Simulations may be realistic in many ways while not being realistic in some aspects. If that is somehow noticeable then we may be able to 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 might be more illuminating to look at the available information about our own universe. Perhaps there is a more conclusive argument to be made. It may go like this:
If this universe is real, we cannot be sure that it is, because a simulation can be realistic too, but at least the laws of reality cannot be breached.
A simulation can have fake properties, so because of (1) we cannot establish that the properties of this universe are the properties of a real universe.
If however the established laws of reality are breached, this is unrealistic, and we have evidence of this universe being a virtual reality.
It follows from (1) and (2) that the properties of this universe reflected in the laws of reality cannot be used to determine whether this universe is real or a simulation. And it doesn’t matter whether the laws of reality are real or not. If they are real, and breached, this universe is a simulation. If they are fake, this universe is a simulation anyway. Science can be used to establish the laws of reality or the properties of this universe, but science cannot determine whether these laws themselves are real or fake.
According to science this universe started 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.
The following properties of our universe are certified by science and can be called established laws of our reality, reflecting what we believe to be realistic:
The laws of physics apply, 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 emerged from chemical processes and is shaped by evolution. There is no evidence for a creator.
We are biological organisms and our consciousnesses reside in our bodies and there is no such thing as 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 flouts the laws of physics from time to time. Evidence for reincarnation suggests that we are not biological organisms. Only, meaningful coincidences can happen by chance. There may be laws of reality we do not know of. 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 a clarification as to why it is the best explanation for our existence. This may be done by demonstrating the following:
The motivations of post-humans may determine whether we are able to establish that we do live inside a simulation and what its purpose is.
Science cannot establish that his universe is a simulation as we do not know the properties of a real universe.
Alternative explanations for the weirdness of this universe seem less plausible as they run into logical inconsistencies.
Evidence suggestive of reincarnation suffices to conclude that our consciousnesses do not reside in our bodies.
Evidence suggestive of ghosts, premonition, and alien abductions suffices to conclude that the laws of physics do not always apply.
The distribution of meaningful coincidences is unlikely to be result of accident, indicating an intelligence coordinating events in this universe.
Establishing that the distribution of meaningful coincidences is an unlikely result of mere accident is perhaps the hardest part. Meaningful coincidences can happen by accident. It is not possible to determine the probability of them happening. There may however be arguments that can be made to establish that mere accident is not so likely. To make the argument more convincing we might consider the following:
Some types of meaningful coincidences are less likely to happen than others. The more elaborate the scheme, the less likely it is the result of mere chance.
If meaningful coincidences happen in relation to the most important historic events, then an intelligence coordinating events appears more likely.
If meaningful coincides are not distributed randomly across people and time-frames, it might suggest interference or even destiny.
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 had proven it. But then we went on a school trip and visited Twickel Castle. The custodian told us there was a ghost upsetting things. It was not an evil entity so we shouldn’t fear it when entering the castle, he said. The custodian seemed a down-to-earth person to me. The following account about Twickel Castle is on the Internet:
Recently I heard a strange story from the temperate 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 in 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
There are plenty of ghost stories. Let’s mention one more. In the summer of 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. DeAnna Simpson, the woman who came forward to tell the story, spoke of several ghosts and other entities that were severely haunting her home. She and her husband had lived in the house for seven years.
She had collected photographs of ghosts caught on film, as well as pictures of guests who have been scratched or otherwise attacked in their home. Simpson had also invited priests, paranormal researchers, and even the TV show ‘The Dead Files’, who then apparently uncovered evidence of grisly deaths that occurred in the house.2 When the Fox43 crew entered the house, their photographer was scratched out of the blue, apparently by an invisible source. He had to admit that this ghostlike phenomenon felt real.
There are countless stories about haunted castles and houses. And there are television series about ghosts, for instance Ghost Adventures. The show is bloated with suggestion, suggesting that it is fake. “It hardly ever happens like that,” an investigator of the paranormal claims.3 But he didn’t deny that ghosts exist. So what to make of this?
The goings-on in Twickel Castle in Delden and the haunted house in Hanover are undoubtedly peculiar. But do they prove the existence of ghosts? Perhaps proof will remain elusive. If we live in a simulation that exists for entertainment, the simulation may play into our imaginations and fears. The phenomena may be as real as we are, but they may elude the measurements of scientists. Indeed, there may not be more to it than that.
If you like this post, then you might also like:
Death: the final frontier
What happens when we die? We don’t know. There is some evidence suggesting an existence after death.
Featured image: Halloween cat from Poland. User Silar (2012). Wikimedia Commons. Public Domain.
1. Betoverd door: haunted houses. Theracoppens.nl. [link] 2. A haunted Hannover home. Civilwarghosts.com. [link] 3. Why those TV ghost-hunting shows are transparently fake. Scott Craven (2019). The Republic. [link]
Socrates was one of the great Greek philosophers. He lived around 400 BC and is seen as the founder of the practice of rational debates. A rational debate is a discussion between two or more people holding different views who wish to establish the truth with the use of reasoned arguments. Socrates saw truth as the highest value and he thought that it could be found through reason and logic in discussions. Many people believe that rational debate is the best way to decide about which course of action needs to be taken.
