§Tech Tree Model of Heuretics

The creation of a heuretic has two steps - conception and implementation

The tech tree model of heuretics is a directed acyclic graph

# Each node on the tech tree can recursively be decomposed into more nodes
At the end of the day, [[New ideas need to come out of a single mind]] so the recursion base case is when the heuretic isn’t viable outside of one person’s brain. This is why [[Brains are wombs for heuretics]]. 

The ‘viability condition’ is admittedly vague. Something can be inviable away from one person’s brain for different reasons. 

In one situation they’ve actually created something that only they can create. For example a glass blowing method that only one person can do. Does that count as a heuretic? In their mind they might literally be unable to separate out the piece that’s different from how everybody else does it, even though they obviously didn’t reinvent ever piece of glass blowing. This example suggests that heuretics can be much more complex and non-modular than we would like to think. [[There are many pieces of equipment or tacit knowledge that only exist  in one or two places in the world]]

What about the situation where someone has a possibly really powerful theory in their head but it’s extremely hard for people to verify or build off of it? This seems to be the case with [[Stephen Wolfram’s Theory of Everything]] and [[Eric Weinstein’s Theory of Everything]]. Where is the line between them and crazy people? At the end of the day, doesn’t a heuretic need to unblock other people in some way? 

Combining nodes can lead to properties that neither of the original nodes contained. [[Emergent behavior happens when a system has properties that none of its subsystems have]]. The emergent behavior is another hint that heuretics can be much more complex and non-modular than we would like to think. 

### Related
- [[Design for roadmapping system]]
- [[Roadmapping could be thought of as backwards-chaining together a series of conception-only heuretics]]
- [[Every time you zoom in on a fractal, it presents the same pattern on a different scale]]
- [[The tech tree is fractal]]
- [[The creation of a heuretic has two steps - conception and implementation]]
- In [[And Suddenly the Inventor Appeared]] it states that every technical system has a super system and sub systems.
- A core piece of [[arthurNatureTechnologyWhat2009]] is the systematic hierarchical nature of technology
-  [[Module assembly and evolution are two ends of a spectrum]]



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Progress frontiers are geographically fractal

The tech tree is fractal

What are the properties of maps?

There are two levels of institution

Scientific inquiry is an abstracting process while engineering design is a narrowing process

Technological and scientific paradigms are each based around exploiting a specific phenomenon

Phenomena are things that happen without a human consciously making them happen

Maps are multi-scale

The difference between specialists and generalists is fractal in nature

olsonManagingModesEffort2020

How can you describe the difference between learning by doing and idea sex?

Each node on the tech tree can recursively be decomposed into more nodes

Conservatism and Progressivism both have good and bad sides

Identity categories are fractal

# Every time you zoom in on a fractal, it presents the same pattern on a different scale
Fractals are Multi- Scale patters
## Questions
## Related
* [[Progress frontiers are geographically fractal]]
## References


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The tech tree creates new nodes either through idea sex or learning by doing

Learning by doing and idea sex happen through the same mechanism

# How can you describe the difference between learning by doing and idea sex?
# How can you describe the difference between learning by doing and idea sex
The difference between [[Learning by doing]] and [[Idea sex]] might have to do with *where* those ideas come from, but the more I think about it that seems like BS. Learning by doing has the sense that there is a lot of experience.

If [[Connections create new knowledge]], maybe the difference is *what* is being connected. Because at the end of the day it can’t be *how* they’re connected. [[Learning by doing and idea sex happen through the same mechanism]]

The distinction is tricky because you could argue that it is actually idea sex because the inventors didn’t just blindly try every shape. They probably went off of some intuition around symmetry, and maybe even used calculus to find the right shape.  Similarly, there are probably many pieces of knowledge that Henry Ford and colleagues brought to bear to get moving slaughterhouse technology to work with cars. 

