The Roman Empire was built on an aristocracy of service

# ‘Have your cake and eat it too’ life-games have led to a toxic society 
*Everybody* wants to play a game with no tradeoffs. [[People and organizations are all playing some game that has different ways of gaining status and power]]. 

In the past all ‘life games’ had perceived tradeoffs.  If you were nobility or wealthy, you traded off religious purity (“It is harder for a wealthy man to enter heaven than for a camel to pass through the eye of a needle.”) If you were royalty, you were expected to be at the greatest risk of physical death. If you were clergy, you were supposed to renounce earthly possessions. Merchants could accumulate great wealth, but had little political power. The idea of these tradeoffs is built into western culture. Of course, the reality often differed from the ideal - Popes accumulating great wealth, Kings buying indulgences etc. but the *idea* that this was wrong is still powerful because it means the system would regress to a mean that enforced the tradeoffs. [[Having more separate games is good for society]].

Life-game tradeoffs were the perceived norm in the 20th century as well. Scientists could achieve great acclaim, but didn’t make that much money. Businessmen could make lots of money but weren’t held in great esteem. Politicians had power but it was understood that they wouldn’t make much money.  Journalists kept everybody in check but claimed few of the spoils.

Now, it seems like the winners in all the categories bleed into each other and everybody is now playing a single game where you can have your cake and eat it too. No tradeoffs. Famous scientists start companies and become fabulously wealthy. Politicians leverage their positions to get paid a ton by lobbyists and speaking tours. Successful entrepreneurs are also celebrities and can now pivot to whatever they want - science, politics, entertainment. 

The two questions that demand answers are “why did this happen” and “why is this bad?” 

### Why is this bad?

Shifting to the evolutionary analogy the single societal game breaks down the group selection hierarchy. [[Institutions are the second level of a group selection evolutionary system]]. It would be like all the cells of all the animals in the world all of a sudden competing with each other. It breaks the brain a little to think about. Is it a return to a primordial ooze? A [gelatinous cube](https://en.wikipedia.org/wiki/Gelatinous_cube)? Some kind of [ooze monster](https://i.pinimg.com/originals/89/99/ce/8999ce8a21742d46e495c4e90c9605ce.jpg)where all the animals have melded together? ![](../BearImages/6EAB3C07-08C0-43F8-91B3-44D37E5A5850-32352-00033E8E7EF8DCFB/8999ce8a21742d46e495c4e90c9605ce.jpg)

Regardless, a single game means that every individual is competing with every other individual, which removes all the advantages of group selection, especially the [[Slack - concept]] to work on things that are not immediately valuable.  

You could also look at the situation as every individual now being a member of every institution, which might be more accurate. Everybody is in business, everybody is a journalist, everybody is a scientist. We’ve been conditioned to think this is a good thing - democratize all the things. I think that would be true *if* people learned and respected the rules of whichever institutional capacity they were acting in and could firewall between the different roles in their lives. Instead it’s all become mushed together (see ooze monster picture.) This admonition to firewall between different roles in the same person is never going to be followed perfectly but it is an ideal that is no longer being strived for at all. 

For example, Ben Franklin was a man of *many* institutions: business, journalism, science, politics. He did a poor job firewalling between his role as a businessman and his role as a journalist - using kind of sketchy business practices to force competitors out of business. However, as far as I can tell, he *didn’t* use his position as a journalist to push his scientific theories or his position in politics to enhance his business. So there was at least some personal fireballing going on. Additionally, he knew and respected the rules of the different games he was playing. So while he was in many institutions he did not break down the barriers between them. 

When everybody wants to be an ‘entrepreneur’ or ‘influencer’ regardless of what they actually want, it increases the noise in the system and basically creates a lottery. A toxic situation. 

