Most people are not malicious or stupid

Incentives are the fundamental forces of peopleland

The incentives in elder care industry are tricky

Want to See My Data? I Thought We Were Friends

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

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

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






The word ‘novel’ is used as an idea bludgeon in academia

Lowering barriers to entry is clutch for healthy institutions

Paradigms outline a set of questions and the rules around answering them

Things that are not paper-worthy enough for academia and not product-focused enough for a startup

Most predictions are not aiming for accuracy

Peer review gives peers veto power over each other

Academia pushes against the creation of new questions

Assumptions of the ICT Writeup

# Most people are not malicious or stupid
It’s easy to chalk badness in the world up to one or more people being either malicious or stupid. While stupidity or malice absolutely could be the root cause of a problem, they should be the last resort for several reasons.

The major upshot of this assumption is that if there is a *systemic* problem, it cannot be attributed to malice or stupidity. 

This makes explaining bad things in the world harder. 

### Related
- [[Incentives]]
- [[Incentives are the fundamental forces of peopleland]]



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

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




Most predictions suck

# Most predictions are not aiming for accuracy
A perhaps more accurate (and charitable?) reason why [[Most predictions suck]] may be that the purpose of the predictions was not to be good predictions in the first place. This explanation would be more consistent with the principle that [[Most people are not malicious or stupid]].  

So why would people make predictions if not to expose non-obvious ways that the world might change so that other people can take action on them? [^1][^2] 

One reason to speculate[^3] is attention. Echoing or riffing on a common opinion about the future is a great way to yell “hello world!”  Putting on a contrarian hat and speculating that the common opinion is wrong can have the same effect. Attention-seeking speculation is closely tied to signaling group affiliation. Different groups hew to different narratives that you can enrich with speculation. As an illustration, imagine if I made predictions about a future where the US government builds recovered alien technology into next-generation military hardware.  

Another reason to speculate is to influence people or a process. Unlike in physics, if you can get enough people to believe that something is true, that thing can actually become true.  [[Faith based on desire works in human systems]]. Real predictions can encourage people to fear or anticipate a specific set of outcomes and take action to prevent or encourage them. If you care more about the actions than predictive accuracy, speculation can often play the same role. Speculating to influence the future isn’t necessarily conscious or nefarious. Great ideas often start off as speculations! “What if …” The core of most “visions” is pure speculation. The only reason many companies and technologies exist is because speculation convinced people to give them enough time and money to make the speculation into a retroactively accurate prediction. It’s possible that the only reasons railroads and the internet were able to become utterly pervasive is because speculative predictions convinced investors and companies to try to make those speculations a reality.  Of course, this is a gambit that can also fail.  [[dancoAreFoundersAllowed2020]]

Telling stories is also just plain fun and speculating is mostly telling stories about the future. So a lot of speculation is just a way of socializing and telling imagination-capturing stories to each other. What’s more interesting than a possibly true story about our own futures?

The [[Robin Hanson]]-Ian analysis would also suggest that many people who are making predictions for non-predictive reasons may *think* that they are genuinely trying to make good predictions. It’s also important to note that even when people are making genuine attempts to create good predictions, they are probably *also* shooting for at least a couple of these other outcomes. When I predict something I certainly don’t mind getting attention for it, influencing how other people act, and having fun in the process.

### Related
- [[A good prediction enables new thoughts and actions by being non-obvious and creating agency]]
- [[Every good prediction should have a science fiction story attached]]

[^1]:Note that [[It’s impossible to know what’s going on inside someone else’s head]] so at the end of the day this is speculation based on internal motivations I’ve had in the past. Perhaps they will ring true for you too. 
[^2]:Michael Crichton touches on some reasons briefly in [[Why speculate]] , but I think there’s more to dig into especially through the lens of “Hansonian analysis” and the proliferation of social media. 
[^3]:It’s pushing the word a bit, but “speculation” (predictions without firm evidence) seemed like the best term for predictions made for reasons other than accuracy. Other contenders were pseudo-prediction, unprediction. The choice between making up new words and appropriating old ones is a rock and a hard place. 

