restaurant_model.nlogo

globals [subjectivity] ;; how subjective the review of a customer is for a restaurant

breed [customers customer]
breed [restaurants restaurant]

customers-own [
  order-probability ;; the probability that a customer will order from a restaurant
  neighbor] ;; the restaurant the customer is assessing and potentially ordering from

restaurants-own[
  quality
  wealth
  review]

to setup
  clear-all

  ask patches [set pcolor 37]

  ;; user chooses how many initial customers
  create-customers initial-customers [
    set shape "person lumberjack"
    set size 1
    setxy random-xcor random-ycor
    set color red
    set order-probability 5]

    ;; user chooses how many initial restaurants and their initial quality, wealth and review
  create-restaurants initial-restaurants [
    set shape "house ranch"
    set size 2
    setxy random-xcor random-ycor
    set quality initial-quality
    set wealth initial-wealth
    set review initial-review
    set color green]

  reset-ticks
end

to go
  ;; The model stops when there are no more restaurants, a duopoly and a monopoly
if not any? restaurants [ user-message "Restaurant industry has collapsed. Call Remi" stop  ]
if count restaurants = 2 [ user-message "Dupoly. Call Anti-Trust Department" stop ]
if count restaurants = 1 [ user-message "Monopoly. Call Anti-Trust Department and the FBI" stop ]

  set subjectivity random (10) ;; the subjectivity of customers is a random constant for every run

  ask customers [ create-link-to one-of restaurants] ;; customers choose a restaurant to assess
  ask customers [
    order
    move
  ] ;; customers choose to order from that restaurant or not and then move
  ask customers [move]

  ask restaurants [
    invest
    cost-cut
    exit
    if quality < 0 [set quality 0]

    if wealth < mean [wealth] of restaurants [move]

    set color scale-color green wealth (max [wealth] of restaurants) 0] ;; color of restaurants changes based on wealth

    if mean [wealth] of restaurants > 100 [create-restaurants 1[
    set shape "house ranch"
    set size 2
    setxy random-xcor random-ycor
    set quality initial-quality
    set wealth initial-wealth
    set review initial-review
    set color green]
  ]
tick
  clear-links ;; customers chooses another restaurant to assess in the next run
end

 to order

 set neighbor one-of link-neighbors
;; the probability of ordering depends on the distance of the restaurant and the past review of that restaurant

 set order-probability ((distance neighbor) / importance-distance + [review] of neighbor) / 2
  ifelse order-probability >= standard ;; the standard is the threshold at which a customer will order
  [ask neighbor [
    set wealth wealth + 1 ;; wealth increases if customers orders
    set review ((quality + subjectivity)/ 2) ]] ;; review is updated, is a function of its inherent quality and the subjectivity of customers
  [ask neighbor [set wealth wealth - 10]] ;; wealth decreases if customer doesn't order
end

to invest ;;restaurants reinvest their wealth to increase their inherent quality
    if wealth > 20 [
    set quality quality + investing-flexibility
    set wealth wealth - investing-flexibility]
  ;; depending on their investing flexibility, they invest more or less
end

to cost-cut ;;restaurants make cuts to their costs to increase their wealth to the detriment of their quality
    if wealth < 10 [
    set quality quality - cost-cutting-flexibility
    set wealth wealth + cost-cutting-flexibility]
  ;; depending on their cost-cutting flexibility, they can rebalance their wealth and quality more easily invest more or less
end

to exit ;; restaurants die if no more wealth
  ask restaurants[
    if wealth < 0 [die]]
end

to move
  rt random 50
  lt random 50
  fd 1
end
@#$#@#$#@
GRAPHICS-WINDOW
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crazy nights
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BUTTON
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NIL
setup
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T
OBSERVER
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NIL
NIL
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BUTTON
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NIL
go
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OBSERVER
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NIL
NIL
NIL
1

SLIDER
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initial-customers
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19.0
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SLIDER
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initial-restaurants
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HORIZONTAL

