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2013-‐09-‐05
1
Philosophy of Science
Why do we have a lecture on the philosophy of science?
• To communicate science, you need to think criCcally about the topic
To do this: • You need to know what science tries to achieve
• You need to know what can go wrong
What does it mean to be “scienCfic”?
• You’ve been studying it intensively for two years
• Many of you are now doing research projects
• Many of you plan to pursue a scienCfic career (doctoral research, medicine…)
So what is it?
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Science is derived from facts
• Historically knowledge was derived from authority
• The idea of systemaCcally tesCng “truths” is oRen aSributed to Galileo in the 17th century (though it was probably more common)
• E.g. Aristotle, Galileo and falling masses
AssumpCon of Facts
1. Careful observaCon will lead the observer to
collect an accurate fact
2. Facts are independent of any theory (they lead
to theory)
3. Facts are firm and reliable (REPEATABILITY)
1.Careful observaCon will lead the observer to collect an accurate fact
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1.Careful observaCon will lead the observer to collect an accurate fact
Two observers don’t necessarily see the same thing
X-‐ray InterpretaCon Fish Eye Colour
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2. Facts are independent of any theory (they lead to theory) • But wait! Lets think about the stair example
Not everyone sees a staircase! Our experiences influence our percepCons
3. Facts are firm and reliable
• Facts change E.g. Earth’s axis changes its Clt, the length of a day increases over Cme, the magneCc poles reverse Our judgement about the validity of an observaCon is guided by what we know or assume. This makes facts just as fallible as our assumpCons.
DeducCve Reasoning
• Proceeding from general statements to a specific conclusion.
• This conclusion must be true given the previous statements
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Logical arguments
1) Philosophy is dull 2) This lecture is on
philosophy
Thus: 3) This lecture is dull
3 is a logical conclusion which flows from 1 and 2 If 1 and 2 are correct, 3 must be correct
1) A bird is a mammal 2) A crow is a bird
Thus: 3) A crow is a mammal
This is also logical It is not correct because Premise 1 is incorrect. We have drawn a false But logical conclusion
1) Copper expands when heated
2) Iron expands when heated
3) Gold expands when heated
Thus: 4) All metals expand when heated
This is not a logically valid argument It is however correct It is possible for 1,2 and 3 to be true but to also find a metal that does not expand when heated.
InducCve Reasoning
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InducCve Reasoning
• A lot of science actually proceed like this we use a finite amount of available evidence to generalize about how all cases will respond.
• Why?
How do we make a good inducCve
conclusion? • Large number of observaCons
– (however this can get ridiculous – should I burn my hand 100 Cmes to demonstrate fire burns?)
• True under a wide variety of condiCons – (this can also get ridiculous – should I do it with different coloured socks?)
• No contradictory cases – (is this fair? There are very few rules that do not have excepCons)
Karl Popper and FalisficaConism • InducCvist theories can be structured so vaguely that any fact can support them.
• FalisificaConists advocate that all scienCfic theories should make testable predicCons
• We set them up to be rigorously tested, and embrace the fact that they can be found false
Sir Karl Popper 1902-‐1994
London School of Economics
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FalsificaCon
Hypothesis 1: All substances expand when heated Hypothesis 2: You may find success today if you pursue a risky venture
FalsificaConists argue: • The best theory makes many specific predicCons and runs the highest risk of being found false.
• Any new hypothesis should be more falsifiable than the one it replaces
• Hypotheses cannot be “true” but can be more widely supported than a previous one
• Allows us to compare the relaCve strength of compeCng hypotheses
Lets see if you get it….
• Hypothesis 1: mars moves around the sun
• Hypothesis 2: all planets move around the sun in an ellipCcal orbit
• Hypothesis 3: all planets move around the sun in closed loops
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• InducCvism: the goal of science is to increase the observaCons which support an hypothesis
• FalsificaCon: the goal of science is to test the falsifiability of an hypothesis or to replace it with a more sophisCcated hypothesis
The problem with FalsificaCon
• A general hypothesis is usually based on a series of interrelated hypotheses
• TesCng a predicCon normally relies on a complicated experiment
• If the hypothesis is falsified, it may be impossible to decide why… a minor sub hypothesis? A technological problem?
The 1960’s = Theory based methods
• The previous philosophies were seen as two rule following
• The response in the 1960s was the emergence of the idea that science should be based on an underlying theory, not the acquisiCon of facts
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Kuhn’s Paradigms
Thomas Kuhn 1922-‐1996
University of California
Berkeley
Pre science
Normal science
Crisis
Paradigm RevoluCon
New-‐normal science
New Crisis
Progress
Paradigm
Lakatos’ Research Programmes
Imre Lakatos 1922-‐1974
London School Of Economics
-‐ Strongly influenced by Popper
-‐ ASempted to “fix” falsificaCon
-‐ Not all theories are equal, if you falsify a theory, you can modify “lesser” components and thus maintain the “research programme”
-‐Programmes can be replaced by one that makes beSer predicCons
Feyerbend’s anarchisCc theory
Paul Feyerbend 1924-‐1994
University of California
Berkeley
-‐ Anything goes
-‐ Argued that scienCsts rarely follow a specific method, and to do so would constrain creaCvity
-‐ Did not think science should be given any special status -‐ Argued that scienCsts should pick any theory they want to follow just as we pick poliCcal parCes
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The Bayesians
Thomas Bayes 1701-‐1761
Kent, UK
-‐ Rediscovered Bayes Theorem -‐ Assign probabiliCes to various hypotheses based on prior evidence -‐ New types of evidence provide more support than addiConal evidence of the same kind -‐ We will return to Bayesian maths in our discussion of phylogeneCcs
Mayo’s experimentalism • Experimentalists have pracCcal methods of measuring effects without the need for theories
• Progress in science is the accumulaCon of experimental data
• This likely encompasses some of what we call applied science and the science of error staCsCcs
Deborah Mayo Virginia Tech , USA
London School of Economics
Summary • Induc&vism: gather lots of data to generate theory • Falsifica&on: theory dependent, theories are those that survive test
• Kuhn’s Paradigms: consensus of the community on what paradigm to use
• Lakatos Research Programmes: protect core ideas and modify peripheral hypotheses
• Feyerbend: rejects “method” in favour of theory • Bayesian's: use probability to evaluate compeCng theories
• Experimentalists: it is possible to use method to evaluate data independent of theory
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What is “science” and how do I know if I am being
“scienCfic”?
Today’s take home message:
• The answer to that quesCon is not obvious
• The answer has changed through Cme
• The answer is not universal between or within disciplines
Clare’s Science (forgive the imperCnence)
• DisCnguish between the goals and method.
Goals of my science: 1) To gather an understanding of cause and
effect relaConships 2) To predict future event based on past
observaCons 3) To explain mechanisms
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Clare’s Science
Methods of my science: 1) Make observaCons 2) Explain observaCons in light of theory 3) Make predicCons which test theory 4) Be willing to acknowledge sources of error 5) Be willing to change theory
Finally: A note on the philosophy of science
• Consider the evoluCon of science philosophy
• Just as we falsify hypotheses, change theory and celebrate the recogniCon of errors, we also revise the philosophical mechanism of study
• What could be more scienCfic?
What is this thing called Science? A.F. Chalmers
If you find this topic interesCng I suggest you read this book. Most of this lecture is based on the text. At my PhD insCtuCon we were all required to read this before we graduated!
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