explanation

What are Functions? A Short and Simple Explanation

Understanding functions is vital for anyone intending to master calculus or learning mathematical physics. For those who have never heard of the concept of the function, here’s a quick introduction. A function f(x) is a mathematical expression, you can think of it as an input-output system, establishing a connection between one independent variable x and a dependent variable y. We “throw” in a certain value of x, do what the mathematical expression f(x) demands us to do, and get a corresponding value of y in return. Here’s an example of this:

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The expression on the right tells us that when given a certain value of x, we need to multiply the square of x by 2, subtract 3x from the result and in a final step add one. For example, using x = 2, the function returns the value:

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So this particular function links the input x = 2 to the output y = 3. This is called the value of the function f(x) at x = 2. Of course, we are free to choose any other value for x and see what the function does with it. Inserting x = 1, we get:

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So given the input x = 1, the function produces the output y = 0. Whenever this happens, a value of zero is returned, we call the respective value of x a root of the function. So the above function has one root at x = 1. Let’s check a few more values:

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The first line tells us that for x = 0, the value of the function lies on y = 1. For x = -1 we get y = 6 and for x = -2 the result y = 15. What to do with this? For one, we can interpret these values geometrically. We can consider any pair of x and y as a point P(x / y) in the Cartesian coordinate system. Since we could check every x we desire and calculate the corresponding value of y using the function, the function defines a graph in the coordinate system. The graph of the above function f(x) will go through the points P(2 / 3), P(1 / 0), P(0 / 1), P(-1, 6) and P(-2 / 15). Here’s the plot:

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Graph of f(x) = 2x² – 3x + 1

You can confirm that the points indeed lie on the graph by following the x-axis, as usual the horizontal axis, and determining what distance the curve has from the x-axis at a certain value of x. For example, to find the point P(2 / 3), we move, starting from the origin, two units to the right along the x-axis and then three units upwards, in direction of the y-axis. Here we meet the curve, confirming that the graph includes the point P(2 / 3). Make sure to check this for all other points we calculated. Of course, to produce such a neat plot, we need to insert a lot more than just six values for x. This uncreative work is best done by a computer. Feel free to check out the easy-to-use website graphsketch.com for this, it doesn’t cost a thing and requires no registration. Note that the plot also shows a second root at x = 0.5, the point P(0.5 / 0). Let’s make sure that this value of x is a root of our function f(x) by inserting x = 0.5 and hoping that it produces the output y = 0:

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As expected. This was all very mathematical, but what practical uses are there for functions? We can use them to establish a connection between the value of one physical quantity and another. For example, through experiments or theoretical considerations we can determine a function f(p) that links the air pressure p to the air density D. It would allow us to insert any value for the air pressure p and calculate the corresponding value for the air density D, which can be quite useful. Or consider a function f(v) that establishes a connection between the velocity v of an object and the frictional forces F it experiences. This is extremely helpful when trying to determine the trajectory of the object, yet another function f(t) that specifies the link between the elapsed time t and the position x of the object. Just to give you one example of this, the function:

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connects the elapsed time t (in seconds) with the corresponding height h (in meters) for an object that is dropped from a 22 m tall tower. According to this function, the object will have reached the following height after t = 1 s:

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Insert any value for t and the function produces the object’s location at that time. In this case we are particularly interested in the root. For which value of the independent variable t does the function return the value zero? In other words: after what time does the object reach the ground? We could try inserting several values for t and hope that we find the right one. A more promising approach is setting up the equation f(x) = 0 and solving for x. This requires some knowledge in algebra.

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Subtracting 22 on both sides leads to:

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Divide by -4.91:

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And apply the square root:

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For this value of t the function f(t) becomes zero (due to inevitable rounding errors, not perfectly though). The rounding errors are also why I switched from the “is equal to”-sign = to the “is approximately equal to”-sign ≈. You should do the same in calculations whenever rounding a value.

