Great Leap ‘Forward’ wishful thinking, poor incentives, hungry

Table 1.3: How to cripple an agricultural economy: Statistics during China’s “Great Leap Forward”

Start (1957)End (1959)

Forecasted output 195 MM tons 525 MM tons (1958)

Grain output 195 MM tons 170 MM tons

Grain imports 2.1 MM tons 3.9 MM tons

Grain procured from communes 46 MM tons 64 MM tons

Grain kept by rural areas 273 kg/capita 193 kg/capita

Rural per-capita calorie consumption

2100 calories/day 1500 calories/day (1960)

% of provinces where workers can not exit communes

20% (1955) 60% (1957)

Centralization, I

Table 1.2: Failure rates in public and private expeditions to the North Pole and Northwest Passage (Karpoff, 2001)

Public (n=35)Private (n=56)

Crew deaths (%) 8.0 6.2

Number of ships (number lost) 1.63 (.53) 1.15 (.24)

% with scurvy 46.7% 13.2%

Unintended consequences

• ...A law enforcement source in Chicago said police see some evidence of soldiers working with gangs here. Police recently stopped a vehicle and found 10 military flak jackets inside. A gang member in the vehicle told investigators his brother was a Marine and sent the jackets home, the source said. (from Sun-Times)

• "We're lowering our standards," [Defense Department gang detective Scott] Barfield said. • "A friend of mine is a recruiter," he said. "They are being told less than five tattoos is not an

issue. More than five, you do a waiver saying it's not gang-related. You'll see soldiers with a six-pointed star with GD [Gangster Disciples] on the right forearm."[....]

• Of particular concern are reports that the Folk Nation, consisting of more than a dozen gangs in the Chicago area, is placing young members in the military in an effort to gather information about weapons and tactics, said FBI Special Agent Andrea Simmons, who is based in El Paso, Texas.

• "Our understanding is that they find members without a criminal history so that they can join, and once they get out, they will have a new set of skills that they can apply to criminal enterprises," Simmons said. "This could be a concern for any law enforcement agency that has to deal with gangs on a daily basis.“

• According to the Tribune, “nearly every one of the cases that we have looked into, it is a young man or woman who thought that the symbol looked cool," said Christopher Grey, spokesman for the Army's Criminal Investigation Command. "We have found some people even get gang tattoos not really knowing what they are, or at least that they have not had any gang affiliation the past."

BEM 146: Some simple games

• Cognitive hierarchy approach– Iterative– easy to compute– Captures individual differences– Explains when Nash succeeds and fails

• Nash equilibrium– Players’ maximize, beliefs are accurate (no surprise

when results are announced)– End of a learning process

• Quantal response equilibrium (Palfrey, Goeree)– Nash+stochastic choice

The thinking steps model• Discrete steps of thinking• Step 0’s choose randomly

K-step thinkers know proportions f(0),...f(K-1)*

Normalize g(h)=f(h)/ h=0K-1 f(h) and best-respond

A j(K)=m (sj,sm) (Pm(0) g(0) + Pm(1) g(1)+... Pm(K-1)g(K-1))

logit probability P j(K)=exp(Aj(K))/ hexp(Ah(K))

• What is the distribution of thinking steps f(K)?

*alternative: K-steps think others are one step lower (K-1)

Poisson distribution of thinking steps• Working memory bound f(k)/f(k-1)1/k f(K)=K/eK! 84 games: median • Heterogeneous (“spikes” in data)• Steps > 3 are rare (Keynes, Binmore, Stahl et al)• Steps can be linked to cognitive measures

Poisson distributions for various

00.05

0.10.15

0.20.25

0.30.35

0.4

0 1 2 3 4 5 6

number of steps

fre

qu

en

cy

Dominance-solvable game (Nash, 1,1; CH .73,1)

COLUMN

Left(.95) Right

Top(.86) 30, 20 10, 18

ROW

Bottom 20, 20 20, 18

Dominance-solvable game results

L R Nash CH Data 06

Data 07

T 30, 20 10, 18 1.00 .73 .81 .86

B 20, 20 20, 18 .00 .27 .19 .14

Nash 1.00 .00

CH .89 .11

Data 06 .95 .05

Data 07 .95 .05

Keynes’s “beauty contest analogy”

• Professional investment may be likened to those newspaper competitions in which the competitors have to pick out the six prettiest faces from a hundred photographs, the prize being awarded to the competitor whose choice most nearly corresponds to the average preferences of the competitors as a whole; so that each competitor has to pick, not those faces which he himself finds prettiest, but those which he thinks likeliest to catch the fancy of the other competitors, all of whom are looking at the problem from the same point of view. It is not a case of choosing those which, to the best of one's judgment, are really the prettiest, nor even those which average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practise the fourth, fifth and higher degrees. (Keynes, GTMEI)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