The resolution of this has something to do with the fractal nature of the Tech tree and also the concept of modularity.  Some options for the difference:
	- Perhaps the difference is substitutability? In learning by doing, you create something that is strictly better than the thing before it. Learning by doing doesn’t change something’s function.
	- Perhaps the difference is modularity - if someone improved non-assembly-line car manufacturing or meat moving technology, that would make the moving assembly line better, but improving calculus wouldn’t make the shape of the rocket engine better. This is pretty weak. 
	- It might also be a qualitative thing where because it’s fractal you just look at whatever level you care about. 
See:
	- [[Modularization allows different groups to work on different pieces]]
	- [[Modularization enables pieces to be used in many different technologies]]
	- [[The tech tree is fractal]]
	- [[Every time you zoom in on a fractal, it presents the same pattern on a different scale]]

### Related
- [[The tech tree advances by idea sex and learning by doing]]
- [[Idea sex]]
- [[Learning by doing]]

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Most roadmaps suck

Maps help you figure out possibilities

Maps help you understand the state of something

Technical Planning Principles

# Maps are multi-scale
You can get some information just by glancing at a map and then more and different information by zooming in.  Fractals were literally discovered by someone mapping coastlines.[[Every time you zoom in on a fractal, it presents the same pattern on a different scale]]. 

 If a map doesn’t give you something useful on first glance, it’s not doing its job. Most pure-text roadmaps fail on this point - it’s very hard to get something useful on first glance from pure text unless it has a well crafted outline. If you can’t get different useful things when you investigate a map more closely it’s not doing its job.

If you can’t get different useful things when you investigate a map more closely it’s not doing its job. Digital maps are wonderful in this way because they can actually load scale-dependent information. Good paper maps did this by being obscenely detailed - cartographers ‘[entire professional life is spent at the magnification level of a postage stamp](https://eyeondesign.aiga.org/for-decades-cartographers-have-been-hiding-covert-illustrations-inside-of-switzerlands-official-maps/).’

### Related
- [[Maps help you figure out possibilities]]
- [[Maps help you understand the state of something]]
- [[What are the properties of maps?]]



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Phenomena-based cycles

arthurNatureTechnologyWhat2009

Science and knowledge of the universe as raw resources

Science is discovering and exploring phenomena while technology is using a principle to exploit phenomena towards a use

# Phenomena are things that happen without a human consciously making them happen
(I am not trying to redefine the word, but to retroactively define a fuzzy term that is used often in the literature)

[[arthurNatureTechnologyWhat2009]] shapes its ideas of technology and technological paradigms around exploiting specific phenomena. This framing addresses a lot of the vagueness associated with paradigms that then cascades into any discussion of [[Technological S curves]], [[Technology readiness level - TRL]], the [[§Tech Tree Model of Heuretics]], and basically any vaguely abstract discussion about science and technology. 

So what is a phenomenon in the context of science and technology? The dictionary definition is that a phenomenon is ‘an observable fact of event.’ Some dictionaries also associate it with a sense of mystery. There are many phenomena with well-understood mechanisms, but it’s important to consider that connotation. That’s a good input, but doesn’t feel very useful. Thanks to [[arthurNatureTechnologyWhat2009]] we have a ‘boundary condition’ that however we’re thinking about phenomena, they need to be ‘exploitable’ into technology. An important feature of something observable and exploitable is that they are both external to you. In order to ‘exploit’ something, it needs to have something you don’t. It wouldn’t make sense to exploit myself typing these words. That sentence doesn’t even parse. 

Like all good definitions, this one is fractal. [[Every time you zoom in on a fractal, it presents the same pattern on a different scale]]. Electricity is a phenomenon (things can get charge that flows! Whoah!) but you can also think of the broader phenomenon of electricity as made up of a whole slew of individual phenomena - the Hall effect, resistance converting electricity to heat, etc.  

According to wikipedia [[Emmanual Kant]] was the first philosopher to think seriously about this. I have not yet read that so I defer to him until I do. 

