;DEFINITION OF THE VARIABLES breeds [constrained random-ones pseudo-rational inverted] turtles-own [pay-off] patches-own [price quantity-demanded f] globals [mean-price max-price min-price pay-off-constrained pay-off-random-ones pay-off-pseudo-rational pay-off-inverted] ;.......................................................................................................... to setup ca setup-agents setup-patches setup-graphics ask patches [setup-plot] end to setup-agents ;AGENT'S CREATION AND THEIR RANDOM PLACEMENT create-constrained 100 - (number-pseudo-rational) - (number-random-ones) ask constrained [set color black] ask constrained [set shape "person"] ask constrained [set size 0.1] ask constrained [fd random 100] create-random-ones number-random-ones ask random-ones [set color blue] ask random-ones [set shape "person"] ask random-ones [set size 0.1] ask random-ones [fd random 100] create-pseudo-rational number-pseudo-rational ask pseudo-rational [set color pink] ask pseudo-rational [set shape "person"] ask pseudo-rational [set size 0.1] ask pseudo-rational [fd random 100] if inverted? [ create-inverted 1 ask inverted [set color white] ask inverted [set shape "person"] ask inverted [set size 0.1] ask inverted [fd random 100] ] end to setup-patches ask patches ;DETERMINE THE VALUE OF THE VARIABLE f FOR EVERY MARKETS (PATCHES) [if (pxcor = -1) and (pycor = 1) [set f random-float 15] if (pxcor = 0) and (pycor = 1) [set f random-float 15] if (pxcor = 1) and (pycor = 1) [set f random-float 15] if (pxcor = -1) and (pycor = 0) [set f random-float 15] if (pxcor = 0) and (pycor = 0) [set f random-float 15] if (pxcor = 1) and (pycor = 0) [set f random-float 15] if (pxcor = -1) and (pycor = -1) [set f random-float 15] if (pxcor = 0) and (pycor = -1) [set f random-float 15] if (pxcor = 1) and (pycor = -1) [set f random-float 15]] ask patches [set quantity-demanded (count turtles-here) * 10] ;INITIAL Qi = number of agents * 10 ask patches [set price (quantity-demanded) * 0.25 + (f)] ; INITIAL Pi = f + Qi * 0.25 set mean-price mean values-from patches [price] ask patches [ if (price / mean-price > (1.10)) [set pcolor red] if (price / mean-price <= (0.90)) [set pcolor green] if (price / mean-price <= (1.10)) and (price / mean-price > (0.90)) [set pcolor yellow] ] set max-price max values-from patches [price] set min-price min values-from patches [price] end to setup-graphics set-current-plot "max-min" set-current-plot "pay-off" end to setup-plot set-current-plot "local-price" set-plot-y-range 0 (mean-price) end ;......................................................................................... to go move-agents update-patches set max-price max values-from patches [price] set min-price min values-from patches [price] update-pay-off update-graphics ask patches [update-plot] end to move-agents ;AGENT'S MOVEMENT ask pseudo-rational ;PSEUDO-RATIONAL [set heading downhill price] ;DIREZIONE DELLO SPOSTAMENTO VERSO IL MERCATO CON P MINORE ask pseudo-rational [ if (pcolor = green) and (price > min-price) ;MOVIMENTO SOLO SE Pi> MinP [ if (random-float 100 < (pr-green)) [fd 1] ] if (pcolor = red) [ if (random-float 100 < (pr-red)) [fd 1] ] if (pcolor = yellow)and (price > min-price) [ if (random-float 100 < (pr-yellow)) [fd 1] ] ] ask random-ones ;CASUALI [set heading random (360)] ;DIREZIONE DELLO SPOSTAMENTO CASUALE ask random-ones [ if (pcolor = green) [ if (random-float 100 < (pr-green)) [fd 1] ] if (pcolor = red) [ if (random-float 100 < (pr-red)) [fd 1] ] if (pcolor = yellow) [ if (random-float 100 < (pr-yellow)) [fd 1] ] ] ask inverted ;INVERSO [set heading uphill price] ask inverted [ if (price < max-price) [fd 1] ] end to update-patches update-quantity-demanded update-price end to update-quantity-demanded ask patches [set quantity-demanded (count turtles-here) * 10] end to update-price ask patches [set price (quantity-demanded) * 0.25 + (f)] set mean-price mean values-from patches [price] ask patches [ if (price / mean-price > (1.10)) [set pcolor red] if (price / mean-price <= (0.90)) [set pcolor green] if (price / mean-price <= (1.10)) and (price / mean-price > (0.90)) [set pcolor yellow] ] end to update-pay-off ask turtles [set pay-off (mean-price) - (price)] ask constrained [set pay-off-constrained mean values-from constrained [pay-off]] ask random-ones [set pay-off-random-ones mean values-from random-ones [pay-off]] ask pseudo-rational [set pay-off-pseudo-rational mean values-from pseudo-rational [pay-off]] ask inverted [set pay-off-inverted mean values-from inverted [pay-off]] end to update-graphics set-current-plot "max-min" set-current-plot-pen "min" plot min-price set-current-plot-pen "max" plot max-price set-current-plot-pen "max-min" plot (max-price) - (min-price) set-current-plot "pay-off" set-current-plot-pen "pay-off-constrained" plot pay-off-constrained set-current-plot-pen "pay-off-random-ones" plot pay-off-random-ones set-current-plot-pen "pay-off-pseudo-rational" plot pay-off-pseudo-rational set-current-plot-pen "pay-off-inverted" plot pay-off-inverted end to update-plot set-current-plot "local-price" set-current-plot-pen "-1,1" if (pxcor = -1) and (pycor = 1) [plot price] end ;..................................................................................... to step-once move-agents update-patches set