Portrait of Socrates in marble, 1st century Roman artwork

Rational debates and historical processes

The discovery of ignorance

Is there progress in ideas? And can we achieve progress in thought by rational debates and persuasion? These questions are not easy to answer, most notably because people disagree. Still, some ideas may be better than others. So, how can we achieve progress or can we not? Socrates was a Greek philosopher who pondered this question. He lived around 400 BC and was the founder of the practice of rational debate. Socratic debates are discussions between two or more people with different viewpoints who wish to establish the truth using reasoned arguments. Asking and answering questions is a critical component of this process. It stimulates critical thinking and draws out ideas and underlying presuppositions.

In his dialogues, Socrates acted as if he was ignorant. According to Socrates, admitting one’s ignorance is the first step in acquiring knowledge, and awareness of ignorance is the beginning of wisdom. The discovery of ignorance can wake you up and push you into pursuing knowledge. For instance, the discovery of America, a previously unknown continent, was a shock to the European worldview. There was an entire continent that nobody in Europe had known. It set in motion the Scientific Revolution. European scientists started to ask themselves what more they did not know. They began to investigate anything they could think of.1 After 500 years, science has completely altered the way we live.

Georg Wilhelm Friedrich Hegel

Hegel’s dialectic

By the year 1800, the idea of progress was firmly established. The impact of scientific discoveries began to increase and the Industrial Revolution took off. Societies began to change and enlightenment ideas were spreading. The American Revolution followed the Glorious Revolution in England. During the French Revolution, the masses mobilised for the first time, and they ended the corrupt old regime. The armies of Napoleon then spread enlightenment ideas over Europe. It was the time when Georg Wilhelm Friedrich Hegel came up with a scheme for rational arguments. It consists of three stages:

  1. A theory is invented. Hegel calls it the abstract.
  2. The theory is criticised or tested. Hegel calls it the negative.
  3. The criticism and testing lead to a better theory. Hegel calls it the concrete.

Alternatively, the three stages of Hegelian dialectic are presented like so: a thesis, giving rise to its reaction; an antithesis, which contradicts or negates the thesis; and the tension between the two being resolved in the synthesis. You can apply it to rational debates. It works like so. First, someone comes up with a proposition. Then someone else brings in an opposing idea. If both parties have valid concerns and are willing to listen to each other, a rational debate between them can lead to a better understanding of the issue. The new understanding can be a thesis in a new argument. And so, the process can repeat, resulting in the progress of ideas.

An example can illustrate this. Suppose that Adam Smith and Karl Marx meet in a conference hall. Suppose further that a discussion between the two could settle the debate between capitalism and socialism. Smith sets out the thesis. He says that capitalism and free markets are great because they create wealth and distribute goods efficiently. Marx then comes up with the antithesis. He argues that the living conditions for workers are miserable and that capitalism distributes its benefits unfairly. He then says that workers should take control of the factories. Smith then objects by saying that workers are poor entrepreneurs so if workers take over businesses, that will cause a drop in living standards.

If both are willing to consider each other’s ideas and understand the issues at stake, they might concur that capitalism creates wealth but that the plight of workers needs improvement. They could agree on minimum wages, unemployment benefits, workplace safety laws, and state pensions. That is the synthesis. It may work for a while. Then a third individual might enter the debate and say that some people abuse welfare schemes. Another person might argue that economic activity will destroy the planet. That could be the beginning of new discussions that lead to measures to reduce fraud with unemployment benefits and investments in making the economy sustainable.

Hegelian dialectic applied to history

For Hegel, historical development proceeds not in a straight line but in a spiral leading upwards to growth and progress. From the opposition of action and reaction, harmony or synthesis emerges.1 In history, progress often involves conflict, and in many cases, there is not a synthesis but an end to the old ideas. For example, in the second half of the eighteenth century, more and more people felt that slavery was morally wrong. It took nearly a century and civil war in the United States to end slavery. And so, activists, planners, and politicians use Hegelian dialectic to enforce change. The Marxists are a prime example. They believed that capitalism would vanish and that socialism would replace it. The Marxists thought they were helping history by trying to end the capitalist world order.