### Related
- [[phenomena]]
- [[Technological and scientific paradigms are each based around exploiting a specific phenomenon]]
- [[The ability to say ‘huh that’s funny’ is important for discovering new phenomena]]
- [[Unexplainable phenomena are not just indistinguishable from magic, but are magic]]
- [[X-punk imagines a world dominated by X]]



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neumannOneProcess2020

Knowledge frontier

Every time you zoom in on a fractal, it presents the same pattern on a different scale

“Should we focus on Type I or Type II progress?” is the wrong question

Debating the abstract relative merits of Type I and Type II progress is a waste of time

Type I and Type II progress are often talked about in the same context

Type I and Type II Progress - publishing version

Being at the frontier in many dimensions helps advance the frontier

Alexey Criticisms on Type I and Type II progress

1 with developed and developing nations

# Progress frontiers are geographically fractal
[[Every time you zoom in on a fractal, it presents the same pattern on a different scale]]. This scale dependence makes it impossible to clearly separate between places that would benefit from Type I vs Type II progress.
In the United States you would expect California to be right on the knowledge frontier - it’s the wealthiest state with the most progressive policies. Yet there are still tons of farm workers who live lives comparable to residents of developing nations - they clearly are not at the frontier and would benefit more from Type II progress than Type I progress.
Zoom in farther to the San Francisco Bay Area. In only a few square miles there are people pushing the limits of quantum technology and infrastructure and policies that look like something out of a developing nation.[^1]
You can’t even say that individuals are the quanta of where type I vs. type II progress happens. Many researchers on the cutting edge of science follow dietary guidelines from the 1970’s. 
This inability to cleanly separate the two types of progress geographically adds evidence to the fact that  [[“Should we focus on Type I or Type II progress?” is the wrong question]].
Another consequence of this fractal nature of progress is that [[Type I and Type II progress don’t correspond 1:1 with developed and developing nations]].

In other words they are [[Nebulous]]

[^1] Some time-dependent evidence of this are the PG&E blackouts in October 2019. 
---



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As a platonic ideal, research is an unscheduled quest for new knowledge and inventions with hard-to-predict outcomes

The iron rule

Glossary

ICT Outline

Peter van Hardenberg Conversation 20 Nov 2020

We only see the science based public stuff from the middle of the century because the engineering was classified

We should give equal status to each rung on both scientific inquiry and engineering design

# Scientific inquiry is an abstracting process while engineering design is a narrowing process
[[As a platonic ideal, research is an unscheduled quest for new knowledge and inventions with hard-to-predict outcomes]]. While the processes of creating new knowledge (scientific inquiry) and new inventions (engineering design) are similar in many ways (hence why it makes sense to put them together under the umbrella of “research”) a key difference between them is which way they move along [[The ladder of abstraction]]. 

Scientific inquiry is an abstracting process. The goal in scientific inquiry is to produce the most abstract piece of useful knowledge you possibly can. You study a physical system, measure it to get data, and then compare those data to create a model - the more abstract the better. The Schrödinger Equation is an excellent example - it correctly describes a large part of the observed universe.[^1]  “Theories of everything” are the paragons of scientific inquiry. 

Engineering design is a narrowing process.  The goal in engineering design is to create the most useful artifact you can. You take an abstract concept, create concrete specifications (either implicitly or explicitly) and then produce a useful system. You don’t care about the knowledge, you care about the capabilities of the end product.[^2]

Another way to look at it is that in engineering design the only thing that matters is a box and what it can do while in scientific inquiry the only thing that matters is what’s inside the box - the only purpose of the box is to show off what the box can do. 

Of course, in reality the best work comes from a mixture of the two. Scientific inquiry might require creating a physical system to study or as a tool to observe another system. Engineering design might require creating a new model in order to design to difficult specifications. [[Phenomena-based cycles]]

It’s also worth noting that not everything that is colloquially called ’science’ is scientific inquiry and not everything called ‘engineering’ is engineering design. The quest for engineering *principles* (which is often what academic engineering is) is scientific inquiry. Similarly, experimentally verifying theories (like a lot of activity at the LHC) is engineering design. Since the two processes feed off of each other, the boundaries between the two are often fractal. [[Every time you zoom in on a fractal, it presents the same pattern on a different scale]]. 