### Why did this happen?
Some of these reasons will inevitably overlap with [[Most institutions have become cancerous]].  It’s also interesting that many of these points are the tails side of a coin - they can be seen as good things as well. 
1. Social media and the internet enabled people to start participating in different institutions - politics, science, journalism - without any sort of introduction to what the rules of the game were. Again, lowering barriers to entry, in and of itself is not a bad thing, but without an additional … *something* (learning? Guidance?) it breaks down the walls between the institutions and creates a single game. 
2. The internet enabled people to not only participate but become major players in a game without needing to pass through any gatekeepers. Again, in a way this is a good thing, but the benefit of gatekeepers is that they can ensure that rules are at least understood before they are broken. It also causes a situation where anybody can try to set themselves up as a gatekeeper. [[Full-time thought leaders try to become gatekeepers to knowledge]]. My sense is that some sort of stability of gatekeeper succession is a good thing. 
3. *Fuzzy* An increasing sense that all institutional games should be fair, and therefore legible to people outside the institution. [[Fair games have legible rules]].  Legible rules made people inside the institution directly answerable to everyone outside of it.  This all sounds good so far! Light is the best disinfectant! However, the exposed rules could not capture all of the implicit rules of the game ([[The Map is not the Territory]])  so all the players are now incentivized to optimize for whatever those legible rules are and ignore all of the illegible rules. This leads to scientists chasing citations and politicians saying “well it’s not strictly illegal!”
4. Institutional democratization created a common currency between institutions: attention. If you can amass a large enough group of followers, people in other institutions will take you seriously. Thus, everybody is now playing the attention game. 

### Related
- [[Institutions shape how individuals interact]], which used to create ‘firewalls’ between different games but [[Most institutions have become cancerous]] so everything bleeds together.
- [[Nobody is resigning for failing to do their institutional role]]
- [[Intra-Elite Competition: A Key Concept for Understanding the Dynamics of Complex Societies]]
- https://twitter.com/ArtirKel/status/1284940928805994497



[Web URL for this note](http://notes.benjaminreinhardt.com/‘Have_your_cake_and_eat_it_too’_life-games_have_led_to_a_toxic_society)

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/‘Have_your_cake_and_eat_it_too’_life-games_have_led_to_a_toxic_society)




Uncertainty always involves risk but risk does not always involve uncertainty

Roadmaps can enable faster progress than blind exploration

# A predictably successful idea is composed of known unknowns that that a team can execute against
Let’s compare Instagram and SpaceX. Arguably both are extremely successful companies but Instagram is an unpredictably successful idea while SpaceX was a predictably successful. 

When Instagram started, there were tons of photo-sharing apps and the thing that would cause one to dominate all the others was totally unknown - there was no way to say “if the instagram team can figure out X, Y, Z then they will win.” (Beyond the obvious ‘if they can get lots of people to use their service’ - X, Y, Z need to be some sort of clear intermediate success factors - *leading metrics*.) 

When [[SpaceX]] started, they could say “it will be frakking hard but if we can get profitable launch price below $100/kg, we will win.” (June 2020 costs are $2700/kg and the fully-reusable starship will supposedly get costs below $10/kg.)  You can add up all the factors contributing to launch cost and systematically work on bringing them down. These are the known unknowns that a team can execute against.

Executing might have a high chance of failure - it might just be too expensive, take too long, be physically impossible, killed by regulation or danger or one of a million other things. 

The description of the inability to find *leading metrics* for an unpredictably successful idea sounds like  [[The Map is not the Territory]]  and why [[§Program Design]] - effectively the ability to describe the territory well with a map - is coupled to predictably successful ideas. To go one step further, because [[Technological roadmapping can give an idea of where technological evolution can go and how to get there]] you could argue that roadmapping is a way to turn ideas into predictably successful ideas. 

Another way to think about it: predictably successful ideas are risky but not uncertain. Or at least they constrain uncertainty. [[Uncertainty always involves risk but risk does not always involve uncertainty]].

### Related
- [[Roadmapping is related to the ability to create leading metrics for success]]



[Web URL for this note](http://notes.benjaminreinhardt.com/A_predictably_successful_idea_is_composed_of_known_unknowns_that_that_a_team_can_execute_against)

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/A_predictably_successful_idea_is_composed_of_known_unknowns_that_that_a_team_can_execute_against)




arthurNatureTechnologyWhat2009

Modularization requires legible interfaces

Modularization allows different groups to work on different pieces

Modularization enables pieces to be used in many different technologies

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

The unrecognised simplicities of effective action number 2 - ‘Systems engineering’ and ‘systems management’ — ideas from the Apollo programme for a ‘systems politics’

More context can decrease the signal to noise ratio of communication for people who already have that context

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

When you communicate you should strive to demystify the world

It is hard to interface different simulations

Interfacing between closer neighbors on the tech tree is easier than between nodes that are farther apart

Making things more mystifying is way to hold on to power

Explicit rules are a substitution for learned pattern matching

Externalized research is a buffer against B players

# Creating legible interfaces is expensive
[[Structuring knowledge is expensive]].  Interface is an entire art in and of itself so this will not do it justice. Ironically this note is not a good interface to interface design. A legible interface minimizes the amount of context you need to know about whatever is behind the interface and also minimizes the amount of context you need aside from the interface to make it work. It is a lot of work to figure out just the right number of gauges and toggles (observation and controls) to allow someone to get all the utility out of the behind-the-interface system without just exposing the whole system. [[The Map is not the Territory]].  You could think of the expense of context separation via an interface as a [[Transaction costs]]. [[The less shared context you have the higher the coordination costs]]. 

