Psychology for Startups

Psychology for Startups

http://msnbcmedia.msn.com/i/MSNBC/Components/Photo/_new/Afghanistan_Dynamic_Planning.pdf Justin Singer - [email protected]

19 February 2013

• Psychology of Intelligence Analysis: http://1.usa.gov/12K7Wc1- Chapter 1 - Thinking about Thinking- Chapter 2 - Perception- Chapter 4 - Strategies for Analytical Judgment- Chapter 6 - Keeping an Open Mind

• Everybody’s an Expert: http://nyr.kr/WVwviv

• Munger’s Worldly Wisdom: http://bit.ly/WVwxXQ

• Wikipedia’s List of cognitive biases: http://bit.ly/1332wsr

• David Foster Wallace - This is Water- Part 1: http://bit.ly/W2D4RM- Part 2: http://bit.ly/W2DgR8

• The Psychology of Human Misjudgment: http://bit.ly/15tDl1N

• The Design of Everyday Things: http://amzn.to/12KctuP

Reading list: http://bit.ly/WVxDCS

ProductStrategy

HiringManaging

MarketingEntrepreneurship depends on robust models of learning

habitbehaviordesireinteractionexpectation

Why psychology?

• Pay close attention to mental models -- they’re the basis for everything

• Our minds are broken, but in predictable ways

• The most important choice you will make is whose advice to take

• Fuck it. Keep moving forward

Today’s arguments

Mental Models

http://friqt.com/worldchil.html

“[M]odels people have of themselves, others, the environment, and the things with which they interact."

- Donald A. Norman. The Design of Everyday Things (1988)

What are mental models?

http://en.wikipedia.org/wiki/File:Cassini_apparent.jpg

Ptolemaic astronomyAssumptions?Useful?

http://en.wikipedia.org/wiki/File:Surplus_from_Price_Floor.svg

Supply and DemandAssumptions?Useful?

http://www.fi.edu/wright/again/wings.avkids.com/wings.avkids.com/Book/History/instructor/jumpers-01.html

Winged flightAssumptions?Useful?

Mental models define how we think the world works, but not necessarily how it actually works

- Me, just now

Mental models are necessarily personalIf a model doesn’t work for you, build a better one

When judging a model’s quality, focus on process, not outcome

What are mental models?

How do we form mental models?

Real world

Interpretation

Feedback

What a video camera would record.

The story we create in our mind.

Is our story confirmed or disconfirmed? (usually we only ask the former)

Single-loop learning

http://en.wikipedia.org/wiki/Mental_model

Real world

DecisionInformation

feedback

Mentalmodel

Decision makingrules

“Insanity is repeating the same mistakes and expecting different results.”

- Narcotics Anonymous. Basic Text, pg. 11(nope, not Einstein)

Single-loop learning

http://amonymifoundation.org/uploads/NA_Approval_Form_Scan.pdf

Want better results? Change your model

Double-loop learning

http://en.wikipedia.org/wiki/Mental_model

Real world

DecisionInformation

feedback

Mentalmodel

Decision makingrules

Learning loops in Product Design

Donald A. Norman. The Design of Everyday Things (1988).

What’s missing?

Donald A. Norman. The Design of Everyday Things (1988).

User feedback should alter the product by altering the design model

Learning loops in Product Design

http://guide.cred.columbia.edu/guide/sec1.html

Just because people are using the same words, doesn’t mean they are thinking the same thing

And remember...

Strong sources of mental models

• Physical laws (especially movement mechanics)

- Elasticity (springs)

- Friction

• Large and representative data sets (empirical observation)

• Careful experimentation (seeking to disconfirm)

• Relevant analogy

• Abstract theory

• Personal experience

• Irrelevant analogy

• Repeated observations (small data sets)

• Single observation (single data point)

• Anecdote/inductive reasoning (Malcolm Gladwell)

• Opinion

Unfortunately, the less data we have, the more heavily we weight it

Weak sources of mental models

Heuristics & Biases

Heuristics are simple, efficient rules people use to form judgments and make decisions

What are heuristics?