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Graph of f(t) = -4.91t² + 22

As you can see, functions are indeed quite useful. If you had trouble understanding the algebra that led to the root t = 2.12, consider reading my free e-book “Algebra – The Very Basics” before continuing with functions.

This was an excerpt from my e-book Math Shorts – Exponential and Trigonometric Functions

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The Placebo Effect – An Overview

There is a major problem with reliance on placebos, like most vitamins and antioxidants. Everyone gets upset about Big Science, Big Pharma, but they love Big Placebo.

– Michael Specter

A Little White Lie

In 1972, Blackwell invited fifty-seven pharmacology students to an hour-long lecture that, unbeknownst to the students, had only one real purpose: bore them. Before the tedious lecture began, the participants were offered a pink or a blue pill and told that the one is a stimulant and the other a sedative (though it was not revealed which color corresponded to which effect – the students had to take their chances). When measuring the alertness of the students later on, the researchers found that 1) the pink pills helped students to stay concentrated and 2) two pills worked better than one. The weird thing about these results: both the pink and blue pills were plain ol’ sugar pills containing no active ingredient whatsoever. From a purely pharmacological point of view, neither pill should have a stimulating or sedative effect. The students were deceived … and yet, those who took the pink pill did a much better job in staying concentrated than those who took the blue pill, outperformed only by those brave individuals who took two of the pink miracle pills. Both the effects of color and number have been reproduced. For example, Luchelli (1972) found that patients with sleeping problems fell asleep faster after taking a blue capsule than after taking an orange one. And red placebos have proven to be more effective pain killers than white, blue or green placebos (Huskisson 1974). As for number, a comprehensive meta-analysis of placebo-controlled trials by Moerman (2002) confirmed that four sugar pills are more beneficial than two. With this we are ready to enter another curious realm of the mind: the placebo effect, where zero is something and two times zero is two times something.

The Oxford Dictionary defines the placebo effect as a beneficial effect produced by a placebo drug or treatment, which cannot be attributed to the properties of the placebo itself and must therefore be due to the patient’s belief in that treatment. In short: mind over matter. The word placebo originates from the Bible (Psalm 116:9, Vulgate version by Jerome) and translates to “I shall please”, which seems to be quite fitting. Until the dawn of modern science, almost all of medicine was, knowingly or unknowingly, based on this effect. Healing spells, astrological rituals, bloodletting … We now know that any improvement in health resulting from such crude treatments can only arise from the fact that the patient’s mind has been sufficiently pleased. Medicine has no doubt come a long way and all of us profit greatly from this. We don’t have to worry about dubious healers drilling holes into our brains to “relieve pressure” (an extremely painful and usually highly ineffective treatment called trepanning), we don’t have to endure the unimaginable pain of a surgeon cutting us open and we live forty years longer than our ancestors. Science has made it possible. However, even in today’s technology-driven world one shouldn’t underestimate the healing powers of the mind.

Before taking a closer look at relevant studies and proposed explanations, we should point out that studying the placebo effect can be a rather tricky affair. It’s not as simple as giving a sugar pill to an ill person and celebrating the resulting improvement in health. All conditions have a certain natural history. Your common cold will build up over several days, peak over the following days and then slowly disappear. Hence, handing a patient a placebo pill (or any other drug for that matter) when the symptoms are at their peak and observing the resulting improvement does not allow you to conclude anything meaningful. In this set-up, separating the effects of the placebo from the natural history of the illness is impossible. To do it right, researchers need one placebo group and one natural history (no-treatment) group. The placebo response is the difference that arises between the two groups. Ignoring natural history is a popular way of “proving” the effectiveness of sham healing rituals and supposed miracle pills. You can literally make any treatment look like a gift from God by knowing the natural history and waiting for the right moment to start the treatment. One can already picture the pamphlet: “93 % of patients were free of symptoms after just three days, so don’t miss out on this revolutionary treatment”. Sounds great, but what they conveniently forget to mention is that the same would have been true had the patients received no treatment.