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

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






Graduate students are the labor in academic science and engineering

Academia is not a good environment for systems engineering

Academia incentivizes novelty, not focus

Academic credit is a first-to-the post system

We have no flying cars - failures of paradigm-shifting engineering

Academia judges via novelty because there are no metrics but there are no metrics when the thing is still research

People are incentivized to work on nodes higher up the tech tree

Peer review is a great system for preventing waste

Legible novelty is the worst

# The word ‘novel’ is used as an idea bludgeon in academia
“Novelty” is one of the constant criteria by which academic research is judged, both in terms of funding and publication. 

Whether something is novel enough not purely about the magnitude of the contribution (the characterization of reactions that barely differ from other reactions can be considered novel) or the actual newness of the idea (If I argued that the transition between turbulent and laminar flow was mediated by conscious 11-dimensional elves it would be a new idea, but not in the good way.) Instead, the worthy novelty of a contribution revolves around its relationship to existing paradigms.  [[Paradigms outline a set of questions and the rules around answering them]].  

A contribution has massive headwinds if the paradigm judges the question being answered to be already answered or out-of-scope or the answer is not in an approved form (the 11-dimensional elves again.) 

It’s easy to say “argh! The system is full of risk aversion, politics, and narrow mindedness!” But [[Most people are not malicious or stupid]] and that goes for academics as well. Most ideas that answer old questions in new ways or have completely different answers are crackpot ideas (11-dimensional ideas!) so it’s not irrational to default dismiss them given limited time and bandwidth[^1]. 

If a question has existed unanswered for a long time, inevitably many people have tried to answer it. Every failed answer rationally increases the priors that the next answer will also fail. Therefore it’s perfectly reasonable prior that a new answer to an old question will also fail. It’s also technically correct that proposing a new answer to an old question is not “novel” creating a rational excuse for people not to waste their time with crackpot answers. However, the novelty criteria become a cudgel when people use it  to avoid spending the time to actually engaging with an idea. The problem is that it’s not actually clear where to set the filter between “take every crackpot idea seriously” and “reject every answer to long-standing problems.” 

The incentives around new answers to old questions also apply to the creation of new questions. [[Academia pushes against the creation of new questions]]. New questions cannot be measured by their performance on standard metrics or data sets. At least in early 21st century machine learning, it’s easier to get a paper accepted by improving performance on a standard dataset from 98.2% to 9.85% than it is to argue that your system is doing something important independent of an established test or *heaven forbid* has performance that can’t be measured numerically. Again, I suspect this situation at least in part arises from the number of people all clamoring for attention and everyone’s limited bandwidth [[Attention is the ultimate scarce resource]]. New questions can also be completely useless. One out of a thousand times “What if we ran it backwards?” Is the key to saving the universe, but most of the time it’s someone trying to be clever with a bad idea.

These two situations parallel startups heavily. It’s rational to have a prior that a company trying ideas that are littered with the bodies of other companies (memexes, new social networks, elder care, etc.) will fail. However, [[It’s rational to try something that failed in the past as long as you explicitly call out why you will succeed when they failed]] but that requires time and precision from both the founders and appraiser. In parallel with new ideas in science, startups trying to introduce a product that “nobody knew they wanted” usually fail as well.  

The upshot is there are good reasons to dismiss new ideas that are both novel and not novel. Therefore “novelty” is a relatively arbitrary idea discriminator and easy for people to use to dismiss ideas they don’t want to engage with. 

### Relevant
- [[§Academia Constraints]]
- [[Mysteries vs puzzles]]
- [[Peer review has failed as an institution]]
- [[Novelty and Heresy - PG]]

[^1]: The coupling between bandwidth and ability to address crackpot ideas on a case-by-case basis of course plays into my bias that insufficient [[Slack - concept]] is a core source of friction in the system. 



[Web URL for this note](http://notes.benjaminreinhardt.com/The_word_‘novel’_is_used_as_an_idea_bludgeon_in_academia)

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/The_word_‘novel’_is_used_as_an_idea_bludgeon_in_academia)




How does money work in PARPA?

The way to get around journals is to focus on non-paper artifacts

PARPA needs a constitution with checks and balances

Mark Johnson the Elder Conversation 4 Jan 2021

Possible PARPA Missions

PARPAs ideal legal structure comprises both a nonprofit and a for profit

ICT Distillation

An Institute for Creative Technology - Riffing on ARPA

ICT Outline

# Things that are not paper-worthy enough for academia and not product-focused enough for a startup
There are many heuretics (discoveries/inventions/innovations/ideas etc.) that don’t fall within the constraints of the institutions we expect to support them. “Things that are not paper-worthy enough for academia and not-product focused enough for a startup” is shorthand for one class of these heuretics. Note that there are many others! 

