Similarity between Design Patterns and Mental Models
I think the concept of Design Patterns
In software engineering, a design pattern is a general repeatable solution to a commonly occurring problem in software design. source
a pattern can contain the description of certain objects and object classes to be used, along with their attributes and dependencies, and the general approach to how to solve the problem Source
from Software Engineering seems to be an instantiation of Mental Models in software world, particularly bearing resemblance to the following definitions, in that they all prescribe ways to look at relationships between entities which allow for solutions, explanations, predictions.
Differences between Design Patterns and Mental Models
As someone who have been a tutor and a student, I observe a phenomenon where Design Patterns are very difficult to teach to people who haven’t had professional software development experience. My conjecture is that Design Patterns abstract away contexts that gave rise to them in the first place, which is probably a result of the need to formalize them in order to communicate meaningfully with fellow engineers.
In contrast, mental models have been intuitive for me, and some models I explained to my friends were well-received. This difference in comprehensibility may come from the fact that mental models model relationships in the real world, and design patterns model relationships in the software world.
An idea: Can we formalize Mental Models the way we formalized Design Patterns ?
Design patterns usually contain the following information source
Name that describes the pattern
Problem to be solved by the pattern
Context, or settings, in which the problem occurs
Forces that could influence the problem or its solution
Solution proposed to the problem
Context for the solution
Rationale behind the solution (examples and stories of past successes or failures often go here)
It seems that these information may be used to describe Mental Models.
61 - Match Quality in Learning is the idea that you want new information being processed by someone to connect with their prior knowledge such that they are able to understand the new information being taught.
A very easy way to understand this concept is to think about reading. You don’t hand a student a history textbook before they are able to read. They need to have the prior knowledge of letters and words to understand the textbook.
If the match quality is bad then the student becomes confused and frustrated. On the other hand the match quality can be bad because it isn’t new information. For example, say your name is Bob Loblaw. If I repeat the words “your name is Bob Loblaw” over and over again then I am wasting your time because you already know this.
The first layer is creating a unified medium of storage such that it makes physical storage of the information manageable. Since paper is the most common form of writing, that is a good starting point. I could technically implement it using crayons and the walls of my house but that’d be subpar (still better than chaos).
The second layer is having a central storage point. I don’t leave my slips of paper all over the house or town because that would make the retrieval of the note a pain in the ass. Instead I put them in the same container/cabinet.
Third layer is having the notes on a restricted size of paper. Luhmann used pieces of paper in DIN-A-6 format. Others prefer using card stock. Fill out why small pieces of paper#todo
Fourth layer is having unique identifiers, which in turn allows you to point towards another piece of relevant information in any given note. Basically allows you to create connections. / Sub Sequence UID Debate #todo
Fifth Layer is using a branching system for the unique identifiers instead of a sequential system. Fill out why you’d want to do branching#todo / Sub Sequence folgzettel Debate #todo - Luhmann did not do [[Thematic Branching]] with his IDs, where you are essentially creating a thematic tree structure. Instead his branching was arbitrary.
66 - Layers of Structure are restrictions you place upon your workflow or tools that add order, which in turn creates repeatability. Repeatability is important for effective note taking because it allows for a greater amount of applicability per note. This is the same idea behind creating connections between notes and keeping notes atomic.
Once you get a sufficient amount of layers of structure together then they make up a workflow or system such as zettelkasten, PARA, or IMF.
68 - Knowledge Work is the creation, manipulation, and expression of knowledge. Usually it is done with the aim of a solving a problem using cognitive skills. The creation of knowledge can take the form of the remixing ideas (e.g. new music, lateral thinking with withered technology), cutting edge research, and new perspectives. Expression of knowledge can be a teacher explaining concepts to students, a presentation at work, or a doctor giving you a diagnosis.
Knowledge Work is important because it is becoming an ever valuable form of work, especially if you can excel at it. In Cal Newport’s book on skills he puts forth the idea that you can become very successful by learning how to improve in knowledge work because so many suck at doing so. The reason being because the path to improvement isn’t as clearly defined as it is with areas such as sports or more traditional work.
17c - Manipulation of Knowledge
17d - Expression of knowledge
17e - Improving Knowledge Work
17f - Cognitive Skills
68 - Future of Work - as science advances and we find new ways to streamline or automate tasks, being able to work with knowledge becomes increasingly important. Because of this it is important to develop skills around knowledge work.
Where I think this might be wrong is if society fails to live up the challenges presented to us and the social order starts to deteriorate. In my paper zettelkasten I have notes comparing the decline of the Roman Empire to modern times. In this scenario, skills related to everyday living becomes more and more important. This is the everyday work that is less abstracted (farming, resource collection, supply chain management, politics, etc). Note - If I wasn’t optimistic and hopeful than I wouldn’t be writing this zettelkasten.
70 - Future Skills and Abilities - Professionals of the future need to adopt more flexibility in how they work.
This means meeting people where they are at, engaging with them over social media or other more preferable platforms (Susskind 2015, pg 106). Imagine being able to instant message your doctor.
