Spring in London

Hello Everyone!

 

This last month without classes has been wonderful.  London has been warming up, so I’ve had some great opportunities to walk around a bit more and find some nice gardens to study in.  Because spring is my favorite season I thought I’d focus this post on nature.

 

Parks in London

Since I spend a lot of my time walking around London, I’ve seen many of its parks by this point.  Here are some of my favorite pictures from this spring.

 

Holland Park, situated just west of Kensington Palace, with tulip patches, the Kyoto Garden, and this nice peacock (I took a video of him calling to a female and raising his tail, but I’m not sure how to upload that).

 

Regents Park, near my residence off Regent’s Canal, situated near Primrose hill and the London Zoo.

 

Hyde Park and Kensington Gardens, established by Henry VIII in 1536 as hunting grounds and opened to the public in the early 1600’s.

 

The Peace Pagoda at Battersea Park, completed in 1985 by Rev. Gyoro Nagase and 50 volunteers to promote peace and global harmony and to oppose nuclear weapons.

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Kew Gardens (or the Royal Botanic Gardens), a 326 acre site that contains numerous gardens and greenhouses open to the public and is also a botanical research and conservation site home to an internationally recognized seed bank.  Founded in 1759 by Princess Augusta, it is home to the world’s largest and most diverse collection of living plants.  The Dutch House, what remains of the larger complex of Kew Palace, is situated in the back of the gardens and is more aptly described as a pink country home.  It was used on and off through the 18th century to house close relatives of the crown (for example as a schoolhouse for the future George IV).  (www.kew.org)

 

Tate Britain

I also can’t help mentioning the Tate Britain, the 4th or 5th London museum that I’ve visited, and my favorite by far.  Not only does it have two works by Henry Fuseli, a 19th century Swiss painter I fell in love with after I saw The Nightmare at the Detroit Institute of Art, but there are numerous other works that I could just sit and stare at for hours.

 

First though, I’ll mention a conundrum I’ve faced: to take photos in museums or not to take photos?  On the one hand I would rather enjoy my experience than spend the whole trip obnoxiously cataloging every moment of it on my phone (or, shudder, taking selfies with the paintings), but on the other hand the immensity of the number of fantastic exhibits I’ve forgotten keeps me up at night.  Given that most museums these days allow non-flash photography, my compromise is to take photos sparingly, but so I might remember my favorite artists and styles for the future.

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Henry Fuseli’s Shakespearean depictions of (1) ‘Lady Macbeth and Seizing the Daggers’, and (2) Tatiana and Bottom (from a Midsummer Night’s Dream).  (3)The Nightmare, on exhibit at the DIA, sensualizing the vulnerability of unconsciousness, and perhaps alluding to sleep paralysis (final photo taken from https://en.wikipedia.org/wiki/The_Nightmare).

 

Here are a few of my other favorites from the Tate:

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(1) The Archers (1769) by Joshua Reynolds depicts two 18th century aristocrats dressed in ‘quasi-historical’ Renaissance costumes posed heroically and on the hunt.  I thought this painting was hilarious and wonderful because it shows that even 250 years ago people were dressing up in cosplay to live out heroic fantasies.

(2) Proserpine (1874) by Dante Gabriel Rossetti, depicting Proserpine in the underworld, holding the pomegranate which sealed her fate as the wife of Hades.  In the painting Proserpine’s face is bathed in light from Earth and she looks longingly towards the living world.

(3) The Annunciation (1892) by Arthur Hacker and influenced by Spanish and Moroccan styles of the day, shows Mary receiving The Annunciation from an invisible angel.  It’s harder to see in a photograph, but the lighting on this painting was just phenomenal.

(information for the above three descriptions taken from signs at the Tate Britain)

 

Cheers!

Ashley

 

Nice, and the End of Lent Term

Hello everyone!

 

Last Friday was my last day of class (wow that went really quickly!!).  It feels like it was just a few days ago that I was writing here about the start of Lent Term, and now it’s over.  From here I have until the end of May to study for my exams, which are the first week of June, and the rest of the summer to complete my dissertation, which is due mid-August.  I’m not going to reflect now on everything I’ve learned being at LSE, because that would make me too sad about how my time in London is almost over, so I’ll make a note to be extra reflective this August.  For the time being I’m content to live in the present and to keep building on what I’ve done.

