BAMERS.nlogo

; Bottom-up Adaptive Macroeconomics

extensions [palette array] ; arrays are used to enhance performance.

breed[firms firm]          ; the firms in the simulation, 500 by default.
breed[workers worker]      ; the workers or households, 5 * number of firms by default.
breed[banks bank]          ; the banks, max of credit-market-h + 1 and number of firms / 10 by default.

globals [
  quarters-average-price   ; an array storing the average price for the last 4 quarters.
  quarters-inflation       ; an array storing the inflation for the last 4 quarters.
  confiscated-money
  gini-index-reserve
  lorenz-points
  ; current stats recorded of incumbent firms used to replace bankrupt ones
  new-firms-production-Y
  new-firms-minimum-wage-W-hat
  new-firms-wage-offered-Wb
  new-firms-net-worth-A
  new-firms-individual-price-P
]

firms-own[
  production-Y                         ; labor * labor productiviy
  labor-productivity-alpha             ; labor productivity > 0
  desired-production-Yd
  expected-demand-De
  desired-labor-force-Ld               ; desired production / labor productivity
  my-employees                         ; an agent set of current employees.
  current-numbers-employees-L0         ; number of current employees.
  number-of-vacancies-offered-V        ; max of desired labor force - current labor force and 0.
  minimum-wage-W-hat                   ; mandatory minimum wage set by law
  wage-offered-Wb                      ; contractual wage: if no vacancies the max of previous wage
                                       ; and minimum wage; otherwise previous wage is also pondered
                                       ; by an idiosyncratic shock, e.g., 1 + wage-shock-xi
  net-worth-A                          ; previous net worth + profits - dividends (profits * 1 - profits-delta)
  total-payroll-W                      ; the desired wage bill
  loan-B                               ; max of desired wage bill - net worth and 0
  my-potential-banks                   ; credit-market-H < number of banks (less than firms/10)
  my-bank                              ; the current bank of the firm
  my-interest-rate
  amount-of-Interest-to-pay
  inventory-S                          ; inventory of unsold goods
  individual-price-P
  revenue-R
  gross-profits
  net-profits
  retained-profits-pi
  ; for visual representation
  x-position
  y-position
  ; extortion variables
  being-extorted?
  being-punished?
  amount-of-pizzo
  amount-of-punish
]

workers-own[
  employed?                   ; a boolean, true iff the agent is employed.
  my-potential-firms
  my-firm                     ; the current firm if employed.
  contract
  my-wage                     ; current wage
  income
  savings
  wealth
  propensity-to-consume-c
  my-stores
  my-large-store
  ; extortion variables
  extorter?
  firms-to-extort
  firms-to-punish
  in-jail?
  time-in-jail
]

banks-own[
  ;  Repayment schedule (eq. 3.6, p. 53) is represented in firms-pay procedure as in p.50:
  ; "If gross profits are high enough, they “validate” debt commitments, i.e. firms pay back both the principal and the interest to the bank"
  ; r-bar (instead r-hat) (eq. 3.7, p. 53) is represented as a reporter (interest-rate-policy-rbar)
  total-amount-of-credit-C    ; a multiple of its equity base in terms of capital req. coeff (buy default v= 0.23).
  patrimonial-base-E
  operational-interest-rate
  my-borrowing-firms
  interest-rate-r
  bad-debt-BD
  bankrupt?
]

; Setup procedures
to setup
  clear-all
  ;reset-timer

  start-firms number-of-firms
  start-workers round (number-of-firms * 5)
  start-banks max (list (credit-market-H + 1) round (number-of-firms / 10))
  initialize-variables

  reset-ticks
end

to initialize-variables
  ask firms [
    set production-Y 1
    set labor-productivity-alpha 1
    set desired-production-Yd 0
    set expected-demand-De 1
    set desired-labor-force-Ld 0
    set my-employees no-turtles
    set current-numbers-employees-L0 0
    set number-of-vacancies-offered-V 0
    set minimum-wage-W-hat 1
    set wage-offered-Wb minimum-wage-W-hat
    set net-worth-A 10
    set total-payroll-W 0
    set loan-B 0
    set my-potential-banks no-turtles
    set my-bank no-turtles
    set inventory-S 0
    set individual-price-P base-price
    set revenue-R 0
    set retained-profits-pi 0
    set being-extorted? false
    set being-punished? false
  ]
  ask workers [
    set employed? false
    set my-potential-firms no-turtles
    set my-firm nobody
    set contract 0
    set income 0
    set savings 1 + random-poisson base-savings
    set wealth 0
    set propensity-to-consume-c 1
    set my-stores no-turtles
    set my-large-store no-turtles
    set extorter? false
    set firms-to-extort no-turtles
    set firms-to-punish no-turtles
    set in-jail? false
    set time-in-jail 0
  ]
  ask banks [
    set total-amount-of-credit-C 0
    set patrimonial-base-E random-poisson 10000 + 10
    set operational-interest-rate 0
    set interest-rate-r 0
    set my-borrowing-firms no-turtles
    set bankrupt? false
  ]
  set quarters-average-price array:from-list n-values (ifelse-value (quarterly-time-scale?)[4][12]) [base-price]
  set quarters-inflation array:from-list n-values (ifelse-value (quarterly-time-scale?)[4][12]) [0]
end

to start-firms [#firms]
  create-firms #firms [
    set x-position random-pxcor * 0.9
    set y-position random-pycor * 0.9
    setxy x-position y-position
    set color blue
    set size 1.2
    set shape "factory"
  ]

  ask firms [
    move-to one-of patches with [not any? firms-here ]
  ]
end

to start-workers [#workers]
  create-workers #workers [
    setxy random-pxcor random-pycor
    set color yellow
    set size 1 / log number-of-firms 10
    set shape "person"
  ]
end

to start-banks [#banks]
  create-banks #banks[
    setxy random-pxcor * 0.9 random-pycor * 0.9
    set color red
    set size 1.5
    set shape "house"
  ]
end

to go
  ;if (ticks mod 100 = 0)[
  ;  export-view (word "view_" (ticks / 100) ".png")
  ;  export-interface (word "inte_" (ticks / 100) ".png")
  ;]
  if (ticks >= 1000
    ;or (ticks > 600 and fn-unemployment-rate > 0.5)
    ;or (ticks > 600 and abs annualized-inflation > 0.25)
  ) [stop]
  ; Process overview and scheduling
  firms-calculate-production
  labor-market
  credit-market
  firms-produce
  goods-market
  extortion
  firms-pay
  firms-banks-survive
  replace-bankrupt

  update-lorenz-and-gini
  tick
end

;;;;;;;;;; to firms-calculate production ;;;;;;;;;;
to firms-calculate-production
  adapt-expected-demand-or-price
  ask firms [
    set desired-production-Yd expected-demand-De; submodel 2
  ]
  array:set quarters-average-price (ticks mod (ifelse-value (quarterly-time-scale?)[4][12])) mean [individual-price-P] of firms
  let actual-price array:item quarters-average-price (ticks mod (ifelse-value (quarterly-time-scale?)[4][12]))
  let previous-price array:item quarters-average-price ((ticks - 1) mod (ifelse-value (quarterly-time-scale?)[4][12]))
  let quarter-inflation ((actual-price - previous-price) / ifelse-value previous-price = 0 [0.00001][previous-price]) * 100    ; with safe division
  array:set quarters-inflation (ticks mod (ifelse-value (quarterly-time-scale?)[4][12])) quarter-inflation
end

to adapt-expected-demand-or-price
  let avg-market-price average-market-price
  ask firms [
    let minimum-price-Pl ifelse-value (production-Y > 0)[( total-payroll-W + amount-of-Interest-to-pay ) / production-Y] [avg-market-price]
    (ifelse
      (inventory-S = 0 and individual-price-P >= avg-market-price and production-Y > 0)
        [ set expected-demand-De max (list 1 ceiling (production-Y * (1 + production-shock-rho)))]
      (inventory-S > 0 and individual-price-P < avg-market-price)
        [ set expected-demand-De max (list 1 ceiling (production-Y * (1 - production-shock-rho)))]
      (inventory-S = 0 and individual-price-P <  avg-market-price)
        [ set individual-price-P max(list minimum-price-Pl (individual-price-P * (1 + price-shock-eta)))]
      (inventory-S > 0 and individual-price-P >= avg-market-price)
        [ set individual-price-P max(list minimum-price-Pl (individual-price-P * (1 - price-shock-eta)))]
        [ show (word "No selected strategies ")]
    )
  ]
end

