Date post: | 22-Oct-2014 |
Category: |
Documents |
View: | 1,211 times |
Download: | 0 times |
Webit, Istanbul, 10 October 2012
14 Primary Lessons for Black Swans(Decision Theory for Startups)
Jochen Wegner . [email protected] . http://wegner.io . 10/2012
About Jochen
Coder BusinessJournalist
Physicist
Science Editor
ComplexityResearcher
Editor-in-ChiefManaging Director
Startup Entrepreneur
Consultant& Angel - see http://wegner.io
‣publishers‣industry‣startups
Science Writer
Influencers
Jochen Wegner . [email protected] . http://wegner.io . 10/2012
Karl Popper Nassim Taleb, Rolf Dobelli Daniel Kahneman
14 Primary Lessons for Black Swans
>6 million businessesare created every year in the United States alone*
* Kauffman Foundation, US Census Bureau
1 out of 1.000-10.000 will be big.*
* >500 employees after 10 years, estimate based on Kauffman Index, US Census Bureau
What if big success was random?
What if it would be impossible to predict if you will be the next Black Swan?
What if big success was random?
What if it would be impossible to predict if you will be the next Black Swan?
„The majority of funds — 62 out of 100 —failed to exceed returns available from the public markets, after fees and carry were paid.“ (Kauffman Foundation)
What if big success was random?
Some reliable sources suggest exactly that:suggest exactly that:
Successful startups are „Black Swans“ according to Taleb:
‣rare‣extreme impact‣only retrospective predictability
Big startups successshows many properties of arandom process.
Lesson 1
Startup entrepreneurs showthe typical cognitive biases connected with randomness.
Lesson 2
There are 156 cognitive biases.*Let us pick 12 of them.
* assembled by Wikipedia
Your startup will almost certainly be a White Swan (and no big hit)...
Your startup will almost certainly be a White Swan...
...even if everyone else around you is so successful.*
* Selection Bias / Survivorship Bias / Representativeness / „Law of small numbers“
Lesson 3
Your startup will almost certainly be a White Swan...
...even if you got big funding.*
* smart investors „farm black swans“- © Paul Graham
Lesson 4
Your startup will almost certainly be a White Swan...
...even if you got funded by a very successful investor.*
* Selection Bias, „Swimmer‘s Body Illusion“
Lesson 5
Your startup will almost certainly be a White Swan...
...even if you were successful before.*
* Randomness, Selection Bias, Overconfidence
Lesson 6
(...even if your name is Loic, Niklas or Chad.)
Lesson 6
Your startup will almost certainly be a White Swan...
...even if you find a lot of evidence that your model will work.*
* Confirmation Bias
Lesson 7
Please follow your idea - even if it seems not big enough for big investors.*
Lesson 8
* They are solely in the Black Swan Farming Business - see Paul Graham
Please follow your idea - even if it seems a little insane (but could be really big).*
Lesson 9
* „If a good idea were obviously good, someone else would already have done it. So the most successful founders tend to work on ideas that few beside them realize are good. Which is not that far from a description of insanity, till you reach the point where you see results.“ (Paul Graham)
It may be rational not to take money from big investors.
Lesson 10
It may be rational not to take money from small investors.*
* Reciprocity
Lesson 11
Please follow your idea - but not because you worked so hard on it in the past.* Only because of future prospects.
Lesson 12
* Sunk Cost Fallacy
Don‘t listen to success stories too much.*
* Availability Bias
Lesson 13
(Read „Techcrunch“ like you read „TMZ“ or „People“ Magazine.)
Be very careful if you take advice from successful entrepreneurs.*
* Overconfidence, Hindsight, Illusion of Control
Lesson 14
Resources
BooksJudgement under Uncertainty: Heuristics and BiasesFooled By RandomnessThe Black Swan
Articles / BlogsWhy Angel Investors don‘t make moneyBlack Swan FarmingWe have met the enemy - and he is us (PDF, Kauffman)Cognitive biases, risk perception, and venture formation: How individuals decide to start companies
Resources on EntrepreneurshipUS Census BureauKauffman Foundation
Wikipedia: List of Cognitive Biases