When one side of history becomes the right side of history: an analysis

This post was written by the same guest author as part 1. 

My previous post was the first of a two-part series, and I highly recommend that you read that one first before you read this one. In the first post, I laid out the rules and operations behind my model that illustrates how societies adopt certain social systems. Now that the model is in place, in this second post I have three primary objectives. My first is to show not just how the model works but why the model works, in a recounting of how I came up with the model on the basis of first principles. My second objective is to flesh out my model and bring it to life with multiple real-life and hypothetical examples. Lastly, my third objective is to point out variations and idiosyncrasies within the model and go in depth into certain aspects of the model that I had to oversimplify in my last post in order to get the main idea through.

My model is built by combining four premises that I gradually developed over time, mostly out of contemplating how revolutions happen. With these four premises, my entire model can be deduced. The first premise is that leaders have power because people believe they have power. This revelation came to Machiavelli, and then to the general population with the French revolution and enlightenment concepts when the public realized that they, not God chose who sat on the throne. My second premise is that there must be a certain point of time in society when one side of history starts to feel more inevitable than another. When I first came up with this premise, I did not have game theory in mind. I simply thought about a scenario where there are the revolutionaries and the rulers. If the revolutionaries succeed, there must have been a single point in time when the revolutionaries fighting against the status quo (the rulers) become the rulers fighting against what was the status quo (the revolutionaries). This premise takes the same logic as Rolle’s Theorem in calculus, which makes sense when looking at the model. The third premise is that this turning point must be when one side takes control of over 50% of power within society. The fourth and final premise is that there are game theory outcomes which are determined by which decision is more popular. I quickly connected premise two and three to come to the conclusion that this “certain point” when the scales flip is when 50% of the population has adopted the new system. I then connected premise one and four, seeing that the decision to believe in a certain power is a decision that is made under the logic of game theory, with the decision to follow the presiding power being the dominant strategy. As you may have already guessed, my final conclusion combines the two aforementioned conclusions with the idea that network-dependent game theory is what holds governments in power, and the turning point is when over 50% of the population goes for an alternative system, and as a consequence the dominant strategy switches over to following the new leader.

I will illustrate how game theory works in this case and my model in action through two examples. The first is the smallest-scale case, that being a leader and five followers. Assume every individual in this example has equal power. If one follower defects, everyone else will turn against him. Whenever a number of people under the majority defects, it is always safer for the remainder of people to work on the side of the majority. Below is an illustration of this circumstance and the game theory matrix that comes with it.

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Picture 1
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Picture 2

When three people defect, there is a 50-50 split, so the choice on which side to join (the followers or the defectors) does not operate under this game theory conundrum (just how in my model, when the ball is at the 50% mark, the force of gravity (game theory) does not push the ball in either direction, and is therefore nonexistent in determining which side wins). Below is the illustration that depicts this situation:

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Picture 3

Once four people defect, there are more defectors than there are followers, and it suddenly becomes the safest bet to follow the defectors. Notice how whichever side has the majority of followers is better off not just because they have more power, but because it creates a compelling decision-making matrix for everyone else to follow them. In addition, the more people there are in this example, the harder it is for the defectors to win. Below is the illustration that depicts this situation, and the now-flipped game theory matrix because of this situation.

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Picture 4
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Picture 5

Now considering this example, you may ask: when things become 50-50, can’t the defectors choose their new leader and the sides divide into two, with two separate leaders and two respective followers? This is true, and the 50-50 mark is when two systems are most likely to divide in two. If the defectors were presenting a completely new social system, then this divide would create two different societies. However, usually the change is less dramatic. If one system does not quickly win out over the other, the 50-50 mark is where two systems are most likely to linger and coexist. In addition, whichever side wins from the 50-50 point is entirely dependent on the pure power of each side.

This would be a good time to bring up one rule of the model that I lied about previously. There, I said that the scales shift when 50% of the population adopts a new system. The reality is that the scales shift when 50% of those who control the power pertaining the system adopt the new system. For example, if one follower held a knife, giving him the power of two men, then the defectors would need to muster the power of two additional men to have enough power to overcome the group with the knife. The same applies on a larger scale. No matter how many people take to the streets, unless the populace is able to accumulate more power than the military is capable of exerting, they need the military on their side (or police, or any organization that holds the most power). This sadly manifested itself recently in Venezuela, where even though roughly 80% of the population opposes Maduro, they are unable to sum up enough power to make their system of government the dominant strategy – military leaders must break. This is also a powerful argument for the second amendment, because by allowing for the ownership of weapons by the public it distributes power more equally.

