Religions claim that a god or gods have created this universe. More recently, the simulation hypothesis allows us to explain how the gods might have done this. We could all be living inside a computer simulation run by an advanced post-human civilisation. But can we objectively establish that this is indeed the case?
There is sufficient evidence that we live inside a simulation, and this evidence allows us to establish the most likely purpose of our existence. This book is an exercise in applying logic to the evidence. It does not promote a specific religion. It goes along with science, but there are limits to what science can establish. God is beyond those limits.
This book addresses the following topics:
Why our existence is not a miracle that requires a creator.
How the possible motivations of post-humans can help us to establish that we live inside a simulation.
Why there is not proof in real life, not even in science.
Why the simulation hypothesis is not scientific.
How our minds can trick us how to avoid pitfalls in our observations and reasoning.
How the laws of reality can help us to ascertain that we do live in a simulation.
Why the evidence for the paranormal is not scientific but strong enough to count.
How to explain premonition, evidence suggesting reincarnation, ghosts, ufo’s, and meaningful coincidences.
How coincidences surrounding major historical events indicate that everything happens according to a script.
Why many people see 11:11 and other peculiar time prompts.
What predetermination tells us about our purpose.
By reading this book, you will discover that the world makes perfect sense if we assume it to be a simulation and that it does not make sense to presume that this world is real.
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.
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.
Despite what the media would have you believe, we’re actually living in the most peaceful time in human history. There’s no doubt that the world is in a bit more chaos than it was, say, five years ago, but largely, it’s still way better than even fifty years ago. We’re just more connected than ever, giving us a direct glimpse into global human suffering we’ve never had before.
The first thing I learned about ghosts was that they are fake. There is an almighty God, but ghosts are fairy tales. Science has proven it. Then we went on a school trip and visited the Singraven Estate in Denekamp. The custodian told us there was a ghost inside the castle upsetting things. He added that it is not an evil entity, so we should not fear it when entering. He seemed dead-serious and did not appear to be an attention-seeker. Only, it is better not to put too much faith in spook stories about venues that depend on tourist income.
There are plenty of ghost stories to go around. Let’s mention just one more. 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 their home. She had invited priests, paranormal researchers, and the crew of the TV show ‘The Dead Files’, who then ‘uncovered evidence’ of ‘grisly deaths’ that occurred in the house.1 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 claims.2 So what to make of this? The goings-on in Twickel Castle and the house in Hanover are undoubtedly peculiar. And perhaps they aren’t fake, and maybe the laws of physics do not always apply. Are they 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.
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Death: the final frontier
What happens when we die? We don’t know. There is some evidence suggesting an existence after death.