 

Nice

In mid-February, keeping with my New Year’s Resolution to travel more, I visited Nice, on the southern coast of France, and spent a day in Monaco, which is a short bus ride away.  It was wonderful to see the Sea (I was corrected by a local that in Nice, it is the Sea, not the ocean), which was bluer than any water I’ve seen before.  It was a little too cold for me to swim (though some brave locals were in the water), but quite warm enough to walk along the shore and hike to the top of some hills overlooking the water.  I had some delicious cheese, escargot, and rosé, and saw some super big yachts in Monaco.  I had a nice chat with an old philosopher I met on the beach who took me antique shopping and told me that Nice is one of the most relaxing places to live, especially in the off-tourist season when it’s near empty.

 

 

Manipulationist Account of Causation

We talked about a few types of causation in my philosophy of science course, and I thought I’d mention here the one I found to be the most convincing: the manipulationist account.  Essentially, X is a cause of Y if intervention on X produces a change in Y, i.e. X must have manipulative control over Y in order for X to be a cause of Y.  An intervention is defined as a change in Y that can occur only as a result of a change in X.  For example, a strong wind can be said to cause me to have dropped my ice cream if changes in the wind’s velocity result in me dropping the ice cream differently or not at all.  Although of course I could not actually change the wind to determine how it affects my ability to hold on to things, I can imagine changes to its speed and direction and the resulting effects.  Stated more simply, X is said to intervene on Y if all of the following are true:

(1) An intervention on X changes the value of Y (but not all changes to X must do so)

(2) All changes in Y must result from the intervention and not another source

(3) The intervention, I, must travel to Y through X and not through another source

(I → X →Y), not (I →Z →Y and I →X) or (I →Y and I →X)

An important part of defining causation is its effect on how we perceive science.  According to the above model (summarized from James Woodward’s manipulationist account), one can differentiate explanatory knowledge from descriptive knowledge (and explanatory sciences from descriptive sciences).  Explanatory knowledge provides control by defining causal relationships, while descriptive knowledge is merely a systematization of observations.  Woodward gives the history of biology as an example.  Biology was a descriptive science until the invention of new instruments and experimental techniques in the early 20th century (such as the microscope) allowed for interventions, and the development of the explanatory field of molecular biology.  I like this account of causation for two reasons that Woodward mentions: (1) it defines causation in a way that applies both to everyday life and to scientific study, and (2) it provides an account of causation that represents the way that scientists themselves think of causality.

A problem for the account is the so called ‘common cause’ dilemma – where one might be fooled into thinking X is a cause of Y because a change in X resulted in a change in Y, when in reality both X and Y are caused by Z.  For example, one could observe that just before thunderstorms appear, barometers show a drop in pressure and all the cows in the field lie down.  The Manipulationist account could lead one to all sorts of strange conclusions, such as ‘when the barometer does not show a drop in pressure, the cows do not lie down; therefore, a low barometer reading causes cows to lie down.’  Likewise, once could conclude ‘because storms only occur after the cows have lied down, cows lying down is a cause of thunderstorms.’

In reality, it is a low pressure system that (1) draws the storm clouds in, (2) results in the barometer showing a drop in pressure, and (3) causes the cows to sense a change in weather is coming and to lie down.  However, all of the above statements of causality are logically sound given the manipulationist account, meaning one must be careful in assigning causal relationships.

 

As I’m done with classes I’m going to try to see more of the British countryside in the coming weeks (and I hope to study while I’m there), so I’ll try to upload some pictures from those trips as I go.

 

Cheers!

Ashley

 

References:

Woodward, J. (2003) Making Things Happen, Oxford: Oxford University Press, Ch. 1.

 

Winter Break, Lithuania and the Start of Lent Term

Hello Everyone!

 

I’m a few weeks into my Lent term now, after having the majority of December off and getting the chance to return to the states over Christmas.  I thought I’d use this post to discuss my busy break, New Year’s, and my Lent term course schedule.

 

Winter Break

Over Winter Break I flew back to the states, and, on my way to Ann Arbor to attend the Jones fellowship interviews I managed to work in a 2-day layover in New York City to see a friend.  Being a fan of weird comedy, I went to see Oh, Hello at the Lyceum Theater on Broadway – a 90 minute, two-man play by Nick Kroll and John Mulaney.  It was an hour of scripted comedy and thirty minutes of an improvised fake interview show where Alan Alda came on stage and they pranked him by giving him a large tuna fish sandwich (it’s real, I promise: http://ohhellobroadway.com/).  We also managed to get some discount standby tickets to see The Phantom of the Opera, which was one of my favorite books in high school, and, no matter that we had seats pretty far back in the balcony, I was absolutely blown away by the atmosphere of the performance.  On the way to Oh, Hello we stopped at Bemelmans’ Bar at the Carlyle Hotel to have a very expensive (in my consideration) cocktail at one of the most beautiful bars in New York.

