Minimising surprise
A model is a simplified representation of reality, not reality itself. Likewise, a map is not the territory. The same goes for our language. The word tree is not the same as a tree. So, if there is a tree on the map, that says little about the scenery, except that there is probably a tree at the location designated by the map. What we think of as reality is also a model. Our brain doesn’t register reality but merely processes it. The outside world generates inputs via our senses, such as seeing, hearing, and touching, which the brain integrates with our memories and emotions. It is what we call consciousness. What we experience as reality is a model. A dog experiences that same reality very differently, and the dog’s experience centres more around smell. We may see a couch and notice its colour, while a dog smells that a cat has been lying on it recently.
The brain generates our perception of reality, and our consciousness is an integrated experience shaped by that brain, informed by its own expectations and the senses. The brain works with an expectation, and adapts it when the senses disagree. We imagine reality, and correct it if contradicting information emerges. It is efficient because it greatly reduces the need for information processing. The brain aims to minimise surprise by having the best possible representation of reality. If our brain fails, for instance, when we are sleep-deprived, the world begins to look unreal. And so, intense stress can cause psychosis. During dreaming, the integrated experience of consciousness remains active, except that the information may not come from the senses but from the brain itself.
We employ models of reality to explain the world. Together, they form our worldview. We try to minimise surprises because we depend on our worldview to survive. Models are imaginations. We may believe that X causes Y because after X occurs, Y happens. We infer our models of reality from our observations and thoughts, as well as those of others. Models have a wide array of uses, including weather and economic models. Using multiple models can help us to reduce surprise, because different models have different strengths and weaknesses. It is, however, crucial to understand that models are not true or false but aim to minimise surprise.
Your model of reality may say that all swans are white, perhaps because you base it on the swans you have seen. Once you see a black one, you change your view to minimise future surprises. That seems a rather straightforward case. You can also reduce surprise by ignoring evidence, as we have trouble handling complexity and contradictions. We may also do that because we are group animals who cooperate and survive based on shared fantasies. This project, The Plan For The Future, aims to overcome these limits to arrive at a comprehensive and accurate model of the relevant issues to fix the apocalyptic problems and outline the future world civilisation. My aim was also to minimise future surprise by facing the evidence that contradicted my views, and seeking better explanations.
Often, I would write down my views on relevant subjects, and every time new information came to light, I adapted my texts accordingly. Finding evidence and setting out the direction were my primary concerns, not the practical execution. The more accurate the direction is, the more straightforward the execution becomes. Hence, I focused on proving that this universe is a virtual reality, that God is a woman who married Jesus and Muhammad, that we can have a global usury-free financial system, and that we need to socially engineer the future world society to be the most socially advanced. That requires fine-tuning, but if the direction is incorrect, we will end up in the wrong place. And so, this model of reality has undergone over 17 years of work, with numerous revisions, and is far from complete, all in a frantic dash to minimise surprise in case my plans get executed.
Limitations of simplicity
Proverbs state contradictory things. One proverb says that two heads are better than one, while another says that too many cooks spoil the broth. How can both be right at the same time? There is an optimal number of people working on a project. It is a very simplistic view. The qualities of the individuals working on the project also matter, as do the available tools, and countless other factors. These proverbs only scratch the surface and are useless for practical purposes. The headcount is just one variable of many that you need to manage if you run a business or a government. If you decide on these matters, you don’t open up a book with proverbs for guidance.
A one-dimensional spectrum, so a line, with two opposites, ranging from one participant to a large number, can make you fail to see the solution. Perhaps it lies outside that line, so this way of thinking keeps us from seeing it. Maybe the project isn’t worth undertaking in the first place, or perhaps success depends on the right tools. The number of people working on the project may still matter, so you have to consider that also. The debate about capitalism versus socialism has dragged on for over a century and has dominated world politics during the Cold War. So, do we need more or less state interference in markets? Yet, markets and states don’t create agreeable societies on their own.
Economic models include multiple variables, such as employment, inflation, consumption, business investment, and government spending. You can add more, such as workforce education, market flexibility, foreign competition, and regulations. There are countless variables to consider. If you have identified variables, you need data to estimate their impact. Economic data is usually insufficient to achieve certainty, as changes can have multiple causes. How do you know that some other variable didn’t interfere with the outcome? Despite all the models central banks used, they didn’t foresee the 2008 financial crisis. So models can be wrong, and often are. The financial models did get countless other things right, but missing one crucial variable can already make the model fail.
