This page was automatically generated by NetLogo 4.1.1.
The applet requires Java 5 or higher. Java must be enabled in your browser settings. Mac users must have Mac OS X 10.4 or higher. Windows and Linux users may obtain the latest Java from Sun's Java site.
In order for this to work, this file, your model file (DRA-CRRA-EU-revCD-3l2p.nlogo), and the file NetLogoLite.jar must all be in the same directory. (You can copy NetLogoLite.jar from the directory where you installed NetLogo.)
On some systems, you can test the applet locally on your computer before uploading it to a web server. It doesn't work on all systems, though, so if it doesn't work from your hard drive, please try uploading it to a web server.
You don't need to include everything in this file in your page. If you want, you can just take the HTML code beginning with <applet> and ending with </applet>, and paste it into any HTML file you want. It's even OK to put multiple <applet> tags on a single page.
If NetLogoLite.jar and your model are in different directories, you must modify the archive= and value= lines in the HTML code to point to their actual locations. (For example, if you have multiple applets in different directories on the same web server, you may want to put a single copy of NetLogoLite.jar in one central place and change the archive= lines of all the HTML files to point to that one central copy. This will save disk space for you and download time for your users.)
powered by NetLogo
view/download model file: DRA-CRRA-EU-revCD-3l2p.nlogo
This section could give a general understanding of what the model is trying to show or explain.
This section could explain what rules the agents use to create the overall behavior of the model.
This section could explain how to use the model, including a description of each of the items in the interface tab.
This section could give some ideas of things for the user to notice while running the model.
This section could give some ideas of things for the user to try to do (move sliders, switches, etc.) with the model.
This section could give some ideas of things to add or change in the procedures tab to make the model more complicated, detailed, accurate, etc.
This section could point out any especially interesting or unusual features of NetLogo that the model makes use of, particularly in the Procedures tab. It might also point out places where workarounds were needed because of missing features.
This section could give the names of models in the NetLogo Models Library or elsewhere which are of related interest.
This section could contain a reference to the model's URL on the web if it has one, as well as any other necessary credits or references.
;; Following George Szeps' paper in Complexity 1997 (for which I was a referee), I thought I'd take
;; Nigel Gilbert's GA code for NetLogo 3.1.1 and put a fitness function in which agents choose
;; between two lotteries, based on the calculated means and variances of the two lotteries and
;; the agent's Constant Relative Risk Aversion coefficient, rho = gamma*w, after which the chosen lottery would
;; be realised and the prize added to (or subtracted from) the agent's fitness.
;; Thanks to Nigel Gilbert and Luis Izquierdo
;; -- Robert Marks {file-print commented out}
globals [
number-of-turtles ; the number of agents (turtles)
chromo-length ; the length of a chromosome (the number of
; bits)
twochromo-len/two ; half of the range of rho
mean-rho ; mean across turtles
mutation-rate ; avg. number of mutations per
; chromosome per generation
generation ; count of generations
running-mean-rho ; running mean rho
prizes ; list of two randomly chosen prizes
lottery ; list of two probabilities
]
turtles-own [
chromosome ; a list of ones and zeros
rho ; risk-preference parameter, CRRA
fitness ; the fitness of this turtle
cohort ; the geration this turtle was born in
]
;=========== obtain payoff for each agent as it faces a lottery ==============
to find-fitness ; turtle procedure. Report my fitness.
; This procedure will change according to
; the problem that the Genetic Algorithm
; is expected to solve.
set fitness 0
set rho decimal
repeat 1000 [
set fitness (fitness + payoff )
]
end
to-report decimal
let dec 0
foreach chromosome [set dec (dec + dec + ? ) ]
report ((twochromo-len/two - dec) / 100 )
;; show rho
;; report rho
end
to-report lottery-values ; turtle procedure. Set up two three-outcome lotteries, where
; each outcome is chosen randomly from [-10 +10], and the
; two probabilities of each of lottery are chosen.
; Calculate the mean and variance of the lotteries, and
; an outcome of the lotteries. The agent (turtle) chooses one of the
; two lotteries, based on a mean-variance ranking, which assumes a normal
; distribution for each lottery and a CRRA utility function.
; The agent's fitness is its final wealth after N choices.
; (Not yet: If an agent's wealth falls below -10, then
; the agent becomes bankrupt, and "dies". Each agent starts with an
; endowment of 50, before facing N pairs of lotteries to choose between.)
