Progress frontiers are geographically fractal

The tech tree is fractal

What are the properties of maps?

There are two levels of institution

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

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

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

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

Maps are multi-scale

olsonManagingModesEffort2020

The difference between specialists and generalists is fractal in nature

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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Specialists create knowledge within fields

Idea sex probably happens more often in generalists

Notes that get screwed up somewhere in the pipeline

# Generalists create connections between high-degree nodes 	
This note was originally “Generalists create connections between disciplines.” That statement is imprecise because [[The tech tree is fractal]] so the line between disciplines is mushy. Roughly, the higher the degree of the nodes that someone is connecting ([[Connections create new knowledge]]) the more of a generalist they are. 

### Related 
- The idea of elaboration in [[How to Take Smart Notes]]
- [[Connections create new knowledge]] 
- [[The difference between specialists and generalists is fractal in nature]]. 



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Materials and manufacturing underpin civilization

gertnerIdeaFactoryBell2012

Prototyping needs manufacturing in the room

Physical experiments are expensive

Implementation is the actualization of the heuretic in the real world

Bell labs brought many disciplines together under the same roof and broke down barriers between them

Parney Albright conversation 17 Jan 2023

New manufacturing paradigms are more likely than other technologies to generate positive second-order effects

Smart people no longer do base-layer work

One of the most failure-prone points in technological development is when a technology needs to move from an organization that focuses on R+D to one that focuses on marketing and manufacturing

Jeremy Glick conversation 18 May 2022

Tying between technology and the underpinnings of civilization

# Manufacturing is important and often ignored
Physical things need to be manufactured if you want to have more than one of them. There is a whole set of properties that make something more or less manufacturable. 

[[Skunkworks podcast]]  mentioned several times how the key to moving fast was to have people who were going to manufacture the thing sitting in the room to point out where a design might be manufacturable. 

Things can fail in manufacturing in a bajillion different ways. You could build a completely functional prototype of something that is entirely unmanufacturable.   (Can things fail in scaling in a bajillion different ways?) 

The people who handle and have heuristics around manufacturing are usually not the same people who do the prototyping and original building. The informal interaction that ‘having the person in the room’ during the prototyping process enables is a [[Transaction costs]] that is reduced if the people are all part of the same firm. (Thanks [[Robert Coase]]!) The whole point of design reviews is when the people who are manufacturing and designing are not in the same firm (or not in the same org within the same firm.) 

The gap between people who design and prototype and people who manufacture is especially bad in academia. ([[§Academia Constraints]].) From my experience, people doing university research literally do not think about manufacturability at all. This missing perspective is one of the gaps left by [[aroraChangingStructureAmerican2020]]. 

Manufacturing is the atomland instantiation of software scalability in bitland. ([[§Atomland, Bitland, and Peopleland]]  - what is the people land version? Replicability? Systemization?) The difference is that you don’t have to worry about scalability until you increase use by orders of magnitude, manufacturing needs to happen from day 1 for dispersion. Manufacturing is the gap between [[TRL7 - prototype version in real environment]] and [[TRL8 - production version tested in real environment]]. A “production” version has been manufactured in the same way that every other version would be.

 In software when going from TRL7 to TRL8 you can get away with doing almost nothing - maybe changing a few more settings, adding more tests, etc. There are two reasons for this tiny gap. The first reasons is that “Production” versions of software don’t need to scale because the cost to disperse a new production version that does scale later is nothing. The only cost is the up-front cost of building it in the first place. The other reason is that you can develop and prototype using roughly the same ‘tools’ as you would use to produce it thanks to standardized software environments, containerization, etc. In Atomland there are gaps both between the CAD/simulation design, the prototype, and the manufactured product that can be uncrossable if you didn’t take into account where it the thing needs to go. 

In the past, manufacturability was less of an issue because the gap between prototyping tools and manufacturing tools was smaller. Everything was made by hand or with simple tools anyway. 

