Category: Simulation

The last piece of the continental drift puzzle

by Paul Curzon, Queen Mary University of London

Image by Gerd Altmann from Pixabay 

A computer helped provide the final piece in the puzzle of how the continents formed and moved around. It gave a convincing demonstration that the Americas, Europe and Africa had once been one giant continent, Pangea, the pieces of which had drifted apart.

Plate tectonics is the science behind how the different continents are both moving apart and crashing together in different parts of the world driven by the motion of molten rock below the Earths crust. It created the continents and mountain ranges, is causing oceans to expand and to sink, and leads to earthquakes in places like California. The earth’s hard outer shell is made up of a series of plates that sit above hotter molten rock and those plates slowly move around (up to 10cm a year) as, for example, rock pushes up between the gaps and solidifies. or pushes down and down under an adjacent plate. The continents as we see them are sitting on top of these plates.

The idea of continental drift had existed in different forms since the early 19th century. The idea was partly driven by an observation that on maps, South America and Africa seemed almost like two jigsaw pieces that fit together. On its own an observation like this isn’t enough as it could just be a coincidence, not least because the fit is not exact. Good science needs to combine theory with observation, predictions that prove correct with data that provides the evidence, but also clear mechanisms that explain what is going on. All of this came together to show that continental drift and ultimately plate tectonics describe what is really going on.

Very many people gathered the evidence, made the predictions and built the theories over many decades. For example, different people came up with a variety of models of what was happening but in the 19th and early 20th centuries there just wasn’t enough data available to test them. One theory was that the continents themselves were floating through the layer of rock below a bit like ice bergs floating in the ocean. Eventually evidence was gathered and this and other suggestions for how continents were moving did not stand up to the data collected. It wasn’t until the 1960s that the full story was tied down. The main reason that it took so long was that it needed new developments in both science and technology, most notably understanding of radioactivity, magnetism and not least ways to survey the ocean beds as developed during World War II to hunt for submarines. Science is a team game, always building on the advances of others, despite the way individuals are singled out.

By the early 1960s there was lots of strong evidence, but sometimes it is not just a mass of evidence that is needed to persuade scientists en-masse to agree a theory is correct, but compelling evidence that is hard to ignore. It turned out that was ultimately provided by a computer program.

Geophysicist, Edward Bullard, and his team in Cambridge were responsible for this last step. He had previously filled in early pieces of the puzzle working at the National Physical Laboratory on how the magnetism in the Earth’s core worked like a dynamo. He used their computer (one of the earliest) to do simulations to demonstrate this. This understanding led to studies of the magnetism in rock. This showed there were stripes where the magnetism in rock was in opposite directions. This was a result of rock solidifying either in different places or at different times and freezing the magnetic direction of the Earth at that time and place. Mapping of this “fossil” magnetism could be used to explore the ideas of continental drift. One such prediction suggested the patterns should be identical on either side of undersea ridges where new rock was being formed and pushing the plates apart. When checked they were exactly symmetrical as predicted.

Jacques Kornprobst (redesigned after Bullard, E., Everett, J.E. and Smith, A.G., 1965. The fit of the continents around the Atlantic. Phil. Trans. Royal Soc., A 258, 1088, 41-51)

Image reconstruction of Bullard’s map by Jacques Kornprobst
from Wikipedia  CC BY-SA 4.0

In the 1960s, Bullard organised a meeting at the Royal Society to review all the evidence about continental drift. There was plenty of evidence to see that continental drift was fact. However, he unveiled a special map at the meeting showing how the continents on either side of the Atlantic really did fit together. It turned out to be the clincher.

The early suggestion that Africa and South America fit together has a flaw in that they are similar shapes, but do not fit exactly. With the advent of undersea mapping it was realised the coastline as shown on maps is not the right thing to be looking at. Those shapes depend on the current level of the sea which rises and falls. As it does so the apparent shape of the continents changes. In terms of geophysics, the real edge of the continents is much lower. That is where the continental shelf ends and the sea floor plummets. Bullard therefore based the shape of the continents on a line about a kilometre below sea level which was now known accurately because of that undersea mapping.

Maps like this had been created before but they hadn’t been quite as convincing. After all a human just drawing shapes as matching because they thought they did could introduce bias. More objective evidence was needed.