There are also ethical consideration that need to be taken into account. Suppose you wanted to test how your placebo treatment compares to a drug that is known to be beneficial to a patient’s health. The scientific approach demands setting up one placebo group and one group that receives the well-known drug. How well your placebo treatment performs will be determined by comparing the groups after a predetermined time has passed. However, having one placebo group means that you are depriving people of a treatment that is proven to improve their condition. It goes without saying that this is highly problematic from an ethical point of view. Letting the patient suffer for the quest of knowledge? This approach might be justified if there is sufficient cause to believe that the alternative treatment in question is superior, but this is rarely the case for placebo treatments. While beneficial, their effect is usually much weaker than that of established drugs.

Another source of criticism is the deception of the patient during a placebo treatment. Doctors prefer to be open and honest when discussing a patient’s conditions and the methods of treatment. But a placebo therapy requires them to tell patients that the prescribed pill contains an active ingredient and has proven to be highly effective when in reality it’s nothing but sugar wrapped in a thick layer of good-will. Considering the benefits, we can certainly call it a white lie, but telling it still makes many professionals feel uncomfortable. However, they might be in luck. Several studies have suggested that, surprisingly, the placebo effect still works when the patient is fully aware that he receives placebo pills.

Experimental Evidence

One example of this is the study by Kaptchuk et al. (2010). The Harvard scientists randomly assigned 80 patients suffering from irritable bowel syndrome (IBS) to either a placebo group or no-treatment group. The patients in the placebo group received a placebo pill along with the following explanation: “Placebo pills are made of an inert substance, like sugar pills, and have been shown in clinical studies to produce significant improvement in IBS symptoms through mind-body self-healing processes”. As can be seen from the graph below, the pills did their magic. The improvement in the placebo group was roughly 30 % higher than in the no-treatment group and the low p-value (see appendix for an explanation of the p-value) shows that it is extremely unlikely that this result came to be by chance. Unfortunately, there seems to be a downside to the honesty. Hashish (1988) analyzed the effects of real and sham ultrasound treatment on patients whose impacted lower third molars had been surgically removed and concluded that the effectiveness in producing a placebo response is diminished if the patient comes to understand that the therapy is a placebo treatment rather than the “real” one. So while the placebo-effect does arise even without the element of deception, a fact that is quite astonishing on its own, deception does strengthen the response to the placebo treatment.

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Results of Kaptchuk et al. (2010)

Let’s explore some more experimental studies to fully understand the depth and variety of the placebo effect. A large proportion of the relevant research has focused on the effect’s analgesic nature, that is, its ability to reduce pain without impairing consciousness. Amanzio et al. (2001) examined patients who had undergone thoracotomy, a major surgical procedure to gain access to vital organs in the chest and one that is often associated with severe post-operative pain. As handing out sugar pills would have been irresponsible and unethical in this case, the researchers found a more humane method of unearthing the placebo effect: the open-hidden paradigm. All patients received powerful painkillers such as Morphine, Buprenorphine, Tramadol, … However, while one group received the drug in an open manner, administered by a caring clinician in full view of the patient, another group was given the drug in a hidden manner, by means of a computer-programmed drug infusion pump with no clinician present and no indication that the drug was being administered. This set-up enabled the researchers to determine how much of the pain reduction was due to the caring nature of the clinician and the ritual of injecting the drug. The results: the human touch matters and matters a lot. As can be seen from the graph below, every painkiller became significantly more effective when administered in an open fashion. Several follow-on studies (Benedetti et al. 2003, Colloca et al. 2004) confirmed this finding. This demonstrates that the placebo effect goes far beyond the notorious sugar pill, it can also be induced by the caring words of a professional or a dramatic treatment ritual.

