Seven varieties of metaphysics

I was having a discussion with someone recently on metaphysics, so I thought I would blog about it. Here are seven varieties of metaphysics, describing three “layers” of reality (and yes, I am oversimplifying for brevity).

The first is Platonism. Plato believed that there was a hierarchy of Forms (Ideals), of which the highest was The One (Plato’s version of God). These Forms or Ideals were the true reality, and the physical objects we touched, saw, and tasted were only shadows of that true reality (that is the point of the allegory of the cave). The physical orange which we see and eat reflects Ideals such as “Fruit,” “Sphere,” and “Orange.” Neoplatonism continues and extends this point of view.

Saint Augustine and many later Christians held to a Christianised Platonism, in which the Ideals were thoughts in the mind of God (the Christian God, naturally). The physical objects we touched, saw, and tasted had a greater importance in Christian Platonism than they did for Plato – after all, when God created those objects, “God saw that it was good.” Much as with Platonism, the regularities that people see in the physical universe are explained by the fact that God created the universe in accordance with regularities in the Divine thoughts. However, Christian Platonism does not have the metaphysical hierarchy that Platonism or Neoplatonism have – in Christian Platonism, God makes direct contact with the physical universe.

Aristotle also reacted to Plato by increasing the importance of the bottom layer, and Aristotle’s thought was Christianised by Thomas Aquinas as Thomism. However, in Thomism the all-important bottom layer does very little except to exist, to have identity, and to have properties assigned to it. It is also not observable in any way. This can be seen in the Catholic doctrine of transubstantiation. According to the Tridentine Catechism of 1566, the bread and the wine of the Eucharist lose their bottom (“substance”) layer (“All the accidents of bread and wine we can see, but they inhere in no substance, and exist independently of any; for the substance of the bread and wine is so changed into the body and blood of our Lord that they altogether cease to be the substance of bread and wine”), while the bottom (“substance”) layer of the body and blood of Christ becomes metaphysically present instead.

Idealism denies that the physical universe exists at all. The followers of Mary Baker Eddy take this view, for example, as did George Berkeley. Only thought exists. To quote a famous movie line, “there is no spoon.” These thoughts may be independent of whatever God people believe in or, as in monistic Hinduism, they may be actually be the thoughts of God (in which case, only God exists).

The last three kinds of metaphysics deny the existence of any kind of God. In Platonist Materialism, this denial is combined with a Platonist approach to mathematics, about which I have written before. Mathematics exists independently of the physical universe, and controls the physical universe, in the sense that the physical universe follows mathematical laws. Roger Penrose is one of many scientists holding this view.

In what I am calling Extreme Materialism, the existence of an independent mathematical world is also denied, i.e. there is an empiricist approach to mathematics (mathematics simply describes observed regularities in nature). This view seems to be increasing in popularity among non-religious people, although it causes philosophical problems for mathematics.

Finally, the concept of the Mathematical Universe holds that the so-called “physical universe” is itself composed only of mathematical objects – only mathematics exists (which makes this, in fact, a kind of Idealism).

In praise of the humble flagellum

The bacterial flagellum (above) is a fascinating device. It contains a molecular motor which rapidly rotates the filament. Whipping around, the filament drives the bacterium forward. In some bacteria, running the motor in reverse causes a random tumble. Amazingly, the combination of forward motion, random tumbling, and a simple sensor allows a bacterium to “home in” on a target (see four simulated example runs below). The idea is to do a random tumble whenever the sensor shows the bacterium heading in the wrong direction. An actual steering mechanism is not necessary – the bacterium gets to the target in the end.

William Dembski and Michael Behe famously argued (via the somewhat informally articulated concepts of specified complexity and irreducible complexity) that the flagellum was too complex to have evolved. Their argument fell apart with the discovery that the flagellum shares components with other bacterial gadgets, such as the injectisome, and thus could potentially have evolved in stages (although in fact the injectisome seems to have evolved as a simplification of the flagellum, and the evolutionary history of the flagellum remains a mystery).

The fundamental point that Dembski and Behe were attempting to make can be illustrated by the simple experiment summarised in the chart above. This experiment presupposes three genes (A, B, and C) all created by single point mutations on copies of existing genes, such that the combination of all three genes creates a useful widget. In the “flat landscape” case, this combination must arise entirely by chance. This takes a very long time (on the experimental assumptions used, an average of almost 14,000 generations). Dembski and Behe were probably right to suggest that, if the bacterial flagellum had to arise that way, it could not have evolved in the time available since the earth was formed.