;;;;;;;;;; to labor-market  ;;;;;;;;;;
to labor-market
  let law-minimum-wage ifelse-value (ticks > 0 and ticks mod (ifelse-value (quarterly-time-scale?)[4][12]) = 0 )[fn-minimum-wage-W-hat][[minimum-wage-W-hat] of firms]
  ask firms [
    set desired-labor-force-Ld ceiling (desired-production-Yd / ifelse-value labor-productivity-alpha = 0 [0.00001][labor-productivity-alpha]); submodel 3
    set current-numbers-employees-L0 count my-employees; summodel 4
    set number-of-vacancies-offered-V max(list (desired-labor-force-Ld - current-numbers-employees-L0) 0 ); submodel 5
    if (ticks > 0 and ticks mod (ifelse-value (quarterly-time-scale?)[4][12]) = 0 )
    [
      set minimum-wage-W-hat law-minimum-wage; submodel 6
    ]
    ifelse (number-of-vacancies-offered-V = 0)
    [
      set wage-offered-Wb max(list minimum-wage-W-hat wage-offered-Wb); submodel 7
    ]
    [
      set wage-offered-Wb max(list minimum-wage-W-hat (wage-offered-Wb * (1 + wages-shock-xi))); submodels 8 and 9
    ]
  ]
  labor-market-opens
end

to labor-market-opens
  if (sum [number-of-vacancies-offered-V] of firms > 0) [
    let potential-firms firms with [number-of-vacancies-offered-V > 0]
    ask workers with [not employed? and not extorter? and not in-jail?][
      ifelse (not empty? [my-firm] of my-potential-firms)
      [set my-potential-firms (turtle-set my-firm n-of (labor-market-M - 1 ) potential-firms)]
      [set my-potential-firms n-of (min (list labor-market-M count potential-firms)) potential-firms]
    ]
  ]

  hiring-step labor-market-M
  ask workers with [not employed?][
    set my-wage 0
    set income 0
    rt random 360
    fd (random 4) + 1
  ]
  ask firms [
    set label count my-employees
    set color palette:scale-gradient [[68 1 84] [33 144 140] [253 231 37]] net-worth-A 0 max [net-worth-A] of firms
  ]
end

to hiring-step [trials]
  while [trials > 0]
  [
    ask workers with [not employed? and any? my-potential-firms and not in-jail?][
      move-to max-one-of my-potential-firms [wage-offered-Wb]
    ]
    ask firms with [number-of-vacancies-offered-V > 0 ][
      let potential-workers workers-here with [not employed?]
      let quantity count potential-workers
      let workers-hired n-of (min list quantity number-of-vacancies-offered-V) potential-workers
      let wage-employees wage-offered-Wb
      set my-employees (turtle-set my-employees workers-hired)
      set number-of-vacancies-offered-V number-of-vacancies-offered-V - count workers-hired
      set total-payroll-W total-payroll-W + (count workers-hired * wage-offered-Wb)
      ask my-employees with [not employed?] [
        set color green
        set employed? true
        set my-wage wage-employees
        set contract 8 + random-poisson 10
        set my-firm firms-here
        set my-potential-firms no-turtles
      ]
    ]
    set trials trials - 1
    ask workers with [not employed? and any? my-potential-firms][
      set my-potential-firms min-n-of min (list trials count my-potential-firms) my-potential-firms [wage-offered-Wb]
    ]
  ]
end
;;;;;;;;;; to credit-market  ;;;;;;;;;;
to credit-market; observer-procedure
  ask banks [
    set total-amount-of-credit-C patrimonial-base-E / v; submodel 12
    set operational-interest-rate random-float interest-shock-phi; part of submodel 14
  ]

  ask firms with [production-Y > 0][
    if (total-payroll-W > net-worth-A)[
      let leverage desired-production-Yd * wage-offered-Wb
      set loan-B max (list (leverage - net-worth-A) 0); submodel 10
      if (loan-B > 0)[
        set shape "person business"
      ]
    ]
  ]
  credit-market-opens
  firing-step
end

to credit-market-opens; observer procedure
  ask firms with [loan-B > 0][
    set my-potential-banks n-of credit-market-H banks
  ]
  borrowing-step credit-market-H
end

to borrowing-step [trials]; observer procedure
  while [trials > 0][ ; submodel 20
    ask firms with [loan-B > 0][
      move-to min-one-of my-potential-banks [operational-interest-rate]
    ]
    ask banks [
      set my-borrowing-firms firms-here
      if any? my-borrowing-firms
      [
        lending-step my-borrowing-firms
      ]
    ]
    set trials trials - 1
  ]
end

to lending-step [#borrowing-firms]; banks procedure
  while [any? #borrowing-firms and total-amount-of-credit-C > 0][
    let my-best-borrower max-one-of #borrowing-firms [net-worth-A]
    let networth max (list [net-worth-A] of my-best-borrower 1)
    let leverage-of-borrower [loan-B] of my-best-borrower / networth; submodel 19
    ;submodel 17
    let contractual-interest interest-rate-policy-rbar * (1 + ([operational-interest-rate] of self * leverage-of-borrower));
    let the-lender-bank self
    let loan min (list [loan-B] of my-best-borrower total-amount-of-credit-C); part of submodel 14

    ask my-best-borrower [
      set my-bank the-lender-bank
      set my-interest-rate contractual-interest
      set amount-of-Interest-to-pay loan * ( 1 + contractual-interest )
      set net-worth-A net-worth-A + loan
      set loan-B loan-B - loan
      setxy [x-position] of my-best-borrower [y-position] of my-best-borrower
      set shape "factory"
    ]

    let count-borrowers count #borrowing-firms
    set #borrowing-firms min-n-of (count-borrowers - 1) #borrowing-firms [net-worth-A]
  ]
end

to firing-step
  ask firms with [loan-B > 0][
    while [total-payroll-W  > net-worth-A and count my-employees > 1][
      let expensive-worker max-one-of my-employees [my-wage]
      set my-employees min-n-of (count my-employees - 1) my-employees [my-wage]
      set total-payroll-W total-payroll-W - [my-wage] of expensive-worker
      ask expensive-worker[
        set color yellow
        set employed? false
        set my-wage 0
        set income 0
        set contract 0
        set my-firm no-turtles
        rt random 360
        fd (random 4) + 1
      ]
    ]
  ]
end

;;;;;;;;;; to firms-produce  ;;;;;;;;;;
to firms-produce
  ask firms [
    set production-Y labor-productivity-alpha * count my-employees; submodel 1
    set inventory-S production-Y * individual-price-P
    set net-worth-A net-worth-A - total-payroll-W
  ]

  ask workers with [employed?][
    set income my-wage
    set contract contract - 1
    if (contract = 0)[
      set employed? false
      set color yellow
      set my-wage 0
      rt random 360
      fd (random 4) + 1
    ]
  ]

  ; firing employees with expired contract
  ask firms [
    set my-employees my-employees with [contract > 0]
  ]
end

;;;;;;;;;; to goods-market  ;;;;;;;;;;
to goods-market ;; an observer procedure
  let average-savings mean [savings] of workers with [not in-jail?]
  ask workers with [not in-jail?][
    set wealth income + savings
    set propensity-to-consume-c 1 / (1 + (fn-tanh (savings / average-savings)) ^ beta)
    let money-to-consume propensity-to-consume-c * wealth
    set savings ((1 - propensity-to-consume-c) * wealth)
    ifelse (any? turtle-set my-large-store)
      [
        let id-store [who] of my-large-store
        set my-stores (turtle-set my-large-store n-of ( min list (goods-market-Z - 1) count firms with [who != id-store] ) firms with [who != id-store])    ; safe n-of: n-of ( min list NNN count ASET ) ASET
      ]
      [
        set my-stores n-of ( min list goods-market-Z count firms ) firms
      ]
    set my-large-store max-one-of my-stores [production-Y]
    if (count my-stores != goods-market-Z) [show (word "Number of my stores " count my-stores " who " who)]
    buying-step goods-market-Z money-to-consume
  ]
end

to buying-step [trials money]; workers procedure
  while [trials > 0 and money > 0][
    let my-cheapest-store min-one-of my-stores [individual-price-P]
    let possible-goods-to-buy min list money [inventory-S] of my-cheapest-store
    set money money - possible-goods-to-buy
    ask my-cheapest-store [
      ; goods are fractional e.g. liters or pounds, so its possible to buy a fraction
      set inventory-S inventory-S - possible-goods-to-buy
    ]
    ask [patch-here] of my-cheapest-store [
      set pcolor green
    ]
    set trials trials - 1
    set my-stores max-n-of trials my-stores [individual-price-P]; eliminate cheap-store of my-stores with sets
  ]
  if (money > 0)[
    set savings savings + money
  ]
end

;;;;;;;;;; to extortion ;;;;;;;;;;
to extortion
  if (propensity-to-be-extorter-epsilon > 0)[
    become-extortionists
  ]
  if (any? workers with [extorter? and time-in-jail < 1])[
    extortion-search
    execute-extortion
    jail-or-punishment
  ]
  if (any? workers with [time-in-jail > 0])[
    in-jail
  ]
end

to become-extortionists
  let Q1 lower-quartile [savings] of workers with [not extorter? and not in-jail?]  ;; The extorters have already decided.
  ask workers with [not employed? and savings < Q1 ][
    if (random 100 < propensity-to-be-extorter-epsilon)[
      set extorter? true
      set color red
    ]
  ]
end

to extortion-search
  let trials firms-to-extort-X
  while [trials > 0][
    ask workers with [extorter?][
      let my-extorted-firms firms-to-extort
      ; extorter/worker goes randomly to a company (not already extorted by himself) to extort
      ifelse (type-of-search = "random-search")[
        let potential-firm-to-extort one-of firms with [not member? self my-extorted-firms]

