Twist on classic hiring secretary problem. Hire above a threshold, hire above the

1. The Hiring Problem:
Going Beyond Secretaries
Sergei Vassilvitskii (Yahoo!)
Andrei Broder (Yahoo!)
Adam Kirsch (Harvard)
Ravi Kumar (Yahoo!)
Michael Mitzenmacher (Harvard)
Eli Upfal (Brown)

2. The Secretary Problem
Interview n candidates for a position one at a time. After each
interview decide if the candidate is the best.
Goal: maximize the probability of choosing the best candidate.

3. The Secretary Problem
Interview n candidates for a position one at a time. After each
interview decide if the candidate is the best.
Goal: maximize the probability of choosing the best candidate.

4. The Secretary Problem
Interview n candidates for a position one at a time. After each
interview decide if the candidate is the best.
Goal: maximize the probability of choosing the best candidate.

5. The Secretary Problem
Interview n candidates for a position one at a time. After each
interview decide if the candidate is the best.
Goal: maximize the probability of choosing the best candidate.

6. The Secretary Problem
Interview n candidates for a position one at a time. After each
interview decide if the candidate is the best.
Goal: maximize the probability of choosing the best candidate.

7. The Secretary Problem
Interview n candidates for a position one at a time. After each
interview decide if the candidate is the best.
Goal: maximize the probability of choosing the best candidate.

8. The Secretary Problem
Interview n candidates for a position one at a time. After each
interview decide if the candidate is the best.
Goal: maximize the probability of choosing the best candidate.

9. The Secretary Problem
Interview n candidates for a position one at a time. After each
interview decide if the candidate is the best.
Goal: maximize the probability of choosing the best candidate.

10. The Secretary Problem
Interview n candidates for a position one at a time. After each
interview decide if the candidate is the best.
Goal: maximize the probability of choosing the best candidate.

11. The Secretary Problem
Interview n candidates for a position one at a time. After each
interview decide if the candidate is the best.
Goal: maximize the probability of choosing the best candidate.
This is not about hiring secretaries, but about decision
making under uncertainty.

12. The Hiring Problem

13. The Hiring Problem
A startup is growing and is hiring many employees:
Want to hire good employees
Can’t wait for the perfect candidate

14. The Hiring Problem
A startup is growing and is hiring many employees:
Want to hire good employees
Can’t wait for the perfect candidate
Many potential objectives.
Explore the tradeoff between number of interviews & the
average quality.

15. The Hiring model
Candidates arrive one at a time.
Assume all have iid uniform(0,1) quality scores - For
applicant i denote it by iq .
(Can deal with other distributions, not this talk)

16. The Hiring model
Candidates arrive one at a time.
Assume all have iid uniform(0,1) quality scores - For
applicant i denote it by iq .
(Can deal with other distributions, not this talk)
During the interview:
Observe iq
Decide whether to hire or reject

17. Strategies

18. Strategies
Hire above a threshold.

19. Strategies
Hire above a threshold.
Hire above the minimum or maximum.

20. Strategies
Hire above a threshold.
Hire above the minimum or maximum.
Lake Wobegon Strategies:
“Lake Wobegon: where all the women are strong, all the
men are good looking, and all the children are above
average”

21. Strategies
Hire above a threshold.
Hire above the minimum or maximum.
Lake Wobegon Strategies:
Hire above the average (mean or median)

22. Strategies
Hire above a threshold.
Hire above the minimum or maximum.
Lake Wobegon Strategies:
Hire above the average
Side note: [Google Research Blog - March ‘06]:
“... only hire candidates who are above the mean of the
current employees...”

23. Threshold Hiring
Set a threshold t , hire if iq ≥ t .

24. Threshold Hiring
Set a threshold t , hire if iq ≥ t .

25. Threshold Hiring
Set a threshold t , hire if iq ≥ t .
t

26. Threshold Hiring
Set a threshold t , hire if iq ≥ t .
t

27. Threshold Hiring
Set a threshold t , hire if iq ≥ t .
t

28. Threshold Hiring
Set a threshold t , hire if iq ≥ t .
t t

29. Threshold Hiring
Set a threshold t , hire if iq ≥ t .
t t

30. Threshold Hiring
Set a threshold t , hire if iq ≥ t .
t t

31. Threshold Hiring
Set a threshold t , hire if iq ≥ t .
t t t

32. Threshold Hiring
Set a threshold t , hire if iq ≥ t .
t t t

33. Threshold Hiring
Set a threshold t , hire if iq ≥ t .
t t t

34. Threshold Analysis
Set a threshold t , hire if iq ≥ t .
1+t
Easy to see that average quality approaches
1 2
Hiring rate .
1−t

35. Threshold Analysis
Set a threshold t , hire if iq ≥ t .
1+t
Easy to see that average quality approaches
1 2
Hiring rate .
1−t
Quality stagnates and does not increase with time.

