Historiography and Plot Technologies

Plot is useful in the service of an appropriate model of the universe but we should not create a model of the universe to service plot.

A bad plot can rarely be overcome by more plot.

Narrativium, the imaginary element introduced by Terry Pratchett in his Discworld series, represents the power of storytelling to shape our understanding of the world. Stories and narratives are an integral part of human culture and communication. They help us make sense of the world around us, provide entertainment, and convey important messages. However, when it comes to creating narratives, there is a danger of prioritizing plot over accuracy or truth. In other words, we should not create a model of the universe to service plot.

The problem with prioritizing plot is that it can lead to inaccuracies and oversimplifications. When we try to fit the universe into a specific plot or narrative, we risk ignoring or downplaying information that does not fit with our preconceived notions. This can result in a distorted view of reality that does not accurately reflect the complexity and nuance of the world we live in. Narrativium makes analogies and historiography difficult because it leads us to simplify complex issues and create patterns where there may be none. We tend to remember the few who succeed and forget the many who fail, attributing success to skill and hard work rather than variance and luck.

The goal of storytelling should be to convey truth and meaning, not simply to entertain. When we prioritize plot over truth, we risk losing sight of this goal and promoting inaccurate or harmful ideas. Therefore, we should strive to create narratives that are grounded in truth and accuracy, and resist the urge to shape the universe to fit our predetermined plotlines. By doing so, we can create stories that not only entertain but also inspire and inform.

To counteract this tendency, it’s important to recognize the role of chance in shaping events. Nassim Taleb’s concept of alternative histories is useful here: if we were to relive a set of events 1000 times, the range of outcomes would likely be much broader than we imagine. Human history is not deterministic, and we should be wary of creating a simplistic narrative that imposes a teleology or notion of progress onto the past.

One approach to studying these processes is through the use of Bayesian networks. Bayesian networks are probabilistic models that allow us to map out the factors that lead to certain outcomes. They can be used to identify the variables that are most strongly associated with particular inventions or social behaviors. By examining the relationships between these variables, we can begin to gain a deeper understanding of the mechanisms that drive historical change.

For example, imagine we want to understand why the Industrial Revolution occurred in Britain during the 18th and 19th centuries. Using a Bayesian network, we could identify variables such as access to capital, technological innovation, and labor supply as key factors that contributed to the rise of industrialization. We could then examine how these factors interacted with one another, and what role they played in creating a favorable environment for innovation and economic growth.

Another important concept in understanding historical processes is the idea of auto-catalysis. Auto-catalytic processes refer to the ways in which certain factors can feed back on themselves, creating positive or negative feedback loops that amplify or dampen their effects over time. This can be seen in many areas of human history, from the spread of language and culture to the evolution of political systems and economic institutions.

For example, imagine that a particular innovation, such as the printing press, is developed in a particular society. As more people begin to use the printing press, its benefits become more apparent, and demand for printed materials increases. This creates a positive feedback loop, in which the use of the printing press is amplified by its own success. Similarly, negative feedback loops can occur when the consequences of a particular behavior lead to its own inhibition or restriction.

By examining these auto-catalytic processes, we can gain a better understanding of the factors that contribute to historical change. We can see how certain innovations and social behaviors spread through a society, and how they interact with other factors to create new systems and institutions.

Ultimately, the power of narrativium lies in its ability to help us make sense of the world around us. However, we should not create a model of the universe to service plot. Rather, we should strive for a nuanced understanding of the past that recognizes the complex interplay of chance and human agency. By doing so, we can gain a deeper appreciation for the richness and diversity of human history.

In conclusion, to truly understand how human history evolves, we need to look beyond individual actors and events and focus on the underlying processes that shape social behavior. Bayesian networks can help us map out the factors that lead to invention, standardization, and replication, while auto-catalytic processes highlight the ways in which these factors can interact with one another to create complex feedback loops. By studying these processes, we can gain a deeper understanding of the mechanisms that drive historical change, and how they shape the world we live in today.

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