        ; if the randomly selected company has already been extorted by someone else who provides "protection", the worker loses his chance to extort
        if ([not being-extorted? or not being-punished?] of potential-firm-to-extort)[
          ; How many of the observable firms are being extorted?
          let closest-firms 3
          let around-firms min-n-of closest-firms other firms [distance potential-firm-to-extort]
          let expected-risk 100 * (count around-firms with [being-extorted? or being-punished?] / closest-firms)
          ifelse (expected-risk >= rejection-threshold); If the expected risk is high, firm accept to pay the pizzo
          [; A threshold of 0% represents that the company at the slightest hint of extortion in the area will choose to pay the pizzo
            set firms-to-extort (turtle-set firms-to-extort potential-firm-to-extort); succesful extortion
            ask potential-firm-to-extort [ set being-extorted? true]
          ]; If the expected risk is low, firm do not accept to pay the pizzo and go to the list of potential punished
          [set firms-to-punish (turtle-set potential-firm-to-extort)];
        ]
      ][
        ; extorter/worker goes to a nearest firm (not already extorted by himself) to extort
        let potential-firm-to-extort min-one-of firms with [not member? self my-extorted-firms] [distance myself]

        ; if the selected firm has already been extorted by someone else who provides "protection", the worker loses his chance to extort
        if ([not being-extorted? or not being-punished?] of potential-firm-to-extort)[
          ; How many of the observable firms are being extorted?
          let closest-firms closest-observable-firms
          let around-firms min-n-of closest-firms other firms [distance potential-firm-to-extort]
          let expected-risk 100 * (count around-firms with [being-extorted? or being-punished?] / closest-firms)
          ifelse (expected-risk >= rejection-threshold); If the expected risk is high, firm accept to pay the pizzo
          [; A threshold of 0% represents that the company at the slightest hint of extortion in the area will choose to pay the pizzo
            set firms-to-extort (turtle-set firms-to-extort potential-firm-to-extort); succesful extortion
            ask potential-firm-to-extort [ set being-extorted? true]
          ]; If the expected risk is low, firm do not accept to pay the pizzo and go to the list of potential punished
          [set firms-to-punish (turtle-set potential-firm-to-extort)];
        ]

      ]

    ]
    set trials trials - 1
  ]
end

to execute-extortion
  ask workers with [any? firms-to-extort][
    ask firms-to-extort [
      ifelse (type-of-pizzo = "proportion")[
        set amount-of-pizzo max (list 0 (net-worth-A * (proportion-of-pizzo / 100)))
        set net-worth-A net-worth-A - amount-of-pizzo
      ]
      [
        set amount-of-pizzo ifelse-value (net-worth-A > 0) [min (list net-worth-A constant-pizzo)][0]
        set net-worth-A net-worth-A - amount-of-pizzo
      ]
    ]
    set wealth wealth + sum [amount-of-pizzo] of firms-to-extort
  ]
end

to jail-or-punishment
  ; The companies that refused to pay pizzo, call the police, and with a probability of being caught, extortionists are imprisoned
  set confiscated-money 0; reset confiscated money each tick
  let needy-shops no-turtles
  ask workers with [any? firms-to-punish][
    ; greater the number of extorted firms, greater the probability of being imprisoned
    let caught? n-values (count firms-to-punish) [random 100 < probability-of-being-caught-lambda]
    ifelse ( length filter [i -> i = TRUE] caught? > 0 )[
      ; extortionists are imprisoned
      set in-jail? true
      set extorter? false
      set time-in-jail 6
      ask firms-to-extort [set being-extorted? false] ;; These firms are no longer extorted.
      set firms-to-extort no-turtles
      ask firms-to-punish [set being-punished? false] ;; These firms are no longer punished.
      set firms-to-punish no-turtles
      let amount-confiscated max (list 0 (wealth * (percent-transfer-fondo / 100)))
      set confiscated-money confiscated-money + amount-confiscated
      set wealth wealth - amount-confiscated
    ][
      ; Extorters punish firms!
      ask firms-to-punish [
        set being-punished? true
        set amount-of-punish max (list 0 (net-worth-A * ((proportion-of-punish) / 100)))
        set net-worth-A net-worth-A - amount-of-punish
        set needy-shops (turtle-set [firms-to-punish] of workers with [any? firms-to-punish])
      ]
      set wealth sum [amount-of-punish] of firms-to-punish
    ]
  ]
  if (count needy-shops > 0)[
    ifelse (proportional-refund?)[; Refund is proportional
      ask needy-shops [
        set net-worth-A net-worth-A + ( confiscated-money / count needy-shops )
      ]
    ][; Refund "First to come" instead proportional
      while [confiscated-money > 0 and count needy-shops > 0][
        let first-firm-to-come one-of needy-shops
        ask first-firm-to-come [
          set net-worth-A net-worth-A + min (list amount-of-punish confiscated-money)
          set needy-shops other needy-shops
        ]
        set confiscated-money confiscated-money - [amount-of-punish] of first-firm-to-come
      ]
    ]
  ]
end

to in-jail
  ask workers with [time-in-jail > 0][
    set time-in-jail time-in-jail - 1
  ]
  ask workers with [in-jail? and time-in-jail < 1][
    set in-jail? false ;; They are being realeased
    set color yellow
  ]
end

;;;;;;;;;; to firms-pay  ;;;;;;;;;;
to firms-pay
  ask firms [
    set revenue-R individual-price-P * ( production-Y - inventory-S)
    set gross-profits revenue-R - total-payroll-W
    let principal-and-Interest amount-of-Interest-to-pay
    if (amount-of-Interest-to-pay > 0)[
      ifelse (gross-profits > amount-of-Interest-to-pay)[; submodel 42
        ask my-bank [
          set patrimonial-base-E patrimonial-base-E + principal-and-Interest
        ]
      ][
        let bank-financing ifelse-value (net-worth-A != 0 ) [loan-B / net-worth-A][1]
        let bad-debt-amount bank-financing * net-worth-A
        ask my-bank [
          set patrimonial-base-E patrimonial-base-E - bad-debt-amount
        ]
      ]
    ]
    set net-profits gross-profits - amount-of-Interest-to-pay
    set amount-of-Interest-to-pay 0
    if (net-profits > 0)[
      set retained-profits-pi (1 - dividends-delta ) * net-profits
    ]
  ]
end

;;;;;;;;;; to firms-banks-survive ;;;;;;;;;;
to firms-banks-survive
  ask firms [
    set net-worth-A net-worth-A + retained-profits-pi
    if (net-worth-A <= 0
        or production-Y <= 0
      )[
      ask my-bank [
        set bad-debt-BD bad-debt-BD + 1
      ]
      ask my-employees [
        set color yellow
        set employed? false
        set my-wage 0
        set contract 0
        set my-firm no-turtles
        rt random 360
        fd (random 4) + 1
      ]
      die
    ]
  ]
  ask banks with [patrimonial-base-E < 0][
    ask my-borrowing-firms [
      set my-potential-banks no-turtles
      set my-bank no-turtles
    ]
    set bankrupt? true
  ]
end

to replace-bankrupt
  if (count firms < number-of-firms)[
    let incumbent-firms fn-incumbent-firms
    if (any? incumbent-firms) [
      set new-firms-production-Y ceiling mean [production-Y] of incumbent-firms
      set new-firms-minimum-wage-W-hat min [minimum-wage-W-hat] of incumbent-firms
      set new-firms-wage-offered-Wb (1 - size-replacing-firms) * mean [wage-offered-Wb] of incumbent-firms
      set new-firms-net-worth-A (1 - size-replacing-firms) * mean [net-worth-A] of incumbent-firms
      set new-firms-individual-price-P average-market-price
    ]
    create-firms (number-of-firms - count firms) [
      set x-position random-pxcor * 0.9
      set y-position random-pycor * 0.9
      setxy x-position y-position
      move-to one-of patches with [not any? firms-here ]
      set color blue
      set size 1.2
      set shape "factory"
      ;-----------------
      set production-Y new-firms-production-Y
      set labor-productivity-alpha 1
      set my-employees no-turtles
      set minimum-wage-W-hat new-firms-minimum-wage-W-hat
      set wage-offered-Wb new-firms-wage-offered-Wb
      set net-worth-A new-firms-net-worth-A
      set my-potential-banks no-turtles
      set my-bank no-turtles
      set inventory-S 0
      set individual-price-P  new-firms-individual-price-P
      ;-----------------
      set being-extorted? false
      set being-punished? false
      set amount-of-pizzo 0
    ]
  ]

  ask banks with [bankrupt?][
    set total-amount-of-credit-C 0
    set patrimonial-base-E random-poisson 10000 + 10
    set operational-interest-rate 0
    set interest-rate-r 0
    set my-borrowing-firms no-turtles
    set bankrupt? false
  ]
  ; re-paint patches
  ask patches [
    set pcolor black
  ]
end