1 9

17

25

33

41

49

57

65

73

81

89

97

number choices

pre

dic

ted

fre

qu

en

cy

Beauty contest results (Expansion, Financial Times, Spektrum)

0.00

0.05

0.10

0.15

0.20

numbers

rela

tive

fr

eq

ue

nci

es

22 50 10033

average 23.07

0

(2/3) of average (BEM 07 ave.28)

Table: Data and estimates of in pbc games

(equilibrium = 0)    

    data steps of

subjects/game mean std dev thinking

game theorists 19 21.8 3.7

Caltech 23 11.1 3.0

newspaper 23 20.2 3.0

portfolio mgrs 24 16.1 2.8

econ PhD class 27 18.7 2.3

Caltech g=3 22 25.7 1.8

high school 33 18.6 1.6

1/2 mean 27 19.9 1.5

70 yr olds 37 17.5 1.1

Germany 37 20.0 1.1

CEOs 38 18.8 1.0

game p=0.7 39 24.7 1.0

Caltech g=2 22 29.9 0.8

PCC g=3 48 29.0 0.1

game p=0.9 49 24.3 0.1

PCC g=2 54 29.2 0.0

mean 1.56

median 1.30

CDF of bids for $10 with n=2 bidders (bid2) and n=5 (bid5)

Nash: Bid $10 CH: 1-step bid $5 (n=2), $8 (n=5)

Thinking steps in entry games

• Entry games:

Enter or stay out ($.50)

Prefer to enter if n(entrants)<c (earn $1);

not enter n(entrants)>c (earn 0)

All choose simultaneously• Experimental regularity in the 1st period:

Close to equilibrium prediction n(entrants) ≈c

“To a psychologist, it looks like magic”-- D. Kahneman ‘88

Thinking steps in entry games

How entry varies with capacity (c) , experimental data and thinking model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

capacity

% e

ntr

y entry=capacity

experimental data

0-Step and 1-Step Entry

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Percentage Capacity

Pe

rce

nta

ge

En

try

Capacity

0-Level

1-Level`

0-Step and 1-Step Entry

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Percentage Capacity

Pe

rce

nta

ge

En

try

Capacity

0+1 Level`

0-Step + 1-Step + 2 Step Entry

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Percentage Capacity

Pe

rce

nta

ge

En

try

Capacity

0+1 Level

2-Level`

0-Step + 1-Step + 2 Step Entry

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Percentage Capacity

Pe

rce

nta

ge

En

try

Capacity

0+1+2 Level`

BOS: Canonical mixed-motive game

COLUMN

ML MH

FL 0, 0 10, 30

FH 30, 10 0, 0

Results 07 (male-female)

COLUMN

ML MH (.56)

FL 0, 0 10, 30

FH (.50) 30, 10 0, 0

Matching pennies (Nash .5, .33; CH .28, .55)

COLUMN

Left (.43) Right (.57)

Top (.63) 0, 10 10, 0

ROW

Bottom (.37) 20, 0 0, 10

Private information: Hidden information & hidden action

• Hidden information (“adverse selection”)– Cannot measure pre-contract information – E.g.: Acquire-a-company problem– Betting game (Groucho Marx theorem)– Coin auction– Insurance market failure– movie “cold opening”

• How to overcome?– Measure – Exclude (insurance)– Screening or signaling– Efficient? (e.g. jockeys)

Betting game & Groucho Marx theorem

STATEA B C D

1's payoffs +32 -28 +20 -162's payoffs -32 +28 -20 +16

1 learns (A,B) or (C,D)2 learns (A), (B,C) or (D)Should they bet?

• Figure ?: Professor Rafael Robb: Guilty of hubris and murder or neither?

• A possibly ironic touch comes at the very end of the AP story (“Penn Professor Charged in Wife’s Slaying”, jan 8, 2007):

• Penn officials said earlier that they had arranged for someone else to teach Robb's graduate seminar in game theory this semester.