The conflict between capitalism and socialism turned into a power struggle during the Cold War. The United States and its allies advocated capitalism, while the Soviet Union and its satellites promoted socialism. The capitalist block featured freedom of expression, so there was a public debate and ideas could be tested and improved in a Hegelian fashion. Consequently, governments interfered with markets and created welfare states. Their economies became mixtures of capitalist and socialist elements. In the socialist block, there was no freedom of expression or public debate, so the socialist countries did not enhance their economies with capitalist elements. In the end, the leadership of the Soviet Union realised that its mission of uplifting the working class had failed.

In the nineteenth century, workers did not appear to benefit from capitalism. It was hard to envision how socialism works in practice, so it may have been necessary to try it. The Soviet Union did so for seven decades. With the benefit of hindsight, the flaws of socialism appear evident, but if no one had tried it in practice, they probably were not so obvious. The main issue with socialism is that it can make people passive so that they will not take matters into their own hands and wait for the state to solve their problems. Socialism can work well in specific situations. For instance, healthcare in socialist Cuba is cheap and effective compared to the United States. The life expectancy in the United States and Cuba is nearly the same despite the United States spending more on healthcare per person than any country, while Cuba only spends a fraction of that amount. Once upon a time, market-driven healthcare may also have seemed a great idea. By trying, you can find out.

Some Scandinavian countries are more socialist than the United States, and citizens in those countries appear happier with their lives than Americans. The degree to which socialism can work depends on the social trust and work ethic within a group. Scandinavian countries have a Protestant work ethic and are culturally homogeneous. Cultural homogeneity can promote social trust like in Scandinavia, but not necessarily so. Greece is also culturally homogeneous, but the level of social trust is much lower than in Denmark or Sweden. Hence, culturally diverse countries can develop a high level of social trust, even though that is more difficult because of cultural differences. To make socialism work, people should contribute what they can and take what they need, and the needs should not exceed the contributions. Imposed socialism, like in the Soviet Union, will not work. Scandinavian countries are not as socialist as the Soviet Union once was. Their economies are a mixture of capitalist and socialist elements.

Hegel and dialectic conflict

Ideologies like socialism and capitalism are models that describe how society works or is supposed to work. Models are simplifications or abstractions. Models can help us organise our thoughts and establish which ideas have merit and under what circumstances. But for many people, ideologies are like religions. People who use one model all the time tend to be poor problem solvers. If your only tool is a hammer, every problem looks like a nail.

Political debates are often about scoring points rather than reasoning and listening to each other in a Socratic fashion. Parties use the Hegelian dialectic as a tool in political conflict. They frame the discussion by using their models and language. In your model, you are right, and your opponent looks stupid. Consequently, there can be no rational discussion between opponents who live in different realities like liberals and conservatives in the United States.

Scientific progress

In science, ideas advance. Thomas Kuhn came up with a scheme to describe scientific progress. He believed that science moves forward by theories replacing each other. Scientists in a specific field often work with a set of hypotheses. You may not be surprised to learn about that. So let’s call one of those theories the Old Theory. The Old Theory works fine in most situations, but sometimes it does not. Scientists at first ignore these exceptions, for instance, unexpected readings on their instruments. At first, they may think that faults cause these readings. As more and more experiments indicate that something is not right with the Old Theory, some scientists start to question it.

Then one of them then comes up with a revolutionary New Theory that explains a lot more than the previous Old Theory, including the unexplained readings on the instruments. At first, most scientists have their doubts because the New Theory is revolutionary. When experiments confirm the New Theory, scientists gradually embrace the New Theory and the Old Theory gets abandoned. In this case, there is also an argument going on between two sides, but the New Theory is superior to the Old Theory. That is most clear in the exact sciences like physics. In social sciences and economics, there is progress in theories but there are also debates between different approaches that appear unresolved.

An example might clarify how it works. Around 1680, Isaac Newton worked out the laws that explain the motion of objects. Newton’s laws tell us how fast objects fall to the ground and how planets orbit around the Sun. Newton presented his laws in a few mathematical formulas so it became possible to calculate how long it would take before a stone hits the ground if you drop it from the top of the Eiffel Tower.

In the centuries that followed, scientists developed more precise instruments and did measurements they could not explain. These were only small deviations from the values calculated with Newton’s formulas so they did not worry much about them at first. They could be errors. But the more precise the instruments grew, the more sure physicists became that something was not right. Albert Einstein then developed a theory that explained these curious readings, but also the motion of objects.