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Results of Amanzio et al. (2001)

The fact that the human touch is of major importance in any clinical treatment, placebo or otherwise, seems pretty obvious (though its power in reducing pain might have surprised you). Much less obvious are the roles of administration form and treatment ritual, something we shall further explore. For both we can use the following rule of thumb: the more dramatic the intervention, the stronger the placebo response. For example, several studies have shown that an injection with salt-water is more effective in generating the placebo effect than a sugar pill. This is of course despite the fact that both salt-water and sugar pills do not have any direct medical benefits. The key difference lies in the inconveniences associated with the form of delivery: while swallowing a pill takes only a moment and is a rather uncomplicated process, the injection, preparation included, might take up to several minutes and can be quite painful. There’s no doubt that the latter intervention will leave a much stronger impression. Another study (Kaptchuk et al. 2006) came to the exciting conclusion that a nonsensical therapeutic intervention modeled on acupuncture did a significantly better job in reducing arm pain than the sugar pill. While the average pain score in the sham acupuncture group dropped by 0.33 over the course of one week, the corresponding drop in the sugar pill group was only 0.15. Again the more dramatic treatment came out on top.

The experimental results mentioned above might explain why popular ritualistic treatments found in alternative medicine remain so widespread even when there are numerous studies providing ample proof that the interventions lack biological plausibility and produce no direct medical benefits. Despite their scientific shortcomings, such treatments do work. However, this feat is extremely unlikely to be accomplished by strengthening a person’s aura, enhancing life force or harnessing quantum energy, as the brochure might claim. They work mainly (even solely) because of their efficiency in inducing the mind’s own placebo effect. Kaptchuk’s study impressively demonstrates that you can take any arbitrary ritual, back it up with any arbitrary theory to give the procedure pseudo-plausibility and let the placebo effect take over from there. Such a treatment might not be able to compete with cutting-edge drugs, but the benefits will be there. Though one has to wonder about the ethics of providing a patient with a certain treatment when demonstrably a more effective one is available, especially in case of serious diseases.

Don’t Forget Your Lucky Charm

This seems to be a great moment to get in the following entertaining gem. In 2010, Damish et al. invited twenty-eight people to the University of Cologne to take part in a short but sweet experiment that had them play ten balls on a putting green. Half of the participants were given a regular golf ball and managed to get 4.7 putts out of 10 on average. The other half was told they would be playing a “lucky ball” and, sure enough, this increased performance by an astonishing 36 % to 6.4 putts out of 10. I think we can agree that the researchers hadn’t really gotten hold of some magical performance-enhancing “lucky ball” and that the participants most likely didn’t even believe the story of the blessed ball. Yet, the increase was there and the result statistically significant despite the small sample size. So what happened? As you might have expected, this is just another example of the placebo effect (in this particular case also called the lucky charm effect) in action.

OK, so the ball was not really lucky, but it seems that simply floating the far-fetched idea of a lucky ball was enough to put participants into a different mindset, causing them to approach the task at hand in a different manner. One can assume that the story made them less worried about failing and more focused on the game, in which case the marked increase is no surprise at all. Hence, bringing a lucky charm to an exam might not be so superstitious after all. Though we should mention that a lucky charm can only do its magic if the task to be completed requires some skill. If the outcome is completely random, there simply is nothing to gain from being put into a different mindset. So while a lucky charm might be able to help a golfer, student, chess player or even a race car driver, it is completely useless for dice games, betting or winning the lottery.

Let’s look at a few more studies that show just how curious and complex the placebo effect is before moving on to explanations. Shiv et al. (2008) from the Stanford Business School analyzed the economic side of self-healing. They applied electric shocks to 82 participants and then offered them to buy a painkiller (guess that’s also a way to fund your research). The option: get the cheap painkiller for $ 0.10 per pill or the expensive one for $ 2.50 per pill. What the participants weren’t told was that there was no difference between the pills except for the price. Despite that, the price did have an effect on pain reduction. While 61 % of the subjects taking the cheap painkiller reported a significant pain reduction, an impressive 85 % reported the same after treating themselves to the expensive version. The researchers suspect that this is a result of quality expectations. We associate high price with good quality and in case of painkillers good quality equals effective pain reduction. So buying the expensive brand name drug might not be such a bad idea even when there is a chemically identical and lower priced generic drug available. In another study, Shiv et al. also found the same effect for energy drinks. The more expensive energy drink, with price being the only difference, made people report higher alertness and noticeably enhanced their ability to solve word puzzles.

