Claude-Thesis-v1

breed [candidates candidate]
breed [employers employer]

candidates-own [
  skill-score
  narrative-score
  dossier-score
  employed?
  my-employer
  ai-assisted?
]

employers-own [
  hiring-regime
  jd-complexity
  workforce-quality
  vacancies
  bad-hire-count
  hire-count
]

globals [
  market-signal-gap
  narrative-firm-quality
  evidence-firm-quality
]

to setup
  clear-all

  create-candidates num-candidates [
    set shape "person"
    set skill-score     clamp (random-normal 0.5 0.18) 0 1
    set narrative-score clamp (
      skill-score * narrative-skill-correlation
      + random-normal 0 0.2
    ) 0 1
    set dossier-score   clamp (skill-score + random-normal 0 0.05) 0 1
    set employed?       false
    set my-employer     nobody
    set ai-assisted?    (random-float 1.0 < pct-ai-candidates / 100)
    set color           scale-color green skill-score 0 1
    setxy random-xcor random-ycor
  ]

  create-employers num-employers [
    set shape "house"
    set size  2
    set color gray
    set hiring-regime   random-float 1.0
    set jd-complexity   0.3
    set workforce-quality 0.5
    set vacancies       2 + random 4
    setxy random-xcor random-ycor
  ]

  reset-ticks
end 

to-report clamp [val low high]
  report max (list low (min (list high val)))
end 

to go
  if ticks >= max-ticks [ stop ]

  candidate-ai-inflate
  employer-ai-adapt
  run-hiring-round
  update-job-performance
  update-hiring-regime
  labour-market-churn
  update-globals

  tick
end 

to candidate-ai-inflate
  ask candidates with [not employed?] [
    if ai-assisted? [
      let inflation ai-inflation-rate * (1 - narrative-score) * random-float 1.0
      set narrative-score clamp (narrative-score + inflation) 0 1
    ]
    set narrative-score clamp (narrative-score + 0.002 * random-float 1.0) 0 1
    set color scale-color green skill-score 0 1
  ]
end 

to employer-ai-adapt
  let market-narrative mean [narrative-score] of candidates
  ask employers [
    let gap market-narrative - 0.5
    set jd-complexity clamp (jd-complexity + 0.01 * gap) 0.1 1.0
  ]
end 

to run-hiring-round
  ask employers [
    let open-slots vacancies - count candidates with [my-employer = myself]
    if open-slots > 0 [
      let pool candidates with [not employed?]
      if any? pool [
        let scored map [ c ->
          list ((selection-score c) + random-float 0.0001) c
        ] sort pool
        let ranked sort-by [[a b] -> first a > first b] scored
        let n min (list open-slots length ranked)
        foreach (sublist ranked 0 n) [ pair ->
          let c last pair
          ask c [
            set employed?   true
            set my-employer myself
          ]
          set hire-count hire-count + 1
        ]
      ]
    ]
  ]
end 

to-report selection-score [c]
  let narrative-signal [narrative-score] of c - (jd-complexity * 0.3)
  let evidence-signal  [dossier-score] of c
  report (hiring-regime * evidence-signal)
       + ((1 - hiring-regime) * narrative-signal)
       + random-normal 0 0.03
end 

to update-job-performance
  ask employers [
    let staff candidates with [my-employer = myself and employed?]
    if any? staff [
      set workforce-quality mean [skill-score] of staff
    ]
  ]
end 

to update-hiring-regime
  ask employers [
    let quality-gap workforce-quality - 0.5
    set hiring-regime clamp (hiring-regime + 0.02 * quality-gap) 0 1
    let neighbours employers in-radius 5
    if any? neighbours [
      let best max-one-of neighbours [workforce-quality]
      if [workforce-quality] of best > workforce-quality + 0.05 [
        set hiring-regime [hiring-regime] of best
      ]
    ]
    set color scale-color blue hiring-regime 0 1
  ]
end 

to labour-market-churn
  ask candidates with [employed?] [
    if random-float 1.0 < 0.02 [
      set employed?   false
      set my-employer nobody
    ]
  ]
  create-candidates churn-rate [
    set shape "person"
    set skill-score     clamp (random-normal 0.5 0.18) 0 1
    set narrative-score clamp (skill-score * narrative-skill-correlation
                               + random-normal 0 0.2) 0 1
    set dossier-score   clamp (skill-score + random-normal 0 0.05) 0 1
    set employed?       false
    set my-employer     nobody
    set ai-assisted?    (random-float 1.0 < pct-ai-candidates / 100)
    set color           scale-color green skill-score 0 1
    setxy random-xcor random-ycor
  ]
end 

to update-globals
  set market-signal-gap
    mean [narrative-score] of candidates - mean [skill-score] of candidates

  let n-firms employers with [hiring-regime < 0.4]
  let e-firms employers with [hiring-regime > 0.6]
  if any? n-firms [
    set narrative-firm-quality mean [workforce-quality] of n-firms ]
  if any? e-firms [
    set evidence-firm-quality mean [workforce-quality] of e-firms ]
end