;;;;;;;;;; utilities ;;;;;;;;;;

;; this procedure recomputes the value of gini-index-reserve
;; and the points in lorenz-points for the Lorenz and Gini-Index plots
;; Code adapted from:
;; Wilensky, U. (1998). NetLogo Wealth Distribution model.
;; http://ccl.northwestern.edu/netlogo/models/WealthDistribution.
;; Center for Connected Learning and Computer-Based Modeling,
;; Northwestern University, Evanston, IL.
to update-lorenz-and-gini
  let sorted-wealths sort [wealth] of workers
  let total-wealth sum sorted-wealths
  let wealth-sum-so-far 0
  let index 0
  let number-of-workers round (number-of-firms * 5)
  set gini-index-reserve 0
  set lorenz-points []

  repeat number-of-workers [
    set wealth-sum-so-far (wealth-sum-so-far + item index sorted-wealths)
    set lorenz-points lput ((wealth-sum-so-far / total-wealth) * 100) lorenz-points
    set index (index + 1)
    set gini-index-reserve
      gini-index-reserve +
      (index / number-of-workers) -
      (wealth-sum-so-far / total-wealth)
  ]
end

to-report gini-index
  report (gini-index-reserve / round (number-of-firms * 5)) / 0.5
end

; b_1 skewness is calculated following:
; Joanes, D.N. and Gill, C.A. (1998). Comparing measures of sample skewness and kurtosis.
; The Statistician, 47, 183–189.
to-report skewness-of-wealth-regular-worker
  ifelse (ticks > 0)[
    let wealths [wealth] of workers with [not extorter? and not in-jail?]
    let n round (number-of-firms * 5)
    let x-bar mean wealths

    let first-part ((n - 1 ) / n)^ (3 / 2)
    let m-3-part ( reduce + map [i -> ( i - x-bar ) ^ 3] wealths ) / n
    let m-2-part ( reduce + map [i -> ( i - x-bar ) ^ 2] wealths ) / n

    report  first-part * (m-3-part / (m-2-part) ^ (3 / 2 ))
  ][
    report 0
  ]
end

to-report fn-truncated-normal [ m sd ]
  let normal-value random-normal m sd
  report ifelse-value (normal-value > (m - sd)) [normal-value][max list 1 (m - sd)]
end

to-report lower-quartile [ xs ]
  let med median xs
  let lower filter [ x -> x < med ] xs
  report ifelse-value (empty? lower) [ med ] [ median lower ]
end

to-report tiempo
  report timer
end

to-report CPI
  let base base-price
  let current array:item quarters-average-price (ticks mod (ifelse-value (quarterly-time-scale?)[4][12]))
  report (current / base) * 100
end

to-report ln-hopital [number]
  report ifelse-value (number > 0)[ln number][0]
end

to-report base-price
  report 1.5
end

to-report base-savings
  report 2
end

;;;;;;;;;; firm related reporters ;;;;;;;;;;
to-report average-market-price
  report mean [individual-price-P] of firms
end

to-report fn-minimum-wage-W-hat
  let currently-minimum-w min [minimum-wage-W-hat] of firms
  report annualized-inflation * currently-minimum-w
end

to-report fn-incumbent-firms
  let lower count firms * 0.05
  let upper count firms * 0.95
  let ordered-firms sort-on [net-worth-A] firms
  let list-incumbent-firms sublist ordered-firms lower upper
  report (turtle-set list-incumbent-firms)
end

to-report avg-firm-production
  report mean [production-Y] of fn-incumbent-firms
end

to plot-size-of-firms
  histogram map ln-hopital [production-Y] of fn-incumbent-firms
end

to-report avg-interest-rate
  report 100 * mean [my-interest-rate] of firms
end

to-report avg-wage-offered
  report ln mean [wage-offered-Wb] of firms
end

to-report avg-net-worth
  report mean [net-worth-A] of fn-incumbent-firms
end

to-report net-worth-of-firms-to-gdp-ratio
  let sum-net-worth sum [net-worth-A] of firms
  report sum-net-worth / nominal-GDP
end

to-report skewness-of-networth
  ifelse (ticks > 0 and any? firms)[
    let networth [net-worth-A] of fn-incumbent-firms
    let n number-of-firms
    let x-bar mean networth

    let first-part ((n - 1 ) / n)^ (3 / 2)
    let m-3-part ( reduce + map [i -> ( i - x-bar ) ^ 3] networth ) / n
    let m-2-part ( reduce + map [i -> ( i - x-bar ) ^ 2] networth ) / n

    report  first-part * (m-3-part / (m-2-part) ^ (3 / 2 ))
  ][
    report 0
  ]
end

;;;;;;;;;; regular worker related reporters ;;;;;;;;;;
to-report fn-tanh [a]
  report ifelse-value (a < 354.391)[(exp (2 * a) - 1) / (exp (2 * a) + 1)][0.135335283]
end

to-report avg-propensity-to-consume
  report mean [propensity-to-consume-c] of workers
end

to-report avg-propensity-to-consume-regular-worker
  ifelse (any? workers with [not extorter? and not in-jail?])[
    report mean [propensity-to-consume-c] of workers with [not extorter? and not in-jail?]
  ][
    report 0
  ]
end

to-report fn-wealth-of-regular-worker
  ifelse (any? workers with [not extorter? and not in-jail?])[
    report ln-hopital mean [wealth] of workers with [not extorter? and not in-jail?]
  ][
    report 0
  ]
end

to-report wealth-of-regular-worker-to-gdp-ratio
  let sum-wealth sum [wealth] of workers with [not extorter? and not in-jail?]
  report sum-wealth / nominal-GDP
end

;;;;;;;;;; bank related reporters ;;;;;;;;;;
to-report interest-rate-policy-rbar
  report 0.07
end

to-report patrimonial-bank-to-gdp-ratio
  let bank-capital sum [patrimonial-base-E] of banks
  report bank-capital / nominal-GDP
end

;;;;;;;;;; economic variables reporters ;;;;;;;;;;
to-report nominal-GDP
  let output sum [production-Y * individual-price-P] of firms
  report output
end

to plot-nominal-GDP
  plot ln-hopital nominal-GDP
end

to-report real-GDP
  report nominal-GDP / CPI
end

to plot-real-GDP
  plot ln-hopital real-GDP
end

to-report fn-unemployment-rate
  report count workers with [not employed? and time-in-jail < 1] / count workers with [time-in-jail < 1]
end

to plot-unemployment-rate
  plot fn-unemployment-rate
end

to-report logarithm-of-households-consumption
  let output sum [production-Y * individual-price-P] of firms
  let consumption output - sum [inventory-S] of firms
  report ln-hopital consumption
end

to-report households-consumption-to-gdp-ratio
  let output sum [production-Y * individual-price-P] of firms
  let consumption output - sum [inventory-S] of firms
  report consumption / nominal-GDP
end

to-report inventory-to-gdp-ratio
  let stock sum [inventory-S] of firms
  report stock / nominal-GDP
end

to-report quarterly-inflation
  let q-inflation array:item quarters-inflation (ticks mod (ifelse-value (quarterly-time-scale?)[4][12]))
  report q-inflation
end

to-report annualized-inflation
  report reduce * map [i -> (i / 100) + 1] array:to-list quarters-inflation
end

to-report yearly-inflation
  report (annualized-inflation - 1) * 100
end

to plot-annualized-inflation
  plot yearly-inflation
end

;;;;;;;;;; Reporters for extortion variables ;;;;;;;;;;
to-report fn-wealth-of-extorters
  ifelse (any? workers with [extorter?])[
    report ln-hopital mean [wealth] of workers with [extorter?]
  ][
    report 0
  ]
end

to-report wealth-of-extorters-to-gdp-ratio
  let sum-wealth sum [wealth] of workers with [extorter?]
  report sum-wealth / nominal-GDP
end

to-report avg-wealth-of-extorters-to-gdp-ratio
  ifelse (any? workers with [extorter?])[
    let sum-wealth sum [wealth] of workers with [extorter?]
    let extorters count workers with [extorter?]
    let avg-wealth sum-wealth / extorters
    report avg-wealth / nominal-GDP
  ][
    report 0
  ]
end

to-report skewness-of-wealth-extorter
  ifelse (ticks > 0 and any? workers with [extorter?])[
    let wealths [wealth] of workers with [extorter?]
    let n round (number-of-firms * 5)
    let x-bar mean wealths

    let first-part ((n - 1 ) / n)^ (3 / 2)
    let m-3-part ( reduce + map [i -> ( i - x-bar ) ^ 3] wealths ) / n
    let m-2-part ( reduce + map [i -> ( i - x-bar ) ^ 2] wealths ) / n

    report  first-part * (m-3-part / (m-2-part) ^ (3 / 2 ))
  ][
    report 0
  ]
end

to-report N-extorted-firms
  ifelse (any? firms with [being-extorted?])[
    report count firms with [being-extorted?]
  ][
    report 0
  ]
end

to-report N-punished-firms
  ifelse(any? firms with [being-punished?])[
    report count firms with [being-punished?]
  ][
    report 0
  ]
end

to-report N-extorters
  report count workers with [extorter? and time-in-jail < 1]
end

to-report N-extorters-in-jail
  report count workers with [time-in-jail > 0]
end

to-report pizzo-to-gdp-ratio
  report (sum [amount-of-pizzo] of firms / nominal-GDP)
end

to-report punish-to-gdp-ratio
  report (sum [amount-of-punish] of firms / nominal-GDP)
end

to-report avg-propensity-to-consume-extorter
  ifelse (any? workers with [extorter?])[
    report mean [propensity-to-consume-c] of workers with [extorter?]
  ][
    report 0
  ]
end
@#$#@#$#@
GRAPHICS-WINDOW
240
10
566
325
-1
-1
6.0
1
8
1
1
1
0
0
0
1
-26
26
-25
25
0
0
1
ticks
240.0