SLIDER
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standard
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PLOT
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Histogram of Wealth
wealth
firms
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10.0
0.0
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false
"" "set-plot-y-range 0 1\nset-plot-x-range 0 max [wealth] of restaurants + 1"
PENS
"default" 1.0 1 -16777216 true "" "histogram [wealth] of restaurants"

PLOT
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Histogram of Quality
quality
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"" "set-plot-y-range 0 1\nset-plot-x-range 0 max [quality] of restaurants + 1"
PENS
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PLOT
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Histogram of Review
review
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PENS
"default" 1.0 0 -16777216 true "" "histogram [review] of restaurants"

PLOT
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Count of Restaurants
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NIL
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"" ""
PENS
"default" 1.0 0 -16777216 true "" "plot count restaurants"

PLOT
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Quality Distribution
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quality
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"" ""
PENS
"Mean" 1.0 0 -8053223 true "" "plot mean [quality] of restaurants"
"Median" 1.0 0 -15040220 true "" "plot median [quality] of restaurants"
"Max" 1.0 0 -14985354 true "" "plot max [quality] of restaurants"
"Min" 1.0 0 -10022847 true "" "plot min [quality] of restaurants"

PLOT
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345
Wealth Distribution
time
wealth
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10.0
0.0
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true
"" ""
PENS
"Mean" 1.0 0 -2674135 true "" "plot mean [wealth] of restaurants"
"Median" 1.0 0 -13840069 true "" "plot median [wealth] of restaurants"
"Max" 1.0 0 -13791810 true "" "plot max [wealth] of restaurants"
"Min" 1.0 0 -7858858 true "" "plot min [wealth] of restaurants"

PLOT
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190
875
340
Review Distribution
time
review
0.0
10.0
0.0
10.0
true
true
"" ""
PENS
"Mean" 1.0 0 -1604481 true "" "plot mean [review] of restaurants"
"Median" 1.0 0 -8330359 true "" "plot median [review] of restaurants"
"Max" 1.0 0 -5516827 true "" "plot max [review] of restaurants"
"Min" 1.0 0 -2382653 true "" "plot min [review] of restaurants"

MONITOR
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425
357
470
Restaurants
count restaurants
17
1
11

SLIDER
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importance-distance
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NIL
HORIZONTAL

SLIDER
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initial-wealth
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NIL
HORIZONTAL

SLIDER
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187
383
initial-quality
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1
NIL
HORIZONTAL

SLIDER
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187
433
initial-review
initial-review
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5.0
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1
NIL
HORIZONTAL

SLIDER
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cost-cutting-flexibility
cost-cutting-flexibility
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1
NIL
HORIZONTAL

SLIDER
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investing-flexibility
investing-flexibility
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3.0
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HORIZONTAL

@#$#@#$#@
## WHAT IS IT?

The restaurant model shows how consumers make decisions when faced with limited information.

Standard economic theory assumes that all agents are fully rational, taking into account all information available to make utility maximising decisions. This would mean that the most competitive (best quality and cheapest) firms will survive and non-competitive ones disappear. However, reality shows us that this is not the case, agents have bounded rationality and micro decisions might not always seem to make sense. However, on the macro-scale, the aggregate behaviour might or might not make sense.

We are trying to find out what patterns emerge from individual-level decision making. Does a market stay fully competitive, does a duopoly or monopoly emerge?

## HOW IT WORKS

Our marketplace is an online delivery app with (relatively) homogeneous chinese restaurants.

CUSTOMERS (turtles) will make a decision to order from a RESTAURANT (turles) or not based on various pieces of information. It will take into account the proximity of the restaurant, which is a proxy for delivery time and the REVIEW the last CUSTOMER has given it. If the combination of these variables reaches a certain threshold (the standard of the CUSTOMER), it will choose to order from the RESTAURANT. It will then move randomly accross the marketplace

RESTAURANTS are assigned a random location and a random QUALITY grade to start.
They then receive income from CUSTOMERS that have chosen to order from there , increasing their WEALTH. The RESTAURANTS' WEALTH decreases if CUSTOMER choses not to order, as they make a loss (inventory, labour). If their wealth is under the restaurants' average wealth, they chose to move randomly until they have above-average wealth.

