globals [
  attendance        ;; the current attendance at the bar
  history           ;; list of past values of attendance
  home-patches      ;; agentset of green patches
  bar-patches       ;; agentset of blue patches
  crowded-patch     ;; patch where we show the "CROWDED" label
]

turtles-own [
  strategies      ;; list of strategies
  best-strategy   ;; index of the current best strategy
  attend?         ;; true if the agent currently plans to attend the bar
  prediction      ;; current prediction of the bar attendance
  reward          ;; the amount that each agent has been rewarded
]


to setup
  clear-all
  set-default-shape turtles "person"

  ;; create the 'homes'
  set home-patches patches with [pycor < 0 or (pxcor <  0 and pycor >= 0)]
  ask home-patches [ set pcolor green ]

  ;; create the 'bar'
  set bar-patches patches with [pxcor > 0 and pycor > 0]
  ask bar-patches [ set pcolor blue ]

  ;; initialize the previous attendance randomly so the agents have a history
  ;; to work with from the start
  set history n-values (memory-size * 2) [random 100]
  set attendance first history

  ;; use one of the patch labels to visually indicate whether or not the
  ;; bar is "crowded"
  ask patch (0.75 * max-pxcor) (0.5 * max-pycor) [
    set crowded-patch self
    set plabel-color yellow
  ]

  ;; create the agents and give them random strategies
  ;; these are the only strategies these agents will ever have though they
  ;; can change which of this "bag of strategies" they use every tick
  create-turtles 100 [
    set color white
    move-to-empty-one-of home-patches
    set strategies n-values number-strategies [random-strategy]
    set best-strategy first strategies
    set reward 0
    update-strategies
  ]

  ;; start the clock
  reset-ticks
end


to go
  ;; update the global variables
  ask crowded-patch [ set plabel "" ]
  ;;  each agent predicts attendance at the bar and decides whether or not to go
  ask turtles [
    set prediction predict-attendance best-strategy sublist history 0 memory-size
    set attend? (prediction <= overcrowding-threshold)  ;; true or false
       ;; scale the turtle's color a shade of red depending on its reward level (white for little reward, black for high reward)
    set color scale-color red reward  (max [ reward ] of turtles + 1) 0
  ]

  ;; depending on their decision the turtles go to the bar or stay at home
  ask turtles [
    ifelse attend?
      [ move-to-empty-one-of bar-patches
        set attendance attendance + 1 ]
      [ move-to-empty-one-of home-patches ]
  ]

  ;; if the bar is crowded indicate that on the view
  set attendance count turtles-on bar-patches
  ifelse attendance > overcrowding-threshold [
    ask crowded-patch [ set plabel "CROWDED" ]
  ]
  [
    ask turtles with [ attend? ] [
      set reward reward + 1
    ]
  ]

  ;; update the attendance history
  ;; remove oldest attendance and prepend latest attendance
  set history fput attendance but-last history
  ;;  the agents decide what the new best strategy is
  ask turtles [ update-strategies ]
  ;; advance the clock
  tick
end

;; determines which strategy would have predicted the best results had it been used this round.
;; the best strategy is the one that has the sum of smallest differences between the
;; current attendance and the predicted attendance for each of the preceding
;; weeks (going back MEMORY-SIZE weeks)
;; this does not change the strategies at all, but it does (potentially) change the one
;; currently being used and updates the performance of all strategies
to update-strategies
  let best-score memory-size * 100 + 1
  foreach strategies [ the-strategy ->
    let score 0
    let week 1
    repeat memory-size [
      set prediction predict-attendance the-strategy sublist history week (week + memory-size)
      ;; increment the score by the difference between this week's attendance and your prediction for this week
      set score score + abs (item (week - 1) history - prediction)
      set week week + 1
    ]
    if (score <= best-score) [
      set best-score score
      set best-strategy the-strategy
    ]
  ]
end