BUTTON
26
10
99
43
NIL
setup
NIL
1
T
OBSERVER
NIL
NIL
NIL
NIL
1

BUTTON
106
10
169
43
NIL
go
T
1
T
OBSERVER
NIL
NIL
NIL
NIL
0

SLIDER
2
48
194
81
number-of-firms
number-of-firms
10
500
100.0
10
1
NIL
HORIZONTAL

SLIDER
2
84
194
117
wages-shock-xi
wages-shock-xi
0.01
0.5
0.05
0.01
1
NIL
HORIZONTAL

SLIDER
2
121
194
154
interest-shock-phi
interest-shock-phi
0
0.5
0.1
0.01
1
NIL
HORIZONTAL

SLIDER
2
157
194
190
price-shock-eta
price-shock-eta
0
0.5
0.1
0.01
1
NIL
HORIZONTAL

SLIDER
2
194
194
227
production-shock-rho
production-shock-rho
0
0.5
0.1
0.01
1
NIL
HORIZONTAL

SLIDER
2
231
194
264
v
v
0
1
0.23
0.01
1
NIL
HORIZONTAL

SLIDER
2
268
194
301
labor-market-M
labor-market-M
1
10
4.0
1
1
trials
HORIZONTAL

PLOT
941
30
1207
150
Unemployment rate
Time
NIL
0.0
10.0
0.0
1.0
true
false
"" ""
PENS
"default" 1.0 0 -16777216 true "" "set-plot-x-range ifelse-value keep-burning-phase? [ 0 ] [ max (list 0 (ticks - burning-periods))  ] (ticks + 5)\nset-plot-y-range 0  0.3\nplot-unemployment-rate"
"pen-1" 1.0 2 -7500403 true "" "plot 0"
"pen-2" 1.0 0 -2674135 true "" "plot 0.10"

SLIDER
2
303
194
336
credit-market-H
credit-market-H
1
10
2.0
1
1
trials
HORIZONTAL

SLIDER
2
338
194
371
goods-market-Z
goods-market-Z
1
15
2.0
1
1
trials
HORIZONTAL

SLIDER
2
374
194
407
beta
beta
0.01
1
0.87
0.01
1
NIL
HORIZONTAL

SLIDER
2
410
194
443
dividends-delta
dividends-delta
0
0.5
0.15
0.01
1
NIL
HORIZONTAL

PLOT
941
290
1207
410
Net worth distribution
log money
freq
0.0
10.0
0.0
10.0
true
false
"" ""
PENS
"default" 1.0 1 -16777216 true "" "set-histogram-num-bars sqrt count firms\nset-plot-y-range 0 ceiling sqrt count firms\nset-plot-x-range floor ln-hopital min [net-worth-A] of fn-incumbent-firms ceiling ln-hopital max [net-worth-A] of fn-incumbent-firms\nhistogram map ln-hopital [net-worth-A] of fn-incumbent-firms"

PLOT
676
290
942
410
Net worth of firms
Time
Ln
0.0
10.0
0.0
10.0
true
true
"" ""
PENS
"mean" 1.0 0 -16777216 true "" "set-plot-x-range ifelse-value keep-burning-phase? [ 0 ] [ max (list 0 (ticks - burning-periods))  ] (ticks + 5)\nset-plot-y-range 0 max (list 1 ceiling ln-hopital max [net-worth-A] of firms)\nplot ln-hopital mean [net-worth-A] of firms"
"min" 1.0 0 -2674135 true "" "plot ln-hopital min [net-worth-A] of firms"
"max" 1.0 2 -13840069 true "set-plot-pen-mode 2" "plot ln-hopital max [net-worth-A] of firms"

PLOT
941
150
1207
270
Propensity to consume
Time
NIL
0.0
10.0
0.0
1.0
true
true
"" ""
PENS
"mean" 1.0 0 -16777216 true "" "set-plot-x-range ifelse-value keep-burning-phase? [ 0 ] [ max (list 0 (ticks - burning-periods))  ] (ticks + 5)\nplot mean [propensity-to-consume-c] of workers"
"min" 1.0 2 -2674135 true "" "plot min [propensity-to-consume-c] of workers"
"max" 1.0 0 -13345367 true "" "plot max [propensity-to-consume-c] of workers"

PLOT
1471
30
1737
150
Time scale inflation
Time
%
0.0
10.0
0.0
1.0
true
false
"" ""
PENS
"default" 1.0 0 -16777216 true "" "set-plot-x-range ifelse-value keep-burning-phase? [ 0 ] [ max (list 0 (ticks - burning-periods))  ] (ticks + 5)\nset-plot-y-range -5 10\nplot quarterly-inflation"
"pen-1" 1.0 2 -5987164 true "" "plot 0"

PLOT
1206
30
1472
150
Annualized inflation
Year
%
0.0
10.0
-1.0
1.0
true
false
"" ""
PENS
"default" 1.0 0 -16777216 true "" "set-plot-x-range max list 0 (ceiling (ticks / (ifelse-value (quarterly-time-scale?)[4][12])) - 125) ceiling (ticks / (ifelse-value (quarterly-time-scale?)[4][12])) + 1\nif (ticks > 0 and ticks mod (ifelse-value (quarterly-time-scale?)[4][12]) = 0 )[\nplot-annualized-inflation]"
"pen-1" 1.0 0 -7500403 true "" "set-plot-x-range max list 0 (ceiling (ticks / (ifelse-value (quarterly-time-scale?)[4][12])) - 125) ceiling (ticks / (ifelse-value (quarterly-time-scale?)[4][12])) + 1\nif (ticks > 0 and ticks mod (ifelse-value (quarterly-time-scale?)[4][12]) = 0 )[plot 0]"

TEXTBOX
5
445
190
475
Random scaling parameter
12
0.0
1

SLIDER
0
460
192
493
size-replacing-firms
size-replacing-firms
0.05
0.5
0.2
0.01
1
NIL
HORIZONTAL

PLOT
676
30
942
150
Real GDP
Time
NIL
0.0
10.0
0.0
3.0
true
false
"" ""
PENS
"Nom." 1.0 0 -12030287 true "" "set-plot-x-range ifelse-value keep-burning-phase? [ 0 ] [ max (list 0 (ticks - burning-periods))  ] (ticks + 5)\nplot real-GDP"

PLOT
676
150
942
270
Consumption to GDP ratio
Time
NIL
0.0
10.0
0.0
1.0
true
false
"" ""
PENS
"default" 1.0 0 -16777216 true "" "set-plot-x-range ifelse-value keep-burning-phase? [ 0 ] [ max (list 0 (ticks - burning-periods))  ] (ticks + 5)\nplot households-consumption-to-gdp-ratio"

PLOT
941
410
1207
530
Price of firms
Time
Ln
0.0
10.0
0.0
10.0
true
true
"" ""
PENS
"mean" 1.0 0 -16777216 true "" "set-plot-x-range ifelse-value keep-burning-phase? [ 0 ] [ max (list 0 (ticks - burning-periods))  ] (ticks + 5)\nplot ln average-market-price"
"min" 1.0 2 -2674135 true "" "plot ln min [individual-price-P] of firms"
"max" 1.0 0 -13345367 true "" "plot ln max [individual-price-P] of firms"

TEXTBOX
200
60
244
78
100
12
5.0
1

TEXTBOX
199
16
234
51
Init \nvalues
12
5.0
1

TEXTBOX
200
98
274
116
0.05
12
5.0
1

TEXTBOX
201
137
252
155
0.10
12
5.0
1

TEXTBOX
201
172
231
190
0.10
12
5.0
1

TEXTBOX
201
210
236
228
0.10
12
5.0
1

TEXTBOX
205
280
235
298
4
12
5.0
1

TEXTBOX
206
319
236
337
2
12
5.0
1

TEXTBOX
205
352
230
370
2
12
5.0
1

TEXTBOX
204
390
234
408
0.87
12
5.0
1

TEXTBOX
200
425
240
443
0.15
12
5.0
1

PLOT
1471
530
1737
650
Wealth distribution of regular worker
log wealth
freq
0.0
10.0
0.0
10.0
true
false
"" ""
PENS
"default" 1.0 1 -16777216 true "" "set-histogram-num-bars sqrt count workers\nset-plot-y-range 0 ceiling sqrt count workers\nset-plot-x-range floor ln-hopital min [wealth] of workers ceiling ln-hopital max [wealth] of workers\nhistogram map ln-hopital [wealth] of workers with [wealth > 0 and not extorter? and not in-jail?]"