36. Maximum Hiring
Hire only if better than everyone already hired.

37. Maximum Hiring
Hire only if better than everyone already hired.

38. Maximum Hiring
Hire only if better than everyone already hired.

39. Maximum Hiring
Hire only if better than everyone already hired.

40. Maximum Hiring
Hire only if better than everyone already hired.

41. Maximum Hiring
Hire only if better than everyone already hired.

42. Maximum Hiring
Hire only if better than everyone already hired.

43. Maximum Hiring
Hire only if better than everyone already hired.

44. Maximum Hiring
Hire only if better than everyone already hired.

45. Maximum Hiring
Hire only if better than everyone already hired.

46. Maximum Hiring
Hire only if better than everyone already hired.

47. Maximum Hiring
Hire only if better than everyone already hired.

48. Maximum Hiring
Hire only if better than everyone already hired.

49. Maximum Hiring
Hire only if better than everyone already hired.

50. Maximum Analysis
Start with employee of quality q
Let hi be the i-th candidate hired

51. Maximum Analysis
Start with employee of quality q
Let hi be the i-th candidate hired
Focus on the gap: gi = 1 − (hi )q gi

52. Maximum Analysis
Start with employee of quality q
Let hi be the i-th candidate hired
Focus on the gap: gi = 1 − (hi )q gi
Conditioned on gn−1 :

53. Maximum Analysis
Start with employee of quality q
Let hi be the i-th candidate hired
Focus on the gap: gi = 1 − (hi )q gi
Conditioned on gn−1 :
gn ∼ U nif (0, gn−1 )

54. Maximum Analysis
Start with employee of quality q
Let hi be the i-th candidate hired
Focus on the gap: gi = 1 − (hi )q gi
Conditioned on gn−1 :
gn ∼ U nif (0, gn−1 )
gn−1
E[gn |gn−1 ] =
2

55. Maximum Analysis
Start with employee of quality q
Let hi be the i-th candidate hired
Focus on the gap: gi = 1 − (hi )q gi
Conditioned on gn−1 :
gn ∼ U nif (0, gn−1 )
gn−1
E[gn |gn−1 ] =
2
1−q
E[gn ] = n
2

56. Maximum Analysis
Start with employee of quality q
Let hi be the i-th candidate hired
Focus on the gap: gi = 1 − (hi )q gi
Conditioned on gn−1 :
gn ∼ U nif (0, gn−1 )
gn−1
E[gn |gn−1 ] =
2
1−q
E[gn ] = n Very high quality!
2

57. Maximum Analysis
Start with employee of quality q
Let hi be the i-th candidate hired
Focus on the gap: gi = 1 − (hi )q gi
Conditioned on gn−1 :
gn ∼ U nif (0, gn−1 )
gn−1
E[gn |gn−1 ] =
2
1−q
E[gn ] = n Extremely slow hiring!
2

58. Lake Wobegon Strategies

59. Lake Wobegon Strategies
Above the mean:
1
Average quality after n hires: 1 − √
n

60. Lake Wobegon Strategies
Above the mean:
1
Average quality after n hires: 1 − √
n
Above the median:
1
Median quality after n hires: 1 −
n

61. Lake Wobegon Strategies
Above the mean:
1
Average quality after n hires: 1 − √
n
Above the median:
1
Median quality after n hires: 1 −
n
Surprising:
Tight concentration is not possible
Hiring above mean converges to a log-normal distribution

62. Hiring Above Mean
Start with employee of quality q
Let hi be the i-th candidate hired
Focus on the gap: gi = 1 − (hi )q gi

63. Hiring Above Mean
Start with employee of quality q
Let hi be the i-th candidate hired
Focus on the gap: gi = 1 − (hi )q gi
Conditioned on gn : (in+1 )q ∼ U nif (1 − gn , 1)