Assessing what to do

A reasoned debate combined with experimenting may be the best way of assessing what to do. Social sciences, including economics, involve human interactions. The number of variables is high and not all are known. That makes it difficult to ascertain causes and effects or to make accurate predictions. And so, experts in these fields make wrong judgements from time to time. It can be dangerous to blindly trust experts, but ignoring them can be even more hazardous. An ignorant person can be right by accident, while an expert can miss out on something. Sometimes, the difference between expert opinion and mere guessing is obscure. That emboldens the ignorant, and it makes the experts cautious.

Experiments can help to ascertain whether or not an assumption or a theory is correct. In social sciences, that may involve experimenting with humans. And that is not always ethical. And so, we should be careful as to the social experiments we engage in.

Reasoned debates are more common in science than in politics but scientists need research budgets provided by businesses and governments. The issues scientists investigate and the outcomes of scientific research can be influenced by the interests of those who fund the research. And so, the results of their research are not always what you might expect from an unbiased investigation.

Even when actions are based on the outcome of rational debates and experimenting, the actions lead to new issues that may need to be resolved in subsequent discussions and trials. The questions that will arise are often difficult to foresee, and it is even harder to think of how they will be resolved. Marx thought he could predict the future. Using Hegel’s dialectic, he thought he could predict how history would play out. Thinking that you know what will happen is a mistake many people make.

Marx believed in progress as Hegel did. Many people think there is progress. Yet, that is not so obvious. That is why conservatives want to keep things the way they are or go back in time to revert things to as they once were. To put it into perspective, if you live in a developed country, you may ask yourself, ‘Are you happier now than your parents were fifty years ago?’

Featured image: Portrait of Socrates in marble, 1st-century Roman artwork. Eric Gaba (2005). Wikimedia Commons. Public Domain.

1. Hegel’s Understanding of History. Jack Fox-Williams (2020). Philosophy Now. [link]

Book: The Virtual Universe

Religions claim that God or gods have created this world. The simulation hypothesis explains that we might live inside a computer simulation run by an advanced post-human civilisation. But can we know that this is the case? The book The Virtual Universe: Evidence Demonstrating That an Advanced Post-Human Civilisation Has Created Us explores the evidence. A revised simulation argument may establish that we live inside a simulation. Using the information this universe gives us, we might even discover the purpose of our existence.

The argument works like so:

  1. If this universe is genuine, we cannot be sure it is. A simulation can be realistic and come with authentic laws of reality.
  2. This universe may have fake properties, but we cannot find that out because we do not know the properties of an authentic universe.
  3. Breaching the laws of reality is unrealistic in any case. If it happens, we may have evidence of this universe being virtual.

It follows from (1) and (2) that we cannot use the properties of this universe reflected in the laws of reality to determine whether this universe is real or a simulation. Science may establish the laws of physics or the properties of this universe, but science cannot ascertain whether they are real or fake. But if they are breached, that may be evidence of this universe being a simulation.

We may discover that we live inside a simulation if we notice that reality is not realistic, at least in some aspects. There is evidence that the laws of reality may be breached from time to time, for instance, paranormal events, premonitions, meaningful coincidences and memories of past lives. The quality of this evidence appears sufficient to establish that the scientific laws of reality do not always apply.

Post-humans could have similar motivations as we have. They might run simulations of human civilisations for research or entertainment. Research applications could be about running what-if scenarios. Possible entertainment applications are games or dream worlds in which imaginations come true. These simulations may not be realistic in some aspects as they reflect the rules of a game or someone’s imagination.

Simulations of civilisations are complex, so guaranteeing a specific outcome, for instance, someone’s imagination coming true, requires control over everything that happens. That does not apply to games. Unpredictable developments make games more interesting. Looking at how we currently employ computing power, the number of simulations for entertainment likely vastly outstrip those run for research. If we live inside a simulation, we should expect its purpose to be entertainment.

If reality is unrealistic in some aspects, that suggests that our purpose is entertainment. A simulation run for research is more likely to be realistic. Evidence of control indicates that the purpose of this simulation is not a game but to realise someone’s imagination.