![](../BearImages/9F23AD8A-B478-4B83-BF2C-92DD2BAB3551-4422-000847ECCE18DFDA/AB2482A3-D5A1-4559-BF55-545994CDEFE3.png)
### Related
- Concept originally from [[drexlerRadicalAbundanceHow2013]]
- [[Physics risk vs. Engineering Risk]]
- Academia is well set up for scientific inquiry but not engineering design [[Academia is not a good environment for systems engineering]]
- [Differences Between Science And Engineering](https://fs.blog/2013/07/the-difference-between-science-and-engineering/) - Farnam Street blog on this

[^1]:Of course, the Schrödinger Equation can predict almost nothing directly because of the curse of dimensionality. The knowledge that comes out of scientific inquiry is often maximally applicable but is minimally useable. Going back to theories of everything, they technically apply to everything but they can barely make any predictions that other things couldn’t.
 [^2]: I would argue that the capabilities of an artifact include scalability and manufacturability , so a one-off proof-of-concept shouldn’t be the end-goal of engineering design.



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X-punk imagines a world dominated by X

Conquest state of nature

You need to break rules to create new paradigms

Unexplainable phenomena are not just indistinguishable from magic, but are magic

# Technological and scientific paradigms are each based around exploiting a specific phenomenon
One ingredient is to amend the definition of paradigms in [[Technological paradigms and technological trajectories]] to be about [[phenomena]] per [[arthurNatureTechnologyWhat2009]]. So instead of a paradigm being defined by the ‘relevant problem’ a paradigm is defined by the phenomena[^1] being exploited. 

This focus on phenomena at the core of a paradigm is important because throughout history the core questions haven’t really changed: “how do we get more energy? Food?” “Why do objects behave the way they do?” It’s the form of the answers to these questions that change over time. 

Phenomena-based paradigms enable hierarchical paradigms, which seems critical to square with reality. Semiconductor computing is paradigm that exploits the phenomena of semiconductors acting like either a wire or break depending on the charge applied to it.  However, the phenomena we have exploited to *create* the semiconductors has changed over time. So within this unshifted paradigm there have been many paradigm shifts. You could even argue that semiconductor computers are actually a subset of the paradigm that exploits the fact that you can encode logic in bits and smash them into each other in order to do computations at all. 

Like any fractal system, it makes clean boundaries impossible in an absolute sense. [[Every time you zoom in on a fractal, it presents the same pattern on a different scale]]. So this modification of paradigms means that you cannot say *without context* whether a change is evolutionary or revolutionary.  This context-dependence doesn’t kill paradigms and evolutionary/revolutionary changes as useful concepts at all! It just makes it so talking about them requires the overhead of talking about which level you’re talking about. 

Thinking about paradigmic levels can be helpful in and of itself. People are often open to changing some levels and not others. 

### Related
Given this framing, you can resist a number of conepts:
- [[Technological S curves]] - each curve is the output of exploiting a specific phenomenon
- [[narayanamurtiCyclesInventionDiscovery2016]] can be framed around phenomena as discovering new phenomena and then exploiting them to discover more phenomena.
- [[Low Hanging fruit theory of stagnation]] - can be broken apart into “how many ways can we exploit a given phenomena?” and “how can we find and exploit new phenomena?”
- [[X-punk imagines a world dominated by X]] 
- [[Heat cycles for democratization and cutting edge experiments innovation and discovery]] 
- [[Unexplainable phenomena are not just indistinguishable from magic, but are magic]]
- [[neumannOneProcess2020]]
- [[kuhnStructureScientificRevolutions1962]]

[^1]: [[Phenomena are things that happen without a human consciously making them happen]]



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Just having the piece of literature doesn’t solve the problem

Academic research is often just the concept half of creating a heuretic

Technology transfer is a mystifying abstraction

# The creation of a heuretic has two steps - conception and implementation
1. [[Conception is the imaginative design of a heuretic]]
2. [[Implementation is the actualization of the heuretic in the real world]]

[[The process of creating a new heuretic node is like building something out of legos]].