 

This was an excerpt from my Kindle e-book Curiosities of the Mind. To learn more about the placebo effect, as well as other interesting psychological effects such as the chameleon effect, Mozart effect and the actor-observer bias, click here. (Link to Amazon.com)

Recurrence Relations – A Simple Explanation And Much More

Recurrence relations are a powerful tool for mathematical modeling and numerically solving differential equations (no matter how complicated). And as luck would have it, they are relatively easy to understand and apply. So let’s dive right into it using a purely mathematical example (for clarity) before looking at a real-world application.

This equation is a typical example of a recurrence relation:

x(t+1) = 5 * x(t) + 2 * x(t-1)

At the heart of the equation is a certain quantity x. It appears three times: x(t+1) stands for the value of this quantity at a time t+1 (next month), x(t) for the value at time t (current month) and x(t-1) the value at time t-1 (previous month). So what the relation allows us to do is to determine the value of said quantity for the next month, given that we know it for the current and previous month. Of course the choice of time span here is just arbitrary, it might as well be a decade or nanosecond. What’s important is that we can use the last two values in the sequence to determine the next value.

Suppose we start with x(0) = 0 and x(1) = 1. With the recurrence relation we can continue the sequence step by step:

x(2) = 5 * x(1) + 2 * x(0) = 5 * 1 + 2 * 0 = 5

x(3) = 5 * x(2) + 2 * x(1) = 5 * 5 + 2 * 1 = 27

x(4) = 5 * x(3) + 2 * x(2) = 5 * 27 + 2 * 5 = 145

And so on. Once we’re given the “seed”, determining the sequence is not that hard. It’s just a matter of plugging in the last two data points and doing the calculation. The downside to defining a sequence recursively is that if you want to know x(500), you have to go through hundreds of steps to get there. Luckily, this is not a problem for computers.

In the most general terms, a recurrence relation relates the value of quantity x at a time t + 1 to the values of this quantity x at earlier times. The time itself could also appear as a factor. So this here would also be a legitimate recurrence relation:

x(t+1) = 5 * t * x(t) – 2 * x(t-10)

Here we calculate the value of x at time t+1 (next month) by its value at a time t (current month) and t – 10 (ten months ago). Note that in this case you need eleven seed values to be able to continue the sequence. If we are only given x(0) = 0 and x(10) = 50, we can do the next step:

x(11) = 5 * 10 * x(10) – 2 * x(0) = 5 * 10 * 50 – 2 * 0 = 2500

But we run into problems after that:

x(12) = 5 * 11 * x(11) – 2 * x(1) = 5 * 11 * 2500 – 2 * x(1) = ?

We already calculated x(11), but there’s nothing we can do to deduce x(1).

Now let’s look at one interesting application of such recurrence relations, modeling the growth of animal populations. We’ll start with a simple model that relates the number of animals x in the next month t+1 to the number of animals x in the current month t as such:

x(t+1) = x(t) + f * x(t)

The factor f is a constant that determines the rate of growth (to be more specific: its value is the decimal percentage change from one month to the next). So if our population grows with 25 % each month, we get:

x(t+1) = x(t) + 0.25 * x(t)

If we start with x(0) = 100 rabbits at month t = 0 we get:

x(1) = x(0) + 0.1 * x(0) = 100 + 0.25 * 100 = 125 rabbits

x(2) = x(1) + 0.1 * x(1) = 125 + 0.25 * 125 = 156 rabbits

x(3) = x(2) + 0.1 * x(2) = 156 + 0.25 * 156 = 195 rabbits

x(4) = x(3) + 0.1 * x(3) = 195 + 0.25 * 195 = 244 rabbits

x(5) = x(4) + 0.1 * x(4) = 244 + 0.25 * 244 = 305 rabbits

And so on. Maybe you already see the main problem with this exponential model: it just keeps on growing. This is fine as long as the population is small and the environment rich in ressources, but every environment has its limits. Let’s fix this problem by including an additional term in the recurrence relation that will lead to this behavior:

– Exponential growth as long as the population is small compared to the capacity
– Slowing growth near the capacity
– No growth at capacity
– Population decline when over the capacity

How can we translate this into mathematics? It takes a lot of practice to be able to tweak a recurrence relation to get the behavior you want. You just learned your first chord and I’m asking you to play Mozart, that’s not fair. But take a look at this bad boy:

x(t+1) = x(t) + a * x(t) * (1 – x(t) / C)

This is called the logistic model and the constant C represents said capacity. If x is much smaller than the capacity C, the ratio x / C will be close to zero and we are left with exponential growth:

x(t+1) ≈ x(t) + a * x(t) * (1 – 0)

x(t+1) ≈ x(t) + a * x(t)

So this admittedly complicated looking recurrence relation fullfils our first demand: exponential growth for small populations. What happens if the population x reaches the capacity C? Then all growth should stop. Let’s see if this is the case. With x = C, the ratio x / C is obviously equal to one, and in this case we get:

x(t+1) = x(t) + a * x(t) * (1 – 1)

x(t+1) = x(t)

The number of animals remains constant, just as we wanted. Last but not least, what happens if (for some reason) the population gets past the capacity, meaning that x is greater than C? In this case the ratio x / C is greater than one (let’s just say x / C = 1.2 for the sake of argument):

x(t+1) = x(t) + a * x(t) * (1 – 1.2)

x(t+1) = x(t) + a * x(t) * (- 0.2)

The second term is now negative and thus x(t+1) will be smaller than x(t) – a decline back to capacity. What an enormous amount of beautiful behavior in such a compact line of mathematics! This is where the power of recurrence relations comes to light. Anyways, let’s go back to our rabbit population. We’ll let them grow with 25 % (a = 0.25), but this time on an island that can only sustain 300 rabbits at most (C = 300). Thus the model looks like this:

x(t+1) = x(t) + 0.25 * x(t) * (1 – x(t) / 300)

If we start with x(0) = 100 rabbits at month t = 0 we get:

x(1) = 100 + 0.25 * 100 * (1 – 100 / 300) = 117 rabbits

x(2) = 117 + 0.25 * 117 * (1 – 117 / 300) = 135 rabbits

x(3) = 135 + 0.25 * 135 * (1 – 135 / 300) = 153 rabbits

x(4) = 153 + 0.25 * 153 * (1 – 153 / 300) = 172 rabbits

x(5) = 172 + 0.25 * 172 * (1 – 172 / 300) = 190 rabbits

Note that now the growth is almost linear rather than exponential and will slow down further the closer we get to the capacity (continue the sequence if you like, it will gently approach 300, but never go past it).

We can even go further and include random events in a recurrence relation. Let’s stick to the rabbits and their logistic growth and say that there’s a p = 5 % chance that in a certain month a flood occurs. If this happens, the population will halve. If no flood occurs, it will grow logistically as usual. This is what our new model looks like in mathematical terms:

x(t+1) = x(t) + 0.25 * x(t) * (1 – x(t) / 300)    if no flood occurs

x(t+1) = 0.5 * x(t)    if a flood occurs

To determine if there’s a flood, we let a random number generator spit out a number between 1 and 100 at each step. If it displays the number 5 or smaller, we use the “flood” equation (in accordance with the 5 % chance for a flood). Again we turn to our initial population of 100 rabbits with the growth rate and capacity unchanged:

x(1) = 100 + 0.25 * 100 * (1 – 100 / 300) = 117 rabbits

x(2) = 117 + 0.25 * 117 * (1 – 117 / 300) = 135 rabbits

x(3) = 135 + 0.25 * 135 * (1 – 135 / 300) = 153 rabbits

x(4) = 0.5 * 153 = 77 rabbits

x(5) = 77 + 0.25 * 77 * (1 – 77 / 300) = 91 rabbits

As you can see, in this run the random number generator gave a number 5 or smaller during the fourth step. Accordingly, the number of rabbits halved. You can do a lot of shenanigans (and some useful stuff as well) with recurrence relations and random numbers, the sky’s the limit. I hope this quick overview was helpful.

