive/unreliable

- experiment becomes understood to be unable to solve the question

- theoretical advances: question becomes discredited

Observation → Facts → Theory through induction

→ the logically derivation pf a conclusion from premises

• logically valid argument

• doesn’t add to our knowledge

• statement about all to statements about some

an argument is logically valid if and only if it is impossible that the premises are true and the conclusion is false (if premises are true then conclusion must be true)

(common sense view)

• not logically valid

• statements about some to statements about all

when two theories are empirically equivalent meaning that both fit the data equally well → data isn’t rich enough to help us decide between two theories

- make new predictions → explore where theories aren’t empirically equivalent and then make observations for those that aren’t in common

- pragmatic criteria: a theory might be better than another for reasons outside empirical adequacy e.g. it’s simpler but explains facts equally well

- mathematical equations

- concise and simple often elegant

- universal in scope

→ laws are descriptions that just say what happens to be the case

- true universal generalization about specific events

→ laws say not just what the case is but also what must be the case

- descriptions of necessary relations between entities, properties or events

→ an answer to a why question

- gives us understanding of why things are as they are

- must be true to have status of explanation (otherwise its a pseudoexplanation)

(explananda)

that which is to be explained

(explanantes)

that which does the explaining

both can take a deductive form

P1: If A then B

P2: A

C: Therefore B

→ premises need to be true for an explanation but not for prediction

1. Deductive nomological model (DN)

   1. Inductive statistical model (IS)

an explanation is:

- a valid deductive argument

- (formed) from true premises

- includes at least one law or true generalization and description of some particular facts

- provides description of the fact that is to be explained

Structure:

- L1…Ln (laws)

- C1… Cn (Facts)

- E: (Explanandum)

the DN model does not rule out explaining a cause on the basis of its effec

also does not rule out an event on the basis of irrelevant info

→ model is too lax (not sufficiently strict)

an explanation is an argument that establishes that the explanandum had high probability of occurring

- uses probable, not certain reasoning (inductive)

- gives understanding for explanandum

too restrictive

→ some good explanations do not make the explanandum highly likely

an explanation should track causes, not merely state sufficient conditions for occurrence (DN model fails requirement)

explanation of event E is a description of part of the causal interactions and processes that led up to E

a causal-historical account

spatio temporal intersection between two causal processes that modifies both = when two objects intersect in spacetime

physical process able to transmit a mark in a continuous way - something that is extended in space time

difficult to obtain a full causal mechanical explanation (too many contributing factors)

refute, empirically prove that a hypothesis is false

the receptiveness of a theory to being falsified

act of falsifying a theory F

Popper’s claim that a scientific method consists in falsifying a Hypothesis

- usually not testable in isolation (too abstract, theoretical)

→ we have to generate observational implications or predictions which we then test

• Hypothesis H implies prediction O

• we check whether O is true

• we draw conclusions about adequacy of H

1. O is true

   → but H could still be false (possibly another mechanism caused O)

   → does not give us any guarantee of truth value of H

   → confirmation is not deductively valid

2. O is false

   → then H must be untrue

   → if mechanism posited by H exists then O must obtain

   → guarantees H is false

   → falsification is a valid argument scheme

rejects induction by rejecting confirmation

→ took David Hume’s induction problem to be unsolvable

• scientists can only attempt to falsify Hypothesis

a series of not yet falsified hypotheses (not a collection of true/confirmed statements)

the elimination of false theories

any source of inspiration (dreams, observations, esoteric theories)

1. Context of discovery

   - stage of proposing a hypothesis

   - no rules or standards

   - de facto thinking process

2. Context of justification

   - stage in which H is tested

   - logic and rules

   - makes science objective

   - de jure defence of correctness of thought

(what distinguishes science from pseudoscience?)

• practitioners must be able to say which observation would falsify their Hypothesis or which outcomes are excluded by theory → otherwise the discipline is pseudoscience

• statements that take risk of being falsified = good and scientific

  (theories don’t have to be falsified they just have to be falsifiable!)

1. Not as straightforwards as P assumed → H only generates predictions when combined with auxiliary assumptions + it’s difficult to pin blame for failed prediction on a single hypothesis

2. Doesn’t accord with scientific practice → researchers make ad hoc adaptations to theories in order to avoid falsification + dogmatism can have methodological values → sticking with a theory and making modifications

3. Popper ignored possible ways to “save” induction and the confirmation of theories → pragmatic justification of induction → corrobaration (acknowledged a week form of confirmation)

4. Some valuable scientific H. don’t seem falsifiable → may eliminate examples of good science

Pro:

• simple,logical model of science

• scientists as creative , undogmatic , risk-taking (appealing)

Con:

• narrow view of science

• underestimates complexity of o and h testing

• ignores mechanisms of h confirmation and empirical confirmation

Structure:

- theoretical terms have clear and stable definitions

- empirical data provides objective test of adequacy of theory (Naive view of science, Poppers view)

History:

- growth of knowledge is continuous and accumulates

- all scientists in history share same norms of rationality (norms of what counts as evidence, good observation)

a conceptual framework which shapes thinking and work of scientists and defines a period of “normal science” in a branch of science

Consist of :

• assumptions about the world as studied by that science

• examples of how to solve problems

• a style of theorizing

- scientists solve puzzles within a paradigm by imitating examples

- a paradigm gives clear norms for progress (new discoveries), professional stability/career, coordination, concentration of effort

- strict boundaries to creativity

- when scientists find radically new data that cannot be explained within the current paradigm

- sequential phases : new data as anomaly; explanation is necessary; current paradigm is inadequate → scientific revolution

- scientists focus on new data and sketch a new paradigm based on it

- period of revolutionary crisis (split scientific community in conservative and progressive)

- social process of paradigm shift

lack of shared standards

not comparable

- according to Kuhn, subsequent paradigms are incommensurable + not just different

3 types:

• semantical

• observational

• methodological

meaning of terms are gotten from the paradigm and differ depending on the paradigm (different meanings)

→ impossible to translate between paradigms, failure to communicate, no clear logical relations between paradigms

concerning sensory perceptions

→shaped by paradigm we do science in → we see different things in different paradigms

(Gestalt switch, ambiguous figures)

- ontological consequences → scientists exist in different worlds, rejection of realism, “different worlds”

concerning norms of rationality and progress

→ each paradigm has their own view

→ no paradigm independent criteria for theory choice or norms for scientific progress