LIS MBA Shift2 Crit2

1 collaborator

Mike Sheerin (Author)

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Click to Run Model

breed [candidates candidate]
breed [employers employer]
breed [jobs job]

globals [
  avg_match_quality
  transition_success_rate
  false_negatives
]

candidates-own [
  true_capability
  narrative_strength
  evidence_strength
  career_state
  selected?
]

employers-own [
  narrative_weight
  evidence_weight
]

jobs-own [
  required_capability
  filled?
  chosen_capability
  chosen_state
]

to setup
  clear-all

  create-candidates num-candidates [
    setxy random-xcor random-ycor
    set shape "person"
    set selected? false
    set true_capability random 101

    ifelse random-float 1 < transition-share
    [
      set career_state "transition"
      set color orange

      ; 🔴 Strong distortion: narrative severely underestimates capability
      set narrative_strength bounded-score
        (true_capability - 50 + centered-random (noise-level * 3))

      ; 🟢 Evidence remains close to true capability
      set evidence_strength bounded-score
        (true_capability + centered-random (noise-level / 2))
    ]
    [
      set career_state "standard"
      set color blue

      ; narrative slightly inflated but noisy
      set narrative_strength bounded-score
        (true_capability + 10 + centered-random (noise-level * 2))

      ; evidence still cleaner
      set evidence_strength bounded-score
        (true_capability + centered-random (noise-level / 2))
    ]
  ]

  create-employers num-employers [
    setxy random-xcor random-ycor
    set shape "square"
    set color red

    if market-mode = "narrative-first" [
      set narrative_weight 0.9
      set evidence_weight 0.1
    ]

    if market-mode = "evidence-first" [
      set narrative_weight 0.2
      set evidence_weight 0.8
    ]
  ]

  create-jobs num-jobs [
    setxy random-xcor random-ycor
    set shape "circle"
    set color green
    set required_capability random 101
    set filled? false
    set chosen_capability -1
    set chosen_state ""
  ]

  calculate_metrics
  reset-ticks
end 

to go
  ask candidates [
    set selected? false
  ]

  ask jobs [
    set filled? false
    set chosen_capability -1
    set chosen_state ""
  ]

  ask jobs [
    let hiring_employer one-of employers

    let ranked_candidates sort-by
      [[a b] -> candidate_score a hiring_employer > candidate_score b hiring_employer]
      candidates

    let winner first ranked_candidates

    set filled? true
    set chosen_capability [true_capability] of winner
    set chosen_state [career_state] of winner

    ask winner [
      set selected? true
    ]
  ]

  calculate_metrics
  tick
end 

to-report candidate_score [cand emp]
  let narrative_score [narrative_strength] of cand
  let evidence_score [evidence_strength] of cand

  report (([narrative_weight] of emp * narrative_score) +
          ([evidence_weight] of emp * evidence_score))
end 

to calculate_metrics
  let filled_jobs jobs with [filled?]

  ifelse any? filled_jobs
  [
    set avg_match_quality
      mean [100 - abs(required_capability - chosen_capability)] of filled_jobs
  ]
  [
    set avg_match_quality 0
  ]

  let transition_candidates candidates with [career_state = "transition"]

  ifelse any? transition_candidates
  [
    set transition_success_rate
      (count transition_candidates with [selected?] / count transition_candidates)
  ]
  [
    set transition_success_rate 0
  ]

  let strong_transition_candidates candidates with
    [career_state = "transition" and true_capability > 70]

  ifelse any? strong_transition_candidates
  [
    set false_negatives
      count strong_transition_candidates with [not selected?]
  ]
  [
    set false_negatives 0
  ]
end 

to-report bounded-score [raw_score]
  report max list 0 min list 100 raw_score
end 

to-report centered-random [spread]
  report (random-float spread) - (spread / 2)
end

There is only one version of this model, created about 1 month ago by Mike Sheerin.

Attached files

File	Type	Description	Last updated
LIS MBA Shift2 Crit2.png	preview	Preview for 'LIS MBA Shift2 Crit2'	about 1 month ago, by Mike Sheerin	Download

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