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:
If this universe is genuine, we cannot be sure that it is. A simulation can be realistic and come with authentic laws of reality.
This universe may have fake properties, but we cannot establish this because we do not know the properties of an authentic universe.
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.
Already in ancient times philosophers imagined that there is no way of telling that the world around us is real or that other people have a mind of their own. Perhaps I am the only one who is real while the rest of the world is my imagination. This could all be a dream. Some major religions claim that gods created this universe and that we are like them. In the Bible it is written that God said: “Let us make mankind in our image, in our likeness.”
For long it was impossible to clarify why this world might not be real or how the gods might have created it. Recent advances in information technology have changed that. This universe could be a virtual reality. We are inclined to think that what our senses register is real, so we tend to ignore evidence to the contrary. For instance, you may think you see a pipe when watching an image of a pipe.The caption of the famous painting named The Treachery of Images of René Magritte makes you notice: this is not a pipe.
In 1977 science fiction writer Philip K. Dick was the first to claim that we do exist in a computer-generated reality. This is the simulation hypothesis. He came to this insight after experiencing a psychosis. If he is right then his name suggests that our creators do like to joke around. Professor Nick Bostrom explored the probability the simulation hypothesis being true in the simulation argument.
According to Bostrom there could be many different human civilisations. The humans in those civilisations may at some point enhance themselves with bio-technology and information technology, live very long and acquire capabilities ordinary humans don’t have. For that reason these beings aren’t humans anymore and called post-humans. These post-humans might be brains-in-vats or have uploaded their consciousness into a computer and have no physical body. These post-humans may run simulations of their human ancestor civilisations. In that case we may be living in one of those simulations ourselves. Bostrom argues that at least one of the following must be true:
Nearly all real human civilisations end before enter the post-human stage.
In any post-human civilisation only an extremely small number of individuals are interested in running simulations of a human ancestor civilisations.
We almost are certainly living inside a computer simulation.1
It comes with the following assumptions that appear realistic to many experts in the relevant fields, but are not provenbecause we have not managed to do it yet:
The available computing power in post-human civilisations is sufficient to run a very large number of simulations of human ancestor civilisations.
The human consciousness needs not to reside in a biological organism, but can be implemented in a computer, perhaps in a limited form that appears realistic.1
Bostrom then concludes that if you believe that our civilisation will one day become post-human and will run a large number of human ancestor civilisations then you must believe we are currently living inside such a simulation.1 It might be explained like this. We do not know at what point in time we are, before or after the invention of virtual reality universes. If every year has an equal probability of this technology being invented, and we are going to invent it in the next 10, 100 or 1,000 years, then it will not happen later than that, because by then we will have done it. But what are the odds of it happening in the next 10, 100 or 1,000 years compared to the billions of years that already have passed?
There are many uncertainties. The available computing power of post-human civilisations might not be sufficient. It is possible that nearly all civilisations die out before becoming able to build simulations of human civilisations. Maybe post-humans will differ from us to the point that they will not be interested in running these simulations. Bostrom doesn’t try to guess the likelihood of the options. He thinks that we have no information as to whether this universe is real or not. But that may not be true.
My life has always been comfortable. We had a car and television. There was central heating. But it hasn’t always been like that. The childhood lives of my parents was very different. It was the life most people led for centuries. They grew most of their food themselves. The winters were cold. There was only one stove. They had no electricity, telephone, car, radio or television at first. Water they took from a pump. My grantparents were small farmers.
And that was only two decades earlier. There already was electricity in the cities, and in many villages too. But my parents lived in an area called Achterhoek, which translates to Rear Corner. And they didn’t live in a city, not even a village, but on remote farms. Remote in the Netherlands means that the nearest village is a few kilometres away. And a remote farm in Rear Corner was as remote as it could get in the Netherlands.