We see the Earth as flat on maps, but it is of course a sphere, and maps distort shapes to make things fit on the flat surface. What matters for continents is whether the shapes fit when placed and then moved around on the surface of a sphere, not on a flat piece of paper. This was done using some 18th century maths by Leonhard Euler. At school we learn Euclidean Geometry – the geometry of lines and shapes on a flat surface. The maths is different on a sphere though leading to what is called Spherical Geometry. For example, on a flat surface a straight line disappears in both directions to infinity. On a sphere a straight line disappearing in one direction can of course meet itself in the other. Similarly, we are taught that the angles of a triangle on a flat surface add up to 180 degrees, but the angles of a triangle drawn on a sphere add up to more than 180 degrees… Euler, usefully for Bullard’s team, had worked out theorems for how to move shapes around on a sphere.

This maths of spherical geometry and specifically Euler’s theorems form the basis of an algorithm that the team coded as a program. The program then created a plot following the maths. It showed the continents moved together in a picture (see above). As it was computer created, based on solid maths, it had a much greater claim to be objective, but on top of that it did also just look so convincing. The shapes of the continents based on that submerged continental line fit near perfectly all the way from the tip of South America to the northern-most point of North America. The plot became known as the ‘Bullard Fit’ and went down in history as the evidence that sealed the case.

The story of continental drift is an early example of how computers have helped change the way science is done. Computer models and simulations can provide more objective ways to test ideas, and computers can also visualise data in ways that help see patterns and stories emerge in ways that are both easy to understand and very convincing. Now computer modelling is a standard approach used to test theories. Back then the use of computers was much more novel, but science provided a key early use. Bullard and his team deserve credit not just for helping seal the idea of continental drift as fact, but also providing a new piece to the puzzle of how to use computers to do convincing science.

More on …

  • Read the book: Science: a history by John Gribbin for one of the best books on the full history of Science including plate techtonics.

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EPSRC supports this blog through research grant EP/W033615/1. 

The paranoid program

by Paul Curzon, Queen Mary University of London

One of the greatest characters in Douglas Adams’ Hitchhiker’s Guide to the Galaxy, science fiction radio series, books and film was Marvin the Paranoid Android. Marvin wasn’t actually paranoid though. Rather, he was very, very depressed. This was because as he often noted he had ‘a brain the size of a planet’ but was constantly given trivial and uninteresting jobs to do. Marvin was fiction. One of the first real computer programs to be able to converse with humans, PARRY, did aim to behave in a paranoid way, however.

PARRY was in part inspired by the earlier ELIZA program. Both were early attempts to write what we would now call chatbots: programs that could have conversations with humans. This area of Natural Language Processing is now a major research area. Modern chatbot programs rely on machine learning to learn rules from real conversations that tell them what to say in different situations. Early programs relied on hand written rules by the programmer. ELIZA, written by Joseph Weizenbaum, was the most successful early program to do this and fooled people into thinking they were conversing with a human. One set of rules, called DOCTOR, that ELIZA could use, allowed it to behave like a therapist of the kind popular at the time who just echoed back things their patient said. Weizenbaum’s aim was not actually to fool people, as such, but to show how trivial human-computer conversation was, and that with a relatively simple approach where the program looked for trigger words and used them to choose pre-programmed responses could lead to realistic appearing conversation.

PARRY was more serious in its aim. It was written by, Kenneth Colby, in the early 1970s. He was a psychiatrist at Stanford. He was trying to simulate the behaviour of person suffering from paranoid schizophrenia. It involves symptoms including the person believing that others have hostile intentions towards them. Innocent things other people say are seen as being hostile even when there was no such intention.

PARRY was based on a simple model of how those with the condition were thought to behave. Writing programs that simulate something being studied is one of the ways computer science has added to the way we do science. If you fully understand a phenomena, and have embodied that understanding in a model that describes it, then you should be able to write a program that simulates that phenomena. Once you have written a program then you can test it against reality to see if it does behave the same way. If there are differences then this suggests the model and so your understanding is not yet fully accurate. The model needs improving to deal with the differences. PARRY was an attempt to do this in the area of psychiatry. Schizophrenia is not in itself well-defined: there is no objective test to diagnose it. Psychiatrists come to a conclusion about it just by observing patients, based on their experience. Could a program display convincing behaviours?