More generally, the creation of a heuretic always has two steps. First you need to conceive that two other heuretics can be brought together and how they might connect - this is the eureka moment, the hunch, the idea, the concept. Second you need to actually connect the heuretics to create a new heuretic that can be used by someone else as a single unit, this is the implementation.  For now let’s call the first step “concept” and the second “implementation.”

Concept and implementation vary wildly based on the heuretic. In math, the concept is the idea that you could prove something and generally what the pieces of the proof would entail while the implementation is writing down the proof itself. In scientific-method-based science, the concept is the hypothesis and the implementation is actually doing the experiment and analyzing the results. In technology, the concept is the design and the implementation is actually building the thing. 

Similar to many other parts of the tech tree ([[The tech tree is fractal]]) lines between concept and implementation are fractal.  [[Every time you zoom in on a fractal, it presents the same pattern on a different scale]].  So while a design could be just the concept step of a heuretic, the design can also be a heuretic itself. In the case where the design is in itself a heuretic, the “hunch” of which pieces should go together in the design is the concept. Implementation is just recursive conception until you can get to a conceived action that someone can do without coming up with a new thought. ([[Each node on the tech tree can recursively be decomposed into more nodes]])

The amount of work needed by each steps varies significantly based on the heuretic. Some heuretics resemble [[Problems where it is easy to know whether an answer is correct but hard to get to that answer]] in that once you have the concept, implementation is trivial. For other heuretics, the majority of the work is in the implementation. To go back to the Lego analogy, it’s like knowing exactly where a piece needs to go but it’s way in the back of some small opening that requires you to either crack open the entire structure to use tweezers or something. The variable division of work between concept and implementation is one of the reasons that “ideas are cheap.”  In many cases the division of work between concept and implementation is variable. The more precise the concept is, the easier the implementation. [[Make precise predictions]], [[Precise communication enables other people to make decisions]], [[More precise descriptions of technology could enable faster progress]]. 

Just as you need to fiddle with a lego piece to get it to snap into place even when you know exactly where it needs to go, you often need to create new heuretics during the implementation of a heuretic. These new heuretics are often [[Learning by doing]]. 

Clean, legible interfaces to heuretics make both concept and implementation easy and bad interfaces make them hard. Legos are such a joy because they’re basically the paragon of clean interfaces. Legible interfaces make it easier to grok the affordances of a heuretic, which makes it easier to conceive what you can combine with it. So while  [[Creating legible interfaces is expensive]], if you want your heuretic to be used, it is important to focus on legibility. 

This notion that conception requires understanding of constituent heuretics ( [[Understanding a thing has to do with intuiting the affordances of a thing]] ) is important because it helps explain why people who have fuzzy ideas of how things work have trouble implementing their ideas.

### Related
- [[Connections create new knowledge]]
-  [[§Tech Tree Model of Heuretics]] 
- [[Modularization requires legible interfaces]]
- [[The constraint framework is powerful because it shines a light on precise ways to explore new ideas]]
- [[It is important to precisely define problems in an area]]
- [[Brains are wombs for heuretics]]
- [[Heuretics that never leave the conception stage can lead to other heuretics]]
- [[Dispersion is the process of a heuretic impacting the world]]
- [[Academic research is often just the concept half of creating a heuretic]]



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Specialists create knowledge within fields

Generalists create connections between high-degree nodes

# The difference between specialists and generalists is fractal in nature
In the same way that [[Every time you zoom in on a fractal, it presents the same pattern on a different scale]] who is a ‘specialist’ and who is a ‘generalist’ depends on which scale you’re looking at disciplines. 