The owner or owners may use avatars and appear like ordinary human beings to us. If you are familiar with computer games, you know what an avatar is. Once you enter the game, you become a character inside the game, your avatar, and suddenly you have a second virtual body apart from your regular body. Inside the game, you are your avatar, not yourself. Similarly, you might start your personal virtual world in which you make your dreams come true. In this world, you also become someone else with another virtual body.

If beings in the simulation can think for themselves, that raises ethical questions like whether they have rights that the creators should respect. Considering how humans treat each other, it is not a given that these rights would be respected even when our creators acknowledge them. In a realistic simulation, bad things happen to people all the time. In the case of control, the beings inside the simulation are not sentient. It means that we may not think and may not have a will of our own. Hence, we might have no intrinsic value to our creators.

Meaningful coincidences demonstrate that there may be a script, which implies that someone or something controls everything that happens in this universe. In other words, we may live inside a story with a preconceived ending. It suggests that the purpose of this simulation is realising someone’s imagination. That implies the possible existence of a post-human individual we may call God. Presumably, God can use avatars and appear an ordinary human to us.

Another question is how does the owner experience the simulation? If there is a script, God probably does not actively direct events. Perhaps, God is in a dream state, where She is not in control of Her role and follows the script She has selected. That can raise yet another question. Does God know that She is God when She is in this world? And you can go even further because we can imagine gods. So, whose imagination is this world after all? The script appears romantic, favouring the odds that God is a woman. We cannot answer these questions because we cannot know God, but perhaps it is possible to disclose some of God’s avatars.

Latest revision: 13 June 2022

You can find it here:

Simulation argument II: adding information

Simulations could be realistic in many ways while not being realistic in some aspects. If that is somehow noticeable, then we might find out that we do live inside a simulation. Instead of speculating about us living in a simulation by guessing the probability of the existence of post-humans and their abilities, resources, and possible motivations, it seems more illuminating to look at the available information about our universe. Perhaps there is a more conclusive argument to be made. It may go like this:

  1. If this universe is genuine, we cannot be sure that it is. A simulation can be realistic and come with authentic laws of reality.
  2. This universe may have fake properties, but we cannot establish this because we do not know the properties of an authentic universe.
  3. Breaching the laws of reality is unrealistic in any case. If it happens, we may have evidence of this universe being virtual.

It follows from (1) and (2) that we cannot use the properties of this universe reflected in the laws of reality to determine whether this universe is real or a simulation. And it does not matter whether the laws of reality are genuine or not. If they are authentic and breached, this universe is a simulation. If they are fake, this universe is a simulation anyway. Science can establish laws of reality or properties of this universe, but science cannot determine whether they are real or fake.

According to science, this universe kicked off fourteen billion years ago with a big bang. Ten billion years later, life on this planet began to develop out of chemical processes. It took another four billion years for life on Earth to evolve into what it is today. According to science, there is no evidence of an intelligent creator, the laws of physics always apply, and we are biological organisms made out of carbon and water.

Hence, the following properties of our universe have been certified by science. They are among the established laws of our reality, reflecting what scientists believe to be realistic:

  • The laws of physics always apply inside their realm, for instance, Newton’s first law of motion, which states that a change in the speed or direction of the movement of a body requires a force.
  • The universe started with a big bang. Life on this planet emerged from chemical processes, and evolution shaped it. There is no evidence of a creator.
  • We are biological organisms, and our consciousnesses reside in our bodies. There is no spirit or soul.

Evidence to the contrary might indicate that we do live inside a simulation. Meaningful coincidences suggest there is an intelligent force directing events. The paranormal defies the laws of physics from time to time. Evidence for reincarnation indicates that we are not biological organisms. But meaningful coincidences can materialise by chance. And there may be laws of reality we do not know. And there is plenty of evidence of the consciousness residing in the body while only a few people remember a previous life. A convincing case for us living in a simulation requires clarification as to why it is the best explanation for our existence. The clarification might consist of the following parts:

  • Our existence is not a miracle that requires a creator, but this universe can be a simulation.
  • The possible motivations of post-humans may allow us to establish that we do live inside a simulation and what our purpose is.
  • Science cannot determine that his universe is a simulation as we do not know the properties of a real universe.
  • Alternative explanations for strange phenomena seem less plausible as they run into logical inconsistencies.
  • Evidence suggestive of reincarnation might suffice to conclude that our consciousnesses do not reside in our bodies.
  • Evidence suggestive of ghosts, premonitions, and alien abductions might suffice to conclude that the laws of physics do not always apply.
  • The distribution of meaningful coincidences could indicate that an intelligence coordinates events in this universe.