In the “parallel evolution” case, however, each of the genes A, B, and C are assumed to be independently beneficial. The A-B-C combination then evolves very quickly. Evolution of the bacterial flagellum may have included aspects of parallel evolution, if components of multiple older widgets were “co-opted” for the flagellum.

The evolution of the bacterial flagellum is generally assumed to have instead been a case of “sequential evolution” (gene A is beneficial on its own, gene B is beneficial in the presence of gene A, gene C is beneficial in the presence of genes A and B, etc.). However, it is not at all clear what the sequence of genes producing the bacterial flagellum might have been (suggestions on this topic by Liu and Ochman have been criticised), nor is it clear what the sequence of intermediate benefits might have been (given that the injectisome was not an intermediate stage). Further research on the humble, but amazing, bacterial flagellum is clearly still required.

Complexity and Randomness revisited

I have posted before (post 1 and post 2) about order, complexity, and randomness. The image above shows the spectrum from organised order to random disorder, with structured complexity somewhere in between. The three textual examples below illustrate the same idea.

Regular Complex Random
AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA AAAAAAAAAA … It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair, … ShrfT e6IJ5 eRU5s nNcat qnI8N m-cm5 seZ6v 5GeYc w2jpg Vp5Lx V4fR7 hhoc- 81ZHi 5qntn ErQ2- uv3UE MnFpy rLD0Y DI3GW p23UF FQwl1 BgP36 RK6Gb 6lpzR nV03H W5X3z 2f1u8 OgpXy tY-6H HkwEU s0xLN 9W8H …

These three examples, and many intermediate cases, can be distinguished by the amount of information they contain. The leading way of measuring that information is with Kolmogorov complexity. The Kolmogorov complexity of a block of text is the length of the shortest program producing that text. Kolmogorov complexity is difficult to calculate in practice, but an approximation is the size of the compressed file produced by good compression software, such as 7-Zip. The chart below shows the number of bytes (a byte is 8 bits) for a compressed version of A Tale of Two Cities, a block of the letter ‘A’ repeated to the same length, and a block of random characters of the same length:

The random characters are chosen to have 64 possible options, which need 6 bits to describe, so a compression to about 75% of the original size is as expected. The novel by Dickens compresses to 31% of its original size.

But does this chart show information? Grassberger notes that Kolmogorov complexity is essentially just a measure of randomness. On this definition, random-number generators would be the best source of new information – but that’s not what most people mean by “information.”

An improvement is to introduce an equivalence relation “means the same.” We write X ≈ Y if X and Y have the same meaning. In particular, versions of A Tale of Two Cities with different capitalisation have the same meaning. Likewise, all meaningless random sequences have the same meaning. The complexity of a block of text is then the length of the shortest program producing something with the same meaning as that text (i.e. the complexity of X is the length of the shortest program producing some Y with X ≈ Y).

In particular, the complexity of a specific block of random text is the length of the shortest program producing random text (my R program for random text is 263 bytes), and we can approximate the complexity of A Tale of Two Cities by compressing an uppercase version of the novel. This definition of complexity starts to look a lot more like what we normally mean by “information.” The novel contains a large amount of information, while random sequences or “AAAAA…” contain almost none:

Those who hold that information satisfies the rule ex nihilo nihil fit can thus be reassured that random-number generators cannot create new information out of nothing. However, if we combine random-number generators with a selection procedure which filters out anything that “means the same” as meaningless sequences, we can indeed create new information, as genetic algorithms and genetic programming have demonstrated – although Stuart Kauffman and others believe that the evolution of biological complexity also requires additional principles, like self-organisation.

Complexity in medicine: some thoughts

I have been thinking recently about medicine and complexity, as a result of several conversations over many years. In particular, the Cynefin framework developed by Dave Snowden (see diagram below) seems a useful lens to use (this thought is not original to me – see among others, the articles “The Cynefin framework: applying an understanding of complexity to medicine” by Ben Gray and “Cynefin as reference framework to facilitate insight and decision-making in complex contexts of biomedical research” by Gerd Kemperman). I will also refer to two case studies from the book Five Patients by Michael Crichton, which is still quite relevant, in spite of being written in 1969.