Manufacturing is an important place where [[Learning by doing]] is key. 

Manufacturing is a failure point in DARPA’s transitions. [[DARPA has a mixed record on transitioning]].  From what I can tell DARPA doesn’t really consider manufacturability during programs. 

Constraints on manufacturing:
- Tooling
- Time
- Materials
- Accessibility
- Number of parts

### Related
- [[List of things that affect dispersion]]
- [[Manage the Transfer not the Technology]]
- [[Skunkworks podcast]]
- [[Process Knowledge]]
- [[Dan Wang 2019 Letter]]
- [[Technology transfer is a mystifying abstraction]]
- [[New manufacturing paradigms can remove bottlenecks for other paradigms]]
- [[Materials and manufacturing underpin civilization]]
- [[Designing something to be manufactured can make a drastic difference in its success]]



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Fractal boiling cauldrons of bottom-up research work plus transient strike teams are the key to a healthy research ecosystem

Program and project management is important and valuable

Heilmeier — Licklider — Braben — Elon quadrants of Research Management

Spectech Field Strategist Job Description

narayanamurtiGenesisTechnoscientificRevolutions2021

Speculative Technologies Frequently Asked Questions FAQ

Motivation is not context-free

The goal of a research manager is to be able to tell whether a proposal has been thought through

Research management is about providing constraints

Grants only go so far

All funding organizations are research organizations

Day One Policy Proposal Memo Rev 2

Activity design indicates how power and coordination are distributed

Bottling Lightning

Vision is not enough

Podcast List

Pritha Ghosh conversation 17 Nov 2022

An important question is who is doing the research design

Coordination and Coercion in Research Funding

Jacob Majikes conversation 3 Jun 2022

Hunting vs Farming Ideas

Speculative Technologies big hypotheses

Day One Policy Proposal Memo

It is really hard to get people to do things that they do not already want to do

Organizations can do any or none of funding, performing, managing, or consuming research

There is a chicken and egg getting amazing program managers when you need to bootstrap a program

# Research management matters
I realize that many people will object to the term “management” in the context of research. “Management” has strong connotations of “control” and most people agree that agree that autonomy is incredibly important for great creative work, especially research. The term is “research coordination” is apt, but flaccid.  

At some point, unless the work for a project can be done by a single person, someone needs to play a coordinating function. As uncomfortable as it is to say, coordinating involves getting people to do things they wouldn’t do otherwise.  Everything from [[LIGO]] to [[Xerox PARC]] to any [[DARPA]] program to any academic lab with postdocs and grad students had leaders nudging people to do more of some things and less of others. These nudges can be at various levels of abstraction and involve various levels of coercion, but at the end of the day, coordination involves causing two or more people to work together when they would not otherwise do so. In other words, research management matters. 

A common narrative is that, like other creative endeavors, researchers need to be completely unfettered — attempting to manage research is foolhardy and can only hurt. However, 
1. [[Management and autonomy are not diametrically opposed]]
2. [[Management exists either implicitly or explicitly whenever there is coordination or power dynamics around research]] (which is almost everywhere).   
3. There are different sorts of research! Some needs more management than others. ([[Research has many orthogonal and non orthogonal classification axes]], [[List of research axes]], [[List of research classification paradigms]])

Of course, not all management is created equal! Bad management is often worse than no management. Good research management has many dimensions and can manifest in many different ways depending on the context: from Elon Musk’s involvement in minute engineering details to J.C.R. Licklider’s funding labs and keeping communication channels open between them based on a precise vision. (See [[Heilmeier — Licklider — Braben — Elon quadrants of Research Management]])  

### Related
- [[All funding organizations are research organizations]]
- [[Activity design indicates how power and coordination are distributed]]
- [[Motivation is not context-free]]
- [[The goal of a research manager is to be able to tell whether a proposal has been thought through]]
- [[Research management is about providing constraints]]