;; this reports a random strategy. a strategy is just a set of weights from -1.0 to 1.0 which
;; determines how much emphasis is put on each previous time period when making
;; an attendance prediction for the next time period
to-report random-strategy
  report n-values (memory-size + 1) [1.0 - random-float 2.0]
end

;; This reports an agent's prediction of the current attendance
;; using a particular strategy and portion of the attendance history.
;; More specifically, the strategy is then described by the formula
;; p(t) = x(t - 1) * a(t - 1) + x(t - 2) * a(t -2) +..
;;      ... + x(t - MEMORY-SIZE) * a(t - MEMORY-SIZE) + c * 100,
;; where p(t) is the prediction at time t, x(t) is the attendance of the bar at time t,
;; a(t) is the weight for time t, c is a constant, and MEMORY-SIZE is an external parameter.
to-report predict-attendance [strategy subhistory]
  ;; the first element of the strategy is the constant, c, in the prediction formula.
  ;; one can think of it as the the agent's prediction of the bar's attendance
  ;; in the absence of any other data
  ;; then we multiply each week in the history by its respective weight
  report 100 * first strategy + sum (map [ [weight week] -> week * weight ] butfirst strategy subhistory)
end

;; In this model it doesn't really matter exactly which patch
;; a turtle is on, only whether the turtle is in the home area
;; or the bar area.  Nonetheless, to make a nice visualization
;; this procedure is used to ensure that we only have one
;; turtle per patch.
to move-to-empty-one-of [locations]  ;; turtle procedure
  move-to one-of locations
  while [any? other turtles-here] [
    move-to one-of locations
  ]
end


; Copyright 2007 Uri Wilensky.
; See Info tab for full copyright and license.
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## ACKNOWLEDGMENT

This model is from Chapter Three of the book "Introduction to Agent-Based Modeling: Modeling Natural, Social and Engineered Complex Systems with NetLogo", by Uri Wilensky & William Rand.

* Wilensky, U. & Rand, W. (2015). Introduction to Agent-Based Modeling: Modeling Natural, Social and Engineered Complex Systems with NetLogo. Cambridge, MA. MIT Press.

This model is in the IABM Textbook folder of the NetLogo Models Library. The model, as well as any updates to the model, can also be found on the textbook website: http://www.intro-to-abm.com/.

## WHAT IS IT?

El Farol is a bar in Santa Fe, New Mexico.  The bar is popular --- especially on Thursday nights when they offer Irish music --- but sometimes becomes overcrowded and unpleasant. In fact, if the patrons of the bar think it will be overcrowded they stay home; otherwise they go enjoy themselves at El Farol.  This model explores what happens to the overall attendance at the bar on these popular Thursday evenings, as the patrons use different strategies for determining how crowded they think the bar will be. This version extends the El Farol model by changing the color of agents that attend the bar more often. The agents who have visited more are darker red.

El Farol was originally put forth by Brian Arthur (1994) as an example of how one might model economic systems of boundedly rational agents who use inductive reasoning.

## HOW IT WORKS

An agent will go to the bar on Thursday night if they think that there will not be more than a certain number of people there --- a number given by the OVERCROWDING-THRESHOLD.  To predict the attendance for any given week, each agent has access to a set of prediction strategies and the actual attendance figures of the bar from previous Thursdays.  A prediction strategy is represented as a list of weights that determines how the agent believes that each time period of the historical data affects the attendance prediction for the current week.  One of these weights (the first one) is a constant term which allows the baseline of the prediction to be modified.  This definition of a strategy is based on an implementation of Arthur's model as revised by David Fogel et al. (1999).  The agent decides which one of its strategies to use by determining which one would have done the best had they used it in the preceding weeks.

Interestingly, the optimal strategy from a perfectly rational point-of-view would be to always go to the bar since you are not punished for going when it is crowded, but in Arthur's model agents are not optimizing attending when not crowded, instead they are optimizing their prediction of the attendance.