The Cynefin framework developed by Dave Snowden. The central dark area is that of Disorder/Confusion, where it is not clear which of the four quadrants apply (image: Dave Snowden).

The Cynefin framework divides problems into four quadrants: Obvious, Complicated, Complex, and Chaotic. In addition, the domain of Disorder/Confusion reflects problems where there is no clarity about which of the other domains apply. In medicine, this reflects cases where multiple factors are at work – potentially, multiple chronic conditions as well as one or more acute ones. These conditions can exist in all four quadrants. Ben Gray gives the example of a child with a broken arm linked to both a vitamin deficiency and an abusive home environment. Several quite different interventions may be required.

The Obvious Quadrant

The quadrant of the Obvious applies to conditions with clear cause and effect, where there is a single right answer. According to Dave Snowden, the appropriate response is to sense what is going on, categorise the situation as one on a standard list, and then to respond in the way that people have been trained to do. This response may be trivial (a band-aid, say), or it may involve enormous professional skill. In medicine, much of nursing falls in this quadrant, as does much of surgery.

Michael Crichton’s Five Patients discuses the case of Peter Luchesi, a man admitted to Massachusetts General Hospital during 1969 with a crushed arm and nearly severed hand, as the result of an industrial accident:

Three inches above the left wrist the forearm had been mashed. Bones stuck out at all angles; reddish areas of muscle with silver fascial coats were exposed in many places. The entire arm about the injury was badly swollen, but the hand was still normal size, although it looked shrunken and atrophic in comparison. The color of the hand was deep blue-gray.

Carefully, Appel picked up the hand, which flopped loosely at the wrist. He checked pulses and found none below the elbow. He touched the fingers of the hand with a pin and asked if Luchesi could feel it; results were confusing, but there appeared to be some loss of sensation. He asked if the patient could move any of his fingers; he could not.

Meanwhile, the orthopedic resident, Dr. Robert Hussey, arrived and examined the hand. He concluded that both bones in the forearm, the radius and ulna, were broken and suggested the hand be elevated; he proceeded to do this.

Outside the door to the room, one of the admitting men stopped Appel. ‘Are you going to take it, or try to keep it?’

‘Hell, we’re going to keep it,’ Appel said. ‘That’s a good hand.’

Once the surgeons had sensed the problem and categorised it as an arm reconstruction, a team of three surgeons, two nurses, and an anaesthetist (all highly trained in their respective fields) then spent more than 6 hours in the operating theatre, repairing bone, tendons, and blood vessels. Certainly not trivial, but a case of professionals doing what they were trained to do.

The Complicated Quadrant

Public Domain image

The Complicated quadrant is the realm of diagnosis. Information is collected – in medicine, that generally means patient history, blood tests, scans, etc. – and is then subjected to analysis. This identifies the nature of the problem (in an ideal world, at least), which in turn indicates the appropriate response.

Diagnosis by physicians typically searches for the cause of an illness, while diagnosis by nurses typically focuses on severity. This reflects differences in the responses that physicians and nurses have been trained to provide (the triage officer in a modern hospital is typically a nurse).

Decades of work have gone into automating the diagnosis process – initially using statistical analysis, later using expert systems, and most recently using machine learning. At present, the tool of choice is still the human brain.

In general, modern medicine excels when it operates in the Obvious and Complicated quadrants.

The Complex Quadrant

The Complex quadrant is the realm of interactions. It is inherently very difficult to deal with, and cause and effect are difficult to disentangle. The paradigm of information collection and analysis fails, because each probe of the system changes it in some way. The best approach is a sequence of experiments, following each probe with a response that seems reasonable, and hoping to find an underlying pattern or a treatment that works. Michael Crichton provides this example:

Until his admission, John O’Connor, a fifty-year-old railroad dispatcher from Charlestown, was in perfect health. He had never been sick a day in his life.

On the morning of his admission, he awoke early, complaining of vague abdominal pain. He vomited once, bringing up clear material, and had some diarrhea. He went to see his family doctor, who said that he had no fever and his white cell count was normal. He told Mr. O’Connor that it was probably gastroenteritis, and advised him to rest and take paregoric to settle his stomach.