Once you see this you begin to see all sorts of ways in which leaders take advantage of this model (without knowledge of this model of course, but for the same reasons) to stay in power. Under this model it makes sense that it seems as though the power of an individual is proportional to the severity of enforcement. Notice how in authoritarian regimes, a civilian disobeying the orders of the government is brushed aside, a soldier disobeying the orders of the government is put in jail, and a top commander disobeying the orders of the government is killed. Likewise, the stability of a regime can be determined by measuring the ratio between the power of an individual and the the severity of their punishment; if a government kills a civilian for disobeying orders, the ruler probably feels as though he could lose power at any moment, while if the government has a punitive punishment for the defection of a top commander, the leader probably feels secure in their position. By making punishments more severe, the leader is compensating for weakness by strengthening the desire to follow the dominant strategy.

It’s important to remember that this model only applies when the dominant strategy in game theory depends on other people engaging in it. It is tempting to look at the passing of laws, and see how 50% of the population is necessary for a law to pass in a legislature, and therefore connect it to this model. This would be a mistake. To know that this model applies, simply ask the question: “if more people follow the system, does that make a certain individual more likely to follow the system?” If more people vote for a law, does that make one person less likely to vote against it? Maybe sometimes, but usually no.

There are two general categories of systems that I have identified to fit this model. The first are systems that are supported by enforcement, such as following a leader or a government. The second are systems that are supported by network externalities.

The most prominent example of a system that is supported by network externalities operating under this model is social media. Let’s say everyone is using social media app A for one purpose. Then comes social media app B or the same purpose. An individual adopting social media app B falls under a game theory matrix that mirrors that shown previously:

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Picture 6 – the basic model

In the case of my model, the Social Force A (Sf(A)) that would work against the force of game theory would be the quality of the new app, an advertising campaign for the new app, etc. In enforcement based systems the measurement of power is much more relevant, because power is directly tied to enforcement capabilities. Of course, there can be systems that are both supported by enforcement and network externalities.

The beauty of these systems is that it is circular and self-fulfilling, really demonstrating how these kinds of systems stay in place unless acted on by an outside force; every individual makes the individual decision to follow a system because it is the dominant strategy in their own game theory matrix, and following the system is the dominant strategy in the game theory matrix because everyone individually makes that decision.

Now that we’ve dealt with a few examples that relate to the fundamental operations of the model, let’s look at the model itself and see what unexpected insight it provides. The first and most prominent is that which describes what occurs at the 25% range, when the slope is at its steepest. Staying true to the math of the curve, the point of where the slope is the greatest is called the point of inflection, and I think it can be said to be a point of inflection in society as well. Therefore, this model provides us with a discernible point of inflection along a mapped trajectory for a social system, going beyond the currently provided definition of “a time of significant change in a situation; a turning point.” What’s most notable about the point of inflection is that it is the most difficult spot for a system to traverse in its path towards total adoption. This is because this area has the steepest slopes and therefore the greatest force of gravity pushing the ball in the opposing direction, which can especially be the case when the system creates a curve that is already extremely steep.

Considering this area aptly reveals the incredible value of leaders. Leaders can gather a large group of people and have them act in a coordinated manner – in doing so, a leader can make a social system leap-frog across the curve. The more organized and powerful the leadership, the easier it is to change social systems, because a big enough unified bloc of people suddenly acting in the same way allows a system to jump past the most difficult part of the parabola that was described above. Because of this, the presence of leaders gives potential for society to adopt systems with much greater ease. They can also enable societies to adopt extreme systems that would have been impossible otherwise. I argue that this was the case in the Rwandan genocide. Recently I wrote a paper on how genocide cannot happen without leadership, and the crux of my argument fell upon this very same logic:

I argue that Rwandan killer groups could not have formed without a coordinated, instantaneous, and drastic change in social norms. Within hours after the plane crash, the social default left that of a normal society and turned into a “kill or be killed” state. Put simply, the social duty not to kill was suddenly replaced with the social duty to kill. A major change in intersubjective social understanding is only possible if it is a view adopted by the majority of society, and the majority of society cannot adopt such a polar opposite of social understanding in such a short amount of time unless conducted in a coordinated fashion. Thus, this sort of paradigm shift must have and can only have been implemented by the dictum of an organized, capable, and unified leadership.

Here is a depiction of this leapfrogging happening on the model:

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Picture 7

In addition to point of inflection, there are few other terms that come out of contemplating the implications of my model. The first is social momentum. I use the term social momentum as an equivalent the velocity of the ball – if the ball is traveling quickly there is a high amount of social momentum. If the ball’s speed is accelerating, the social momentum is increasing. When a ball falls down a slope, it naturally accelerates. It makes sense that the acceleration is greatest when it is at the second point of inflection, when 75% of the population has adopted the new system and 25% still happen to hold on to the old one. This is because at this point the adoption of the new system should be in full swing, there is still a decently large number of people still waiting to adopt the system, and the slope makes it the most difficult point for the old system to fight back. The acceleration slows down at the ends of the curve as it picks up those at the fringes of society at a slower rate, and the old system has more ease pushing back. It is important to consider that even though the velocity of the ball may be faster, the rate of adoption may be the same, since the area under the curve picked up between two points is much greater when near the middle than on the edges.