Future professionals will need to be able to take advantage of the increase and availability of data to come to insights. This involves both the collection and analyzing of data. Either the professionals themselves will work directly with the data or use a middle man that understands both the data and industry (Susskind 2015, pg 107). They will also need to get better at working with new and emerging technology. Those who can effective work alongside machines will see an increase in efficiency and effectiveness (e.g. Advanced Chess).
The final need is for workers to widen their tool set, either through the effective use of technology or introducing a multi-disciplinary approach (Susskind 2015, 108-109). Imagine a “self-help coach” but who has extensive training, such that they work with doctors and psychologists. Taking a more holistic approach to fixing oneself.
71 - Future Roles In the book The Future of the Professions: How Technology Will Transform the Work of Human Experts, author Richard Susskind lays out 12 different roles that will emerge from a “post-professional society”:
Craftspeople - people who “craft” stuff that require a difficult skill set, such that they can’t be easily replaced by para-professionals or crowd sourced.
Assistants - people who aren’t experts but help out the above mentioned craftspeople (e.g. associates in law firms).
Para-professionals - will take over the spot of experts with the help of ever increasingly competent systems and tools.
Empathizers - people with extremely good people skills, which will always be important because we have a sociality that is ingrained in our evolutionary biology. Machines will fill the gap, but people who can afford the help of other people will prefer doing so.
R&D Workers - research and development will always be desired for the creation of new technology that can allow for a greater reach of solutions or make them more effective.
Knowledge Engineers - people who will be designing systems that draw on the sources of existing expertise to disseminate knowledge to the wider public and para-professionals. Early examples may be wikipedia or thoughtCo.
Process Analysts - will be the ones deconstructing the work of experts to create the systems and tools used by the above mentioned para-professionals.
Moderators - people with deep insight who will help guide the centralization of expertise knowledge either from the masses or a pool of experts. Essentially making sure the quality of “the body of knowledge” stays high.
Designers - People who think up and design the various systems described above. If a service or system isn’t well made then people aren’t going to want to use it. You see this with the high salary and importance of UX designers.
System Providers - people who are actually providing the systems that the knowledge base is built on, whether it be a foundation (e.g. Wikipedia) or a private company (e.g. Quora).
Data Scientists - pretty straight forward field. People who are able to work with big data and come to insights. Two of my data related products are pudding.cool and quid.
Note on the source - I actually own this book and took notes on it. But because I suck and didn’t have a zettelkasten at the time, I have no clue where those notes actually are. One of the helpful layers of structure of the zettelkasten is the centralization of notes.
A good question is how well his argument still holds up 4 years later? Something to think about.
Further planned Research:
The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives - 2020
The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future - 2017
Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will - 2016
Rise of the Robots: Technology and the Threat of a Jobless Future - 2016
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant - 2016
Average Is Over: Powering America Beyond the Age of the Great Stagnation - 2013
74 - Creation of Knowledge - There are two different ways I think about the creation of knowledge, a private and public way.
The first is the private creation of knowledge, which is essentially learning. It means taking information and going through the memory process, which turns it into knowledge. This can be done on a shallow level, which is necessary but not sufficient (spin off into 17b1 #todo). In contrast, when done correctly, you are going one step further and structure building with the information.
The second way is the public creation of knowledge, which encompasses cutting edge research and the remixing/rethinking of ideas. The remixing of ideas can take the form of synthesizing existing knowledge (e.g. Ryan Holiday’s Notebox, Mark Manson, etc - what I’d call the remix genre) or reformulation of existing knowledge. The later is typically done in educational settings, with articles and blog posts.
Creation of knowledge is on of the pillars that cognitive skills (17f) are geared towards (e.g. critical, creative, and three dimensional thinking - 10e5).
The dark circles represent the new knowledge you come across through reading a broad selection of books (or exposing yourself to a broad set of information). You then let it interacts with your prior knowledge (red arrow of expertise, 18c) to help you come up with new ideas and solve novel problems. See lateral thinking #todo
The transparency of the circles reflects the idea that you dive into various ideas at different levels. Sometimes you may go deep in learning a concept, other times you’ll stick to just getting a surface level understanding.
76 - Prior Knowledge is the information you have personally turned into knowledge through a lifetime of learning. It is the knowledge you use for creative problem solving with the knowledge cycle. There are two paths of acquisition for prior knowledge, represented below by A and B.
This web represents the body of knowledge out there.
The first pathway A involves specializing, working your way towards the edge of the current body of knowledge before working on expanding it (research, theorizing, etc).
The second pathway B is the process of starting from the basics in every area of knowledge and slowly expanding your knowledge base. This is essentially what a generalist would do. This is what Farnam Street calls “most useful knowledge is a broad-based multidisciplinary education of the basics”. It is the approach you take until you get to college and start to specialize.
79 - Cognitive Skills - An important part of thriving in the economy is having an understanding of cognition and the important skills that underly it. Having a solid understanding of the cognitive skills will help you improve in how you work with existing knowledge and generate new knowledge