Many people believe they are right and that a rational debate will prove this. And because they are right, they should discard any evidence to the contrary. In fact, any evidence to the contrary proves there are people with hidden agendas out there spreading misinformation. That’s why it isn’t easy to have rational debates.
In his famous dialogues Socrates acted as if he was ignorant. If you are willing to concede that you are ignorant then you may be willing to learn. Socrates was willing to teach. Willingness to learn is the key to progress. The Scientific Revolution took off after European scientists accepted their ignorance after European sailors discovered America, a continent they didn’t know of. European scientists started to ask themselves what more they didn’t know. And so 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 came up with a scheme for rational argument. He also applied it on the course of history. Hegel lived around the year 1800. He believed in progress like most European scientists at the time. His idea was that resolving a conflict of opposing sides can lead to progress in rational debates, but also in history in general. In most cases both parties in a debate or a conflict have valid arguments. Hegel thought that an argument develops in three stages. First, someone will come up with a proposition. Then someone else will bring in an opposing idea. If both have valid arguments, are willing to listen to each other, and understand each other’s arguments, then a rational debate between them can lead to a better understanding of the situation. The new understanding can be a proposition in a new argument.
An example can illustrate this. Suppose that Adam Smith and Karl Marx meet in a conference hall. Smith argues that capitalism and free markets are great because they create a lot of wealth while goods are distributed efficiently. Marx then says that the living conditions for workers are miserable and that goods are distributed unfairly. He then says that workers should take control over the factories. Smith might then object by saying that workers will turn out to be poor entrepreneurs. If both are willing to consider each other’s ideas, they might agree that capitalism is a great way of creating wealth, but that there should be minimum wages, unemployment benefits, laws protecting workers, and state pensions. After they agree a third person might enter the debate and say that economic activity is destroying the planet. This could be the beginning of a new debate. The outcome may be regulations on the use of chemicals and investment in making the economy sustainable.
This is not how history progresses in reality. Issues are often resolved in a historical process that involves conflicts. In many cases power is used rather than arguments. People do not always listen to each other nor is it easy to foresee the consequences of choices. Most people find it hard to deal with contradictions, so they may take a side rather than let the arguments play out in a rational discussion. And a debate is often clouded by interests. Capitalists might argue for capitalism because they feel they can profit from it. Socialists might argue for socialism because they believe they may gain from it. And so a debate can drag on without being resolved.
Historical processes are complex and a Hegelian scheme often isn’t adequate to describe them. Karl Marx and the Marxists believed that Hegelian dialectic would prove him right, not much unlike many people who believe that rational discussion will prove them right. Marx used Hegel’s ideas to promote class struggle. In fact Marxists believed that philosophy can be used to change reality. Marxism promoted social conflict as a way of resolving issues. And so communists tried to take over countries with military force or agitation. In developing countries communist insurgencies became mixed up with struggles for national liberation. For Marxists Hegelian dialectic was a tool in the war on capitalism, and later on, to liberate marginalised groups from social injustice.
In the nineteenth century workers didn’t appear to benefit from the capitalist system. It was hard to figure out how socialism would work out in practice so it may have been necessary to try it. A country called the Soviet Union tried socialism for seven decades. If you look for the Soviet Union on a map, you probably will not find it. That isn’t because it is such a small country but because it doesn’t exist any more. The Soviet Union was dismantled because its leaders realised that the socialist economy performed poorly. With the benefit of hindsight the flaws of socialism seem obvious, but if it hadn’t been tried out, it probably wasn’t that obvious. Neverteless, socialism may work well in specific situations. For instance, health care in socialist Cuba is cheap and effective compared to the United States. Life expectancy in the United States and Cuba is nearly the same despite the United States spending more on health care per person than any other country in the world.
The poor living and working conditions for labourers in the nineteenth century challenged the legitimacy of capitalism and promoted the case for class struggle. Christian democrats and conservatives on European continent tried to introduce a ‘third way’, which was an attempt to resolve the Hegelian question by relying different ways of organising society and the economy rather than markets and governments, for instance via solidarity in communities. The result was a historical process leading to labour regulations, minimum wages and pensions in the democratic societies of Western Europe. As a consequence the economies of these societies came to have a mixture of capitalist, cooperative and socialist features. A rational debate could develop because there was freedom of expression. The Soviet Union was a dictatorship so there wasn’t much of a rational discussion going on there. Progress in history often requires experimenting. At first there was capitalism but conditions for workers were poor. And so pure socialism was tried out but the results were also poor. In the meantime many societies found a middle way that was often a combination of elements of several ideas. This is progress in history the way Hegel may have liked it.
Ideologies like socialism and capitalism are models of society that describe how society can be organised. Models are simplifications or abstractions but they can be useful. Models can help us to organise our thoughts so that we can figure out which ideas are useful and under what circumstances. People who adhere to a specific ideology tend to be poor problem solvers. This is where Hegelian dialectic often goes wrong as it is also a tool employed by parties in a political conflict. They tend to frame the debate with their choice of words. As a consequence, parties may become pitted against each other and live in their own realities as the words describing their realities diverge.