max-price max values-from patches [price] set min-price min values-from patches [price] update-pay-off update-graphics ask patches [update-plot] end @#$#@#$#@ GRAPHICS-WINDOW 554 10 1014 491 1 1 150.0 1 10 1 1 1 0 CC-WINDOW 5 505 1023 600 Command Center BUTTON 22 10 85 43 NIL setup NIL 1 T OBSERVER T NIL BUTTON 22 42 85 75 NIL go T 1 T OBSERVER T NIL SLIDER 86 10 258 43 pr-green pr-green 0 100 4 1 1 NIL SLIDER 86 42 258 75 pr-red pr-red 0 100 4 1 1 NIL SLIDER 86 75 258 108 pr-yellow pr-yellow 0 100 3 1 1 NIL PLOT 259 10 554 134 local-price NIL NIL 0.0 10.0 5.0 50.0 true false PENS "-1,1" 1.0 0 -65281 true PLOT 259 135 553 285 max-min NIL NIL 0.0 10.0 0.0 10.0 true false PENS "max" 1.0 0 -65536 true "min" 1.0 0 -11352576 true "max-min" 1.0 0 -7566196 true PLOT 262 293 557 443 pay-off pay-off NIL 0.0 10.0 0.0 10.0 true false PENS "pay-off-constrained" 1.0 0 -16777216 true "pay-off-random-ones" 1.0 0 -16776961 true "pay-off-pseudo-rational" 1.0 0 -65413 true "pay-off-inverted" 1.0 0 -16711681 true MONITOR 134 378 259 427 NIL pay-off-constrained 3 1 MONITOR 2 378 135 427 NIL pay-off-random-ones 3 1 MONITOR 4 330 155 379 NIL pay-off-pseudo-rational 3 1 SLIDER 61 108 259 141 number-pseudo-rational number-pseudo-rational 0 100 22 1 1 NIL BUTTON -3 74 85 107 NIL step-once NIL 1 T OBSERVER T NIL SLIDER 85 144 257 177 number-random-ones number-random-ones 0 100 43 1 1 NIL MONITOR 156 328 260 377 NIL pay-off-inverted 3 1 SWITCH 112 189 220 222 inverted? inverted? 0 1 -1000 @#$#@#$#@ WHAT IS IT? ----------- We present a very simple model that tries to describe the dynamic behaviour of the prices of an undifferentiated good among different markets under different strategies implemented by the consumers. We assume that in each market operates a representative producer and that there are 4 categories of consumers (constrained, pseudo-rational, random-ones, inverted) that follow different behavioural rules. HOW IT WORKS ------------ The price in each market is set by a simple price-rule: P = F + Q*0.25 where F is typical of the market and Q is total demand for the market. The procedure follow in this way: (i) we generate the 9 markets, the 9 representative producers and the consumers (ii)the consumers are casually placed (iii)following the price rule the representative producers set the initial prices in the 9 markets Then in every period: (iv)the consumers decide if and where to move according to their rules: the "constrained" don't have the possibility to move the "random-ones" move randomly with different probabilities according to the ratio P/average P registered in the former period: if P/average P < 0.9 pr-green, if 0.9 < P/average P > 1.1 pr-yellow, if P/average P > 1.1 pr-red the "pseudo-rational" move to the market where the price was minimum in the former period with different probabilities according to the ratio P/average P: if P/average P < 0.9 pr-green, if 0.9 < P/average P > 1.1 pr-yellow, if P/average P > 1.1 pr-red the "inverted" move to the market where the price was maximum in the previous period (v)after the movement of the consumers the producers set the prices in the 9 markets HOW TO USE IT ------------- With the "setup" button we create the world in which the agents interact and the initial value of the variables (i-iii). With the button "go" it starts the simulation. The button "step-once" simulate just one period. Using the sliders before the Setup one can set the value of the parameters number-random-ones, number-pseudo-rational, inverted? (the number of the consumers pseudo-rational, random-ones and inverted) and indirectly the number of constrained (100 - number-pseudo-rational - number-casual); while pr-green, pr-red, pr-yellow can be change every time during the simulation. There are 3 graphics: "local-price" represent the dynamic of the price in a single market (the one characterized by pxcor=-1, pycor=1), "max-min" plot the max, min price in each period and their difference, "pay-off" plot the average pay-off of each category of consumers (which are also displayed in the 4 monitors). THINGS TO NOTICE ---------------- The key variables to look at in the simulation are the difference between the max and min price (stability of the markets) and the pay-offs of the categories of consumers (in order to analyze the efficiency of their strategies). The patches (markets) are green if P/average P < 0.9, yellow if 0.9 < P/average P > 1.1, and red if P/average P > 1.1 THINGS TO TRY ------------- The value of pr-green, pr-red, pr-yellow can be interpreted as a measure of the flexibility of the prices: low values means that the prices are updated after that few consumers moved. The number of "constrained" can be interpreted as a measure of the opennes of the economy. The presence of the inverted consumer helps to understand if the behaviour of the pseudo-rationl agents is really rational. EXTENDING THE MODEL ------------------- The biggest limit of this model is the absence of really rational agents. Representative producers and every category of consumers have a fixed behavioural rule. An interesting extension could be implemented introducing agents able to learn and modify their behaviour while the model is running (allowing the consumers to coordinate their actions). 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