Establishing that the distribution of meaningful coincidences is not the outcome of chance requires information about probabilities. Meaningful coincidences can happen by accident, and it is impossible to determine the odds of them materialising. Still, there are arguments to be made to certify that mere accident is not so likely. For that, we may consider the following:

  • Some types of meaningful coincidences are less likely to occur than others. The more elaborate the scheme, the less likely it is the result of mere chance.
  • Mere chance is also unlikely when elaborate meaningful coincidences surround the most important historical events.
  • If meaningful coincides are not distributed evenly across people and time-frames, and some people are heavily affected, it suggests interference and perhaps even destiny for those involved.

Naomi Campbell

About models

What are we talking about?

When you hear about models it is often about people like Naomi Campbell or Heidi Klum. Yet, there are far more fascinating models out there. They may not dwell in the spotlights but everyone employs them. Scientists are the most heavy users. These models are simplifications or abstractions of reality and are used to explain things or to make predictions.

Game theory model

Indeed these models are as sexy as the scientists using them so a picture might not have drawn your attention. But then again, sexy is just a temporary phase in life. So what kind of models are we talking about? You can think of:

  • models to calculate the trajectory of the planets in the solar system
  • models to forecast the weather
  • models to predict the spread and the mortality of a virus
  • models to estimate the impact of a proposed measure on the economy
  • models to predict the impact of climate change

In the 1970s weather forecasts were of poor quality compared to today. And they didn’t go a lot farther than the day after tomorrow. Today predictions are more accurate and go up to two weeks in advance, even though the longer term predictions are not as accurate as those for today or tomorrow.

This improvement is the result of weather forecast models and computers. Computer models have improved over time, and a lot of hard work of scientists has gone into them. Usually about 50 different models are used together to make a weather prediction. Models are important tools to make sense of what happens in the world. There has been a course named Model Thinking by Professor Scott E. Page of the University of Michigan on the Internet. Much of what you read here comes from this course.

Why use models?

When making plans for the future, models can be useful. You can ask yourself, what might happen if you choose a particular action. An economist might use models to predict the consequences for economic growth of a proposed policy measure. Predictions made with models do not always come true. For instance, most economists didn’t see the financial crisis of 2008 coming despite all the models they had at their disposal.

In 1972 a group of scientists using a computer model warned that we would have run out of oil and some other crucial natural resources by 2010. They may have been a few decades off the mark but their warning made people and policy makers think about the fact that the resources of our planet are limited.

When models fail people may start to doubt the experts. This can be dangerous. On average experts do better than uneducated guesses. Only, small errors can lead to dramatic misses so an uneducated guess can sometimes be more accurate than an expert calculation. Experts usually don’t make the mistakes laypeople make so they do better on average.

Models can be wrong because they are simplifications and don’t take everything into account. For instance, an economic model to predict demand for goods and services doesn’t include the preferences and budgets of each individual consumer. If you had all that information, you might be able to make very accurate predictions, but that may be impossible.

There are good reasons to become familiar with models and the issues that come with them. Models can make us think clearer. People who use models usually are better decision makers than those who don’t because they have a better understanding of the situation. Models help us to use and understand data. And they assist us with designing solutions for problems and setting out strategies.

Using multiple models together

Proverbs can disagree with each other. Two heads are better than one but too many cooks spoil the broth. And he who hesitates is lost while a stitch in time saves nine. Contradictory statements can’t be true at the same time but they can be true in different situations or times. It may be important to know which advice is best in which situation, or more often, which combination of advice.

Models are better than uneducated guesses and using more models together can lead to better outcomes than using a single model. That is why up to fifty models are used to make a weather prediction. People who use a single model are not good at predicting. They may be right from time to time just like a clock that has stopped sometimes shows the correct time.

Smart people use several models and their personal judgement to determine which models best apply on the situation at hand. Only people using multiple models together make better predictions than mere guessing but they can be wrong. Still, models can help us to think more logically about how the world works, and eliminate a lot of errors we would make otherwise.