As a consequence, the interface needs to be used enough that the expense is worth it. Therefore, heavily used things tend to have better interfaces, which make them easier to use and creates a positive feedback loop.

This also holds true for interfacing with other people. [[It takes bravery to demystify things]].

There is a tradeoff between communication and the necessity for interface legibility. The more communication between groups working on different pieces, the less legible the interfaces need to be. More communication means more shared context. [[Context is important and underrated for knowledge transfer]].

### Related
- [[Fully open online discussions are low quality because of trust and context]]
- [[Jargon is mystifying to people without the relevant context]]



[Web URL for this note](http://notes.benjaminreinhardt.com/Creating_legible_interfaces_is_expensive)

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/Creating_legible_interfaces_is_expensive)




Fair games can be gamed

Making a game more fair reduces its variance

Government money needs to be fair

§Institutions and Games

Complicated rules are a way of creating a nominally fair game that is not fair by any common use of the word

# Fair games are self-limiting
[[Fair games have legible rules]] and if a game has legible rules, it’s easier to predict whether you have a shot of winning. Marathons have extremely legible rules and I will bet a lot of money that I will never be world class marathoner. As a result I’m not even going to try. In other words, games with predictable success are self-limiting. 

In the name of fairness, people have been pushing for more legibility in the form of transparency, metrics, and explicit criteria. However, for disciplines that haven’t been systematised, like science or starting companies, [[The Map is not the Territory]] and these attempts to create legibility do not (cannot?) capture all the important things. However, the fairness measures will still create the self-limiting effect and people who could potentially crush it at the illegible unfair game don’t even play. For example [[Months of grant writing is one of the things that makes people think twice about academia]].  

In a way, making a game fair actually *raises* the barriers to entry: only those who can afford to optimize the game can get over the high, but uniform, wall. [[Fair games can be gamed]]. 

Of course, the fairness measures are in place to try to get rid of other things that freeze people out of a game - the classic illegible example is “it’s all about who you know.” This is tricky.  While it is not fair, ‘who you know’ is actually a reasonable rule *in a healthy institution.* In that situation, the people in the institution should want to maximize the game’s output, so they should be open to knowing people who might be great at it, even if they aren’t in their social circle. You see this throughout history - the young outsider pounding down the door of the master who recognizes their talent and helps them win. It is *completely unfair* and yet creates some amazing outputs - Alexander Hamilton, Warren Buffet, etc. However, [[Most institutions have become cancerous]] - so the drive towards fairness is not unreasonable but won’t fix the underlying problem.  

Another way to look at the effect of fairness measures is that they homogenize the people playing the game around ‘people who are good at whatever is being measured.’ For marathons, this is fine because you’ll homogenize around people who are really good at running 32.5 miles as fast as they can. That’s literally all a marathon is so in this case, the map *is* the territory. In science, on the other hand, you’re homogenizing around people who are really good at publishing highly cited papers in prestigious journals, pulling in grant money, and getting attention ([[Successful professors are not necessarily the best researchers]].)  Is that all science is? 

### Related
- [[When ‘talent’ isn’t code for ‘specialized training’ it means the role or industry has not been systematized]]



[Web URL for this note](http://notes.benjaminreinhardt.com/Fair_games_are_self-limiting)

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/Fair_games_are_self-limiting)




Stagnation Quote Grab Bag

Many things cannot be measured well

People who have done a thing should be in charge of a thing

Opacity is important to DARPA’s outlier success

Fair games have legible rules

The Categories Were Made for Man, Not Man for the Categories

Legibility poisoning

Corruption potential and efficiency are anti-correlated

You need to break rules to create new paradigms

The purpose of the Scientific Paper system is to generate legible artifacts of work, award credit for them, and direct funding towards generating more legible artifacts

21st century riffs on ARPA should be private

Feedback loops matter even if there is no control explicitly being exerted

# Legible games enable people outside of an institution to control it
If you want to make sure that someone isn’t up to any shenanigans, you need a way of measuring their outputs. In order to have measurable outputs you need to make the game they’re playing at least somewhat legible. The importance of legibility is the idea behind KPIs, Taylor’s scientific management, and a lot of modern economics. Similarly, if you want to make sure that an institution isn’t corrupt, you want a legible way of seeing how money is spent and people are hired to make sure that they aren’t spending money on fancy dinners and cigars or hiring their unqualified buddy from the golf club’s son over the highly qualified immigrant kid. 