What a difference a few decades make. My son grew up with computers, Internet and smartphones. Compared to the dramatic changes my father and mother have witnessed, the changes that came later were rather insignificant. My father likes to talk about the old times. Before he went to school he had to milk the cows. There were lots of chores to do. My mother’s childhood had been like that too but she rarely talked about it. My mother’s family was quiet and reticent while my father’s family was noisy and outgoing.
My mother had three sisters and three brothers. My father had two brothers and two sisters. Both lived on a small farm. My father’s parents grew a few crops. They had a horse, a few cows, some pigs, and chicken. Neighbours were very important. If a farmer fell ill, the neighbours would step in and run the farm as long as needed. After the war my grandfather erected a windmill with batteries. They were one of the first in the area to have electric lights. Electricity from the grid came in 1952.
Then a local shop owner came by and showed them a radio, my father recalled. My grandfather didn’t want to spend money on a luxury item so the shop owner said he could try the radio a month for free. After a month my grandmother and my aunt had discovered a great radio show and wanted to keep it. And so my grandfather was pressed into buying a radio. In the same fashion a television set came in a decade later.
My father recalled when he saw a car for the first time. He was biking with his father. He said: “When I grow up I want to have a car too.” My grandfather then tried to teach him some realism: “You will never own a car. Only the physician, the notary and the mayor have cars.”
By the end of the 1960s the Netherlands had become wealthy. I was born in 1968 and have never known poverty. It may be easy to forget that most people in history have been poor and that many people today still are. But for me that was not so easy. An important lesson my parents taught me was that our comfortable lives come from hard work and that we shouldn’t take it for granted. My father worked long hours as a manager of a road construction company. “To give us a good life,” he said.
He is an outdoors man, a hunter, and well aware of what happens in nature, for instance the struggle for survival in the animal kingdom. Most people nowadays go to the supermarket to buy their food. At best they have a vague notion about farmers, crops and livestock. He grew up on a farm so it is hard for him to accept that city people take the living conditions farm animals seriously. “They know nothing about farm life or nature,” he says. And he balks at the idea of artificial meat.
My father is politically conservative, but he is also innovation-minded and very interested in improving things. He was keen on learning the newest management techniques from Japan about giving people on the workplace more responsibility to manage their own affairs. When the first home computers became available, he bought one for me. “Computers will be the future and you must learn about them,” he said to me. That was in 1984.
The lives of people completely changed in a few decades. It is happening everywhere. Millions of people in China can tell similar stories. In the past people worked with their hands and used their own judgement. Now we sit behind screens and watch graphs and check parameters. And perhaps our lives will be quite different a few decades from now.
But poverty is still on our doorsteps. We are running out of resources and pollution is running out of control. If societies break down, we will not gracefully return to subsistence farming. Many of us will starve. Most people live in cities nowadays and do not have the skills to survive. But perhaps we can fundamentally change our lifestyles in two decades. It has been done in the past.
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.
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.
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.
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.
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 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.
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 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 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:
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.
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.
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.
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:
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.
Negative interest rates may be here to stay. Interest rates are the result of supply and demand for money and capital in financial markets. The factors that have caused interest rates to go down are still in place and may not go away. And so it may be better to allow interest rates to go down further and into negative territory. For that reason, we may need a holding fee on central bank money.
Most money is in bank accounts. It is loaned to banks by depositors. Central bank money is different. It is not a loan. The central bank money we all know is cash. But banks have accounts at the central bank. The balances in those accounts are central bank money too. So, if the central bank sets the interest rate, it is the interest rate on central bank accounts.
Cash comes with an interest rate of zero. Cash can be an attractive investment when interest rates on bank accounts are negative. Depositors may take their money from the bank and put their savings in cash. In Switzerland, where interest rates are the most negative, bank notes of 1,000 francs and safe deposit boxes are in short supply. Hence, interest rates can’t go further down as long as cash remains the way it is.