Model thinking

When you plan to work with models, you need to think logically from assumptions to conclusions, and then verify the outcomes with the use of experiments or gathered data. This way of working is called model thinking. It gets even more complicated when you use different models together as the outcomes may differ. And so you might have to consider which models apply best on the situation at hand and evaluate the different outcomes. Model thinking usually consists of the following steps:

  • name the parts

A model consists of parts. For instance, if you want to figure out which people go to which restaurant, you need to identify the individual people as well as their preferences and budgets. You also need to identify the restaurants and their menus and the price of those menus. And so the parts are the individual people, their preferences, the restaurants, their menus and the price of each of those menus.

  • identify the relationships between the parts

A model comes with relationships between the parts. For instance, the financial system is interconnected because financial institutions lend money to each other. If one bank fails, loans may not be repaid, and other institutions may get into trouble too. And so it might be a good idea to identify the relationships between financial institutions and how much they depend on one another.

  • work through the logic

Suppose you want to calculate the length of a rope that you want to tie around the earth at one metre above the surface. Assume the Earth’s circumference to be 40,000 kilometres. The formula for circumference C is: C = πD, where D is the diameter of the Earth. In this case C = π(D + 2m) = πD + (π * 2m) = 40,000 km + 6.28m.

  • doing experiments

You can design a model on a drawing board and then reality may turn out to be quite different. Model need a reality check. For instance, if people are often jammed near the exit of a room, you could explore the effects of putting a post before the exit to prevent people from pushing each other.

  • identify logical boundaries

With the use of models it may be possible to identify boundaries. For instance, if you think of allowing interest rates to go negative, you may want to estimate how low interest rates can go. If interest rates go below a certain level, for instance -3%, most people may stop saving so the interest rate can’t go lower. To estimate that interest rate, you may need a model predicting savings at different interest rates.

  • communicate the findings

If you have used a model then you may have to expain your findings, and therefore the use of the model. For instance, to explain why interest rates can’t go below -3%, you may discuss how you have used the model to come to your conclusion. To support your model you may have used a survey asking people at which interest rate they will stop saving.

Outcomes

Models come with different types of outcomes. Models can help us predict which of type of outcomes will materialise in reality. Possible types of outcomes are equilibrium, cycle, random, and complex.

  • equilibrium

Equilibrium outcomes end at a specific value and stay there until conditions change. For instance, if you set the thermostat of the central heating to 20°C while the room is 17°C, it will turn on the heating until the room is 20°C and stop once the temperature has reached this level. By then the water in the device might be heated to the point that the room will heat up further to 21°C.

But the heater will remain off as long as the temperature is above 20°C so the room will cool down after some time as long as the outside temperature is lower. The heater will only start again once the temperature goes below 20°C. So after some time the temperature will be close to 20°C and remain so until you set the thermostat to another temperature.

  • cycle

Outcomes of the type cycle show a repeating pattern. For instance, there is a business cycle in the economy causing growth to alternate with slumps. Therefore a model for economic growth could identify a trend, which is the average economic growth over a longer period of time as well as cycles of growth and slumps.

  • random

Random outcomes are impossible to predict even though there may be boundaries or a limited number of possible outcomes. For instance, if you play a game of cards, it is impossible to know on beforehand which cards you will get even though you may know that you won’t get a joker card if it is not in the game. Likewise, if you throw a dice, you can’t predict the number but it will be between one and six.

  • complex

Complex outcomes are hard to predict but they are not random. For example, the demand for oil and the supply of oil tend to slope up in a fairly predictable manner. The price of oil depends on all kinds of things, such as reserves, people in markets, and politics, so an oil-price model is probably complex. The model might be wrong quite often too but it may do better than mere guessing.

Using and understanding data

An important application of models is using and understanding data. If you can make sense of data, you may find information that you can use. This can be done in the following ways:

  • understand patterns

There may be patterns in the data. For example, there may be fluctuations in economic growth that can be explained by a business cycle model.

  • make predictions for individual cases

A model can give a relationship between different variables so you can predict an unknown variable if the other variables are known. For example, the price of a house may depend on the neighbourhood and the number of square metres. So, if you know the neighbourhood and the number of square metres, and the relationship between these variables and price, you can predict the price of a house.