PLOT
1206
410
1471
530
Size of firms
log Production
freq
0.0
10.0
0.0
10.0
true
false
"" ""
PENS
"default" 1.0 1 -16777216 true "" "set-histogram-num-bars sqrt count firms\nset-plot-y-range 0 ceiling sqrt count firms\nset-plot-x-range floor ln-hopital min [production-Y] of fn-incumbent-firms ceiling ln-hopital max [production-Y] of fn-incumbent-firms\nplot-size-of-firms"

PLOT
1206
290
1471
410
Production of firms
Time
Quantity
0.0
10.0
0.0
1.0
true
true
"" ""
PENS
"mean" 1.0 0 -16777216 true "" "set-plot-x-range ifelse-value keep-burning-phase? [ 0 ] [ max (list 0 (ticks - burning-periods))  ] (ticks + 5)\nset-plot-y-range 0 ceiling (max (list 1 [production-Y] of firms))\nplot mean [production-Y] of fn-incumbent-firms"
"max" 1.0 0 -13840069 true "" "plot max [production-Y] of fn-incumbent-firms"
"min" 1.0 0 -2674135 true "" "plot min [production-Y] of fn-incumbent-firms"

PLOT
1471
290
1736
410
Desired production
Time
Quantity
0.0
10.0
0.0
10.0
true
true
"" ""
PENS
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"max" 1.0 0 -13840069 true "" "plot max [desired-production-Yd] of firms"
"min" 1.0 0 -2674135 true "" "plot min [desired-production-Yd] of firms"

PLOT
676
530
941
650
Contractual interest rate
Time
%
0.0
10.0
0.0
10.0
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false
"" ""
PENS
"mean" 1.0 0 -16777216 true "" "set-plot-x-range ifelse-value keep-burning-phase? [ 0 ] [ max (list 0 (ticks - burning-periods))  ] (ticks + 5)\nplot 100 * mean [my-interest-rate] of firms"

PLOT
1206
530
1471
650
Avg Wealth of workers
Time
Ln
0.0
10.0
0.0
1.0
true
true
"" ""
PENS
"regular" 1.0 0 -14439633 true "" "set-plot-x-range ifelse-value keep-burning-phase? [ 0 ] [ max (list 0 (ticks - burning-periods))  ] (ticks + 5)\nset-plot-y-range 0 max (list 1 ceiling ln-hopital max [wealth] of workers)\nplot fn-wealth-of-regular-worker"
"extorter" 1.0 0 -5298144 true "" "plot fn-wealth-of-extorters"

PLOT
676
410
941
530
Inventory-S
Time
Ln
0.0
10.0
0.0
10.0
true
true
"" ""
PENS
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PLOT
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1206
650
Banks patrimonial base
Time
Ln
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10.0
0.0
10.0
true
true
"" ""
PENS
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General extortion parameters
12
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propensity-to-be-extorter-epsilon
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HORIZONTAL

SLIDER
241
540
546
573
firms-to-extort-X
firms-to-extort-X
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3
1.0
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1
trials
HORIZONTAL

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941
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% of Workers being extortionists
Time
%
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"" ""
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SLIDER
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663
proportion-of-pizzo
proportion-of-pizzo
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100
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HORIZONTAL

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1471
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Pizzo & punish to GDP ratio
Time
NIL
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"" ""
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probability-of-being-caught-lambda
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HORIZONTAL

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Wage offered Wb
Time
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TEXTBOX
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15%
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TEXTBOX
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30%
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PLOT
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Lorenz curve of workers
Pop %
Wealth %
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95.0
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100.0
true
true
"" ""
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"Equal" 100.0 0 -13840069 true "plot 0\nplot 100" ""

PLOT
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150
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Gini index
Time
Index
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"" ""
PENS
"default" 1.0 0 -16777216 true "" "set-plot-x-range ifelse-value keep-burning-phase? [ 0 ] [ max (list 0 (ticks - burning-periods))  ] (ticks + 5)\nplot (gini-index-reserve / round (number-of-firms * 5)) / 0.5"

TEXTBOX
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Pizzo payment parameters
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SLIDER
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HORIZONTAL

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50%
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Searching strategy
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CHOOSER
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quarterly-time-scale?
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TEXTBOX
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Otherwise, first to come
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Otherwise, monthly scale
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proportion-of-punish
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HORIZONTAL

TEXTBOX
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25%
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PLOT
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% of Firms being...
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PENS
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Wealth distribution of extorters
log wealth
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@#$#@#$#@
The Bottom-up Adaptive Macroeconomics under Extortion Racket Systems (BAMERS) model extends the BAM model @DelliGatti2011 (NetLogo code by @Platas-Lopez2020 in https://www.comses.net/codebases/9dacc220-8d7f-4038-b618-92bb9b1333f0/) by introducing extortionists in the system. In what follows, the model is described in detail following the ODD protocol.

Overview
========

Purpose
-------

BAMERS is designed to explore the effect of individual extortion activities in macroeconomic signals like GDP, inflation, unemployment rate, and Gini index.

Entities, state variables, and scales
-------------------------------------

-   Agents: Firms (also know as producers), workers (also know as consumers and households) and banks.

-   Environment: Originally the grid environment where the agents are situated was used exclusively for debug purposes, displaying information useful for validating the behavior of the system, e.g., the number of workers laboring in a firm, their presence when working and buying goods, etc. Beyond this, the BAMERS model uses neighborhood in this space when computing some decisions,   even when such relation has not geographical meaning, i.e., neighbors are not   assumed to be geographically closed.

-   State variables: Firms and workers, as originally defined in the   BAM model, are now extended with the variables shown in  next Table for registering the effect of extortion   activities. Unemployed workers can become extortionists and keep a list of   firms to extort and possibly punish. When captured, an extortionist will be in   jail a given number of periods (`time-in-jail`). Firms register if they are   being extorted and keep.

<center>
<table style="width: 400px; height: 50px;">
<tbody>
<tr class="odd">
<td style="text-align: right;"><strong>Firm</strong></td>
<td style="text-align: left;"></td>
<td style="text-align: right;"><strong>Worker</strong></td>
<td style="text-align: left;"></td>
</tr>
<tr class="even">
<td style="text-align: left;"><strong>Attribute</strong></td>
<td style="text-align: left;"><strong>Type</strong></td>
<td style="text-align: left;"><strong>Attribute</strong></td>
<td style="text-align: left;"><strong>Type</strong></td>
</tr>
<tr class="odd">
<td style="text-align: left;"><code>being-extorted?</code></td>
<td style="text-align: left;">Bool</td>
<td style="text-align: left;"><code>extortionist?</code></td>
<td style="text-align: left;">Bool</td>
</tr>
<tr class="even">
<td style="text-align: left;"><code>amount-of-pizzo</code></td>
<td style="text-align: left;">Float</td>
<td style="text-align: left;"><code>firms-to-extort</code></td>
<td style="text-align: left;">AgSet</td>
</tr>
<tr class="odd">
<td style="text-align: left;"><code>amount-of-punish</code></td>
<td style="text-align: left;">Float</td>
<td style="text-align: left;"><code>firms-to-punish</code></td>
<td style="text-align: left;">AgSet</td>
</tr>
<tr class="even">
<td style="text-align: left;"></td>
<td style="text-align: left;"></td>
<td style="text-align: left;"><code>time-in-jail</code></td>
<td style="text-align: left;">Int</td>
</tr>
</tbody>
</table>
</center>

-   Scales: Time is represented in discrete periods, each step   representing a month.


Process overview and scheduling
-------------------------------

Extortion in  the BAMERS model occurs after closing the goods market and before firms pay loans and dividends, i.e., in between steps 5 and 6 of the BAM model schedule. The new steps added to the schedule are as follows:

<span>5.</span> A) Goods market opens. B) Good market closes.
&nbsp;&nbsp;<span>E1.</span> Unemployed poor workers decide whether to become extortionists or not.
&nbsp;&nbsp;<span>E2.</span> Extortionists look for firms to extort.
&nbsp;&nbsp;<span>E3.</span> Firms pay the pizzo or refuse to pay and denounce.
&nbsp;&nbsp;<span>E4.</span> Extortionists punish firms that refused to pay or, when captured, they     go to jail and lose their wealth.
&nbsp;&nbsp;<span>E5.</span> Prisoners that have served their time in prison are released as regular     unemployed workers.
<span>6.</span>  Firms will pay loans and dividends.


Design concepts
===============

### Basic Principles

Economic pressure and socioeconomic status induce a process of decision making on how to respond to basic needs, even considering the idea of becoming criminals. @Abrahamsen1949 [p.140] wrote about this:

> There are a few questions that are frequently asked in regard to our findings that family tension is the basic cause of criminal behavior.  The first has to do with economics. It is reasonable to assume,  intellectually speaking, that when one is without what is necessary  for subsistence and cannot get it, he will simply take it for himself  and his loved ones. This is instinctive, and it has to do with  self-preservation.