 

Left: picture taken from http://ohhellobroadway.com/.  Right: Bemelmans Bar, with its famous walls as painted by its namesake Ludwig Bemelmans, an Austria-Hungary born American painter and illustrator, in 1947.

 

After New York I spent a couple of days in Ann Arbor to attend the interviews for the next Jones Fellow.  It was a strange experience being back on campus, but very nice to see some familiar faces again.  With the nostalgia a lot of my engineering work came flooding back to me, which was a good reminder that all this philosophy is helping me contribute something greater through my work as a scientist.  It’s unfortunately been easy while in London to jump into philosophy while disregarding my engineering background, so going ahead I hope to integrate the two more in my papers and possibly in my Master’s dissertation, which has been looming with increasing prominence in my mind.

 

New Years

Following Michigan I was able to spend a few weeks at home with family in Minnesota (which somehow managed to be even colder and snowier than Ann Arbor), before flying back to London in time for New Year’s.  I spent New Year’s on Westminster Bridge and saw a wonderful fireworks display over the London Eye with a friend:

 

 

I made a few New Year’s resolutions, and I think that if I publish them online I’ll be more likely to stick to them (fingers crossed).  First, I hope to travel more.  In the fall I became very caught up in my studies and so mostly stayed around the London area.  I’ve decided now to try to travel once a month.  Second, I’d like to learn to swim.  I can swim reasonably well from swimming in lakes as a kid, but I’ve never been able to swim laps in a pool.  As my gym gives free weekly swimming lessons, I had planned to take lessons beginning last fall, but I never worked up the motivation, so I’d like to attend those more regularly this year.

 

As for my travel resolution, I’m off to a good start as I travelled to Vilnius, Lithuania for a few days in January.  Though very cold in the winter, Lithuania is still very lively and has very good food and interesting architecture.  Having been occupied twice by the USSR, with an intermediate occupation by Nazi Germany, Lithuania has a rather tragic 20th century history, which is well-documented in the Museum of Genocide Victims, located in what were once the KGB offices in Vilnius.  However, Lithuania was the first nation to proclaim its independence from the Soviet Union, which it did following elections in 1990.  Lithuania joined the European Union in 2004 and, though it is not among the wealthiest of countries in the EU, according to the National Museum of Lithuania is currently one of the fastest growing economies in Europe.

 

 

Lent Term

And finally, I thought I’d cover my Lent term course schedule.  Two classes I’m taking, Philosophy of Science, and Rationality and Choice, are full units, so they were taught both during the fall and in the spring.  I took one half unit in the fall, Evidence and Policy, and so am taking a second half unit this spring (to fulfill LSE’s three unit requirement for MSc students).  As there were a few half unit courses in Lent term that I’m interested in, I am taking one half unit and auditing two others: (1) Society, Technology, and Resistance, (2) Effective Philanthropy, and (3) Genes, Brains, and Society.

 

Society, Technology, and Resistance

Tarde (1980) famously argued that creativity and invention have none or little regularity, while the diffusion of new ideas and practices follows the ‘laws of imitation’. This idea remains very influential in the models of diffusion of innovation and the linear model of science translated into technical engineering and marketing. The course will examine critically how this model is only valid hen there is no or little resistance in the process which, however, is rare. More common are efforts of techno-scientific mobilizations that encounter resistance, and resistance changes the process by focusing attention where needed; enhancing the ‘collective we-image’, evaluating on-going efforts of mobilization and urging strategic adaptation and delays to the plan. We will explore various conceptions of ‘resistance’ across the social sciences and develop the functional analogue to ‘pain’ in relation to collective activity (Bauer, 1991, 1995 and 2015). In this light, we will examine public resistance, public engagement with science and its debates and impact on the developments of nuclear power, genetic engineering and information technology leading into current mobilizations for Nanotechnology, synthetic life forms, and robotic automation.

 

Genes, Brains, and Society

This course examines, from a philosophical perspective, the ways in which recent developments in genetics and neuroscience challenge our conceptions of what we are — and what we could become.