The number of potential strategies an agent has is given by NUMBER-STRATEGIES, and these potential strategies are distributed randomly to the agents during SETUP. As the model runs, at any one tick each agent will only utilize one strategy, based on its previous ability to predict the attendance at the bar.  In this version of the El Farol model, agents are given strategies and do not change them once they have them, however since they can change their strategies at any time based on performance, the ecology of strategies being used by the whole population changes over time.  The length of the attendance history the agents can use for a prediction or evaluation of a strategy is given by MEMORY-SIZE.  This evaluation of performance is carried out in UPDATE-STRATEGIES, which does not change the strategies, but rather updates the performance of each strategy by testing it, and then selecting the strategy that has the best performance given the current data.  In order to test each strategy its performance on MEMORY-SIZE past days is computed.  To make this work, the model actually records twice the MEMORY-SIZE historical data so that a strategy can be tested MEMORY-SIZE days into the past still using the full MEMORY-SIZE data to make its prediction.

## HOW TO USE IT

The NUMBER-STRATEGIES slider controls how many strategies each agent keeps in its memory. The OVERCROWDING-THRESHOLD slider controls when the bar is considered overcrowded. The MEMORY slider controls how far back, in the history of attendance, agents remember. To run the model, set the NUMBER-STRATEGIES, OVERCROWDING-THRESHOLD and MEMORY size, press SETUP, and then GO.

The BAR ATTENDANCE plot shows the average attendance at the bar over time.

## THINGS TO NOTICE

Does the color scaling visualization help you identify interesting patterns of behavior in the model?

## NETLOGO FEATURES

Lists are used to represent strategies and attendance histories.

`n-values` is useful for generating random strategies.

## RELATED MODELS

Arthur's original model has been generalized as the Minority Game and is included in the models library.
Wilensky, U. (2004).  NetLogo Minority Game model.  http://ccl.northwestern.edu/netlogo/models/MinorityGame.  Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

There is a participatory simulation version of the Minority Game model in the models library.

Stouffer, D. & Wilensky, U. (2004). NetLogo Minority Game HubNet model. http://ccl.northwestern.edu/netlogo/models/MinorityGameHubNet. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

There is an alternative implementation of this model with more parameters that is part of the NetLogo User Community Models.

## CREDITS AND REFERENCES

This model is adapted from:

Rand, W. and Wilensky, U. (1997). NetLogo El Farol model. http://ccl.northwestern.edu/netlogo/models/ElFarol. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

This model is inspired by a paper by W. Brian Arthur. "Inductive Reasoning and Bounded Rationality", W. Brian Arthur, The American Economic Review, 1994, v84n2, p406-411.

David Fogel et al. also built a version of this model using a genetic algorithm.  "Inductive reasoning and bounded rationality reconsidered", Fogel, D.B.; Chellapilla, K.; Angeline, P.J., IEEE Transactions on Evolutionary Computation, 1999, v3n2, p142-146.

## HOW TO CITE

This model is part of the textbook, “Introduction to Agent-Based Modeling: Modeling Natural, Social and Engineered Complex Systems with NetLogo.”

If you mention this model or the NetLogo software in a publication, we ask that you include the citations below.

For the model itself:

* Rand, W., Wilensky, U. (2007).  NetLogo El Farol Extension 1 model.  http://ccl.northwestern.edu/netlogo/models/ElFarolExtension1.  Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL.

Please cite the NetLogo software as:

* Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Please cite the textbook as:

* Wilensky, U. & Rand, W. (2015). Introduction to Agent-Based Modeling: Modeling Natural, Social and Engineered Complex Systems with NetLogo. Cambridge, MA. MIT Press.

## COPYRIGHT AND LICENSE

Copyright 2007 Uri Wilensky.

![CC BY-NC-SA 3.0](http://ccl.northwestern.edu/images/creativecommons/byncsa.png)

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.  To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

Commercial licenses are also available. To inquire about commercial licenses, please contact Uri Wilensky at uri@northwestern.edu.

<!-- 2007 Cite: Rand, W., Wilensky, U. -->
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