At the end of the day, all of these metrics make an entity (individual, group, or institution) appear legible to outsiders. Of course [[Many things cannot be measured well]] and [[The Map is not the Territory]] so the metrics will vary on the accuracy of the picture they give. However, the the appearance of legibility is enough for outsiders to make judgements on how an entity is performing. Generally this corresponds to making some numbers go up and other numbers go down. 

Once you know how an entity is performing, you can then start to form opinions about what sort of inputs you should give it to try to make the numbers do what you want. More money and awards if the numbers are doing what you want; less money, shame, or sackings if the numbers are not doing what you want. It’s a control system: inputs informed by measured outputs. 

Bureaucracies depend on legible games because in effect [[Bureaucracies are human algorithms]] that need clear inputs. As a result, the bureaucratisation of an institution is usually coupled to making that institution more legible - academia, government, and large companies are clear examples of this. 

[[Scott Alexander]] argues that a lot of the thesis of [[Seeing like a State]] is that creating legibility is a [significant part of how governments exert control over their citizens](https://slatestarcodex.com/2017/03/16/book-review-seeing-like-a-state/). 

The desire to measure and control an institution often comes from a good place and can lead to good results - reducing corruption, incentivizing inefficiency, and making them more fair. The benefits of legibility are legion. 

However, making an institution more legible can also have many downsides. [[Optimizing for fair gatekeeping games is often disjoint for optimizing for the real game]], [[Fair games can be gamed]], and external control of an institution makes it more political: [[Politicization of an institution means that the people in the institution are more aligned with external goals than the institutions mission]].

Over time, people outside of institutions seem to have become more interested in controlling what happens inside of them. There are many possible explanations: 
- Perception that institutions are not delivering on their perceived mission  ([[Institutions have implicit, explicit, and perceived missions]]), 
- A trend towards caring about behavior in all situations, 
- A trend towards wanting more fairness, 
- more value placed on measurability, 
- A trend towards wanting more transparency.  

In order to create this control, institutions have been pushed to become ever more fair and legible. My biggest concern is the impact that this trend has had on high variance institutions - specifically research. [[Making a game more fair reduces its variance]]. 

### Related
- [[Streetlight Effect]]
- [[Formal process lets people outside the organization trust in the process instead of the people]]
- [[Opacity is important to DARPA’s outlier success]]
- [[Shared values allow you to break rules without breaking the spirit of the rules]]



[Web URL for this note](http://notes.benjaminreinhardt.com/Legible_games_enable_people_outside_of_an_institution_to_control_it)

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/Legible_games_enable_people_outside_of_an_institution_to_control_it)




Most roadmaps suck

What are the properties of maps?

Maps help you understand the state of something

Maps are multi-scale

Roadmaps need to be coupled to people

# 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.  Star maps help you figure out which constellations you could draw or where you could look in the night sky. 

If you can’t figure out possible (and unallowable moves) from a map, it’s not doing its job. Many ‘competitive’ landscapes fail on this front. 

A good map makes you aware of the tradeoffs between different possible moves.  Google Maps also helps you figure out possibilities by showing alternate possibilities and making you aware of the time and distance between those possibilities. The parameters of these tradeoffs vary drastically between maps - on a hiking map, elevation is important while on a driving map it is almost irrelevant. On a research map, cost is important while on a star map the concept of cost doesn’t even make sense. 

[[The Map is not the Territory]] - a good map will capture the information that you need to make decisions and exclude information that is distracted. 

*Roadmaps* ([[§Program Design]]) explicitly point out those possibilities in an opinionated way.  The difference between a map and a roadmap is the difference between google maps and the directions the map gives you to a location. 