Topics covered include:

Human nature: Does the concept of ‘human nature’ have any biological basis? Can we distinguish between those traits which are part of ‘human nature’ and those which are not? And is ‘human nature’ fixed, or can it be altered by technological means?

Sex and gender: Are ‘sex’ and ‘gender’ the same thing? Are gender categories natural or social? Are there robust psychological differences between men and women? If so, are they explained by genes or by culture? And should we reconcile ourselves to these differences, or should we try to eliminate them?

Race: Do races exist? Is there any objective biological basis for racial categorization, or are races socially constructed? Does the concept of ‘race’ have a future, or will human societies soon become racially undifferentiated?

Free will and responsibility: Has neuroscience debunked the notion of ‘free will’? If so, can we still be held responsible for our actions? Should neuroscientific data be used to predict—and prevent—wrongdoing?

Right and wrong: Has neuroscience shown that morality is more a matter of emotion than reason? Can we use neuroscience to help us choose between ethical theories, and to help us improve our own behavior?

 

Effective Philanthropy

The course will address key questions in philosophy and social science concerning philanthropy, including:

  • Which motives actually drive philanthropy and which motives should drive it? • What is the nature and extent of our moral obligations to philanthropy? • Is the proper aim of philanthropy to ‘do the most good’? • How should the good aimed at be conceived of and measured? • How, if at all, should people’s rights and the risks of causing harm constrain the pursuit of the good? • What are a charitable organization’s duties of accountability towards its stakeholders (e.g. donors and employees) and those whose lives it aims to affect? • Which career and personal choices should one make in order to further philanthropic aims? • Which moral principles govern the relationship between the state and private philanthropy? Between corporations and charities?

 

(course outlines taken directly from the LSE graduate course guide, found here: http://www.lse.ac.uk/resources/calendar/courseGuides/PH/2016_PH427.htm)

 

 

 

Cheers!

Ashley

 

Cumberland Lodge

Hi Everyone!

A couple weeks ago I got the chance to attend the LSE philosophy retreat at Cumberland Lodge, so I thought I’d tell you all about it. The retreat was interesting, there were about 40 philosophy students there, and we spent the weekend attending lectures, drinking around an exceptionally cozy fireplace, and exploring Windsor Great Park (the Lodge is located a few miles south of Windsor Castle, the royal residence).

 

First, it was really great to get out of the city and see more of the English countryside. I’ve been so busy with school that I hadn’t gotten the chance to really enjoy fall (there aren’t many trees in London, besides in a few groomed parks, and those don’t count), so it was nice to go somewhere and see the leaves change.  I unfortunately signed up for the conference pretty late and so was unable to stay in the Lodge itself.  Instead I stayed in the adjacent building, The Mews.  ‘Mews’, I learned, means a stable that has been adapted for residential purposes.  Luckily there haven’t been horses in the building since the 1700s and it was very nice inside.

 

The Lodge itself was beautiful; we received a short history lesson upon our arrival and learned that it was built by army captain John Byfield in the 1650’s after he was sold the land by Oliver Cromwell, whose intention was to pay off debts incurred during the recent civil war. After the captain’s death and the Restoration, King Charles II reclaimed the land.  For hundreds of years following, the Lodge was used to house the Ranger of the Great Park, who tended to be a close friend of the King or Queen.  Then, in 1947, King George VI granted the Lodge to the St Katharine’s Foundation for use as an educational establishment.  Its purpose since has been to gather together students to discuss scientific, social, and ethical issues in order to avoid another catastrophe of the scale of WWII.  The Foundation’s founder, Amy Buller, believed that a large contributing factor to the rise of Nazism was the lack of such open discussion in the German education system of the 1920s and 30s.

 

 

I took a walk with some other students on Saturday through the park and saw a group of cows in a green, misty field – which felt like a decidedly English moment so I’m happy I got a picture.  There was also a lovely stuffed bird in the Lodge – I got a picture of that too.

 

 

Cheers!

Ashley

Epistemic Dependence

Hello Everyone!

 

I’ve been taking a course titled ‘Evidence and Policy’ where each week we have a discussion about the relation between science and public policy – both what that relation is and what it should be. We’ve been talking a lot about the idea of ‘epistemic autonomy’, and question of whether or not citizens in a democracy truly make decisions for themselves. This gets into the concept of epistemic dependence, or believing an idea because an ‘expert’ believes it. In wake of this week’s election it seems prudent to think about the validity of epistemic dependence.