First, let’s address some misconceptions about what falls into this category.  Wacky ideas and long-term projects are two things that many people assume don’t get supported in universities or startups. This is not true.
	- Long-term projects get support *if* they hit consistent milestones *and* they’ll be able to capture a lot of the value they create if they succeed. 
	- Wacky ideas get support *as long* as they don’t threaten paradigms too badly *or* someone thinks they can make money off of them. 
	- Research projects that require a lot of people or coordination *as long as* the final product is a paper they can all put their names on. See the LHC. 
	- Research projects that require some engineering effort *as long as* the engineering effort resembles something that has been done before: building a bigger telescope/particle collider/linear accelerator/space probe, etc.

The reasons that ideas are not paper-worthy or product-worthy *make sense* given the incentive frameworks in their relative institutions.  [[Most people are not malicious or stupid]]. 

### What makes something not paper-worthy?
‘Paper-worthy’ is meant as shorthand for ’things academics are incentivized to do.’ So things that are not paper worthy are those that don’t obey [[§Academia Constraints]].  It’s important to explicitly call those implications out because that call-out can shine a light on where to dig for hidden gold. 

1. **An idea isn’t paper worthy if not considered “novel enough” or “not a contribution to the literature” by peer reviewers.** [[The word ‘novel’ is used as an idea bludgeon in academia]] and weeds out many classes of ideas. 

A non-exhaustive list of what gets shot down by peer review: 
- New approaches to old “solved” problems, 
- Better implementations of an existing idea that’s already been implemented, 
- Efficiency improvements along a different metric than the ones the field normally cares about, 
- Scaling existing techniques, 
- Creating something for a very specific application as opposed to a general principle/purpose, 
- Further work on things that have already gotten to proofs of concept
- System integration of several already-existing ideas.

For more see:  [[Academia is not a good environment for systems engineering]]. [[Academia incentivizes novelty, not focus]].  [[Academic research is often just the concept half of creating a heuretic]]. 

2. **An idea generally isn’t worth writing a paper about if it’s not expected to get a lot of citations.** Even if it’s published, low-citation ideas languish in obscurity. It’s valid to ask ‘are there things that are valuable that don’t get a lot of citations?’ 

I would argue yes, valuable projects that wouldn’t get many citations include:
	- Projects that are intermediate steps to a larger vision without a strong conclusion besides “we did a thing!”
	- Engineering work in a theory-heavy field or theory work in an engineering-heavy field.  More generally, work in a field that the field doesn’t think is valuable. 
	- Ideas that are far enough out of mainstream won’t get many citations because people don’t have the bandwidth to dig in and see if it’s worthwhile and the default answer is “no” (see #3 below) 
	
3. **It’s hard to publish ideas that are too outside of the approved way of doing things.** [[Paradigms outline a set of questions and the rules around answering them]].  Ideas can go too far outside of accepted paradigms when when you’re asking different questions, or measures success differently than standard ways. [[Antedisciplinary Science]] often falls into this trap as well, when it’s too much like discipline X to be published in a journal for discipline Y but it’s too much like discipline Y to be published in a journal for discipline X.  This situation is of course tricky because many (most?) ideas that go outside of paradigms are legitimate crackpottery. [[Anyone on the knowledge frontier is a bit of a crackpot]]. 

4.  **It’s hard to write a paper if you can’t even get the money to do the research to write the paper.**
The requirement to align with current funding priorities is particularly pernicious because [[The government grant process depends on politics and committees]]. Additionally, one reason to write papers is to continue playing the game, so if a paper doesn’t hint at your continued ability to do work that aligns with funding priorities, you’re incentivized not to work on it. [[People giving out grants try to derisk them as much as possible]].

