Feedback: I’m trying to reduce the length of this article. If you find anything redundant or that I could move into a footnote please let me know. I’m also unsure how clear some of the mentioned concepts are to readers less familiar with epistemology or progress studies. Please lmk if something is unclear or unfamiliar.
While most of the emerging field of progress studies is focused on downstream inputs, such as energy consumption, institutional design, or the history of innovations, it has neglected the most upstream, enabling input—knowledge.
Knowledge of how to create a fire solves the problem of freezing.
Knowledge of how to cure cancers solves the problem of cancer.
Knowledge of how to engineer a fleet of nuclear space ships solves the problem of asteroid impacts.
Many of the dominant questions in the progress studies field, like "Why have we stopped building X?", "Why haven't we invented Y earlier?" or "What happened to our vision of Z" can be answered uniformly—because we lacked to required knowledge. For example, because we lack the knowledge of how to prevent regulation inflation, we lack good explanations of the phenomenon enabling an invention, and we lack knowledge about how to spread and retain societal optimism. This article is highly inspired by David Deutsch's work, The beginning of Infinity, and the ideas of critical rationalism. Deutsch, and his influence Karl Popper, are likely the most underappreciated resource for understanding human progress. In this essay, I'll formulate a model that integrates the neglected input of knowledge and argue that human progress is achieved via intellectual progress, supported by education and cognitive augmentation.
Inputs
Intelligence, Cognition, Creativity
Many definitions of intelligence have been proposed. Max Tegmark, for example, argued that intelligence is the ability to accomplish complex goals. AI researcher Francois Chollet gave a more comprehensive definition in his seminal paper On the Measure of Intelligence as the ability to turn experience into future skills. David Deutsch defines the related concept of creativity as the capacity to create new explanations, i.e., an endless stream of innovation. To solve a new problem, we create explanations about the problem to understand it, conjecture a solution and eliminate bad explanations by criticizing them. This process requires creativity. I argue that on the scale of humanity, achieving goals, attaining future skills, or solving problems, can be equated with making progress. Importantly, intelligence, i.e., cognitive processes, can be augmented by Cognition Augmentation Technology, as I'll elaborate on below.
Knowledge
Problem Solution Definition
While awaiting that sight, they had numerous fears as well: fear of storms; fear of mighty, unknown creatures; fear of sickness on board; fear of being becalmed; fear of that wavy immensity opening up all around them; fear of uncertainty. But they also had their charts, their instruments, the technology used to build their vessels. They had knowledge.—Chiara Marletto.
Deutsch summarizes his view on progress in his Principle of Optimism—All evils are caused by insufficient knowledge. "Evils" include all natural and technological problems, intentional and unintentional. The evil of nuclear war is solved by the knowledge of game theory and international relations. The evil of cancer can be eliminated if we figure out the required gene editing or drug technology. The evil of governments that regulate flying cars or supersonic planes can be solved by better regulatory institutions. We can solve these problems because "[e]ither a given technology is possible, or else there must be some reason (say, of physics or logic) why it isn’t possible.". Knowledge is what allows us to develop solutions to all our problems.
Accumulation and Evolution
Knowledge evolves. All growth of knowledge happens through a process of variation and selection. Just as the knowledge in our genes grows through blind mutation and natural selection, human knowledge analogously grows through conjecture and criticism. Unlike genetic knowledge, human knowledge evolves accumulated and is not contained by any genomic bottleneck (for comparison, the algorithm that also makes up our intelligence is contained by the 50-100k protein-coding genes). The amount of knowledge we can store and process is infinite, given the technology to do so. Once physically embodied in a suitable environment, knowledge tends to cause itself to remain so. Once we invent planes the knowledge of their construction lives on in our society, in our brains, in our books, and in the physical plane itself. Note that it only has the tendency. Knowledge can be superseded, get lost, or simply get burned (as in the case of the library of Alexandria).
A downstream force against the accumulation and evolution of knowledge are errors or anti-rational memes, especially in our societies' rules and organizations. Errors, among others, consist of conformity seeking, the suppression of curiosity and creativity, and a lack of mechanisms for error correction. On the level of society, "error-corrections" means the removal of bad leaders, policies, and regulations. Anti-rational memes are ideas that rely on disabling the critical faculties of the individual host to cause themselves to replicate. One such meme is religion that does not allow its followers to question the worldview or laws of nature proclaimed by that religion. Once a child is indoctrinated with that religion they don’t question it and in turn, teach it to their own children. They might even help kill some heretic scientists like Giordano Bruno. The Italian philosopher, astronomer, and mathematician was the first to theorize that the universe is infinite but was burned at the stake by the catholic church for his unorthodox ideas. A contemporary example is how schools inculcate standard patterns of behavior through psychological pressure instead of training and encouraging students’ creativity and curiosity.