In lecture we first discussed the difference between freedom and autonomy. One could say that freedom is a stronger form of the idea: to be free is to act unconstrained by control or influence. In contrast, to be autonomous is to act according to one’s principles – someone who is autonomous is not being controlled, or told what to believe, but may be influenced. At the point where influences over a person become strong enough that they diminish or change a person’s principles rather than adding context to them, that person is no longer autonomous.

For example, a man who eats carrots for the vitamin A is acting autonomously, but a boy who dislikes carrots but who eats them because his mom tells him to is not acting autonomously. The line becomes blurred when one asks at what point in his life does the boy eat carrots of his own volition? To some extent, even as a man, he might believe that carrots are healthy because this was instilled in him as a child, and he may continue to dislike the taste. But as his mom is presumably no longer watching him eat his dinner, he must now be acting on his own principle, one that he adopted from his mother.  In addition, is his knowledge that carrots contain vitamin A an autonomous belief if it was taught to him and he did not come to the conclusion independently?  Must he instead teach himself about medicine and conduct the studies himself to be free of all controlling influence?  This is an impossibility when the entirety of knowledge is considered.

John Hardwig challenges the assertion that epistemic autonomy is requisite for a true democracy. Consider again the case of a mechanic. To be autonomous, a person with car troubles would have to investigate his problems independently by teaching himself how cars work, and could then go to the mechanic and pay to have the problems fixed. Hardwig claims that this person is rational to instead defer to the mechanic’s judgment. The man must rationally determine that the mechanic is to be trusted by choosing which mechanic to go to (say he chooses mechanic A because mechanic B is notorious for cheating customers), but he must not be personally knowledgeable of cars to do so. He in this case took his knowledge of the mechanic ‘s expertise as evidence that the mechanic’s diagnosis of his car troubles would be correct.

 

Farther-reaching examples of epistemic dependence include citizen assumptions that smoking causes lung cancer and that the Earth revolves around the Sun. Especially in examples of healthcare, it would be impossible in practice for all people to be able to diagnose their own illnesses. In the context of voting, a person may rationally support a war because of his trust in a respected politician  (he is rational so long as he put thought into trusting the politician – and note that ‘rationality’ does not imply rightness or ethical superiority). Another person can be equally rational in opposing that war if she has greater respect for a politician on the other side of the issue.  However, a third person who supports the war because she blindly follows a politician is not acting rationally. This third case begs the question, what is and what is not rational justification for depending on someone? Can I support a politician because he is a good speaker? Because my mom supports him? Because I know he and I agree on four issues (even though I may not know his opinion on thirty other issues)? If someone supports a politician for his economic policies but not his stance on climate change, is that person partly to blame if after he’s elected he removes limits on coal mining, which the person may oppose (and in this instance, has the person lost autonomy when it comes to coal mining, as the person is no longer in control and the politician will surely contradict the person’s principles)?

 

Support, Hardwig claims, is a response to evidence of a person’s expertise.  Hardwig claims that person A’s epistemic dependence on person B is not support which lacks evidence, but rather that person A’s belief in person B’s credentials is itself evidence for person A that person B’s belief is true. To argue against this is to claim that most of our beliefs are irrational. After all, most people believe that there is no breathable air in space, but have never been there.  A problem with Hardwig’s claim is that calling non-expert support in experts ‘evidential’ is to label all subjective trust evidential, effectively placing the non-expert’s opinion on par with the expert’s

A response to Hardwig’s argument, by Elizabeth Fricker, claims that the above is not evidence. She claims that person A cannot count belief in person B as evidence of person B’s beliefs. As person B is the expert in the matter, only person B’s thoughts on the matter are hard evidence. However, person A can take person B’s opinion on the matter as a form of testimony: person B’s word is evidence to person A, and person A’s opinion of that evidence (such as person A’s belief in person B) is merely an interpretation of that testimonial evidence. I think this is a cleaner way of defining evidence because it removes the problem introduced by Hardwig’s theory where a layman’s opinion of an expert is evidence equal to that expert’s opinion. According to Fricker, a layman’s interpretation of an expert’s evidence may lead them to a conclusion, but that conclusion is not as founded as the expert’s is. So a man supporting a war because his favorite politician does is not evidence of equal weight to a politician supporting a war because he has researched the pros and cons of the endeavor. Fricker reintroduces the idea that one must do the work for one’s opinion to be valid – it’s not enough to regurgitate ideas without deeper reflection.

 

References

John Hardwig Epistemic Dependence (1985)

Elizabeth Fricker Testimony and Epistemic Autonomy (2006)

 

 

Cheers!