![](../BearImages/57FE1074-F3ED-4A30-A4EB-C75575380032-456-00003424E76F85DB/9F042C35-37C6-4A14-8DCC-2066389EFB4E.png)
(Wardley)
![](../BearImages/D46F1E2D-1AC7-43B6-B703-46E7BA120E26-456-0000343CA6138B63/A9DE43E5-B06E-442A-8C30-4ED7643D2317.png)

### Related
- [[The less shared context you have the higher the coordination costs]]
- [[Context is important and underrated for knowledge transfer]]
- [[Maps are multi-scale]]
- [[Maps help you understand the state of something]]
- Wardley points out how useless a strengths/weaknesses/opportunities/threats analysis would be for a battle [On being lost - wardleymaps - Medium](https://medium.com/wardleymaps/on-being-lost-2ef5f05eb1ec)
	- [[Wardley Maps]]

[Web URL for this note](http://notes.benjaminreinhardt.com/Maps_help_you_figure_out_possibilities)

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/Maps_help_you_figure_out_possibilities)






Maps help you figure out possibilities

# 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 are always an abstraction ([[The Map is not the Territory]]) so there is always something about the state of the thing being mapped that the map will not capture. However, this is not always a bad thing! You can think of a map as compressing and structuring knowledge similar to how [[Jargon is a mechanism for compressing information]]. 

The compression mechanism means that a map is always going to be opinionated about what is signal and what is noise. Therefore, the design of a map should never be independent of its audience. [[More context can decrease the signal to noise ratio of communication for people who already have that context]].

A [[Stock and Flow Models]] *can* be a map, if it reflects the state of reality, but is not necessarily a map. 

### Related
- [[Structuring knowledge is expensive]]
- [[Maps are multi-scale]]
- [[Maps help you figure out possibilities]]



[Web URL for this note](http://notes.benjaminreinhardt.com/Maps_help_you_understand_the_state_of_something)

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/Maps_help_you_understand_the_state_of_something)




Life Style Guide

Organizations run by practitioners have less bureaucratic bs

What traits do high-potential PMs have?

Pieces of the Action Forward Intro Notes

Successful professors are not necessarily the best researchers

Bell Labs management was extremely light

Ideally corporate R+D enables work on a general purpose technology before it’s specialized

PARPA needs a constitution with checks and balances

People do fundable work

fuEnablingMissionImpact2021

Bell Labs empowered young people

Comparing and contrasting Bell labs, DARPA, and Venture Research

Where is the best physical place for PARPA?

Bottling Lightning

Fingerspitzengefühl — Finger Knowledge

The good idea fairy is the failure mode of a single decision maker

The people setting research priorities at AFRL are often not technical

Is it more impactful to find and train undiscovered diamonds or to unlock well-positioned but constrained researchers?

Tool accessibility is underrated for heuretic development

ICT Distillation

Tacit knowledge about the tradeoffs inherent in drivers of change is important for well run governments

ICT Outline

Is it possible that we’re actually getting less research because we have more researchers?

Short term feedback loops are hard when you need to be doing two things at the same time

# People who have done a thing should be in charge of a thing
*I’m going to focus on research because that’s the primary concern that’s driving this note and I think that’s a better cruising altitude on the ladder of abstraction. However “people who have done a thing should be in charge of a thing” extends to almost everything.*[^1]

[[Understanding a thing has to do with intuiting the affordances of a thing]]. It’s extremely hard to build up intuition for a thing without having done it. So at the end of the day ‘having done a thing’ is actually a heuristic for ‘has intuition about this thing.’ 

This heuristic is important to call out because it is not always true! People who have done a thing do not always have intuition for a thing and people who have not done a thing can sometimes build intuition for a thing! As far as I can tell, the only way to tell whether someone has an intuition for a thing is a track record of things that could only be accomplished through having an intuition for the thing or if you have intuition for the thing yourself. It’s [[The Map is not the Territory]] problems all the way down. So while ideally you could know someone’s intuition directly, the probability of intuition in someone who has done the thing is high and the probability of intuition in someone who has not done the thing is low. 

In research especially, there are very few metrics you can use on their own distinguish whether a project is a good idea or a bad idea or how it is progressing once it’s started. [[Many things cannot be measured well]]. Even in the case of “everything is working great!” it is easy for researchers to fool themselves, optimize for the wrong thing, or overlook a core problem.  

“Being in charge of a thing” is a rather fuzzy concept. I think what I’m grasping at is along the lines of “controlling the fate of thing” - this includes deciding whether a project/person gets approval funding, how well it’s doing, coming up with ideas for what should be done in the first place. Abstracting: the levels are binary go/no-go, progress measurement, and creation. You need different levels of intuition for each of these - they’re ordered from least intuition to most. 