Key people to know: Herbert A. Simon, Amos Tversky, Daniel Kahneman

Heuristics usually work well, but can lead to systematically irrational outcomes. These errors are called biases

Three major heuristics to know

Availability

Representativeness

Anchoring and adjustment

Overweights the probability of events that are recent, vivid, or dramatic

Overweights the probability of events that match our expectations

Overweights the importance of the first piece of information we receive

The more vivid or recent an event, the more likely we are to overestimate its likelihood

Availability heuristic

http://www.cdc.gov/nchs/fastats/deaths.htm

http://www.state.gov/j/ct/rls/crt/

http://www.dot.gov/mission/budget/nhtsa-fy-2010-budget-estimate

http://report.nih.gov/categorical_spending.aspx

http://en.wikipedia.org/wiki/Transportation_Security_Administration

Availability heuristic Deaths vs. Dollars

$6.814

$0.867

$0.448

$1.076

$5.448

$2.049597,689

574,743

69,071

83,494

35,332

3,023

Heart Disease

Cancer

Diabetes

Alzheimer’s

Car Accidents

Terrorism

Annual deaths Annual spending ($B)

All deaths since 2000

NHTSA budget

TSA budget

Availability heuristicHow feature creep happens

Just because a few people bitch about it doesn’t mean you should change it. Dig deeper and use your judgment

https://twitter.com/vacanti/status/184003264361148416

The fact that something “looks” like you’d expect does not make it more likely to be what you’re looking for

Representativeness heuristic

Representativeness heuristicWhat does random look like?

HHHHHTTTTHHTHHHTHTHT

Representativeness heuristicWhat does random look like?

HHHHHTTTTHHTHHHTHTHT

Random

Not random

Gambler’s fallacy: the belief that small samples will reflect the populations they’re drawn from

Proof by exampleWe tend to vastly overweight the evidentiary value of small, not necessarily representative samples

Base rate fallacyWhen making judgments, we tend to ignore prior probabilities and focus on expected similarities

http://www.businessinsider.com/how-andreessen-horowitz-chooses-investments-2013-2?op=1

To be fair, this is a bit of a cherry pick -- the next slide in the deck is more nuanced

Representativeness heuristic :: hiringWhat does a designer look like?

http://topics.nytimes.com/top/reference/timestopics/people/f/shepard_fairey/index.html

http://karakreative.blogspot.com/2013/02/graphic-designer-of-month-paul-rand.html

http://vimeo.com/putorti

http://tech.fortune.cnn.com/2011/06/27/quoras-designing-woman/

http://topics.nytimes.com/top/reference/timestopics/people/f/shepard_fairey/index.html

http://karakreative.blogspot.com/2013/02/graphic-designer-of-month-paul-rand.html

http://vimeo.com/putorti

Representativeness heuristic :: hiringDesigners look like everyone else!

http://tech.fortune.cnn.com/2011/06/27/quoras-designing-woman/

Jason Purtorti

Paul Rand Rebekah Cox

Shepherd Fairey

Representativeness heuristic :: hiringWho do you want to work with?

• Great people are...- Thoughtful- Productive- Team-oriented- Quick studies- Patient teachers- Empathetic- Pragmatic- Comfortable with

uncertainty- A strong cultural fit

• Great people are not necessarily...

- Ex-FB/Paypal/Google/etc. (also, fundamental attribution error)

- Graduates of Stanford/CMU/Wharton/Columbia/college

- Arrogant

- Overly deferential

- Aggressively passionate

- On Twitter

- Morally superior

- “Design-y”

Fundamental attribution errorWe tend to overvalue personality-based explanations and undervalue situational explanations for the actions of others

Self-serving biasWe tend to attribute our successes to personal/internal factors and attribute our failures to situational/external factors

Representativeness heuristic :: skill vs. luck

What’s more likely?

http://money.cnn.com/2007/11/13/magazines/fortune/paypal_mafia.fortune/index.htm

Or, that a large group of smart people happened to meet and work together at the right place at the right time?

That a large group of Super Businessmen happened to work together at Paypal...

http://www.inc.com/articles/201109/then-and-now-venture-capital.html

What’s more likely?

Or, that a large group of smart people happened to meet and work together at the right place at the right time?

That a large group of Super Businessmen happened to work together at Fairchild Semiconductor...

Judging outliersWhen it comes to judging outliers, we tend to overestimate the effect of skill and wildly underestimate the effect of luck

The law of exponential returnsAny great entrepreneur can build a $10M* business on skillNo great entrepreneur can build a $1B business without luck

* Amounts aren’t meant to be taken literally

Representativeness heuristic :: skill vs. luck

The tendency to base subsequent judgments on the first piece of information we gather (even when the information is entirely irrelevant)

Anchoring and adjustment

Anchoring and adjustmentNegotiating strategies

• When you receive a lowball offer, reject it out of hand (i.e., don’t make a counteroffer)

• Corollary: if making the first offer, aim for just beyond acceptable (i.e., not so high or low as to elicit rejection)

• Don’t send an agreeable person to the negotiating table

• Decide walkaway points before negotiating and stick to them

• Be wary of framing effects

• Smile! Sadness tends to exacerbate the anchoring effect

• Practice! Anchoring effects diminish with experience

“The fox knows many things; the hedgehog one great thing.”