  • produce bounds

For example, if you use models to estimate predict the weather two weeks from now, there is too much uncertainty to come up with an exact temperature, so a model will probably produce a range with a lower bound and an upper bound of the temperatures that might occur.

  • test

You can use models with the data to ‘predict’ the past. In this way you can test models and check how good they are. For example, if you have the economic data from 1950 to the present, and you have a model that predicts the unemployment rate based on the economic data of previous years, you can use the data from 1950 to 1970 in the model to predict the unemployment in 1972, and then check whether or not the prediction is close to the real unemployment figure of 1972.

  • predict other things

For example, you may have made a model that predicts the unemployment rate, but as a side benefit it might also predict the inflation rate. Another example is that early models of the solar system and gravity showed that there must be an unknown planet, which turned out to be Neptune.

  • informed data collection

For example, if you want to improve education, and make a model that predicts school results, you have to name the parts, such as teacher quality, the education level of parents, the amount of money spent on the school, and class size. The model determines which data should be collected. There is no reason to collect data on school size if you don’t use it in you model.

  • estimate hidden parameters

Data can tell us more about the model and the model can tell us more about reality. For example, a model for the spread of diseases is the Susceptible, Infected, Recovered (SIR) model. If you have the data of how many people are getting the disease, you can predict how the disease will spread over time.

  • improve

After you have constructed a model, you can use data to improve it and make it closer to the real world.

Making decisions, strategies and designs

Models can help with making decisions, setting out strategies and designing solutions. A few examples can illustrate that:

Financial contagion risk
Financial contagion risk model
  • decision aides

Models can be used to make decisions. For instance, at the time of the financial crisis of 2008, you could have made a model of financial institutions like Bear Sterns, AIG, CitiGroup, and Morgan Stanley with the relationships between them in terms of how their success depends on another. As some of these companies were starting to fail, the government had to decide whether or not to save them. This model can help to make that decision. The numbers represent how much one institution depends on another.

So, if AIG fails then how likely is it that JP Morgan fails? The number 466 is big. The number 94 represents the link between Wells Fargo and Lehman Brothers. If Lehman Brothers fails, this only has a small effect on Wells Fargo and vice versa. Lehman Brothers only has three lines going in and out and the numbers associated with these lines are relatively small. For the government this can be a reason not to save Lehman Brothers. AIG has much larger numbers associated with AIG and can be a reason to save AIG because a failure of AIG cancause the whole system to fail. This is why some financial institutions were deemed ‘too big to fail’.

  • play out different scenarios

History only runs once. But with models of the world, you can play out different scenarios. For example, in April 2009, the Federal Government decided to implement an economic recovery plan. You can run models of the economy and look at the unemployment rate with and without the recovery plan. It doesn’t mean that what a model shows would really have happened without the recovery plan, but at least the model provides some understanding of its effect.

  • identify and rank levers

It can be worthwhile to implement the measures that have the most effect. For example, one of the big issues in climate change is the carbon cycle. The total amount of carbon on Earth is fixed. It can be up in the air or down on the earth. If it is down on the earth then it doesn’t contribute to global warming. If you think about intervening, you may ask where in this cycle are there big levers? Surface radiation is a big number. If you think about where to interfere, you want to think about it in terms of where those numbers are large.

  • help to choose from policy options

Suppose there will be a market for pollution permits. We can make a simple model and tell which one is going to work better. Suppose a city has to decide about creating more parks. More parks might seem a good thing but if people want to move there and developers build large apartment buildings around them, it might not be such a good idea after all.

Featured image: Naomi Campbell at Festival de Cannes. Georges Biard (2017). Wikimedia Commons. Public Domain.

1. Model Thinking [link]

Halloween cat from Poland. User Silar.

Ghost stories

The first thing I learned about ghosts was that they are fake. Ghosts are fairy tales, at least so I was told. Then I went on a school trip and visited the Singraven Estate near Denekamp. The custodian told us a spook inside the manor was upsetting things, but he added that we should not fear it when entering. He seemed dead-serious and did not appear to be an attention-seeker. Still, it is better not to put too much faith in spook stories about venues that depend on tourist income.

As a teenager, I once visited Twickel Castle in Delden, not far from Denekamp. Recently I found out that this castle also features a ghost. It is not advertised. There is only one source on the Internet mentioning it, and if it is true, the laws of physics went out of the window, at least temporarily. The author preferred me to quote her work literally.