The “level” of a generalist may have to do with the number of nodes in the [[§Tech Tree Model of Heuretics]]  that a person could build new nodes from.  [[The difference between specialists and generalists is not clean but depends on which nodes they can extend]]

### Related
[[Theory of Expertise - James Dama]]
[[§Specialists vs. Generalists]]



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There are scientific and technological problems that make other technology advances irrelevant by altering the environment

boydenArchitectingDiscoveryModel2019

Macroinventions are the tail end of the impact power law distribution clashing with our intuition

Good roadmaps work backwards from a goal

The difference between specialists and generalists is not clean but depends on which nodes they can extend

We can cleanly attribute philosophical ideas to people but we cannot do the same with science or technology

# The tech tree is fractal
[[Every time you zoom in on a fractal, it presents the same pattern on a different scale]] - in this case, every time you zoom in on a piece of knowledge, there are multiple pieces of knowledge that gave rise to it. 

From a theoretical standpoint, it’s intuitive that this is basically how human brains work. Our brains smush together different pieces of knowledge to create new pieces of knowledge. ([[Thoughts don’t spring fully formed out of heads]].) 

From an empirical standpoint, a fractal tech tree fits the observed phenomena:
- Fractal tech trees explain the power law distribution of impact in [[neumannOneProcess2020]]. Fractals give rise to power laws in general (see [[Self-Organized Criticality: An Explanation of 1 over f Noise]]) 
- Fractal tech trees explain how some inventions *feel* different than others but upon closer inspection actually have precedent just as much as other inventions.  [[Macroinventions are the tail end of the impact power law distribution clashing with our intuition]].
- Fractal tech trees explain why you can infinitely pick apart any category of technology.  You can break apart guns into rifles, pistols etc. and trace all of their histories, down to exactly what every human did over the course of history.

### Related
- [[The Map is not the Territory]]
- [[§Tech Tree Model of Heuretics]]

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Institutions are the second level of a group selection evolutionary system

§Institutions and Games

Why aren’t people building more institutions?

Enabling creative work that is not currently happening will require breaching institutional barriers

19 Jul 2020

# There are two levels of institution 
According to Oxford English Dictionary, an institution can be:
1. a society or organization founded for a religious, educational, social, or similar purpose.
	- The US government 
	- A department of the US government
	- A specific university 
	- A specific newspaper
	- A specific VC firm
	- A specific company
	- A specific nonprofits
2. an established law, practice, or custom.
	- The institution of marriage 
	- Governments as a whole
	- Academia
	- Journalism
	- Venture Capital 
	- The startup ecosystem (“Silicon Valley”) 
	- Charity
	- Finance (“Wall Street”)

Type I institutions are specific organizations, or subset of an organization. An organization is not necessarily an institution. It’s fuzzy to say, but an organization needs some amount of gravitas and longevity. Type I institutions can become paragons of Type II institutions: for example the NYT standing in for journalism, or Sequioa standing in for venture capital. You can start an organization that becomes a Type I institution.

Type II institutions are emergent behaviors - you can’t start one directly. The closest you can come is to create a type I institution that inspires imitators and becomes a paragon for the new type II institution.  Type II institutions are by their nature fuzzy

Every type I institution seems to have a dual Type II institution, but does every type II institution have type I institutions? At first glance, it looks like the answer should be yes . But looking at marriage, what are the corresponding Type I institutions? Would it be churches, or married couples themselves? I think you could convince me that each married couple was a Type I institution whose dual was the Type II institution of marriage. 

There are clearly many Type I institutions that comprise a Type II institution, but is every Type I institution a member of only one Type II institution? Normatively, we want the answer to be no, for each newspaper to purely belong to Journalism, for each university to purely belong to Academia. But in reality the answer is no - each organization is part of multiple Type II institutions: newspapers and universities are also businesses, startups can be both part of Silicon Valley and whatever institution they’re ‘disrupting’ - Journalism, Finance, Insurance, you name it. I suspect that these divided institutional loyalties may be part of the cause for increased institutional mistrust. 

Both types of institution are fractal. [[Every time you zoom in on a fractal, it presents the same pattern on a different scale]]  There’s governments, and within governments there are Federal governments, city governments, etc. Similarly, there is the US government, and different parts of the US government. 