Still, models can be helpful. The more models we use, the more errors we can eliminate, but even then, we may still miss something important. Our minds have constraints. We often take a perspective and reason from there. A socialist economist might be good at pinpointing why capitalism fails but may not see what is wrong with socialism. Economic theories explain specific phenomena under certain conditions. You might find additional explanations in psychology, sociology, or even history. You might want to check your ideas before trying. You can run the idea through several economic theories. That might give new insights. You can’t be sure you are right, but models help you eliminate errors.
Natural Money is research into an interest-free financial system. It draws from economic theories, monetary economics, banking, psychology and even history. I reviewed that idea using existing theories and historical evidence to investigate how it might work in practice. During the research process, unforeseen issues came to light. Once interest rates in Europe fell below zero, resistance to negative interest rates emerged. Savers prefer 2% interest and 10% inflation over -2% and 0% inflation, it turned out. That is irrational, but normal human behaviour, so humans are not, as economics may suggest, rationally calculating individuals.
We seem to measure our gains and losses in currency units rather than purchasing power. And behavioural economics says we give more weight to losses than gains. It is a reason why inflation and money printing meet little resistance, while negative interest rates make people go berserk. A 4% loss in interest income impresses us more than the 10% reduction in inflation because we count our money units. Emotions can be strong and make us act irrationally. And so, it is pointless to try to convince savers that they are better off. Once that became apparent, I could look for a fix for this particular human feature by making negative interest rates appear as inflation.
Insights models give
Models yield better results than uneducated guesses, and combining models yields better outcomes than using a single model. Models tie us to a mast of logic so that we are not carried away by our thoughts and can figure out which ideas are useful to us under what circumstances. Using data, we can calibrate our models, so if our model is wrong, we can investigate why and correct the error. Scientific disciplines like economics, sociology, linguistics and biology use models. According to Scott Page, models share the following characteristics:
- They simplify by omitting irrelevant details and focusing on the essentials. Models abstract from reality.
- They formalise by making precise definitions and providing a framework for logical thinking to explain and predict.
- They are somewhat wrong because they simplify and omit details. The use of multiple models can reduce the errors coming from that.
Weather models make calculations, such as predicting that the maximum temperature in Amsterdam will be 26°C eight days from now. That is a precise calculation, but likely it is wrong. And so, weather forecasters use up to fifty models to make weather predictions. The average of these models is more reliable than the individual models. People who use a single model do poorly at predicting. They may be correct occasionally, just like a clock that has stopped is sometimes accurate, and then tout their few successes while forgetting about their much longer list of failures. Experts know that their models have limitations. When constructing a model, we can choose from the following approaches:
- Aiming for realism by describing relevant parts and relationships. Climate models work with CO2 levels and their effects on temperatures.
- Using an analogy by describing a process as similar to another. You can compare the ruinous growth of human activities with a cancer.
- Exploring possibilities if limits change. You can explore what will happen when unlimited energy becomes available at no cost.
When making a plan for humanity that lasts a thousand years or more, numbers and mathematics become meaningless, and we have to do it with words. You can’t measure culture, but it is the central ingredient. And still, we must try to pin down things by modelling humans to predict what we can do and how it might work. You get observations like religion can save us because humans are imaginary creatures who cooperate on the basis of myths. This is a strong and meaningful statement without mathematics, and part of the model of reality that may save us all. And there must be an explanation why humans are like that and why they fail in any realistic scenario.
To illustrate the use of models, I have taken two examples from the course Model Thinking by Scott Page on Coursera that investigate why people of the same ethnicity often live together, and why revolutions are difficult to predict.
Sorting and peer effects

Groups of people who hang out together tend to look alike, think alike and act alike. If you look at the map of Detroit, you see people of the same ethnicity living together. Blue dots represent black people, and red dots represent white people. We can’t change our skin colour, so if we hang around with people who look like us, that is sorting. We also adapt our behaviour to match that of others around us. When you hang around with smokers, you may start smoking too. Alternatively, if you hang out with people who don’t smoke, you might quit smoking. That is the peer effect. Both sorting and peer effects create groups of similar people who hang out with each other. Models can help us understand how these processes work by modelling them.1
Schelling’s segregation model offers insight into the segregation process. It says nothing about why people move. That we seek out people that are like us is an observation. Why would people move? That is an interesting question, most notably if it points to problems. If we believe that skin colour shouldn’t matter, then why does it matter? Yet, the model doesn’t say anything about that. So, that is a limitation. Schelling developed a model in which individuals follow simple rules. Suppose everyone lives in a block with eight neighbours. Red boxes represent homes where rich people live, and grey boxes are homes where poor people dwell. The blank box is an empty home. Assume now that everyone has a threshold of similar people who will make them stay.