5.  **A project is unlikely to lead to a paper if it’s not within scope for a PhD student or untenured professor.** 
[[Graduate students are the labor in academic science and engineering]]. In exchange for cheap labor, grad students expect to produce research within a 4-7 year time frame that can help them move on to the next rung of the academic career ladder. (Despite the fact that [[There is an increasingly wide gap between the number of graduate students training for PhDs and academic positions for them to fill]].) While a tenured professor might be willing to work on something crazy that will take 10 years and might be a complete dud, no grad student would rationally work on it.  The incentive for PhD students to produce high-impact papers for the sake of their careers serve to amplify all the other constraints, and add the timescale constraint. Additionally, PhD students in science get most of their funding via research grants, so research they work on is especially susceptible to #4. 

6. **Projects that produce a lot of structured data or reusable code don’t led themselves to papers.** Academic papers are built around the paradigm of scientific inquiry, where collecting data and writing code are only in service to producing an abstract model or theory - the more generalizable the better. There is little reward to producing high quality, reusable datasets or code, despite the value they can create. 

7. **A project is unlikely to lead to a paper if it involves a lot of coordination.** Academia emphasizes individual agency. This is a good thing, but if a project cannot be effectively modularized it will end up requiring a lot of grungy coordination work that doesn’t count as a contribution unless you’re in charge of the project. A big reason to go into academia in the first place is to avoid having a boss and because you like doing your own thing, your own way. Obviously there are exceptions like the LHC, but that is a situation where the experimental particle physicists are locked into a paradigm and have no other option. 

Coordination aversion hits hard projects that don’t fit into a particular disciplinary bucket or require integration between components built in different labs. Projects of a particular scale require coordination by their very nature. See[[A new structure for scalable research]] for a much deeper dive.  

### What makes something not product-focused enough for a startup
‘Product-focused’ is meant as shorthand for ’things startups are incentivized to do.’ So things that are not product-worthy are those that don’t obey [[§Startup Constraints]].  It’s important to explicitly call those implications out because that call-out can shine a light on where to dig for hidden gold. 

1. **There’s no convincing story for how the project’s output produce a massive and competitive ROI on a reasonable timescale for a VC-funded startup** 
*Massive ROI* Venture capitalists need to believe that any investment in their portfolio could potentially return the fund. 
*Competitive ROI* Investing isn’t just about absolute returns - any investment is in leu of another investment. So if the stock market is absolutely crushing it, there’s less incentive to invest in higher risk technology. Even within the VC realm, many atom-based technologies have the same risk or higher than a software startup but expected return is lower (especially when you take timeline into account) or farther in the future. From the cold hard numbers SaaS is often just a better investment.  
*Timescale* [[J.C.R. Licklider]] started work on the ARPA project to build what we would consider “modern computing” in 1962 and perhaps the first real ‘value capture’ event based on it was Apple’s IPO in 1980. [[Venture capitalists need to fund things that increase valuation in a few years and return in 5-7 years]] so this trajectory would have been untenable as a startup. Venture timescales often lead to projects being pushed to focus on a specific application or being acquired before achieving the technical goals they set out to hit. There is nothing wrong with either of these routes per se, but they can lead to technologies failing to live up to their potential. 
2. **It will take a lot of development work to sell anything for a bootstrapped startup**
Startups do not *need* to raise money from VCs. However, that does mean that if the founders are not extremely wealthy ([[SpaceX]], [[Blue Origin]]) the startup needs revenue early on. If the project will take a lot of development work before it can sell anything the startup needs to get revenue somewhere. Some bootstrapped startups use consulting work to create revenue to fund development. Unless it’s wildly profitable, the consulting approach severely limits the amount of time and money that can be spent on development, possibly dragging it out forever. [[Some projects need effort above a certain threshold to move forward]]. [[SBIR]] grants can provide another option, but they are relatively small (order $1m over several years.) 
3. **It’s hard to capture the value the project creates**
Markets are great, but there is no law of economics that people can always capture the value they create if they are clever enough. (See [[Schumpeterian Profits in the American Economy - Theory and Measurement]].)  Research often falls into this trap because [[Innovation requires multiple people]]. Often the person who figures out how to make a lot of money off of a discovery or invention is not the person who created it, nor are they even in the same organization.  “But what about intellectual property ?” There are *many* reasons intellectual property does not ensure everybody involved in an innovation capture the value they create but a short list includes: many people in many organizations contribute to most innovations, patents only work for a narrow set of innovations, and patents have massive downsides - including potentially capping how much value an innovation can create. 