Recently I heard a strange tale from the phlegmatic steward of Twickel Castle in Delden. An English restorer who had come to restore some antique cupboards was given permission by her to stay overnight in an attic room of the castle. After he had been there for a few days, she saw that he had put his mattress on the floor.

She asked him why he slept on the floor and not on the bedstead? He answered her unmoved that he had been pushed out of bed for three consecutive nights. To prevent it from happening again, he had decided to sleep on the floor from then on. He had not been bothered since then. The steward asked him if he didn’t find that creepy? His answer was calm and clear: ‘No, I’m from England.’1

That is what the stiff upper lip is all about. There are plenty of ghost tales. Often the accounts appear credible, but it is impossible to verify what happened. On the Internet, you can find lists of ghost tales. One of them is 10 Eerie Real-Life Paranormal Encounters to Creep You Out on Listverse.com.2 The list is fact-checked, which probably means most stories happened. You are about to read two stories from this list.

The first is about Nina De Santo. She was closing up her New Jersey hair salon one Saturday evening in 2001 when she saw Michael, one of her customers, standing outside the shop’s window. He had become a good friend of hers over the years. He had been going through a tough time after his wife left him and he lost custody of his children.

Nina had tried her best to cheer Michael up whenever he came to the salon and had given him a chance to talk. So when she opened the door to him that night, Michael seemed transformed and even happy. He smiled at her and said he could not stay long but just wanted to thank Nina for everything she had done for him. They chatted a bit, and then each went their own way for the evening.

The following day, Nina received a call from one of her employees. She told her that Michael’s body had been found the previous morning, around nine hours before Nina had spoken to him at the salon. He had committed suicide.2

The second story concerns Redditor tooabstract788. He was home alone one night, playing games. His dog started barking at the closet. He got up to see what was upsetting the dog when he heard scratching sounds coming from the inside. As he got closer, the scratching stopped. Then he heard a loud crash followed by the sound of falling items. He ran outside at once and only went back inside when his friend came over. Inside the closet, they found clothes and hangers scattered over the floor.2

In 2014, a couple named the Simpsons asked the regional news channel Fox43 in the United States to visit their haunted house in Hanover, York County. The wife, DeAnna Simpson, spoke of several entities that were severely haunting their home. She and her husband had lived there for seven years. She caught ‘ghosts’ on film while guests had been scratched or even attacked in this house. She had invited priests, paranormal researchers, and the TV show ‘The Dead Files’ crew, who then ‘uncovered evidence’ of ‘grisly deaths’ there.3 When the Fox43 staff came in, their photographer was scratched, apparently by something invisible.

Television series such as Ghost Adventures are suggestive, giving the impression that they are at least partially fake. ‘It hardly ever happens like that,’ an investigator of the paranormal claimed.4 So what to make of this? The events mentioned in these stories are undoubtedly peculiar. There are many similar tales, but mentioning more of them is pointless because this booklet is not intended as a collection of creepy tales, but to provide a possible explanation for these occurrences. So is this evidence of ghosts? Not necessarily. If we live in a simulation built for entertainment, the simulation can play into our imaginations and fears. Indeed, there may not be more to it than that.

Strange incidents occurred in my house too. In March 2018, my wife woke me up in the middle of the night. She said, ‘The bathroom door is locked, and our son is sleeping in his bed.’ You can only lock the door from the inside. The lock needs some force, so this cannot happen by accident, so my wife feared that a burglar was hiding inside. I took a knife from the kitchen to unlock the door while she was standing behind me, holding a heavy object to smash into the head of the burglar. Only, I never believed there was a burglar. So many unusual things had happened already that strange events like this one did not impress me anymore. And I was right. There was no burglar. My wife was baffled, and I went back to bed.

Latest revision: 24 May 2022

Featured image: Halloween cat from Poland. User Silar (2012). Wikimedia Commons. Public Domain.

1. Betoverd door: haunted houses. Theracoppens.nl.
2. 10 Eerie Real-Life Paranormal Encounters to Creep You Out. Listverse.com (2022).
3. A haunted Hannover home. Civilwarghosts.com. [link]
4. Why those TV ghost-hunting shows are transparently fake. Scott Craven (2019). The Republic. [link]