Insurance company exclusions• Air traffic control

Building, movingChemical/rubber manufacturingCircus or carnival workConcrete or asphalt workCrop dustingFirefightingFurniture and fixtures manufacturingLumber work, including wood chopping, timber cutting and working in a sawmillMigrant laborOil well or refinery workPolice workRoofingSandblastingSports, semi-pro or professionalStockyard work, with or without butcheringStables, all employeesStunt workTelecom installationTransportation and aviationTree climbingTunnel workWar reportingWindow work at heights exceeding three stories

• Lipitor (cholesterol)Zocor (cholesterol)Nexium (heartburn, ulcers)Prevacid (heartburn, ulcers)Advair (asthma)Zoloft (depression)Singulair (asthma)Protonix (heartburn, ulcers)

• Hidden action (“moral hazard”)– Cannot enforce choice of post-contract action– E.g., trust games– Air traffic control

Moral hazard in air traffic control?

• 2nd Career program changes in ’74

• 100% of pay if injury is “disabling” + 2 yrs job training

• Could choose own MD or psychologist

• Increase in “system errors” (<3mi or 1000 vertical ft)

• No increase in near misses

Table ? : Increases in diagnoses after rule changes in “Second Career” program for air traffic controllers (Staten and Umbeck, 1982)

DisorderPre-Second

Career Program Change incidence

Post-Second Career

Program Change incidence

% change

Respiratory 1.9 1.5 -21

Muscles .5 .4 -20

Ear,nose,throat 6.7 8.0 +19

Abdominal 16.7 20.4 +22

Eye 5.6 7.4 +32

Bones and joints 2.3 4.6 +39

Cardiovascular 22.1 32.5 +47

Neuropsychiatric 10.9 27.2 +150

How to avoid hidden info & action? • Monitoring

– WaWa stores, undercover retail checkers

• Reputations– Internet! (Ebay, dontdatehimgirl.com)

• 3rd party assurance (“social collateral”)• Honor code! (Caltech)• Make moral people

– Socialization?– Early-childhood nutrition (Adrian Raine Mauritius

study) reduces ASPD?

  PROFILE CITY VIEWS

1.

             Velazquez, Joel "Slim, patches," 

NEW added: Thursday, 1:47:00 PM

killeen , 0

2.

             Beecroft, Bart "bbuzz68" 

NEW added: Thursday, 1:27:00 PM

Long Beach State, Texas A&M, 11

3.              Ross, Jonathan "Johnny, JR, Scooby" 

NEW added: Thursday, 1:19:00 PM

Miamisburg, USA 33

4.

             Harris, Marion "Tyke" 

NEW added: Thursday, 1:06:00 PM

Petersburg, 23

5.

             Green, James "MIKE" "Mike, Whitechocotate" 

NEW added: Thursday, 12:50:00 PM

Virginia Beach, Indianapolis, 31

displaying 1 to 5 of 18581 of alleged cheaters

Theories of human nature• Crucial for organizational design

• Are people good & need opportunity (Theory Y) or bad & need constraint (theory X)? (a la Maslow hierarchy)

• Models:– States vs traits (sorting good from bad)– Differences in social preferences

• “self-interest seeking with guile” (opportunism) as limiting case (or ASPD?)

– Social image & moral wriggle room

States vs traits• Behavior due to situational “states” or personal

(immutable) “traits”? • Attribution theory (Kelley, Nisbett-Ross):

– (Western) tendency to overestimate effect of global traits & ability to do trait inference (e.g. interviews)

– E.g. question-answer study – E.g. Asian vs Caucasian “brains vs work” in

educational success– Self-serving tendency to blame state for bad outcome,

claim trait credit for good outcome (annual reports, oil company executive pay)

Social preferences

• Will sacrifice money to help/hurt others• Dictator game• Ultimatum game• One view (inequality-aversion):

– Dislike envy & guilt– Prefer equal shares

• Another view– Rawlsitarian (like $, minimum, total)

• Best guess?: 40% selfish, 50% conditional cooperators, 10% “saints”

The right view of human nature? (from Dawes and Thaler 1988)

Ultimatum vs dictator “games” (Forsythe et al 1994) NB: Dictator games are “weak situations”, more variance

This is your brain on unfairness: Areas that are differentially active facing unfair offers (1-2) versus fair offers (4-5)

(Sanfey et al 04 Science)

Ultimatum offer experimental sites

Ultimatum offers across societies (mean shaded, mode is largest circle…)

A subject complaining post-experiment (Zamir, 2000)

Social image• People do not care directly about others• People care about how others perceive them

(2nd-order belief)– Few large anonymous donations– Choose $9 or Play $10 dictator game Information avoidance

• Dana-Weber-Kuang/Feiler studies• High-risk people avoiding an AIDS test• Cross the street to avoid a homeless person• “Plausible deniability”• HP Chief Ethics Officer (?): “I shouldn’t have asked”