### Adaptation

Following the basic principle enunciated above, every time the goods market closes, unemployed workers belonging to the poorest quartile in the population decide as to whether they will become extortionists or not with a propensity *ϵ*. The propensity to become a criminal is orthogonal to any other variable, e.g., the probability of being imprisoned *λ*. This enables a controlled complete parameter exploration to evaluate the macroeconomic effect of extortion. When *ϵ* is equal to zero, our model corresponds to the baseline macroeconomic model without extortion, i.e., the BAM model.

### Sensing

Firms can observe a given number of firms to know if they are paying for pizzo or not.

### Interaction

Interaction happens when an extortionist finds a firm and asks for the payment of pizzo. Firms can in turn accept, or refuse to pay and denounce.

### Stochasticity

Processes exhibiting randomness include becoming an extortionist, being imprisoned, and trying to extort a firm. The search for potential firms to extort is also stochastic.

### Observation

Along simulation are observed:

-   Logarithm of real GDP.

-   Unemployment rate.

-   Annual inflation rate.


At end of simulation are computed:

-   Distribution of the size of firms.


Details
=======

Initialization
--------------

Next Table shows the default parameter initialization for the BAMERS model. Parameter values are influenced by the work of  [@elsenbroich2016; @Troitzsch2015a; @Nardin2016a; @Nardin2017]. Their use is fully described below, in the submodels section.

<center>
<table>
<thead>
<tr class="header">
<th style="text-align: left;"><strong>Parameter</strong></th>
<th style="text-align: left;"></th>
<th style="text-align: right;"><strong>Value</strong></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;"><span class="math inline">&epsilon;</span></td>
<td style="text-align: left;">Propensity to become an extortionist</td>
<td style="text-align: right;">20%</td>
</tr>
<tr class="even">
<td style="text-align: left;"><span class="math inline">&lambda;</span></td>
<td style="text-align: left;">Probability of being imprisoned</td>
<td style="text-align: right;">30%</td>
</tr>
<tr class="odd">
<td style="text-align: left;"><span class="math inline">R<sub>t</sub></span></td>
<td style="text-align: left;">Rejection threshold</td>
<td style="text-align: right;">15%</td>
</tr>
<tr class="even">
<td style="text-align: left;"><span class="math inline">X</span></td>
<td style="text-align: left;">Number of attempts to extort a new firm</td>
<td style="text-align: right;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;"><span class="math inline">CF</span></td>
<td style="text-align: left;">Number of observable closest firms</td>
<td style="text-align: right;">3</td>
</tr>
<tr class="even">
<td style="text-align: left;"><span class="math inline">P<sub>i</sub></span></td>
<td style="text-align: left;">Proportion of net worth asked as pizzo</td>
<td style="text-align: right;">20%</td>
</tr>
<tr class="odd">
<td style="text-align: left;"><span class="math inline">P<sub>u</sub></span></td>
<td style="text-align: left;">Proportion of net worth taken as punish</td>
<td style="text-align: right;">30%</td>
</tr>
<tr class="even">
<td style="text-align: left;"><span class="math inline">C<sub>m</sub></span></td>
<td style="text-align: left;">Proportion of wealth as confiscated money</td>
<td style="text-align: right;">50%</td>
</tr>
<tr class="odd">
<td style="text-align: left;"><span class="math inline">T<sub>j</sub></span></td>
<td style="text-align: left;">Number of time periods in jail</td>
<td style="text-align: right;">6</td>
</tr>
</tbody>
</table>
</center>

Submodels
---------

1.  The number of extortionists in the system can change every time step. Each unemployed worker in the quartile with the lower savings become a criminal if the propensity to be an extortionist *ϵ* is greater than a randomly generated value with a uniform distribution between 0 and 100.

2.  An extortionist has *X* attempts to find a new victim. If an extortionist selects a firm that is already being extorted, it is considered as a failed attempt.

3.  Extortionists choose their victims by searching randomly over all firms. 

4.  Firms can allow the extortion or refuse to pay. A firm refuses to pay the pizzo when the rejection threshold (*R*<sub>*t*</sub>) is greater than the expected risk (*ER*), which is calculated as follows:

    *ER = EF / CF*

    where, *CF* denotes number of observable firms (three by default), i.e., the closest ones; and *EF* denotes how many of them are being extorted. This cognitive mechanism is based on the work of @elsenbroich2016. *R*<sub>*t*</sub> = 0 makes firms to pay the pizzo at the slightest hint of extortion activity.

5.  Firms that accept to pay the pizzo are asked for a proportion of their net worth, set by default to *P*<sub>*i*</sub> = 20% as suggested by [@Troitzsch2015a; @Nardin2016a; @Nardin2017].

6.  Firms that refuse to pay the pizzo always denounce. The probability that a extortionist is imprisoned is set to *λ* = 30%. Imprisoned extortionists stay *T*<sub>*j*</sub> = 6 periods in jail. Additionally, a proportion *C*<sub>*m*</sub> = 50% of their wealth is confiscated for supporting the victims of extortion that had denounced. All the previous default parameter values were adopted from the findings by [@Troitzsch2015a; @Nardin2016a; @Nardin2017].

7.  Extortionists that are not imprisoned punish the firm that denounced them, if any. The amount of money taken as punishment is a proportion of the net worth of the firm set by default to *P*<sub>*u*</sub> = 30%.

8.  Firms who have denounced and being punished receive an equal proportion of the victim support fund raised from 50% of imprisoned extortionists wealth. 

9.  Extortionists who served their sentence become regular workers in the labor market, in the immediate following period.


References
========
Abrahamsen, D. (1949). Family tension, basic cause of criminal behavior. *Journal of Criminal Law and Criminology (1931-1951)*, *40*(3), 330--343 <http://www.jstor.org/stable/1138546>

Delli Gatti, D., Desiderio, S., Gaffeo, E., Cirillo, P. & Gallegati, M. (2011). *Macroeconomics from the bottom-up*, vol. 11. Springer. <http://dx.doi.org/10.1007/978-88-470-1971-3>

Elsenbroich, C. & Badham, J. (2016). The extortion relationship: A computational analysis. *Journal of Artificial Societies and Social Simulation*, *19*. <http://dx.doi.org/10.18564/jasss.3223>

Platas-López, A., Guerra-Hernández, A., Cruz-Ramı́rez, N., Quiroz-Castellanos, M., Grimaldo, F., Paolucci, M. & Cecconi, F. (2020). Towards an agent-based model for the analysis of macroeconomic signals. In *Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications*, (pp. 551–565). Springer International Publishing. <http://dx.doi.org/10.1007/978-3-030-35445-9_38>

Troitzsch, K. G. (2014). Distribution effects of extortion racket systems. In *Lecture Notes in Economics and Mathematical Systems*, (pp. 181--193). Springer International Publishing. <http://dx.doi.org/10.1007/978-3-319-09578-3_15>

Nardin, L. G., Andrighetto, G., Conte, R., Székely, Á., Anzola, D., Elsenbroich, C., Lotzmann, U., Neumann, M., Punzo, V. & Troitzsch, K. G. (2016). Simulating protection rackets: a case study of the sicilian mafia. *Autonomous Agents and Multi-Agent Systems*, *30*(6), 1117--1147. <http://dx.doi.org/10.1007/s10458-016-9330-z>