In the afternoon, Mr. O’Connor began to feel warm. He then had two shaking chills. His wife suggested he call his doctor once again, but when Mr. O’Connor went to the phone, he collapsed. At 5 p.m. his wife brought him to the MGH emergency ward, where he was noted to have a temperature of 108 °F [42 °C] and a white count of 37,000 (normal count: 5,000–10,000).

The patient was wildly delirious; it required ten people to hold him down as he thrashed about. He spoke only nonsense words and groans, and did not respond to his name. …

One difficulty here was that John O’Connor could not speak, and so could not provide information about where he felt pain. He appeared to suffer from septicaemia (blood poisoning) due to a bacterial infection in his gall bladder, urinary tract, GI tract, pericardium, lungs, or some other organ. Antibiotics were given almost immediately, to save his life. These eliminated the bacteria from his blood, but did not tackle the root infection. They also made it difficult to identify the bacteria involved, or to locate the root infection, thus hampering any kind of targeted response. In the end (after 30 days in hospital!) John O’Connor was cured, but the hospital never did locate the original root infection.

Similar problems occur with infants (Michael Crichton notes that “Classically, the fever of unknown origin is a pediatric problem, and classically it is a problem for the same reasons it was a problem with Mr. O’Connor—the patient cannot tell you how he feels or what hurts”). As Kemperman notes, medical treatment of the elderly often also falls in the Complex domain, with multiple interacting chronic conditions, and multiple interacting drug treatments. Medical treatment of mental illness is also Complex, as the brain adapts to one treatment regimen, and the doctor must experiment to find another that stabilises the patient.

Similarly Complex is the day-to-day maintenance of wellness (see the Food and Wellness section below) which often falls outside of mainstream medicine.

The Chaotic Quadrant

The Chaotic quadrant is even more difficult than the Complex one. Things are changing so rapidly that information collection and experimentation are impossible. The only possible response is a dance of acting and reacting, attempting to stabilise the situation enough that it moves from Chaotic to Complex. Emergency medicine generally falls in this quadrant – immediate responses are necessary to stop the patient dying. In the airline industry, the ultimate (and extremely rare) nightmare of total engine failure shortly after takeoff (as in US Airways Flight 1549) sits here too – each second of delay sees gravity take its toll.

Success in the Chaotic domain requires considerable experience. In cases where the problem is a rare one, this experience must be created synthetically using simulation-based training.

Food and Wellness

Michael Crichton notes that “The hospital is oriented toward curative treatment of established disease at an advanced or critical stage. Increasingly, the hospital population tends to consist of patients with more and more acute illnesses, until even cancer must accept a somewhat secondary position.” There is, however, a need for managing the Complex space of minor variations from wellness, using low-impact forms of treatment, such as variations in diet. Some sections of this field are reasonably well understood, including:

Traditional culture often addresses this space as well. For example, Chinese culture classifies foods as Yin (cooling) or Yang (heaty) – although there is little formal evidence on the validity of this classification.

There remain many unknowns, however, and responses to food are highly individual anyway. There may be a place here for electronic apps that record daily food intake, medicine doses, activities, etc., along with a subjective wellness rating. Time series analysis may be able to find patterns in such data – for example, I might have an increased chance of a migraine two days after eating fish. Once identified, such patterns suggest obvious changes in one’s diet or daily schedule. Other techniques for managing this Complex healthcare space are also urgently needed.

Looking back: 2009

Washington, DC in June 2009

In 2009, I had the privilege of visiting the United States twice (in June and November).

This was the year that saw the launch of the Lunar Reconnaissance Orbiter (which imaged, among other things, the Apollo 11 landing site), the Kepler space telescope (designed to look for exoplanets), the Herschel space observatory (an infrared telescope studying star formation), the Planck spaceprobe (which studied the cosmic microwave background), and the Wide-field Infrared Survey Explorer (an infrared telescope looking for minor planets and star clusters).

Apollo 11 landing site, imaged by the LRO (with photographs from 1969 inset)

More metaphorically, Bitcoin and the programming language Go were also launched. US Airways Flight 1549, on the other hand, was skillfully landed in a river. In archaeology, hoards were discovered in Staffordshire (gold and silver metalwork) and Shrewsbury (Roman coins). Australian Hospital Ship Centaur, torpedoed in 1943, was discovered off the Queensland coast.