- Archilochus

http://www.etsy.com/listing/60007735/woodland-animal-pair-hedgehog-and-foxExpert Prediction

Every feature suggestion opinion piece of advice is a prediction

What does this have to do with startups?

Who should you listen to?How much credence should you give?

http://www.theatlantic.com/technology/archive/2012/05/twitter-tech-elite-seriously-overstimated-facebooks-closing-price/257406/

What will Facebook close at on its IPO day?

http://collider.com/mark-zuckerberg-reviews-the-social-network/

http://www.theatlantic.com/technology/archive/2012/05/twitter-tech-elite-seriously-overstimated-facebooks-closing-price/257406/

Oopsies...

http://collider.com/mark-zuckerberg-reviews-the-social-network/* required significant price support from underwriters

$38*

Blurbed by Burton Malkiel Blurbed by FNMA ‘s Chief Economist

"Freddie Mac and Fannie Mae are fundamentally sound. They're not in danger of going under…I think they are in good shape going forward."

- Barney Frank (D-Mass.) House Fin. Svcs. Comm. chairman, July 14, 2008Placed into conservatorship in September

"I think you'll see [oil prices at] $150 a barrel by the end of the year" - T. Boone Pickens, May 20, 2008

$100/bbl in May - $135/bbl in July - $38/bbl in November

“The subscription model of buying music is bankrupt. I think you could make available the Second Coming in a subscription model and it might not be successful.”

- Steve Jobs, Rolling Stone, Dec. 3, 2003Spotify and Rdio would beg to differ

Why?

These are very, very smart people who were very, very wrong.

http://www.stratabridge.com/2011/08/putting-the-t-into-leadership/t-shaped/

What does it mean to be T-shaped?

Fox-Experts Hedgehog-Experts

Fox-Dilettantes

Hedgehog-Dilettantes

One model for thinking about advisors

FoxKnows many things well

HedgehogKnows one thing well

ExpertExpert in the subject at hand

DilettanteExpert in a related subject (but not the one at hand)

When it comes to China, the Chinese Ambassador is an expert and the British Ambassador is a dilettante

Tetlock, Philip E., Expert Political Judgment: How Good Is It? How Can We Know? (2005), fig. 3.4

Refers to political extremism regardless of party

If advice is a prediction, then whose advice deserves your attention?

Turns out that a lot of knowledge in a single area is a dangerous thing

Short-term advice1. Fox-Experts2. Fox-Dilettantes3. Hedgehog-Dilettantes4. Hedgehog-Experts

Long-term advice1. Fox-Dilettantes2. Fox-Experts3. Hedgehog-Dilettantes4. Hedgehog-Experts

How to recognize a fox

• skeptical of deductive approaches to explanation and prediction

• disposed to qualify tempting analogies by noting disconfirming evidence

• reluctant to make extreme predictions of the sort that start to flow when positive feedback loops go unchecked by dampening mechanisms

• worried about hindsight bias causing us to judge those in the past too harshly

• prone to a detached, ironic view of life

• motivated to weave together conflicting arguments on foundational issues in the study of politics, such as the role of human agency or the rationality of decision making

Tetlock, Philip E. Expert Political Judgment: How Good Is It? How Can We Know? 2006.

“Everyone is totally blind, feeling around in the dark, trying to succeed at building this thing we call a ‘business’.”

- Dan Shipper

There is no textbook for this

http://danshipper.com/how-to-make-a-million-dollars

The best you can hope for is to develop a robust learning process

Treat your models as hypotheses

Make sure they’re testableModels that can’t be disproven are aren’t model -- they’re beliefs

Actively seek to disprove themWelcome disproof -- a model disproved is a lesson learned

Look for hidden assumptionsTreat secondhand data as assumptions until proven otherwise

Question their predictabilityThe same event may be evidence of many different hypotheses

Models don’t care about your loyaltyIf a model doesn’t work, change it

Uncertainty stops most people in their tracks, but it’s only by movement that uncertainty can be resolved

“Strong opinions, weakly held.”- Paul Saffo

In the meantime, read widelythink deeplystay humblechose your advisors wiselyimprove your model setmove forward.

“Our brains have just one scale, and we resize our experiences to fit.”

http://xkcd.com/915/

Jerry Neumann - [email protected] Singer - [email protected]