64. Hiring Above Mean
Start with employee of quality q
Let hi be the i-th candidate hired
Focus on the gap: gi = 1 − (hi )q gi
Conditioned on gn : (in+1 )q ∼ U nif (1 − gn , 1)
n+1 1
Therefore: gn+1 ∼ gn + U nif (0, gn )
n+2 n+2

65. Hiring Above Mean
Start with employee of quality q
Let hi be the i-th candidate hired
Focus on the gap: gi = 1 − (hi )q gi
Conditioned on gn : (in+1 )q ∼ U nif (1 − gn , 1)
n+1 1
Therefore: gn+1 ∼ gn + U nif (0, gn )
n+2 n+2
U nif (0, 1)
= gn 1−
n+2

66. Hiring Above Mean
U nif (0, 1)
gn+1 = gn 1−
n+2

67. Hiring Above Mean
U nif (0, 1)
gn+1 = gn 1−
n+2
t
U nif (0, 1)
Expand: gn+t = gn 1−
i=1
n+i+1

68. Hiring Above Mean
U nif (0, 1)
gn+1 = gn 1−
n+2
t
U nif (0, 1)
Expand: gn+t = gn 1−
i=1
n+i+1
Therefore:
n
U nif (0, 1)
gn ∼ (1 − q) 1−
i=1
i+1

69. Hiring Above Mean
U nif (0, 1)
gn+1 = gn 1−
n+2
t
U nif (0, 1)
Expand: gn+t = gn 1−
i=1
n+i+1
Therefore:
n
U nif (0, 1)
gn ∼ (1 − q) 1−
i=1
i+1
n
1 1
E[gn ] = (1 − q) 1− = Θ( √ )
i=1
2(i + 1) n

70. Hiring Above Mean
Conclusion:
1
Average quality after n hires: 1 − Θ( √ )
n
Time to hire n employees: Θ(n3/2 )
Very weak concentration results.

71. Hiring Above Median
Start with employee of quality q
When we have 2k + 1 employees.
Compute median mk of the scores
Hire next 2 applicants with scores above mk

72. Hiring Above Median
mk

73. Hiring Above Median
mk
New hires: with quality above mk

74. Hiring Above Median
mk

75. Hiring Above Median
mk+1

76. Hiring Above Median
mk

77. Hiring Above Median
mk
Inductive hypothesis: U nif (mk , 1)

78. Hiring Above Median
mk
Inductive hypothesis: U nif (mk , 1)
New hires: are U nif (mk , 1)

79. Hiring Above Median
mk
U nif (mk , 1)

80. Hiring Above Median
mk+1
U nif (mk , 1)

81. Hiring Above Median
mk+1
U nif (mk+1 , 1)

82. Hiring Above Median
mk+1
U nif (mk+1 , 1)
Induction holds.

83. Hiring Above Median
mk+1
U nif (mk , 1)

84. Hiring Above Median
mk+1
U nif (mk , 1)
The median:

85. Hiring Above Median
mk+1
U nif (mk , 1)
The median: smallest of k + 1 uniform r.v., each U nif (mk , 1)

86. Hiring Above Median
mk+1
U nif (mk , 1)
The median: smallest of k + 1 uniform r.v., each U nif (0, gk )

87. Hiring Above Median
mk+1
U nif (mk , 1)
The median: smallest of k + 1 uniform r.v., each U nif (0, gk )
gk+1 |gk ∼ gk Beta(k + 2, 1)

88. Hiring Above Median
k
Expand (like the means): gk ∼ g Beta(i + 1, 1)
i=1

89. Hiring Above Median
k
Expand (like the means): gk ∼ g Beta(i + 1, 1)
i=1
1
E[gn ] = Θ( )
n

90. Hiring Above Median
k
Expand (like the means): gk ∼ g Beta(i + 1, 1)
i=1
1
E[gn ] = Θ( )
n
log n
Caveat: this is the median gap! The mean gap is: Θ( )
n

91. Hiring Above Median
Conclusion:
log n
Average quality after n hires: 1 − Θ( )
n
Time to hire n employees: Θ(n2 )
Again, very weak concentration results.

92. Extensions
Interview preprocessing:
Self selection: quality may increase over time
Errors:
Noisy estimates on iq scores.
Firing:
Periodically fire bottom 10% (Jack Welch Strategy)
Similar analysis to the median.
Many more...

93. Thank You