At some point someone needs to be in charge and they can’t have done everything, so how do you both have a [[§Trusted Hierarchy]] and stick to the assertion that people who have done a thing should be in charge of a thing? Delegation! 

The “gotcha!” comes in determining what counts as ‘having done the thing.’ Does someone who worked in a biology lab doing experiments on rat brains count as someone who has ‘done’ biology? Should they be in charge of a molecular genetics project? This is where you need to defer back to the fact that the actual [[Highest order bit]] is 

Of course, the contrapositive also holds:
`People who have not done a thing should not be in charge of a thing`  Specifically people who have not done research should not be in charge of research

A corollary to this is that a PM’s (project, program, or product) power over a thing should be attenuated according to how much they’ve done the thing they’re PMing over. 

In terms of investing at least, a counterpoint brought up by [[Sarah Cone]] is that sometimes someone will overspecialize and end up being *less* qualified to be in charge of a thing in an area that’s normally quite close. For example, someone with a PhD in molecular biology but doesn’t understand CRISPR applies an old mindset to a new technique and isn’t willing to update their priors. While on the other hand, an excellent, applied, and diligent person may be able to be better at investing at least.  

### Related
- This tweet/video is an amazing example of what happens when someone who has no technical experience with a thing being in charge of a thing https://twitter.com/chittimarco/status/1338810527565361152?s=12
- [[Venkatesh Narayanamurti Podcast 20 May 2020]]
-  [[Organizations run by practitioners have less bureaucratic bs]] 
- [[Legible games enable people outside of an institution to control it]]
- [[Institutions need to differentiate between individuals inside and outside the institution]]
- [[Book Review - Seeing Like a State]]

[^1]: Of course there are exceptions. I suspect the strength of the assertion varies with the dimensionality of the thing. 



[Web URL for this note](http://notes.benjaminreinhardt.com/People_who_have_done_a_thing_should_be_in_charge_of_a_thing)

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/People_who_have_done_a_thing_should_be_in_charge_of_a_thing)




Legible games enable people outside of an institution to control it

Creating legible interfaces is expensive

The tech tree is fractal

Fair games are self-limiting

Undiscovered Public Knowledge (Paper)

A predictably successful idea is composed of known unknowns that that a team can execute against

Every simulation is an engineering approximation

Ontologies should be context dependent

‘Have your cake and eat it too’ life-games have led to a toxic society

Good quotes

# The Map is not the Territory



[Web URL for this note](http://notes.benjaminreinhardt.com/The_Map_is_not_the_Territory)

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/The_Map_is_not_the_Territory)




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

Generalists create connections between high-degree nodes

Good roadmaps work backwards from a goal

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

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]]

[Web URL for this note](http://notes.benjaminreinhardt.com/The_tech_tree_is_fractal)

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/The_tech_tree_is_fractal)






Undiscovered public knowledge discovery - UPKD

You need a human to resolve conflicting information

Using published literature to find causal relationships that can be connected to lead to a new causal relationship