A note for the advanced: here’s how you turn a differential equation into a recurrence relation. Let’s take this differential equation:

dx/dt = a * x * exp(- b*x)

First multiply by dt:

dx = a * x * exp(- b * x) * dt

We set dx (the change in x) equal to x(t+h) – x(t) and dt (change in time) equal to a small constant h. Of course for x we now use x(t):

x(t+h) – x(t) = a * x(t) * exp(- b * x(t)) * h

Solve for x(t+h):

x(t+h) = x(t) + a * x(t) * exp(- b * x(t)) * h

And done! The smaller your h, the more accurate your numerical results. How low you can go depends on your computer’s computing power.

Einstein’s Special Relativity – The Core Idea

It might surprise you that a huge part of Einstein’s Special Theory of Relativity can be summed up in just one simple sentence. Here it is:

“The speed of light is the same in all frames of references”

In other words: no matter what your location or speed is, you will always measure the speed of light to be c = 300,000,000 m/s (approximate value). Not really that fascinating you say? Think of the implications. This sentence not only includes the doom of classical physics, it also forces us to give up our notions of time. How so?

Suppose you watch a train driving off into the distance with v = 30 m/s relative to you. Now someone on the train throws a tennis ball forward with u = 10 m/s relative to the train. How fast do you perceive the ball to be? Intuitively, we simply add the velocities. If the train drives off with 30 m/s and the ball adds another 10 m/s to that, it should have the speed w = 40 m/s relative to you. Any measurement would confirm this and all is well.

Now imagine (and I mean really imagine) said train is driving off into the distance with half the light speed, or v = 0.5 * c. Someone on the train shines a flashlight forwards. Obviously, this light is going at light speed relative to the train, or u = c. How fast do you perceive the light to be? We have the train at 0.5 * c and the light photons at the speed c on top of that, so according to our intuition we should measure the light at a velocity of v = 1.5 * c. But now think back to the above sentence:

“The speed of light is the same in all frames of references”

No matter how fast the train goes, we will always measure light coming from it at the same speed, period. Here, our intiution differs from physical reality. This becomes even clearer when we take it a step further. Let’s have the train drive off with almost light speed and have someone on the train shine a flashlight forwards. We know the light photons to go at light speed, so from our perspective the train is almost able to keep up with the light. An observer on the train would strongly disagree. For him the light beam is moving away as it always does and the train is not keeping up with the light in any way.

How is this possible? Both you and the observer on the train describe the same physical reality, but the perception of it is fundamentally different. There is only one way to make the disagreement go away and that is by giving up the idea that one second for you is the same as one second on the train. If you make the intervals of time dependent on speed in just the right fashion, all is well.

Suppose that one second for you is only one microsecond on the train. In your one second the distance between the train and the light beam grows by 300 meter. So you say: the light is going 300 m / 1 s = 300 m/s faster than the train.

However, for the people in the train, this same 300 meter distance arises in just one microsecond, so they say: the light is going 300 m / 1 µs = 300 m / 0.000,001 s  = 300,000,000 m/s faster than the train – as fast as it always does.

Note that this is a case of either / or. If the speed of light is the same in all frames of references, then we must give up our notions of time. If the light speed depends on your location and speed, then we get to keep our intiutive image of time. So what do the experiments say? All experiments regarding this agree that the speed of light is indeed the same in all frames of references and thus our everyday perception of time is just a first approximation to reality.