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§Program Design

raoGUTSGrandUnified2018

Program design is a coordination tool

An ontology around peoples attitudes competency and roles is important for program design

weaverUnderstandingProgramsProjects2010

Institution builders are initially judged based on their programs

A taxonomy of research organizations and activities

How to think about the differences between FROs and private ARPA programs

It is important to differentiate between the needs wants and activities of funders, institution designers, program managers, and researchers in the context of precommercial scitech

Geoff Anders conversation 7 Jun 2021

Research projects have a halting problem

# The distinction between areas, programs, and projects 
# The distinction between areas, programs, and projects 
- **Projects** *are chartered to create a speciﬁed “deliverable” as efﬁciently as possible*[^1]. They are are the ‘discrete’ unit of work. Of course, projects can still have [[Knightian Uncertainty]], but they have a clear definition of what “done” looks like (is this true?) 
- **Programs** *focus on the coordination of a number of related projects and other activities, over time, to deliver benefits to the organization*.[^2] 
- **Areas** are broad groups of interest like “climate” or “space exploration.”  Unlike programs, they are both not necessarily focused on delivering benefits and don’t require coordination.

Note that these distinctions can span all the quadrants in [[raoGUTSGrandUnified2018]]/[[obengAllChangeProject1996]]. 

### Related
- [[§Program Design]]
- [[Program design is a coordination tool]]

[^1]: From [[weaverUnderstandingProgramsProjects2010]]
[^2]: Also from  [[weaverUnderstandingProgramsProjects2010]]

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neumannOneProcess2020

§Tech Tree Model of Heuretics

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

Generalists create connections between high-degree nodes

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

Environment shapes our thoughts

Legibility forces you to fall back on conventional framings which causes you to think about the thing in a conventional way

DeepMind combines bottom-up research work with top-down strike teams

Architecture matters for creativity

Outlier ideas tend to come from people who are outliers in other ways

Comparing and contrasting Bell labs, DARPA, and Venture Research

# Where do ideas come from?
# Where do ideas come from
While not strictly “where do ideas come from”, the idea of *idea flow* is intriguing. That is, where are ideas incepted, who do they need to get to next in order to survive, etc.[^1] 

It’s probably worth splitting out idea flow and impetus flow? Or perhaps impetus is the ‘magnitude’ of an idea vector and it can get increased or decreased as the idea flows around.[^2] Even ideas that we think of as ‘top down’ probably didn’t just strike a CEO like a bolt of lighting while in the shower. Someone probably mentioned at least the seed of it. (Which of course gets back to the real question of *where do ideas come from*) However, the most salient thing that pushes us to call it top down is that the *impetus* came from the CEO.

For example, in the [[§ARPA model]] in the dominant mode, the ideas come from the PMs who are not actually the bottom[^3] nor are they at the top. [[Top down problems and bottom up solutions]]. Of course, sometimes the ideas do come from the top (we need a vertical take off and landing boat!) or from researchers. 

### Related
- [[olsonManagingModesEffort2020]]

[^1]: This language makes me think of virology. Perhaps instead of idea flow we could call it ideology or memeology? Mimetics is a think already isn’t it? 
[^2]: This would suggest that we should think of a multidimensional idea vector field.
[^3] An aside: it’s intriguing when you actually poke at what we mean by “bottom” and “top” in “bottom-up” and “top-down.” A first instinct is to say that it has to do with a power hierarchy, but that doesn’t quote work. It’s pretty uncontroversial that in the VC-Startup system ideas that come from startups are “bottom up” At first pass one might say in the VC-Startup relationship the VCs have all the power, but that’s not really true. Sometimes startups are asking VCs whether they would invest, but sometimes VCs are asking startups whether they could invest. However, the startups are the ones *doing the implementation* which I suspect is at the core of our perceptions of bottom-up and top-down. There is probably a mid-20th century economist who has done a much more thorough dissection of what we actually mean by top-down and bottom-up than me. 