It was tested by doing a variation of the Turing Test: Alan Turing’s suggestion of a way to tell if a program could be considered intelligent or not. He suggested having humans and programs chat to a panel of judges via a computer interface. If the judges cannot accurately tell them apart then he suggested you should accept the programs as intelligent. With PARRY rather than testing whether the program was intelligent, the aim was to find out if it could be distinguished from real people with the condition. A series of psychiatrists were therefore allowed to chat with a series of runs of the program as well as with actual people diagnosed with paranoid schizophrenia. All conversations were through a computer. The psychiatrists were not told in advance which were which. Other psychiatrists were later allowed to read the transcripts of those conversations. All were asked to pick out the people and the programs. The result was they could only correctly tell which was a human and which was PARRY about half the time. As that was about as good as tossing a coin to decide it suggests the model of behaviour was convincing.

As ELIZA was simulating a mental health doctor and PARRY a patient someone had the idea of letting them talk to each other. ELIZA (as the DOCTOR) was given the chance to chat with PARRY several times. You can read one of the conversations between them here. Do they seem believably human? Personally, I think PARRY comes across more convincingly human-like, paranoid or not!

Activity for you to do…

If you can program, why not have a go at writing your own chatbot. If you can’t writing a simple chatbot is quite a good project to use to learn as long as you start simple with fixed conversations. As you make it more complex, it can, like ELIZA and PARRY, be based on looking for keywords in the things the other person types, together with template responses as well as some fixed starter questions, also used to change the subject. It is easier if you stick to a single area of interest (make it football mad, for example): “What’s your favourite team?” … “Liverpool” … “I like Liverpool because of Klopp, but I support Arsenal.” …”What do you think of Arsenal?” …

Alternatively, perhaps you could write a chatbot to bring Marvin to life, depressed about everything he is asked to do, if that is not too depressingly simple, should you have a brain the size of a planet.

More on …

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Issue 16 cover clean up your language

This blog is funded through EPSRC grant EP/W033615/1.

The ping pong vaccination programming challenge

Vaccination programmes work best when the majority of the population are vaccinated. One way scientists simulate the effects of disease and vaccination programmes is by using computer simulations. But what is a computer simulation?

Lots of multi-coloured ping pong balls

You can visualise what a simulation is with ping pong balls bouncing around a crowd. Imagine having a large room full of people. A virus is represented by a ping pong ball, bouncing from person to person, infecting each person it touches. Each person who is hit by a ping pong ball and not already infected becomes infected. That means they toss that ping pong ball back into the crowd to infect more people, but they also toss an extra one too (and then they sit down: dead). Start with a few ping pong balls. Quickly the virus spreads everywhere and lots of people sit down (die). You have run a physical simulation of how a virus spreads!

Now start again but ‘vaccinate’ 80 per cent of the people first: give them a baseball cap to wear to show who is who. If those people get a ping pong ball, they just destroy it: they infect noone else. Start with the same number of ping pong balls. This time, the virus quickly dies out and only a few people sit down (die). Not only are the vaccinated people protected but they protect many of the un-protected people too who might have died.

Now (if you can program) you can write a program to do the same thing, and so simulate and explore the spread of infection, which is easier perhaps than getting a thousand people to chuck ping pong balls about. Create a grid (an array) of 1000 cells. Each represents a person. They can be infected or not. They can also be vaccinated or not. Start with five random cells (so people marked as infected). Run a series of rounds. After each round, a newly infected cell randomly chooses two others to infect. If not infected already and not vaccinated, then they become newly infected. If already infected or vaccinated, they do not pass the infection on.

You can run lots of different experiments with different conditions. For example, experiment with different proportions of people infected at the start or explore what percentage of people need to be vaccinated for the virus to quickly die out. Is 50 per cent enough? You could also change how many people one person infects, or for how long a person can infect others before dying. Perhaps they each keep causing new infections for three rounds before stopping instead of only one. In what situations does the virus infect lots of people and when does it die out quickly?

What you are doing here is computer modelling or simulating the effects of the virus in different scenarios, and that is essentially how computer scientists make the predictions that governments use to make decisions about lockdowns and mask wearing, if they are “following the science”. Of course, such models are only as good as the data that goes into them, such as how many other people does each person infect. In reality, this is data provided by surveys, experimental studies, and so on.

– Paul Curzon, Queen Mary University of London, Spring 2021

Download Issue 27 of the cs4fn magazine on Smart Health here.

This post and issue 27 of the cs4fn magazine have been funded by EPSRC as part of the PAMBAYESIAN project.