When people stop lending money and take their money from the bank, the economy runs into trouble. With a holding fee on central bank money of 10% per year, it can be attractive to lend out money at negative interest rates like -2% because you don’t pay the holding fee on money lent. That includes money in bank accounts. And so you may keep your money in the bank even when interest rates are negative.
Cash as a loan to the government
Only, a holding fee of 10% per year would make cash unattractive. And in Wörgl people had to buy stamps and glue them to the banknotes to keep them valid. This is cumersome. If the interest rate on cash would be a bit lower than interest rates on bank accounts, that might be enough to stop people from hoarding cash. And if we do not have to glue stamps on banknotes, cash would remain practical to use.
So if cash is a loan to the government rather than central bank money, the interest rate on short term loans to the government could be applied. That rate would be much better than the holding fee, for example -3%. There would be an exchange rate between cash and central bank money. The value of cash could gradually decrease at a rate of 3% per year so you don’t have to glue stamps on bank notes to keep them valid.
Negative interest rates reduce the balance in your account while inflation is stealthy. Wage changes are more visible than price changes as some prices go down while others go up. Even when negative interest rates and deflation are a better deal, people might not opt for it. Psychologists have found that for most people the pain of a loss outstrips the pleasure of a similar gain, which makes them risk-averse.
When interest rates are negative, money disappears so inflation will probably be lower. There might even be deflation, which means that prices on average go down. That could be a better deal than printing money to produce inflation as this money usually ends up in the hands of the rich while everyone else pays for the inflation. But most people simply do not like see their account balance go down because of a negative interest rate.
They prefer the illusion of a small gain that amounts to a loss in reality to the illusion of a small loss that is a gain in reality. And there is a risk that the expected benefits from negative interest rates do not materialise. It is not rational but human psychology is the way it is. There may be a fix for that by hiding negative interest rates and make them look like inflation. To explain how, we have to look at the characteristics of Natural Money:
Central bank money carries a holding fee of approximately 10% per year. If you own central bank currency then € 1,00 turns into € 0,88 after a year. This can make lending at negative interest rates attractive.
Interest rates on bank accounts might be around -2% per year in terms of central bank money, so people don’t pay the holding fee but the interest rate banks offer.
Cash is a short-term loan to the government and carries the interest rate of short-term government loans, which might be -3%.
Central bank money and cash are separate currencies. They have an exchange rate. Cash gradually loses value relative to central bank money.
Making cash the money in people’s mind
If balances of bank accounts are expressed in cash rather than central bank money, negative interest becomes hidden from the public. The interest rate on short-term government loans is one of the lowest. Banks must be able to offer at least this interest rate so people won’t see their money disappear because of negative interest. And if prices in shops are expressed in cash, cash will become the currency in people’s minds.
If the interest rate on cash is -3%, the value of cash goes down by 3% per year in terms of central bank money. So if a bank offers an interest rate of -2%, and the account is settled in cash, it appears as if the interest on the bank account is +1%. And if the deflation rate is 1%, prices go down by 1%. Meanwhile cash goes down 3% in value so that it appears there is an inflation rate of 2% as cash prices go up by 2%.
It is a trick to prevent people from acting against their best interest. Nowadays the interest rate on bank accounts is 0% and inflation is 2% so you would lose 2% in purchasing power per year by holding money in a bank account. In the example above the loss is 1%, which is a better deal. Natural Money can be a better deal for account holders. The economy is expected to do better so real interest rates can be higher.
Critics might argue that we could be fooled by this scheme, just like we were fooled before by inflation. We won’t notice the negative interest rate, just like we didn’t notice inflation previously. But separating cash from central bank money and expressing prices and the value of bank accounts in cash can clear the psychological barrier that stands in the way of adopting negative interest by the public.
Central bank money should remain the accounting unit in the financial system. Bank accounts should be accounted in central bank money, just like debts and interest rates as well as prices of financial assets like stocks and bonds. A similar situation existed in Europe between 1999 and 2002 when the digital euro was already introduced while cash was still the national currency.