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The distinction between areas, programs, and projects

# obengAllChangeProject1996


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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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Research has many orthogonal and non orthogonal classification axes

Quadrant Model of Scientific Research

Critical path

When should an idea that smells like research be a startup?

Systems research requires a lot of work that is expensive and uninteresting

Efficiency biases systems towards false negatives

It takes a long time to explain research

Critical paths enable efficiency

Astro Teller conversation 1 Feb 2022

Some ideas take a large chunk of resources to assess

Evidence either implicitly or explicitly involves a critical path

When you do not have a critical path you need to throw the idea of efficiency out the window

Startups are the upper left corner of the divergent-convergent lean-fat 2x2

People time is important when there is a clear critical path

# raoGUTSGrandUnified2018
![](BearImages/C6CDCB55-EB4E-42A2-91D9-4AA20DDAFAEA-76832-000626F9E189B040/E9D75888-5BEE-4A02-A798-442F57B2E804.png)

This is a really powerful framework, along with [[olsonManagingModesEffort2020]], so I’m going to mush them together. 

- This emphasizes why I am unsatisfied with the framing of a bottleneck analysis. [[Bottlenecks]] analysis works in convergent situations where you’ve identified one or multiple global critical paths and there is something stopping it from happening.  
- Generalized [[Critical path]] are *zones where there is a tight dependence of ends on means* 
	- Critical paths require two conditions
		1. You’re optimizing a system for a *single goal* 
		2. You have a global model for how to achieve that goal
- People react to uncertainty about goals by turning everything into portfolio optimization theory
- Fat thinking happens in complex situations where you have a why but can only do short stretches of critical paths
- Different views
	- **Value view** —Goals are where the value is — it exists in different places in c
	- **Criticality view** — are the critical paths connected or disconnected
	- **Context view** — focus is why context restriction — find a patch of value
- Ambiguity tolerance and uncertainty tolerance
	- Ambiguity tolerance is tied to focus / goals
	- Uncertainty tolerance is tied to criticality / process 
		- [[Knightian Uncertainty]]
	- Activities either lower ambiguity or uncertainty 
- Fundamentally fat and divergent problems
- Breakthroughs let you move a problem into another quadrant

### Thoughts
- Related to [[stanleyWhyGreatnessCannot2015]]’s thesis that “goal-oriented work” is counterproductive. This is true in divergent quadrants, but not all creative work is divergent. 
- [[People like to fund projects]] 
	- A contrapositive based on this framework is that people hate funding fat processes because by definition *you can’t say a priori what the money will be used for*— there is inherent [[Knightian Uncertainty]] around what will happen. You can’t say a-priori what will happen. Fat processes are, *by definition* wasteful. If you knew which routes to not explore, it would be a lean process. 
	- To the point of this piece — most people have low uncertainty tolerance when they are *removed from the process* 
- As 
- A big role for research management  
- Sometimes you need to do work to figure out what the goal should be — this work might be different than exploring the space more broadly. One common approach in disciplines is to just do full exploratory work and assume that the goal will emerge. Yes, both types of work are play, but there is a different vision/urge driving each of them and the actual work will then be different. 
- The conceit of a lot of startup/other funding is presenting what is really one of play, quest, exploration as a project. [[Projects need to build narratives of perceived progress on top of pieces of evidence]]. Narratives are a sketch of a critical path and a goal in a space even if none exists #action/to-draw [[Narratives are how we deal with uncertainty]] -- to some extent this is always necessary in order to do project-level action. 
- How do you know whether a problem is “fundamentally fat or divergent”

#### Published
#### Authors
#### Link
### Related
- [[olsonManagingModesEffort2020]]
- [[Fund people not projects]]
- [[The distinction between areas, programs, and projects]]
- [[alexanderStudiesSlack2020]]
- Cites
	- [[lindblomScienceMuddling1959]]
	- [[obengAllChangeProject1996]]

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Benʼs working notes

olsonManagingModesEffort2020

Published

Authors

Link

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