Books of 2009 included Wolf Hall by Hilary Mantel (set in 1500–1535; a TV series of 2015), The Windup Girl by Paolo Bacigalupi (dystopian science fiction; Nebula Award winner), and The Maze Runner by James Dashner (young adult dystopian sci-fi; a film of 2014). Books that I later reviewed include The Lassa Ward by Ross Donaldson and God’s Philosophers by James Hannam.

Movies of 2009 included Avatar (rather disappointing), 2012 (a little silly), Angels & Demons (a travesty), Up (Pixar/Disney), Coraline (designed to give children nightmares), District 9 (designed to give adults nightmares), Julie & Julia (a film about cooking), The Imaginarium of Doctor Parnassus (a film about mirrors), and Sherlock Holmes (a lot of fun). On the whole, a good year for films.

In this series: 1978, 1980, 1982, 1984, 1989, 1991, 1994, 2000, 2004, 2006, 2009.

Recreational mathematics

The wolf, the goat, and the cabbages

Dancing alongside the more serious practitioners of mainstream mathematics are the purveyors of mathematical puzzles and problems. These go back at least as far as Diophantus (c. 200–284), the Alexandrian “father of algebra.” Alcuin of York (c. 735–804) produced a collection of problems that included the the wolf, the goat, and the cabbages (above); the three men who need to cross a river with their sisters; and problems similar to the bird puzzle published by Fibonacci a few centuries later. In more modern times, Martin Gardner (1914–2010) has done more than anyone else to popularise this offshoot of mathematics. It is often called “recreational mathematics,” because people do it for fun (in part because they are not told that it is mathematics).

Particularly popular in recent times have been Sudoku (which is really a network colouring problem in disguise) and the Rubik’s Cube (which illustrates many concepts of group theory, although it was not invented with that in mind). Sudoku puzzles have been printed in more than 600 newspapers worldwide, and more than 20 million copies of Sudoku books have been sold. The Rubik’s Cube has been even more popular: more than 350 million have been sold.

A Soma cube, assembled

Recreational puzzles may be based on networks, as in Hashi (“Bridges”). They may be based on fitting two-dimensional or three-dimensional shapes together, as in pentominoes or the Soma cube. They may be based on transformations, as in the Rubik’s Cube. They may even be based on arithmetic, as in Fibonacci’s problem of the birds, or the various barrel problems, which go back at least as far as the Middle Ages.

In one barrel problem, two men acquire an 8-gallon barrel of wine, which they wish to divide exactly in half. They have an empty 5-gallon barrel and an empty 3-gallon barrel to assist with this. How can this be done? It is impossible to accurately gauge how much wine is inside a barrel, so that all that the men can do is pour wine from one barrel to another, stopping when one barrel is empty, or the other is full [highlight to show solution → (8, 0, 0) → (3, 5, 0) → (3, 2, 3) → (6, 2, 0) → (6, 0, 2) → (1, 5, 2) → (1, 4, 3) → (4, 4, 0)]. There is a similar problem where the barrel sizes are 10, 7, and 3.

The barrels

Apart from being fun, puzzles of this kind have an educational benefit, training people to think. For this reason, Alcuin called his collection of problems Propositiones ad Acuendos Juvenes (Problems to Sharpen the Young). Problems like these may also benefit the elderly – the Alzheimer’s Association in the United States suggests that they may slow the onset of dementia. This is plausible, in that thinking hard boosts blood flow to the brain, and research supports the idea (playing board games and playing musical instruments are even better).

Looking back: 2004

In 2004, I was privileged to visit Middle Earth (aka New Zealand) with a colleague and to present the paper “Network Robustness and Graph Topology.” A major event of that year was the landing of the Mars rovers Spirit and Opportunity. Intended to operate for 90 Martian days (92 Earth days), Spirit kept going until 2010 (as xkcd remarked on in the comic above) and Opportunity set a record by operating until 2018. Also in 2004, the Stardust spaceprobe collected some comet dust.

On a more sombre note, 2004 saw the Boxing Day Tsunami. In the field of technology, Facebook and Gmail both launched in 2004, and Vint Cerf and Bob Kahn shared the Turing Award (for having invented the Internet).

This was an excellent year for cinema. Examples from different genres include National Treasure, Troy, Van Helsing, Man on Fire, Hotel Rwanda, The Village, Howl’s Moving Castle, and The Passion of the Christ. I certainly have memories that I treasure.

In this series: 1978, 1980, 1982, 1984, 1989, 1991, 1994, 2000, 2004, 2006.