Ashley

 

 

P.S. The picture above is the ‘Enlightenment Room’ in the British Museum.  It seemed appropriate to the topic.

 

 

What is Science?

Hello everyone!

 

Due to my inability to use WordPress correctly, I’ve accidentally been saving my posts as drafts rather than posting them! Due to that, I’ve put up two posts back to back today and will try to be more careful in the future.

 

I’m now in my 5th week of classes, though it often still feels like I’ve just started.  For example, what I’d initially thought to be an interesting albeit trivial question, ‘what is science’, has in fact become more complicated the more I learn about it.

I’d been a bit taken aback my first week of classes when I learned that the typically accepted definition of science (by ‘accepted’ I mean the definition of science taught in elementary education – in University level science courses the question of what does and doesn’t constitute science rarely comes up other than to debunk poor research techniques) of observation-based conclusions used to prove and disprove theories is not generally accepted as ‘science’ from an epistemic point of view.  This topic had been debated heavily in my Philosophy of Science class – as one might expect.

Popper

We first looked at the Popperian view of science, which introduced the concept of falsifiability.  Popper says that an idea is more scientific the more falsifiable it is, the more predictive power it has, and the riskier it is.  Falsifiability, in this sense, concerns a concept’s capacity to be tested. So a theory such as intelligent design is not very scientific because one cannot test the claim that an intelligent being designed the world – this does not prove that intelligent design is a ‘false’ theory, just that it is not a scientific one by this standard.  Additionally, by this definition theories are never proven true, but are either falsified through testing or ‘survive’ for another round.  Predictive power is another necessity of a scientific theory.  A theory that merely explains events of the past (there have been unusual levels of flooding along the Louisiana coast for the past 10 years) is unscientific, while a theory that hypothesizes future events based on the past is more scientific (recent trends in flooding along the Louisiana coast suggest that more powerful storms are to come).  And where predictability is concerned, Popper says that the more specific a prediction is (and thus the more easily falsifiable), the more scientific it is.

My problem here is that I find it difficult to dismiss all conception of truth. While I don’t believe absolute truth is necessarily obtainable, I certainly think one theory can be more true than another, rather than just not-yet-proven-false.

Kuhn

We next looked at Kuhn’s interpretation of scientific development as it concerns paradigms. Rather than that science is a universal pursuit resulting in progressive accumulating knowledge, Kuhn asserts that science evolves in a messy fashion characterized by periods of ‘normal science’ and ‘revolutionary science’. A paradigm consisting of a central theory, auxiliary hypotheses, heuristic models, and various methods is developed during period of revolutionary science.  From this point, the paradigm grows as its auxiliary hypotheses are adapted and developed during periods of normal science where little questioning of the paradigm occurs.  Within this period, anomalies, or problems that the paradigm cannot solve, are discovered.  Given time a secondary paradigm will develop, under a new period of revolutionary science, which will take the place of the current paradigm (a paradigm shift) – here science leaps forwards, carrying with it some knowledge from the previous paradigm but leaving behind ideas that are incommensurable.  A paradigm is said to be ‘better’ than another if it solves problems more accurately, more consistently, with a broader scope, and/or with a simpler theory.

A typical example here is the switch from the Ptolemaic theory of planetary motion where the planets revolve around the Earth to the Copernican theory of planetary motion where the planets (including the Earth) revolve around the Sun. Ptolemy’s theory had been adapted during periods of normal science to continue to fit the growing knowledge of planetary motion, such that now the planets revolved on an axis which revolved around a location adjacent to Earth (i.e. Ptolemy’s theory was becoming excessively complicated in order to account for inconsistencies between theoretical and observed motion).  According to Kuhn, Copernican theory not only better explained planetary motion, but did so with simpler reasoning.

Lakatos

Just this last week we started looking at Lakatos’ concept of scientific research programs, an idea that attempts to find common ground between Popper and Kuhn’s ideas. Lakatos starts by distinguishing refutation from rejection.  He states that while particular evidence may refute a theory, by no means does this necessitate that the theory as a whole be rejected, as a naïve conception of Popper might suggest.  This, he argues, would lead to the rejection of all theories.  Thus, a systematic method of determining at what point refutation should lead to rejection must be developed, and, in the case of rival theories, it must be determined how one can determine which of a pair of refuted theories should be accepted, and which should be rejected.