Nardin, L. G., Székely, Á. & Andrighetto, G. (2017). GLODERS-S: a simulator for agent-based models of criminal organisations. *Trends in Organized Crime*, *20*(1), 85--99. <http://dx.doi.org/10.1007/s12117-016-9287-y>
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@#$#@#$#@
NetLogo 6.1.1
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
<experiments>
  <experiment name="thesis-extortion" repetitions="20" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>real-GDP</metric>
    <metric>fn-unemployment-rate</metric>
    <metric>quarterly-inflation</metric>
    <metric>yearly-inflation</metric>
    <metric>gini-index</metric>
    <metric>skewness-of-wealth-regular-worker</metric>
    <metric>fn-wealth-of-regular-worker</metric>
    <metric>wealth-of-regular-worker-to-gdp-ratio</metric>
    <metric>households-consumption-to-gdp-ratio</metric>
    <metric>avg-propensity-to-consume</metric>
    <metric>avg-net-worth</metric>
    <metric>skewness-of-networth</metric>
    <metric>avg-firm-production</metric>
    <metric>ln average-market-price</metric>
    <metric>avg-interest-rate</metric>
    <metric>avg-wage-offered</metric>
    <metric>inventory-to-gdp-ratio</metric>
    <metric>patrimonial-bank-to-gdp-ratio</metric>
    <metric>fn-wealth-of-extorters</metric>
    <metric>avg-wealth-of-extorters-to-gdp-ratio</metric>
    <metric>skewness-of-wealth-extorter</metric>
    <metric>N-extorted-firms</metric>
    <metric>N-punished-firms</metric>
    <metric>N-extorters</metric>
    <metric>N-extorters-in-jail</metric>
    <metric>pizzo-to-gdp-ratio</metric>
    <metric>punish-to-gdp-ratio</metric>
    <steppedValueSet variable="propensity-to-be-extorter-epsilon" first="0" step="5" last="100"/>
    <steppedValueSet variable="probability-of-being-caught-lambda" first="0" step="5" last="100"/>
    <enumeratedValueSet variable="rejection-threshold">
      <value value="15"/>
      <value value="45"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="type-of-search">
      <value value="&quot;random-search&quot;"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="type-of-pizzo">
      <value value="&quot;proportion&quot;"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="proportion-of-pizzo">
      <value value="10"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="proportion-of-punish">
      <value value="25"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="quarterly-time-scale?">
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="percent-transfer-fondo">
      <value value="50"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="proportional-refund?">
      <value value="true"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="thesis-baseline" repetitions="100" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>fn-unemployment-rate</metric>
    <metric>mean [wealth] of workers</metric>
    <metric>(gini-index-reserve / round (number-of-firms * 5)) / 0.5</metric>
    <metric>skewness-of-wealth</metric>
    <metric>count workers with [extorter?]</metric>
    <metric>count firms with [being-extorted?]</metric>
    <metric>sum [amount-of-pizzo] of firms</metric>
    <metric>sum [amount-of-punish] of firms</metric>
    <metric>mean [net-worth-A] of fn-incumbent-firms</metric>
    <metric>mean [production-Y] of fn-incumbent-firms</metric>
    <metric>mean [propensity-to-consume-c] of workers</metric>
    <metric>quarterly-inflation</metric>
    <metric>(annualized-inflation - 1) * 100</metric>
    <metric>real-GDP</metric>
    <metric>logarithm-of-households-consumption</metric>
    <metric>ln average-market-price</metric>
    <metric>100 * mean [my-interest-rate] of firms</metric>
    <metric>ln mean [wage-offered-Wb] of firms</metric>
    <metric>ln-hopital mean [inventory-S] of firms</metric>
    <metric>ln-hopital mean [patrimonial-base-E] of banks</metric>
    <metric>fn-wealth-of-extorters</metric>
    <enumeratedValueSet variable="type-of-search">
      <value value="&quot;random-search&quot;"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="type-of-pizzo">
      <value value="&quot;proportion&quot;"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="propensity-to-be-extorter-epsilon">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="proportion-of-pizzo">
      <value value="3"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="proportion-of-punish">
      <value value="25"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="quarterly-time-scale?">
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="rejection-threshold">
      <value value="15"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="probability-of-being-caught-lambda">
      <value value="20"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="percent-transfer-fondo">
      <value value="100"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="proportional-refund?">
      <value value="true"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="thesis-epsilon" repetitions="20" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>real-GDP</metric>
    <metric>fn-unemployment-rate</metric>
    <metric>quarterly-inflation</metric>
    <metric>yearly-inflation</metric>
    <metric>gini-index</metric>
    <metric>skewness-of-wealth-regular-worker</metric>
    <metric>fn-wealth-of-regular-worker</metric>
    <metric>wealth-of-regular-worker-to-gdp-ratio</metric>
    <metric>households-consumption-to-gdp-ratio</metric>
    <metric>avg-propensity-to-consume</metric>
    <metric>avg-net-worth</metric>
    <metric>skewness-of-networth</metric>
    <metric>avg-firm-production</metric>
    <metric>ln average-market-price</metric>
    <metric>avg-interest-rate</metric>
    <metric>avg-wage-offered</metric>
    <metric>inventory-to-gdp-ratio</metric>
    <metric>patrimonial-bank-to-gdp-ratio</metric>
    <metric>fn-wealth-of-extorters</metric>
    <metric>avg-wealth-of-extorters-to-gdp-ratio</metric>
    <metric>N-extorted-firms</metric>
    <metric>N-punished-firms</metric>
    <metric>N-extorters</metric>
    <metric>N-extorters-in-jail</metric>
    <metric>pizzo-to-gdp-ratio</metric>
    <metric>punish-to-gdp-ratio</metric>
    <enumeratedValueSet variable="type-of-search">
      <value value="&quot;random-search&quot;"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="type-of-pizzo">
      <value value="&quot;proportion&quot;"/>
    </enumeratedValueSet>
    <steppedValueSet variable="propensity-to-be-extorter-epsilon" first="0" step="5" last="100"/>
    <enumeratedValueSet variable="proportion-of-pizzo">
      <value value="10"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="proportion-of-punish">
      <value value="25"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="quarterly-time-scale?">
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="rejection-threshold">
      <value value="15"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="probability-of-being-caught-lambda">
      <value value="30"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="percent-transfer-fondo">
      <value value="50"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="proportional-refund?">
      <value value="false"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="thesis-caught" repetitions="20" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>real-GDP</metric>
    <metric>fn-unemployment-rate</metric>
    <metric>quarterly-inflation</metric>
    <metric>yearly-inflation</metric>
    <metric>gini-index</metric>
    <metric>skewness-of-wealth-regular-worker</metric>
    <metric>fn-wealth-of-regular-worker</metric>
    <metric>wealth-of-regular-worker-to-gdp-ratio</metric>
    <metric>households-consumption-to-gdp-ratio</metric>
    <metric>avg-propensity-to-consume</metric>
    <metric>avg-net-worth</metric>
    <metric>skewness-of-networth</metric>
    <metric>avg-firm-production</metric>
    <metric>ln average-market-price</metric>
    <metric>avg-interest-rate</metric>
    <metric>avg-wage-offered</metric>
    <metric>inventory-to-gdp-ratio</metric>
    <metric>patrimonial-bank-to-gdp-ratio</metric>
    <metric>fn-wealth-of-extorters</metric>
    <metric>avg-wealth-of-extorters-to-gdp-ratio</metric>
    <metric>N-extorted-firms</metric>
    <metric>N-punished-firms</metric>
    <metric>N-extorters</metric>
    <metric>N-extorters-in-jail</metric>
    <metric>pizzo-to-gdp-ratio</metric>
    <metric>punish-to-gdp-ratio</metric>
    <enumeratedValueSet variable="type-of-search">
      <value value="&quot;random-search&quot;"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="type-of-pizzo">
      <value value="&quot;proportion&quot;"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="propensity-to-be-extorter-epsilon">
      <value value="20"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="proportion-of-pizzo">
      <value value="10"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="proportion-of-punish">
      <value value="25"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="quarterly-time-scale?">
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="rejection-threshold">
      <value value="15"/>
    </enumeratedValueSet>
    <steppedValueSet variable="probability-of-being-caught-lambda" first="0" step="5" last="100"/>
    <enumeratedValueSet variable="percent-transfer-fondo">
      <value value="50"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="proportional-refund?">
      <value value="false"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="test-economy" repetitions="1" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <timeLimit steps="200"/>
    <metric>fn-unemployment-rate</metric>
    <metric>gini-index</metric>
    <metric>skewness-of-wealth</metric>
    <metric>avg-net-worth</metric>
    <metric>avg-propensity-to-consume</metric>
    <metric>real-GDP</metric>
    <metric>average-market-price</metric>
    <metric>avg-interest-rate</metric>
    <enumeratedValueSet variable="credit-market-H">
      <value value="2"/>
      <value value="3"/>
      <value value="4"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="labor-market-M">
      <value value="2"/>
      <value value="3"/>
      <value value="4"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="goods-market-Z">
      <value value="2"/>
      <value value="3"/>
      <value value="4"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="wages-shock-xi">
      <value value="0.05"/>
      <value value="0.1"/>
      <value value="0.15"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="price-shock-eta">
      <value value="0.05"/>
      <value value="0.1"/>
      <value value="0.15"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="interest-shock-phi">
      <value value="0.05"/>
      <value value="0.1"/>
      <value value="0.15"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="production-shock-rho">
      <value value="0.05"/>
      <value value="0.1"/>
      <value value="0.15"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="test-troitzsch" repetitions="20" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <timeLimit steps="250"/>
    <metric>fn-unemployment-rate</metric>
    <metric>gini-index</metric>
    <metric>skewness-of-wealth</metric>
    <metric>avg-net-worth</metric>
    <metric>avg-propensity-to-consume</metric>
    <metric>real-GDP</metric>
    <metric>average-market-price</metric>
    <metric>fn-wealth-of-extorters</metric>
    <steppedValueSet variable="rejection-threshold" first="15" step="15" last="90"/>
    <steppedValueSet variable="probability-of-being-caught-lambda" first="10" step="10" last="70"/>
    <steppedValueSet variable="propensity-to-be-extorter-epsilon" first="20" step="10" last="50"/>
  </experiment>
  <experiment name="tiempo" repetitions="10" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>tiempo</metric>
    <steppedValueSet variable="number-of-firms" first="45" step="5" last="510"/>
    <enumeratedValueSet variable="propensity-to-be-extorter-epsilon">
      <value value="20"/>
    </enumeratedValueSet>
  </experiment>
</experiments>
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