When we talk about institutions we carelessly jump back and forth between the two levels - “Synecdoching.”



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§Program Design

Workshops could be a way to start small building a private ARPA

Most roadmaps are not execution oriented

Roadmaps need to be coupled to people

# What are the properties of maps?
# What are the properties of maps
“Maps”, “Mapping”, and “Roadmaps” feel like [[Suitcase Handle Word]]s but  there is a nugget of desire in using them. What are we really yearning for from a map?

- [[Maps are multi-scale]]. You can get some information just by glancing at a map and then more and different information by zooming in.  Fractals were literally discovered by someone mapping coastlines.[[Every time you zoom in on a fractal, it presents the same pattern on a different scale]]. 
	- If a map doesn’t give you something useful on first glance, it’s not doing its job.
		- Most pure-text roadmaps fail on this point - it’s very hard to get something useful on first glance from pure text unless it has a well crafted outline. 
	- If you can’t get different useful things when you investigate a map more closely it’s not doing its job. 
-  [[Maps help you understand the state of something]].
	- If you don’t understand the state of something better after looking at a map, it’s not doing its job. 
- [[Maps help you figure out possibilities]]. Even the medieval T-O map let you know that you could get to Africa from Europe by crossing the mediterranean.  
	- *Roadmaps* explicitly point out those possibilities in an opinionated way. 
	- If you can’t figure out possible (and unallowable moves) from a map, it’s not doing its job.

![](BearImages/A1F27DD7-DF6B-467B-96E4-599D26CC393D-32352-00023ECE922974A5/DEE0FD40-B247-4C7C-82BD-5CB1EB003417.png)

### Types of maps
- Conceptual 

### Related
- [[Wardley Maps]]



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raoGUTSGrandUnified2018

Where do ideas come from?

People like to fund projects

# olsonManagingModesEffort2020
![](BearImages/D5D9E905-C612-4F5E-A6B0-2454A5757D39-76832-000626DD4800F73B/D204A86B-6791-4676-93CF-C60E4F1A6A3A.png)
![](BearImages/68A86E34-2464-438C-844D-6C26F3794959-76832-000626E368A04A26/6A893012-596F-41E4-8224-246B7FBB19BC.png)
- The key insight on top of [[raoGUTSGrandUnified2018]] is that the different modes are hierarchical.
	- My expansion on this is that the modes are actually fractal — when you’re in Play mode there are sub-quests, explorations, or projects. [[Every time you zoom in on a fractal, it presents the same pattern on a different scale]]. A quest has sub-play, exploration, or projects. Etc. Even projects have sub-quests, explorations, and play. Even if the path forward seems straightforward, you might need to figure out a creative solution based on a new context, etc. (This is why even if a prototype works, manufacturing the thing is hard [[Manufacturing is important and often ignored]])
		- [[The tech tree is fractal]] and 
- Different modes call for different management styles ([[Research management matters]])
	-  Lean — know how to manage the *method* — “mercenary lean-six-sigma stonecutter types” in other words craftsmen dominate here. 
		- My extension would be that this is where specialists matter [[Specialists create knowledge within fields]]
	- Fat — know how to strategically reduce scope to do various lean things — focus on *leading talented people* -- more [[Generalists create connections between high-degree nodes]]
	- Divergent — roles rather than tasks
	- Convergent — alignement and assigning tasks 
- The trick is identifying and arguing for which situation is which
	- Recommended questions
		- What level of abstraction is the objective?

#### Published
#### Authors
#### Link
### Related
- Expands directly on [[raoGUTSGrandUnified2018]]
	- Shifts “muddling through” to “quest” from [[obengAllChangeProject1996]] which I like
- [[The distinction between areas, programs, and projects]]
- Cites
	- [[obengAllChangeProject1996]]
	- [[bahcallLoonshotsHowNurture2019]]

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Benʼs working notes

Every time you zoom in on a fractal, it presents the same pattern on a different scale

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