If their WEALTH surpasses a certain level, they will be able to invest a share of that to improve their QUALITY grade.
If their WEALTH goes under a certain level, they will use cost-cutting techniques, which will increase their WEALTH but decrease their QUALITY.

Every time a CUSTOMER orders, they REVIEW the restaurant. That REVIEW is based partly on the QUALITY of the food they get and partly on a subjective (modelled as random) component.

When RESTAURANTS reach a WEALTH of 0, it will die (firm exit).

## HOW TO USE IT

The user first chooses the density of CUSTOMERS and RESTAURANTS in the marketplace using the initial-customers and initial-restaurants sliders.

The user then chooses the initial-wealth, initial-quality and initial-review of restaurants, for the initial time-step of the model.

They can then choose the importance of distance in the decision making with the importance-distance slider. The higher the  number, the less distance is important in the decision making of CUSTOMERS, and thus reviews are more heavily weighted in that decision.

They then choose what threshold CUSTOMERS need to reach to dine at a RESTAURANT. This is the standard slider, the higher it is, the higher standard there is, so RESTAURANTS must have a good overall "rating" for CUSTOMERS to dine there.

Finally, users can decide how easy RESTAURANTS can reinvest their WEALTH to improve their QUALITY or cost-cut to increase their WEALTH, at the detriment of their QUALITY. The higher the investing-flexibility AND cost-cutting-flexibility slider is, the more they can reinvest or COST-CUT, respectively.

We can view the histogram and time-series of the mean, median, min and max of the WEALTH, the QUALITY and the REVIEW of RESTAURANTS. We also view the count of restaurants.


## THINGS TO NOTICE

The model stops when there are no more restaurants or when a monopoly or duopoly is established.

## EXTENDING THE MODEL

RESTAURANTS could have various quality metrics such as customer service, the menu, food portion, food quality, a unique selling point, marketing, delivery and preparation time. Then we could make some customers value more or less (depending on their own peculiarity) each metric when reviewing those restaurants.

One could also give RESTAURANTS the ability to move locations based on demand, or have the ability to influence the REVIEW CUSTOMERS give them. They could also have a capacity constraint. Limiting their offer to CUSTOMERS if it becomes too popular.

Finally, we could model firm entries, new RESTAURANTS entering the market every few ticks, simulating a truly dynamic marketplace.

CUSTOMERS could also incur a cost when dining at a restaurant, taking that into accout when making a decision to dine at a RESTAURANT.

CUSTOMERS could assess all available restaurants when making their order decision instead of assessing one at each time step.

## NETLOGO FEATURES

I was not able to model the average REVIEW for all past CUSTOMERS, so the REVIEW is the one of the last customer who has dined at that RESTAURANT.

The color of the RESTAURANTS is scaled according to its WEALTH or QUALITY, which can be chosen in the code tab.

## CREDITS AND REFERENCES

DB for the motivation ;)
@#$#@#$#@
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@#$#@#$#@
NetLogo 6.2.0
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
<experiments>
  <experiment name="ABM CE" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <timeLimit steps="1000"/>
    <metric>count restaurants</metric>
    <metric>median [wealth] of restaurants</metric>
    <metric>median [quality] of restaurants</metric>
    <metric>median [review] of restaurants</metric>
    <enumeratedValueSet variable="importance-distance">
      <value value="1"/>
      <value value="5"/>
      <value value="10"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="initial-review">
      <value value="1"/>
      <value value="5"/>
      <value value="10"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="initial-quality">
      <value value="1"/>
      <value value="5"/>
      <value value="10"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="investing-flexibility">
      <value value="1"/>
      <value value="3"/>
      <value value="5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="initial-customers">
      <value value="10"/>
      <value value="20"/>
      <value value="30"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="initial-wealth">
      <value value="1"/>
      <value value="5"/>
      <value value="10"/>
    </enumeratedValueSet>
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</experiments>
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