Here Lakatos puts forth the concept of a research program, which he distinguishes from a paradigm. A research program consists of a hard core (the unchanging definitive part of a theory), a protective belt (adaptable theories designed to protect the hard core), and a negative and positive heuristic.  The negative heuristic mandates that the protective belt be sacrificed to preserve the hard core.  The positive heuristic determines how the protective belt should be modified – what should be added to preserve the core without compromising the integrity of the research program.  For example, in order to preserve the core of the Ptolemaic conception of planetary motion in the wake of contradictory evidence, the equant was introduced and further epicycles were added.  While this allowed the research program to survive, it also introduced what Lakotos calls ad hoc changes to the theory, which are auxiliary hypotheses introduced to correct for contradictory evidence but which fail to increase a theory’s predictive power.  Ideally, an auxiliary hypothesis will be substantiated by the introduction of a previously unknown ‘novel fact’ which independently supports the new hypothesis.  Thus initial conditions may be changed to account for the strange orbit of Mercury, but this new hypotheses merely accounts for an anomaly, it is not substantiated through correct prediction of other phenomena.

Lakatos says that a research program is progressing when it consistently adds empirical content (new hypotheses) and intermittently has empirical progression (new, substantiated hypotheses), and a research program is degenerative when it fails to produce both of the above.  A new research program may supersede an old research program if it is progressive while the other is degenerative.  Here Lakatos has married Popper’s conception of falsifiability (as the more complex rejection and refutation) with Kuhn’s general conception of paradigms.

I particularly like Lakatos’ differentiation between rejection and refutation. Having worked in a few research settings, it’s clear to me that being forced to reject any theory that has imperfect data would be mad.  As all models contain assumptions it should be implicit that not all data will fit and that the best model, rather than the right model, should be sought.  Where in vitro studies and computational studies are concerned, the best model is often one that is purposefully incorrect in that it is known to be a highly simplified representation of the true environment which allows for faster and cheaper data acquisition.  I appreciate that Lakotos’ idea of research programs allows for a more applicable philosophic interpretation of science than either Popper or Kuhn’s ideas do.

 

While I’d like to give my own complex response to each of these analyses of science, I strangely enough feel less comfortable now with the idea of ‘science’ as a concrete thing than I used to.  While I’ve started to question my basic notions of science as a tangible, mathematical field of purely rational evaluations of observable qualities, I don’t yet have a good representation of my current feelings on the matter.  Intuitively in my mind, subjects like quantum physics and biomedicine are scientific in nature, and subjects like intelligent design and psychoanalysis are not, but what differentiates these disciples? Is it that scientific theories are adaptable and aim to best fit the evidence while non-scientific theories instead twist evidence to avoid changes in doctrine? Is it that scientific theories are testable and non-scientific theories are not, is it that scientific theories make predictions and non-scientific theories are ad hoc?  It seems wrong to differentiate the scientific from the non-scientific based on gut feelings, but it also feels wrong to overcomplicate the definition of science to the point of inapplicability.  So for now I’ll leave the question open.

 

 

Cheers!

Ashley

 

P.S. I finally found the Michigan alumni group in London and I got a chance to get together with some fans at a bar this weekend to watch the Michigan – MSU football game, which was a ton of fun (especially considering we won)! Go Blue!!  🙂

 

 

References

Popper, K. (1963) ‘Science: Conjectures and Refutations’, in M. Curd et al. (eds.) Philosophy of Science: The Central Issues, second edition, New York: W.W. Norton & Company, 2012.

Kuhn, T. S. (1962) ‘The Nature and Necessity of Scientific Revolutions’, in M. Curd et al. (eds.) Philosophy of Science: The Central Issues, second edition, New York: W.W. Norton & Company, 2012.

Lakatos, I. (1968) ‘Criticism and the Methodology of Scientific Research Programmes’, Proceedings of the Aristotelian Society, vol. 69: 149–186. (Read Sections 3-4, pp. 167-186).

Introduction to Philosophy of Science

 

Hello everyone!

I wrote this two weeks ago and for some reason saved it as a draft instead of posting it, so here it is now:

 

To help get my own thoughts in order and to jump into this whole “philosophy” thing, I’d like to define a few definitions from my early classes.

 

Deduction vs. Induction

Deduction is an argument whose conclusion is guaranteed by it’s premises (assuming the premises are true).  An example: It is windy in London.  If it is windy, John is wearing a windbreaker.  Therefore John is wearing a windbreaker.