Concluding, knowledge evolves, accumulates, and is the raw, abstract resource we need to solve problems and make progress. However, knowledge accumulation is also hindered by anti-rational memes.
Indirect Inputs and Knowledge Infrastructure
Now that I outlined the two main inputs of progress let's look at the indirect inputs of the model.
Cognitive augmentation
Humans have used tools for millions of years, augmenting their bodies, writing for thousands of years, augmenting externalizing and sharing of knowledge. Only a few decades ago, we invested in computers. Cognitive Augmentation or Augmented cognition in the field of using computers and other tools to augment our cognitive and intellectual abilities.
Because we rely on these capabilities to solve problems effectively, they contribute directly to our problem-solving ability, i.e., progress.
Cognitive augmentation enabled innovation and problem-solving. Humans could only design nuclear reactors or space ships in the first place because we developed complex simulation and computer-aided design software. These simulations and computations would not have been possible with the unaided brain that evolution created. Similarly, without information technology, including writing, we'd likely never have been able to accomplish the productivity increase that led to a 65,64% decline in extreme poverty from 1820 to 2018.
Education
Education is the institutionally supported process of acquiring new knowledge in a systematic way to solve complex problems. (Modern universities are more of a bundle offering a credential, a social network, and many other services, but I'll focus on the knowledge transmission property in this section). Because our world and problems are complex, we can't, for ex, expect a child to be able to participate in the design of an electoral system. One way to support the child to get there is to provide them with increasingly difficult examples or simulations of the problems they want to solve once they graduate.
Later in their lives, they might actually start working on designing an electoral system for an innovative charter city and publish a seminal paper on social-choice theory.
If our education system (and our knowledge infrastructure) works efficiently, this knowledge will now be presented and explained to young students curious about social-choice theory.
I.e., it is effectively fed back into our collective pool of knowledge once the knowledge is created.
Unfortunately, this process is massively inefficient, little driven by market demands or the students' curiosity. I'll expand on its flaws in later articles. For now, I recommend Brian Caplan's work for a rigorous economic analysis.
Knowledge Infrastructure
I first grasped the concept of Knowlege Infrastructure while reading my friend Will Bryk's article on the High-Quality Information Society. It starts with two aliens talking about humans, our accomplishments, and our flaws.
Humans are smart. It took us 124 years to discover atoms to splitting them. It took us 58 years, from our first flight to our first space flight.
Humans are also stupid. Many of our most significant inventions were driven by war, i.e., the goal of killing each other. Our educational system is largely the same as it was 300 years ago.
Knowledge Infrastructure is the process and technology we utilize to transfer knowledge from one person to another. This process includes informing others about the open scientific, technological, business, and moral problems. An inefficient knowledge infrastructure does not communicate what problems other scientists are working on, so a lot of work gets duplicated. It also includes processes that eliminate false theories and criticize erroneous conjectures. One example of such a false claim is the harmfulness of GMO foods. This idea was mainly spread by the non-profit Green Peace and caused millions of people to die because they were prohibited from being supplied with gene-modified rice. Knowledge infrastructure is part of every step/entity in the discussed model. I'll expand more on this concept and category in the future.
Model
Thus I propose this abstract model of progress driven by intellectual progress. The primary input of this model is our existing knowledge and our cognitive processes.
These are combined to solve problems, i.e., create solutions, explanations, inventions, and companies, which lead to progress, i.e., technical, moral, political, scientific, and economic progress. This problem-solving process is facilitated by education (mainly in the first part of a person's life) and cognitive augmentation. New knowledge generated is fed back into our input pool of knowledge via education and our knowledge infrastructure.
Conclusion
The model shows that if we want to make progress, humanity should devote more of our thinking and resources to improving our education system, knowledge infrastructure, and cognitive augmentation technology. Since there is no infallible, true knowledge and every system has flaws, we need to create processes that allow for criticism and error correction in all parts of society. In the last few thousands of years, we've developed a dynamic society, one that actively encourages and facilitates conjecture and criticism. However, during the previous 50 years, we've become more static, regulated, and conservative. We can't accept this stasis if we want to see humanity thrive and progress.
What's next on Scaling Knowledge?
In future articles, I'll explore the (complexity science) scaling laws of knowledge infrastructure, the burden of knowledge, curiosity, epistemology, and cognitive augmentation and their intersection.