setup-prizes
create-lottery
let utprize1 ((fitness + (item 0 prizes)) ^ (1 - rho )) / (1 - rho )
let utprize2 ((fitness + (item 1 prizes)) ^ (1 - rho )) / (1 - rho )
let exputil ((item 0 lottery) * utprize1) + ((item 1 lottery) * utprize2)
let l-prob random-float 1
let act-val-lot
ifelse-value (l-prob <= (item 0 lottery))
[(item 0 prizes)]
[(item 1 prizes)]
;; show act-val-lot
report (list exputil act-val-lot )
end
to setup-prizes
set prizes 0
set prizes n-values 2 [max-abs-prize - random (2 * max-abs-prize + 1)]
;; two random prizes between -max-abs-prize and +max-abs-prize
;; show prizes
end
to create-lottery
let probabilities 0
set probabilities n-values 2 [random-float 1]
let sum-of-probabilities (sum probabilities)
set lottery map [? / sum-of-probabilities] probabilities
;; show lottery
;; lottery is a list of two random probs summing to 1
end
to-report payoff ;; turtle procedure. Choose the highest-utility (CRRA) lottery of three
let lot1 lottery-values
let lot2 lottery-values
let lot3 lottery-values
report ifelse-value (item 0 lot1 >= item 0 lot2)
[ifelse-value (item 0 lot1 >= item 0 lot3)
[(item 1 lot1) ]
[(item 1 lot3) ]]
[ifelse-value (item 0 lot2 >= item 0 lot3)
[(item 1 lot2) ]
[(item 1 lot3) ]]
end
;======================== end of my-fitness procedures========================
to mutate ; turtle procedure. Flip a bit with
; a probability of 1 in 1/mutation-rate
let place 0
let old 0
let new 0
set place (random (chromo-length / mutation-rate))
if (place < chromo-length) [
set old (item place chromosome)
ifelse (old = 0)
[set new 1]
[set new 0]
set chromosome (replace-item place chromosome new)
]
end
to cross [ mate ] ; turtle procedure. Exchange a chunk of my
; and my mate's chromosomes by swapping
; the genes from the beginning to a
; randomly chosen stopping place
let place 0
let my-gene 0
let your-gene 0
repeat random (chromo-length) [
set my-gene (item place chromosome)
set your-gene [item place chromosome] of mate
set chromosome
(replace-item place chromosome your-gene)
; set [chromosome] of mate
ask mate [ set chromosome
(replace-item place [chromosome] of mate my-gene) ]
set place place + 1
]
end
to breed-a-turtle [ parent ] ; turtle procedure.
; Make new turtle and give it
; the genes of its parent turtle.
create-turtles 1 [
set cohort generation + 1
set chromosome [chromosome] of parent
]
end
to-report select-a-turtle ; turtle procedure.
; Use a tournament selection.
; The turtle reported is the
; fitter of two chosen at random.
let turtleA 0
let turtleB 0
set turtleA
one-of turtles with [ cohort = generation ]
set turtleB
one-of turtles with [ cohort = generation ]
ifelse ([fitness] of turtleA > [fitness] of turtleB)
[report turtleA]
[report turtleB]
end
to colour-turtle ; turtle procedure. Set the
; colour of the displayed turtle,
; using the binary value of
; the chromosome to index the
; colour scale
set color wrap-color reduce [ ?1 * 2 + ?2 ] chromosome
end
to setup ; observer procedure.
clear-turtles
clear-all-plots
set generation 0
set mean-rho 0
set running-mean-rho 0
set chromo-length 21
set twochromo-len/two ( 2 ^ (chromo-length - 1))
set number-of-turtles 100
set mutation-rate mutatn-rate
set-current-plot "Fitness"
set-current-plot-pen "axis"
auto-plot-off
plotxy 0 0
plotxy 1000000000 0
auto-plot-on
set-current-plot "rho"
set-current-plot-pen "axis"
auto-plot-off
plotxy 0 0
plotxy 1000000000 0
auto-plot-on
create-turtles number-of-turtles [
set cohort 0
set fitness 0
; make the chromosome a list of random 0s and 1s
set chromosome n-values chromo-length [ random 2 ]
]
ask turtles [
find-fitness
display-turtles
]
end
to display-turtles ; turtle procedure. Display the
; turtle on the perimeter of
; a circle
colour-turtle
set heading who * (360 / number-of-turtles)
fd 10
end
to go ; observer procedure.
let turtleA 0
let turtleB 0
; Breed 60% of the new population and mate them to
; produce two offspring
repeat (0.3 * number-of-turtles) [
set turtleA select-a-turtle
set turtleB select-a-turtle
ask turtleA [ cross turtleB ]
breed-a-turtle turtleA
breed-a-turtle turtleB
]
; Just copy 40% of the population into the
; new generation
repeat (0.4 * number-of-turtles) [
breed-a-turtle select-a-turtle
]
; Kill off the old generation
ask turtles with [ cohort = generation ] [ die ]
; Mutate the new generation, display them, and
; find their fitness
ask turtles [
mutate
display-turtles
find-fitness
]
set mean-rho mean [rho ] of turtles
set running-mean-rho ((running-mean-rho * generation ) + mean-rho) / (generation + 1)
; file-open "./mean-rhos.txt"
; file-write mean-rho
; file-print running-mean-rho
; file-close
set generation generation + 1
plot-results
if (generation = 1150) [ stop ]
end
to plot-results ; observer procedure. Plot the current
; mean fitness
set-current-plot "Fitness"
set-current-plot-pen "average"
plot mean [ fitness ] of turtles
set-current-plot-pen "min-f"
plot min [ fitness ] of turtles
set-current-plot-pen "max-f"
plot max [ fitness ] of turtles
set-current-plot "rho"
set-current-plot-pen "avrho"
plot mean [rho ] of turtles
set-current-plot-pen "maxrho"
plot max [rho ] of turtles
set-current-plot-pen "minrho"
plot min [rho ] of turtles
end