chanSOLVENTMixedInitiative2018

Many approaches to augmented knowledge generation ignore humans

Designed serendipity program

 techniques and performance review

 A Ten Year Update

# Undiscovered Public Knowledge (Paper)
- Two aspects of Popperian philosophy (p2)
	- Critical approach in science
	- The distinction between objective and subjective knowledge
- This also applies to humanities
- Belief that science - facts and knowledge derived from those facts - is only valid and certain form of knowledge is “positivism” -  19th-early 20th c
- In [[The Logic of Scientific Discovery]] Popper argued that positivism was not correct because science came from conjectures or free inventions and that clashing inventions with the world weeded out the false ones.
	- Even if a theory passes many tests it’s not necessarily “True” because it could still contain hidden flaws
- Positivism required objective measurements and scientists are never fully objective and in the humanities people are *very* non objective
- Therefore science needs to be decoupled from positivism and then is a good model for knowledge acquisition 
- Therefore science is a series of guesses
- Guesses that can be tested against the world are better than ones that can’t
- Guesses that pass a test are better than those that have failed the test
- Facts of the world are not immediately available to our perceptions
- Perception must be preceded by expectation
- You know social reality exists because you know there are psychotics who are out of touch with social reality 
- Objective knowledge = recorded knowledge = public knowledge (p106)
- Subjective knowledge = thing in your head (p107)
- Public knowledge is larger than anybody (or everybody) can hold in their heads
- Popper described three worlds
	- World 1 - physical world
	- World 2 - subjective knowledge
	- World 3 - objective knowledge
- World 3 affects world 1 via world 2
- World 3 is not fully knowable
- World 3 includes anything people have written down *and any implications of those things*
- Argues that there are parts of world 3 that are not discovered only because we have limited ability to IOR
	- Index
	- Organize
	- Retrieve information
- Argues that information retrieval is an iterative process and is important to bridging world 2 and 3 (p108)
- “Central problem of information retrieval” is to find all public knowledge relevant to a theory
- Theories can have states of 
	- Testing
	- Criticism
- Example of someone who asserts “all swans are white” and there is a report somewhere that mentions black swans. The refutation of the original theory exists in world 3 and world 1 but not in world 2. (P109)
- The hypotheses and refutation have both been published, but no one person connected them.
- For two piece of knowledge to affect the future growth of knowledge, they need to exist in the same person’s head (p110)
- Because there is a hypothesis A->C that can be synthesized *only* from thesis that already exist in world 3, it is genuinely new knowledge
- Example of Raynaud’’s disease(C) and fish oil(A) that act through blood viscosity (B) (p111)
- The Reynaud’s example doesn’t give a conclusion but a testable hypotheses
- Popperian severity is p(e,h) - p(e) where p(e,h) is the probability of the effect given the hypotheses and p(e) is the probability of the effect in the absence of the hypothesis. (P112)
- If a person brings together a ton of weak (low severity) examples they can produce a stronger suggestion.
	- Example of smoking -> lung cancer: the hypothesis had actually withstood more tests because of the number of small tests
- Ideal is to be able to inspect every piece of information but that is impossible (p113)
- Each document has “points of access” - ie searchable attributes
- It’s impossible to encode all possible meanings or relevance of a document 
- Search function hypothesis : “All pieces of recorded information relevant to a given theory can be described and found with the correct search function for the theory” (p114)
- Search function hypothesis is false, therefore there will always be uncertainty over whether you’ve found all possible documents or that there might be some missing piece of info
- Example of where text-based analysis falls down: looking for a mathematical analysis of how a child pumps a swing.
- To map between the different meanings a thesaurus would need to be all of World 3 (p115)
- Search functions are like scientific theories for world 3 - they are conjectures about it
- Searching needs to be interactive - services should facilitate this (p116)
- Arguments depend on logical relationships involved in testing theories
- Two types of undiscovered knowledge: If A -> B and B -> C therefore A -> C are all true, you could either have A -> C be undiscovered or A -> C through B be undiscovered. 
- World 3 is “an endless frontier”
- Example: Fish oil affecting Raynaud’s disease via blood viscosity

## Questions
- Is economics stuck in a situation where none of the propositions are testable?
- Has anyone shown that the expectation effect works on societal levels
- What about information retrieval of things in world 2 that have not gotten to world 3 
- Or information retrieval purely via world 2, which probably effectively indexes a lot of world 3
- Is there a CS approach to the search function theory?
- How does this interact with oure data driven pieces
## Thoughts
- World 2 is not a subset of World 3, they overlap. 
- Perhaps there is a world 4 which is “all knowledge”
- Swanson uses World 3 I think differently than Popper meant it - Poppers objective knowledge could be known or unknown
-  The key piece of IOR is “relevance” 
- Could “hypothesis” and “refutation” be useful in an encoding scheme?
- At the end of the day Swanson agrees with [[Andy Matuschak 17/2/20]] that new knowledge can only come out of one person’s head but I feel like he’s more optimistic that we can algorithmically get the other pieces in there
- The causal diagrams in [[The Book of Why]] feel really related to this method
	- Really want to compare contrast part about lung cancer
- The argument about bringing together a number of weak examples to build a strong case reminds me of historians or any book with a ton of case studies
- The impossibility of encoding everything useful about a document is basically the [[The Map is not the Territory]] situation
## Related
- Cites [[The Logic of Scientific Discovery]]
## Authors
- [[Don R Swanson]]
## Link



[Web URL for this note](http://notes.benjaminreinhardt.com/Undiscovered_Public_Knowledge_(Paper) )

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/Undiscovered_Public_Knowledge_(Paper) )




Benʼs working notes

The Map is not the Territory

Links to this note