Psychology for Startups


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Slides from a talk I gave to Columbia Engineering students in Managing Technological Innovation, taught by Jerry Neumann.

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Psychology for Startups

  1. Justin Singer - Psychology for Startups 19 February 2013
  2. Reading list: • Psychology of Intelligence Analysis: - Chapter 1 - Thinking about Thinking - Chapter 2 - Perception - Chapter 4 - Strategies for Analytical Judgment - Chapter 6 - Keeping an Open Mind • Everybody’s an Expert: • Munger’s Worldly Wisdom: • Wikipedia’s List of cognitive biases: • David Foster Wallace - This is Water - Part 1: - Part 2: • The Psychology of Human Misjudgment: • The Design of Everyday Things:
  3. Why psychology? Product Strategy Hiring Managing Marketing Entrepreneurship depends on robust models of learning habit behavior desire interaction expectation
  4. Today’s arguments • 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
  5. Mental Models
  6. What are mental models? “[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)
  7. Ptolemaic astronomyAssumptions?Useful?
  8. Supply and DemandAssumptions?Useful?
  9. Winged flightAssumptions?Useful?
  10. What are mental models? Mental models define how we think the world works, but not necessarily how it actually works - Me, just now Mental models are necessarily personal If a model doesn’t work for you, build a better one When judging a model’s quality, focus on process, not outcome
  11. How do we form mental models? Real world What a video camera would record. Interpretation The story we create in our mind. Is our story confirmed or disconfirmed? Feedback (usually we only ask the former)
  12. Single-loop learning Real world Information Decision feedback Decision making Mental rules model
  13. Single-loop learning “Insanity is repeating the same mistakes and expecting different results.” - Narcotics Anonymous. Basic Text, pg. 11 (nope, not Einstein) Want better results? Change your model
  14. Double-loop learning Real world Information Decision feedback Decision making Mental rules model
  15. Learning loops in Product Design What’s missing? Donald A. Norman. The Design of Everyday Things (1988).
  16. Learning loops in Product Design User feedback should alter the product by altering the design model Donald A. Norman. The Design of Everyday Things (1988).
  17. And remember... Just because people are using the same words, doesn’t mean they are thinking the same thing
  18. 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
  19. Weak sources of mental models • 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
  20. Heuristics & Biases
  21. What are heuristics? Heuristics are simple, efficient rules people use to form judgments and make decisions Heuristics usually work well, but can lead to systematically irrational outcomes. These errors are called biases Key people to know: Herbert A. Simon, Amos Tversky, Daniel Kahneman
  22. Three major heuristics to know Overweights the probability of events Availability that are recent, vivid, or dramatic Overweights the probability of events Representativeness that match our expectations Anchoring and Overweights the importance of the first adjustment piece of information we receive
  23. Availability heuristic The more vivid or recent an event, the more likely we are to overestimate its likelihood
  24. Availability heuristic Deaths vs. Dollars Annual deaths Annual spending ($B)597,689 Heart Disease $2.049 574,743 Cancer $5.448 69,071 Diabetes $1.076 83,494 Alzheimer’s $0.448 35,332 Car Accidents $0.867 NHTSA budget All deaths since 2000 3,023 Terrorism $6.814 TSA budget
  25. Availability heuristic How feature creep happens Just because a few people bitch about it doesn’t mean you should change it. Dig deeper and use your judgment
  26. Representativeness heuristic The fact that something “looks” like you’d expect does not make it more likely to be what you’re looking for
  27. Representativeness heuristic What does random look like? HHHHHTTTTH HTHHHTHTHT
  28. Representativeness heuristic What does random look like? Random HHHHHTTTTH HTHHHTHTHT Not random Gambler’s fallacy: the belief that small samples will reflect the populations they’re drawn from
  29. Proof by example We tend to vastly overweight the evidentiary value of small, not necessarily representative samples
  30. Base rate fallacy When making judgments, we tend to ignore prior probabilities and focus on expected similarities To be fair, this is a bit of a cherry pick -- the next slide in the deck is more nuanced
  31. Representativeness heuristic :: hiring What does a designer look like?
  32. Representativeness heuristic :: hiring Designers look like everyone else! Paul Rand Rebekah Cox Jason Purtorti Shepherd Fairey
