Probability Primer, Khakis, and the Tricky “Science” of Male Fashion

Probability & Risk Essentially, probability is a generalization of the concept of percentage: for example: - one has a 50/50 chance of tossing “heads” Always between 0 (no probability) and 1 (absolute certainty)

Pascal’s Wager “God is, or he is not. Which way should we incline? Reason cannot answer.” This is the theory of decision-making when the outcome is uncertain. Core of Pascal’s argument: The value of any bet (or choice) equals the likelihood of winning times the size of the potential payoff … Expected Value (of the bet/choice) = likelihood of winning x size of the potential payoff EV (bet/choice) = LW x SPP ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ For Pascal then… - Size of Potential Payoff (if “God is”) is infinite - Likelihood of Winning (from “God is”) is roughly 50% Hence, the Expected Value (the bet) of being an atheist—despite more worldly pleasure— can never be as great as that of being a “believer.”

Probability & Risk Large-scale risk sharing and Diversification An individual merchant who makes 1 major shipment/per year with a probability of loss at 20%: Arrives safely = $10,000 (80% probability) Shipment lost = $0 (20% probability) Expected value = $10,000 x 80% = $8,000 [or the average result of 10 trips over 10 years] Two merchants, who both make 1 major shipment, agree to hedge their risk and split their combined earnings: Both arrive safely = $10,000 (64% probability) 1 arrives safely = $5,000 (32% probability) Both lost = $0 ( 4% probability) Expected value = ($10,000 x 64%) + ($5,000 x 32%) = $8, Three merchants, who all make 1 major shipment, agree to hedge their risk and split their combined earnings: All arrive safely = $10,000 (51% probability) 2 arrive safely = $6,667 (39% probability) 1 arrives safely = $3,333 ( 9% probability) All lost = $0 ( 1% probability) Expected value = ($10,000 x 51%) + ($6,667 x 39%) + ($3,333 x 9%) + ($0 x 1%) = $8,000

Syphilis Tipping Point, Baltimore : exponential increase Different explanations... -increase in crack cocaine (overall context of the disease = the “Power of Context”) -reduction in medical services (the disease itself = the “Stickiness Factor”) -destruction of housing projects and exodus from old row houses (people carrying the disease = the “Law of the Few”)

The “Law of the Few” the 80/20 rule; the “Influentials” (the 1-2 Americans in 10 who tells the other 8-9 how to vote, where to eat, and what to buy); medical costs; costs of caring for the homeless; polluting cars; RA write-ups; Rx’s by MDs) The behavior of a few, unique individuals: - Darnell “Boss Man” McGee, Nushawn Williams, Gaetan Dugas

The “Stickiness Factor” HIV strains in the 1950s vs. the 1980s Influenza 1918 “A diamond is ______”; “Frosted Flakes, they’re ___!”; “Got M----?”; “BMW, the ultimate driving _______”; Miller Lite: “Less Filling/…”

The “Power of Context” Syphilis cases and weather patterns Kitty Genovese’s death in Queens and the “bystander problem”

Can You Intentionally Trigger a “Tipping Point”? “Levi’s one-hundred-per-cent-cotton Dockers. If you’re not wearing Dockers, you’re just wearing pants.” unique commercials: - no heads seen (invisibility) - set in living rooms and at the office (wide-bandwidth) - all shot in the same style (the “canned laughter” problem)

Can You Intentionally Trigger Another “Tipping Point”? “Nice Pants” advertising campaign Why did it work?