A rich person might stay as long as at least 30% of his neighbours are rich. Assume a rich person lives at X. In this case, three out of seven or 43%, of the neighbours are rich, like the person living at X. If one of the wealthy neighbours moves out, and a poor person takes that place, 29% of the neighbours will be rich, and the person living at X will move.

A computer can simulate how that works out over time. Assume there are 2,000 people; 1,000 are poor, represented by yellow dots, and 1,000 are rich, represented by blue dots. Suppose they are randomly distributed at the start, and everyone wants to live among at least 30% similar people. In that case, the average is 50% alike, and only 16% are unhappy because less than 30% of their neighbours are alike. As a result, people start moving, and you end up with an average of 72% similar and 0% unhappy.

Even when everyone likes to live in a diverse neighbourhood where only 30% of their neighbours are like them, segregation occurs. Segregation may not be the intention of the individuals involved, as they might be tolerant people requiring only a minority of similar people living in their neighbourhood.1 Whether that is indeed the case remains to be seen. But if it is correct, and if we believe segregation is undesirable, social engineering the ethnic or wealth characteristics of neighbourhoods by mixing homes for different income groups makes sense. Some countries plan neighbourhoods that way.
Peer effects cause people to act alike. Contagious phenomena are peer effects. They often start suddenly, seemingly out of nowhere. In uprisings and revolutions, extremists frequently determine what happens, as in the French, Bolshevik, and Maidan revolutions. Things could have proceeded differently. It is difficult to predict revolutions. Granovetter’s model offers possible insight into why that is so.
Suppose there is a group of individuals. Each individual has a threshold for participating in an event like an uprising and will join if at least a specific number of others join. If your threshold is 0, you do it anyway. If your threshold is 50, you start if you see 50 participants. The outcome varies depending on the thresholds of other people that might get involved.
Suppose there are five individuals, and the behaviour is wearing a suit. One individual has a threshold of 0, one has a threshold of 1, and three have a threshold of 2. The following will happen: one individual starts wearing a suit because her threshold is 0. The second individual joins because his threshold is 1. Then, the three remaining individuals join in because their threshold is 2.
If the thresholds had been 1, 1, 1, 2, 2, nobody would have worn a suit despite the group, on average, being more open to the idea. If the thresholds had been 0, 1, 2, 3, 4, and 5, everyone would have worn a suit after five turns, even though the group, on average, was less keen on doing this. In this case, extremist suit-wearers determine the outcome.
It indicates that collective action is more likely to occur with lower thresholds and greater variation. The influence of variation is surprising. It might explain why it is challenging to predict whether something like an uprising will occur. Not only do you need to know the average level of discontent, but you must also see the spread of discontent among the population and connections between individuals and groups.1
Yet, what motivates people to join a movement greatly determines the tresholds, and the model gives no insight into that. The British Tea Tax, which sparked the outrage that led to the Americans to declare independence, was actually a good deal for the Americans that would lower tea prices. The Americans had a good life, only no influence on British policies. They didn’t suffer like the Russians that started the Bolshevik Revolution. Yet, they risked life and limb for independence in the war the war that followed.
Proof in the pudding
Do similar people hang out together because of sorting or the peer effect? That is the identification problem. Sometimes, it is clear. Segregation by race occurs due to sorting as you can’t change your skin colour. Other situations are less clear, and you can’t tell whether it is sorting or peer effect as the outcomes are the same. Happy people hang out with each other, as do unhappy people. Both sorting and peer effects may have caused this.1 Happy people may seek each other’s company because they don’t like to hear the whining of unhappy people or the company of happy people makes you happier.
Models can provide insights as to why similar people hang out together and why revolutions are difficult to predict. Yet, they don’t tell the whole story. Other models help us investigate what might happen under which circumstances. Thus, models explore the dimensions of complex questions and help us identify the spots where the best solutions hang out. In this way, models can assist us. If we intend to get people to contribute to a cause, we might want to model human behaviour to see how we can do that.
If we see humans as rational beings, we can convince them with arguments to do the right thing. If we see humans as myth-belieiving creatures, an inspiring story can prompt them to act. If we think humans are calculating individuals, incentives and punishments can make them do the right thing. If we see humans as status seekers driven by pride seeking recognition, we might achieve the objective by telling them how great they are. You may get the best result if you use a combined approach, so multiple models. Not everyone is the same, so each approach may appeal best to a particular group of people.
Latest revision: 29 August 2026
Featured image: Line And Dot On A Grey Rectangle. The artist wishes to remain anonymous because who wants to be as famous as Piet Mondriaan?
1. Model Thinking. Scott Page. Coursera (2014). [link]