Induction, in turn, is the act of basing predictions on past observations.  For example: Every time I’ve come near fire, my skin has been burned.  Therefore all fire burns skin.

Whereas deductive reasoning uses evidence to guarantee the truth of the conclusion, induction merely offers support of the conclusion.  I have no way of knowing that every fire is hot without testing it every time, but the notion that all fire is hot is supported by my past observations.

David Hume offers a famous objection to the validity of induction.  First, he refutes the “uniformity of nature” (the idea that nature follows consistent rules that allow the future to be predicted by the past).  As an example of this non-uniformity, Hume offers the example of a chicken that has been raised by a farmer.  No matter that for every day of the chicken’s life it has received food from the farmer, one day the farmer will instead wring the chicken’s neck for his own dinner.  In this instance the chicken inductively relying on the farmer for daily food will one day lead to the chicken’s death.  Nevertheless, Hume admits that some uniformity exists in nature, allowing for often true predictions to come out of inductive reasoning.  His problem with induction comes from its justification.  He says that induction is justified because it often works – often, one can observe, the past can be effectively used to predict the future.  This is in itself a form of induction, and to base the validity of induction on inductive reasoning is a cyclical (and therefore invalid!) argument.

Karl Popper has a response to this, writing that it is wrong to discuss the validity of induction because predictions (or theories) are not in fact based on induction.  Rather, theories are put forth based on preliminary observation, but rather than being “confirmed” by subsequent observations, theories can only be falsified or corroborated – they can be shot down or survive for further trials.  Therefore it cannot be stated that “all fire is hot” is a true statement; instead, one could say that no fire has yet been found that is not hot.

Another example, called “The New Rule of Induction” offers an example of a failure in inductive reasoning:

The new riddle of induction introduces the language L’, identical to English except that blue and green are replaced with “grue” and “bleen”.

An object is “grue” if it is green before time t or blue after time t.

An object is “bleen” if it is blue before time t or green after time t.

Given a series of observations (before time t) that all emeralds found are green (or “grue” in L’), an English-speaking agent would induce that subsequent emeralds will be green.  Similarly an L’-speaking agent would induce that subsequent emeralds will be “grue”, which, after time t means that the agent expects emeralds to appear blue (as after time t “grue” is a blue object).  This leads to translational contradiction, as the two agents, using the same inductive logic, came to two different conclusions about the appearance of future emeralds.

I’m not fully convinced by the new riddle of induction.  I think that, if language L’ existed, agents would not only note the physical appearance of the emeralds in observations, but would also note the time of the observation.  As very few objects change color cyclically over time (the sky being a notable exception), L’-speaking agents would, in my opinion, not be so stupid as to expect the color of stones to change with time just because their language is time-sensitive.  Inductive reasoning would tell them that most “grue” objects which are green before time t (lily pads, grass, etc ..) become “bleen” after time t because they remain green.  Therefore if they observed a series of green emeralds before time t they would expect to see “bleen” emeralds after time t because this how green objects have in the past behaved.

 

Definition of Science

Another interesting concept I’ve been introduced to is Karl Popper’s definition of science.  While most people would instinctively define science as theories methodically based on observation and tested under constrained conditions, Popper offers a different definition.  He says that science is defined by “risky” predictions – the riskier, the more scientific. Scientific theories must be testable, refutable, and interesting.  A “safe” prediction (you will get sick in the next year) may be testable, but is less scientific than a more specific prediction (you will have a cough starting from November 4th and lasting 11 days).  This of course allows many seemingly non-scientific theories to call themselves science.  A medium can claim to have scientific predictions about the future of your love-life and offer incredibly specific visions, but this does not make them “good” science.  Such theories are generally either easily falsified or are so vague as to make them not very scientific to begin with.

 

 

I’ve also included some pictures I’ve taken of London and from my trip to the Cliffs of Dover last weekend.

 

 

Cheers!

Ashley

 

 

 

Information taken from:

Goodman, N. ([1954] 1983) Fact, Fact, and Forecast, 4th ed., Cambridge(MA): Harvard UP, Ch. 3.

Norton, J. (2005) ‘A Little Survey on Induction’, in P. Achinstein (ed.), Scientific Evidence: Philosophical Theories and Applications, Baltimore: John Hopkins University Press, pp. 9–34.

Popper, K. (1963) ‘Science: Conjectures and Refutations’, in M. Curd et al. (eds.) Philosophy of Science: The Central Issues, second edition, New York: W.W. Norton & Company, 2012.