  33. Representativeness heuristic :: hiring Who do you want to work with? • Great people are... • Great people are not necessarily... - Thoughtful - Ex-FB/Paypal/Google/etc. (also, fundamental attribution error) - Productive - Team-oriented - Graduates of Stanford/CMU/ Wharton/Columbia/college - Quick studies - Arrogant - Patient teachers - Overly deferential - Empathetic - Aggressively passionate - Pragmatic - On Twitter - Comfortable with - Morally superior uncertainty - A strong cultural fit - “Design-y”
  34. Representativeness heuristic :: skill vs. luck Fundamental attribution error We tend to overvalue personality-based explanations and undervalue situational explanations for the actions of others Self-serving bias We tend to attribute our successes to personal/internal factors and attribute our failures to situational/external factors
  35. What’s more likely? That a large group of Super Businessmen happened to work together at Paypal... Or, that a large group of smart people happened to meet and work together at the right place at the right time?
  36. What’s more likely? That a large group of Super Businessmen happened to work together at Fairchild Semiconductor... Or, that a large group of smart people happened to meet and work together at the right place at the right time?
  37. Representativeness heuristic :: skill vs. luck Judging outliers When it comes to judging outliers, we tend to overestimate the effect of skill and wildly underestimate the effect of luck The law of exponential returns Any great entrepreneur can build a $10M* business on skill No great entrepreneur can build a $1B business without luck * Amounts aren’t meant to be taken literally
  38. Anchoring and adjustment The tendency to base subsequent judgments on the first piece of information we gather (even when the information is entirely irrelevant)
  39. Anchoring and adjustment Negotiating 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
  40. “The fox knows many things; the hedgehog one great thing.” - ArchilochusExpert Prediction
  41. What does this have to do with startups? Every feature suggestion opinion piece of advice is a prediction Who should you listen to? How much credence should you give?
  42. What will Facebookclose at on its IPO day?
  43. Oopsies... $38* * required significant price support from underwriters
  44. Blurbed by Burton Malkiel Blurbed by FNMA ‘s Chief Economist
  45. "Freddie Mac and Fannie Mae are fundamentally sound. Theyre not indanger of going under…I think they are in good shape goingforward." - Barney Frank (D-Mass.) House Fin. Svcs. Comm. chairman, July 14, 2008 Placed into conservatorship in September"I think youll 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 youcould make available the Second Coming in a subscription model andit might not be successful.” - Steve Jobs, Rolling Stone, Dec. 3, 2003 Spotify and Rdio would beg to differ
  46. These are very, very smart people whowere very, very wrong. Why?
  47. What does it mean to be T-shaped?
  48. One model for thinking about advisors Fox Fox-Experts Hedgehog-Experts Knows many things well Hedgehog Knows one thing well Expert Expert in the subject at hand Fox-Dilettantes Hedgehog-Dilettantes Dilettante Expert 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
  49. Refers to political extremism regardless of partyTetlock, Philip E., Expert Political Judgment: How Good Is It? How Can We Know? (2005), fig. 3.4
  50. If advice is a prediction, then whose advicedeserves your attention? Short-term advice Long-term advice 1. Fox-Experts 1. Fox-Dilettantes 2. Fox-Dilettantes 2. Fox-Experts 3. Hedgehog-Dilettantes 3. Hedgehog-Dilettantes 4. Hedgehog-Experts 4. Hedgehog-Experts Turns out that a lot of knowledge in a single area is a dangerous thing
  51. 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.
  52. There is no textbook for this “Everyone is totally blind, feeling around in the dark, trying to succeed at building this thing we call a ‘business’.” - Dan Shipper The best you can hope for is to develop a robust learning process
  53. Treat your models as hypotheses Make sure they’re testable Models that can’t be disproven are aren’t model -- they’re beliefs Actively seek to disprove them Welcome disproof -- a model disproved is a lesson learned Look for hidden assumptions Treat secondhand data as assumptions until proven otherwise Question their predictability The same event may be evidence of many different hypotheses Models don’t care about your loyalty If a model doesn’t work, change it
  54. Uncertainty stops most people in their tracks, but it’sonly by movement that uncertainty can be resolved In the meantime, read widely think deeply stay humble chose your advisors wisely improve your model set move forward. “Strong opinions, weakly held.” - Paul Saffo
  55. “Our brains have justone scale, and weresize our experiencesto fit.”
  56. Jerry Neumann - mti@neuvc.comJustin Singer -