A more subtle issue than the question of value capture itself is that trying to capture the value created by an innovation can severely hamstring the total value it creates. Consider the [[Graphical User Interface]] and other personal computing innovations created at [[Xerox PARC]]. Arguably they’ve created trillions of dollars of value for the world. Would that be the case if Apple had a patent on the GUI and mouse? Probably not. This situation is not unique. 

[[It is hard to capture value from research]]

4. **The project is changing one or more components of a bigger system or process** 
A startup that is trying to improve or change a component of a bigger system needs to either convince whoever runs that system to adopt the change (which is especially hard when no particular entity runs the system) or the startup needs to build the entire system themselves with the change incorporated. 

Obviously, the change needs to happen eventually, but many paradigm shifts (especially in complex systems) initially *lower* performance, at least on traditional metrics. [[Metrics can cause paradigm lock-in]]. Change happens either when the new paradigm gets enough reps to catch up to the old way of doing things or people see the new paradigm in action and accept that the traditional metrics weren’t capturing all the important features of a system. Both of these situations are hard for a traditional startup to achieve because both approaches take a lot of time and resources before showing ‘traction’ and the potential customers (the people running the systems or consuming their outputs) will assert that the idea is dumb up until its not. 

See [[fillerFundamentalManufacturingProcess2020]] for more.

5. **Innovations that can’t be encapsulated into a product**
There are some innovations that it’s just hard to productize. Process innovations often fall into this category - especially those that don’t depend on new technology per se, but on a new way of doing the same thing with basically the same tools. The [[Bessemer Process]], for example, used basically the same crucibles but you put different things in them. Social process changes (like Kaizen[^1]) are even harder to productize. 

In these situations, the vast majority of the value generally accrues to the end-product producer or consumers. The naive advice would be “start a firm that’s doing the end-to-end thing” and outcompete the incumbents. While that should theoretically work, it often runs into the reality of extremely complex products that you would need to reinvent from the ground up (like if you had a better way of making passenger jets), heavily regulated industries where it would take massive lobbying just to use a new process, and other scenarios. A startup doesn’t usually have the time or resources to tackle these scenarios. The alternative approach is to take on a change-management consulting role, which usually doesn’t capture enough value to be VC-fundable so it needs to be profitable early on. Additionally consulting runs head-long into the issues in point #4 above. 

In some ways this point is intertwined with #4. 

6. **Startups shouldn’t tackle market, channel, AND technical risk** 
New technological paradigms usually come with all three of technical, market, and channel risk. You simultaneously need a lot of work to get the technology to a point where it’s competitive with alternatives, to figure out who wants to use it, and to establish how they’re going to buy it. 

It’s common wisdom among VCs that it’s not a good idea to invest in companies that are taking on two, let alone three of these risks. This heuristic isn’t misguided.  The chance of failure goes up *dramatically* when an organization tries to tackle tow, let alone three of those risks. Startups should only tackle one (or *maybe* two) of these.  The reason therapeutics focused startups can exist is that they have almost no market or channel risk - there is a well established pipeline to being acquired by a pharma company and insurance companies are guaranteed to pay for drugs that go through FDA approval and target big conditions.

At the end of the day, the work you need to do to drive adoption of a technology is often very different from getting it to a certain performance level. The work priorities drive the type of organization you create. As much as we like to think that startups are about building technology, they are actually about selling technology and adopting it for specific markets so that they can do that better. 

### Related
- [[§Academia Constraints]]
- [[§Startup Constraints]]
- [[§Corporate R&D Constraints]]
- [[§Philanthropy and Non-Profit Constraints]]
- [[Systems research requires a lot of work that is expensive and uninteresting]]

[^1]: [[Kaizen is Toyotas process for continuous business process improvement]] 



[Web URL for this note](http://notes.benjaminreinhardt.com/Things_that_are_not_paper-worthy_enough_for_academia_and_not_product-focused_enough_for_a_startup)

[Comment on this note](http://via.hypothes.is/http://notes.benjaminreinhardt.com/Things_that_are_not_paper-worthy_enough_for_academia_and_not_product-focused_enough_for_a_startup)




Benʼs working notes

Most people are not malicious or stupid

Related

Links to this note