Science works in a similar fashion. A philosopher named Karl Popper came up with a scheme for scientific progress. He believed that scientific progress is achieved by theories replacing each other. Scientists in a specific field often work with theories. 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 doesn’t. Most scientists at first ignore the glitches like weird readings on their instruments because the Old Theory has proven to be very useful. They may convince themselves that the unexplained measurements were caused by faulty instruments. As more and more experiments indicate that there’s something wrong 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 so revolutionary. They feel that only a crazy person could think of it. But as experiments confirm the New Theory, and because the New Theory explains a lot of things the Old Theory couldn’t, scientists embrace it 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. In social sciences and economics both schemes occur. There is progress in theories but also a debate between different approaches that might be resolved in a Hegelian fashion.
An example might explain the thoughts of Popper. Around 1680 the mathematician Isaac Newton worked out a few laws that explain the motion of objects. Newton’s laws tell us that objects fall to ground and don’t float in the sky. It may seem rather pointless to make laws telling us that but his laws have other applications too. For instance, they can explain how the Earth orbits around the Sun. Newton presented his laws in a few nice mathematical formulas so that 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. And that’s really cool.
Over the years scientists developed more precise instruments. After a few centuries they found some measurements they couldn’t explain. These were only small deviations from the values you could calculate with the use of Newton’s formulas so scientists didn’t worry much at first. But a physicist named Albert Einstein took these glitches seriously and he developed a theory that explained the curious readings on the instruments but also the motion of objects, so that this new theory explained more. Scientists were sceptical at first and Einstein was a weirdo, but when experiments confirmed his theory, they finally embraced it.
A reasoned debate may seem the best way of figuring out what to do. Only, social sciences including economics involve human interactions. The number of variables are high and all of them are known while individual variables can’t be isolated in a controlled environment. And so it becomes difficult to ascertain causes and effects or to make predictions. Experts in these fields therefore often make wrong judgements.
It is dangerous to blindly trust experts but may be even more dangerous to ignore them. An ignorant person can be right by accident while an expert can be wrong because he or she missed out on something. And that something might have appeared insignificant on beforehand. Making predictions in social sciences may sometimes look like gambling and the difference between knowledge and ignorance may be obscure. This can embolden the ignorant while it can make experts cautious.
Sometimes experiments may prove whether or not some assumption or theory is correct but that involves experimenting with humans. Trying out communism in the Soviet Union killed millions of people. For instance, there were famines in the 1930s in Ukraine. We should therefore be careful as to what kinds of social experiments we engage in. And we shouldn’t draw the wrong conclusions. Millions of people have died of capitalism too, simply because capitalists didn’t think they could profit from letting these people live.
In theory reasoned debates are more common in science than in politics but scientists need research budgets that are provided by businesses and governments. The things scientists investigate are often determined by governments and businesses and the outcomes of scientific research can be influenced by the interests of those who fund the research. And so the results of research projects are not always what you might expect from a reasoned and unbiased investigation. For instance, in the United States there are several think tanks that do political and economic research. The research of liberal think tanks tends to support liberal views while the research of conservative think tanks tends to support conservative views, and perhaps that doesn’t surprise you.
When actions taken are based on the outcome of rational debates, this often leads to new issues that may be resolved in subsequent debates. People may think about what these new issues might be and what the solutions for these issues could be. Yet 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 the scheme of Hegel, he thought he could predict how history would play out. Many people make the same mistake. They think they somehow know what will happen in the future.
Marx believed in progress like Hegel did. And many people still do believe that there is progress. Yet this is not so obvious. There should be some point to the general direction of history otherwise you can’t call it progress. To put it all into perspective, you can ask yourself: “Are we happier now than our parents were fifty years ago?”
Even if that isn’t the case, that doesn’t contradict the case for using rational debates to resolve social issues. In debates all kinds of arguments can be made. That can be frustrating because there are so many stupid ideas. Sadly, it is not always obvious which ideas are stupid and which aren’t. There are several techniques to frustrate debates used in situations of conflict between opposing sides. And perhaps even more worrying is the fact that many important decisions are made without a proper debate, simply because people aren’t interested in the subject. Progress is more likely to happen if debates focus on important matters and are conducted in an open and honest way. For a rational debate to flourish, all parties involved must feel free to speak. Absence of violence and threats are basic preconditions for such a debate to take place.
There’s something else that should make us cautious. Technology is progressing and it is about to completely alter human existence. Humans may be about transform themselves into a new kind of beings that live for thousands of years and entertain themselves in their own virtual realities. If this technology becomes cheap then everyone may be able to enjoy it. Politics as well as economics may become meaningless as a consequence. For the time being it can still be worthwhile to pursue social progress using rational debates as it can mean a huge difference as to how we enter the new era.
Featured image: Portrait of Socrates in marble, 1st century Roman artwork. Eric Gaba (2005). Wikimedia Commons. Public Domain.
1. A Brief History Of Humankind. Yuval Noah Harari (2014). Harvil Secker.