Another term is social potential energy, which is an equivalent to the potential energy of the ball in physics, measured by the height of the ball. The higher the ball, the greater the potential for social change.

It’s important to keep in mind that outside social forces can work on the ball in any direction at any time. Therefore, there are scenarios where it is possible for the ball to be moving at any speed and towards either direction on all points on the curve. It is just that the forces relevant to whatever point the ball sits will still apply.

Although the terms and the properties of each point remain the same, different curves that describe different social systems can vary greatly. These variations happen by altering the steepness of certain parts of the curve.

There can be curves in which for some reason the peak is not at 50%, because the dominant strategy flips when adoption is at 25% or 80%. There can also be curves where the slope is on one side is much steeper than the other. This would be the case when one competing social system has a much stronger enforcement mechanism or network-externality than the other social system, even though they compete on the same field. Take the use of language pertaining to political correctness, for example. There is much more social pushback (enforcement mechanism) behind using a politically correct word than a politically incorrect word. If someone only uses politically correct words, they will largely go unnoticed unless they use a loaded politically correct word and it sort of annoys a few people who are very politically aware. Contrast that to using a politically incorrect word, where if one uses it they will be scolded or shunned. The curve would play out as drawn below, where the ball is the use of a politically correct word or set of words, system A is being politically correct, and system B is not being politically correct.

 

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This depicts how the slope can seem much steeper coming from one side over the other, because of an enforcement mechanism privileging one side. Clarification in a future edit or post.

One fault of my model is that it is very difficult to express my forces in numerical terms, and therefore calculate certain values. I refrain from making a scale with certain set numbers, because the value of a certain number pertaining to what it reflects on society is entirely subjective. For example, the velocity of 10 m/s can mean totally different things to different people depending on how much subjective value they place on that number. The percent adoption number is the most important number, and luckily it is also the number that is easiest to measure and the most objective. If someone pleased, they could construct an elaborate scoring system such that every value could be converted to some number and the values could be mathematically calculated through the physics equations I provided to produce a final score which would mean something to them. Although it would require extensive social surveying to come up with an accurate score for each value, it can be done in a way that the conclusions would yield value, and doing so would not be a foreign procedure.

Although it would be amazing to see this be put into action, it is certainly not the only way to yield value from my model, in fact, I would not entirely recommend it. I see the best and most accessible way for the model to be used is for someone to come up with a social system which fits the requirements, think about the properties of that social system and the state of society, and sketch out a curve with a ball according to how that someone feels about how each part of the model should be portrayed. Then that someone should imagine how the ball would move, and generally just think about the state of the model that person constructed with all the existing variables in mind. Just doing this, I believe, can be an extremely effective springboard for insights, conclusions, and discoveries. Think about all of the examples I have used so far – I haven’t had to substitute variables for hard numerical values in any of them.

Finally, I believe this model can offer value in the parts that it is made of. In other words, the conclusions and theoretical underpinnings of my model can be taken and expanded to any sort of new concept or construction of how society operates. If not anything I have put forth so far, if someone identifies a system to fit the requirements, they can at least come to a set and sure amount of determinations just by knowing what percent of society has adopted that system.

I’ll wrap this post up with a BBC article I came across recently. This article talked about a researcher who looked at hundreds of political campaigns over the last century and found that although the exact dynamics will depend on many factors, it was shown that it takes around 3.5% of the population actively participating in the protests to ensure serious political change. It’s hard to say which types of political change were game-theory depended and which weren’t, but this does show that there is serious research which plays into my model. “But the results say only 3.5% of the population participated, not 50%!” You say. Yes, but the 3.5% is only the percent of people devoted enough to actively participate. If 3.5% are actively participating, then most likely another 20% of the population strongly supports the issue, another 30% supports it mildly, and probably another 20% supports it slightly. Regardless, the fact that there was identifiable cutoff plays into my conclusions that there is a discernible point of adoption where things start to fall one direction. Funnily, this recent article did exactly what I described in one of the first sentences in my previous post: “Social scientists and historians often use contextual evidence and a chronology of events describe the process of how social systems flip. But society is a machine with each individual operating as a node, and these sorts of explanations merely put together a coherent narrative of the outputs of this machine rather than its inner workings.” If my model is right, now you know the gears.