# A template for bootstrapping new models.
# See https://helipad.dev/ for complete documentation
#===============
# SETUP
# Instantiate the object and add parameters, breeds, and goods below.
#===============
from helipad import Helipad
# from utility import CobbDouglas
heli = Helipad()
heli.name = 'Model Name'
heli.order = 'random' #Can be changed to 'linear' or 'match'
heli.stages = 1 #Change to create a multi-stage model
# heli.addParameter('name', 'title', 'type (slider, menu, or check)', dflt=default, opts={depends on type})
# heli.addGood('good1','hex color', lambda breed: endowment)
# heli.addBreed('name1', 'hex color')
# heli.addBreed('name2', 'hex color')
#===============
# BEHAVIOR
# A list of hooks and their function signatures can be found at http://helipad-docs.nfshost.com/hooks/
#===============
#Any variables or properties the agent should keep track of should have default values set here.
@heli.hook
def agentInit(agent, model):
#agent.myAgentProperty = 0
# A sample spatial model with agents eating grass off patches.
# No visualization as of yet
#===============
# SETUP
#===============
from helipad import Helipad
heli = Helipad()
heli.name = 'Grass Eating'
heli.order = 'random'
heli.stages = 5
heli.addParameter('energy', 'Energy from grass', 'slider', dflt=2, opts={'low': 2, 'high': 10, 'step': 1})
heli.addParameter('smart', 'Smart consumption', 'check', dflt=True)
heli.addParameter('e2reproduce', 'Energy to reproduce', 'slider', dflt=25, opts={'low': 0, 'high': 100, 'step': 5})
heli.addParameter('maleportion', 'Male portion reproduction', 'slider', dflt=40, opts={'low': 0, 'high': 100, 'step': 5})
heli.addParameter('maxLife', 'Max Lifespan', 'slider', dflt=200, opts={'low': 100, 'high': 1000, 'step': 10})
heli.addParameter('grassrate', 'Grass Rate', 'slider', dflt=10, opts={'low': 1, 'high': 100, 'step': 1})
heli.params['num_agent'].opts = {'low': 1, 'high': 200, 'step': 1}
heli.param('num_agent', 200)
heli.addBreed('male', 'blue')
heli.addBreed('female', 'pink')
heli.addGood('energy', 'red', 5)
from random import choice, randint
from numpy import mean
# Replicates the repeated PD tournament from Axelrod (1980)
# Paper here: https://www.jstor.org/stable/173932
# Strategies indexed here: https://axelrod.readthedocs.io/en/stable/reference/overview_of_strategies.html#axelrod-s-first-tournament
# Strategies requiring chi-squared tests for randomness or such not yet implemented
# Difference from the original: The tournament matches stochastically, rather than being a round-robin, and repeats.
#===============
# SETUP
#===============
from helipad import Helipad
import numpy.random as random
from numpy import mean
heli = Helipad()
heli.name = 'Axelrod Tournament'
heli.order = 'match'
heli.params[
'num_agent'].type = 'hidden' #So we can postpone breed determination until the end
#Initial parameter values match the payoffs in Table 1
heli.addParameter('cc',
'C-C payoff',
'slider',
dflt=3,
opts={
'low': 0,
'high': 10,
'step': 0.5
})
# A model of the long-run cyclical dynamics of urbanization and human capital.
from collections import namedtuple
import pandas, random as rand2
from helipad import Helipad
from math import sqrt, log, floor
from numpy import *
heli = Helipad()
#================
# CONFIGURATION
#================
heli.addBreed('urban', '#CC0000')
heli.addBreed('rural', '#00CC00')
heli.addGood('consumption', '#000000')
#Constrain the parameter space to values resulting in H* = 100
def constrain(model, var, val):
if var=='city': model.params['rent'].disabled(val)
elif model.param('city'):
if var=='fixed':
model.params['rent'].enable() #Tkinter won't let you update the value of a disabled widget...
model.param('rent', .3099+.4598*val)
model.params['rent'].disable()
elif var=='rent': model.param('fixed', 2.1748486*val-.67398869)
heli.addParameter('city', 'City?', 'check', True, desc='Whether agents have the possibility of moving to the city', callback=constrain)
heli.addParameter('breedThresh', 'Breeding Threshold (φ)', 'slider', dflt=20, opts={'low':5, 'high': 500, 'step': 5}, desc='Proportional to the minimum wealth necessary to breed')
heli.addParameter('movecost', 'Moving Cost (ω)', 'slider', dflt=15, opts={'low':0, 'high': 150, 'step': 1}, desc='Cost incurred by moving location')
# A decentralized price discovery model with random matching
#===============
# SETUP
#===============
from helipad import Helipad
from helipad.utility import CobbDouglas
from math import sqrt, exp, floor
import random
heli = Helipad()
heli.order = 'random'
heli.addParameter('ratio',
'Log Endowment Ratio',
'slider',
dflt=0,
opts={
'low': -3,
'high': 3,
'step': 0.5
})
heli.addGood('shmoo', '11CC00', lambda breed: random.randint(1, 1000))
heli.addGood(
'soma', 'CC0000',
lambda breed: random.randint(1, floor(exp(heli.param('ratio')) * 1000)))
#===============
# BEHAVIOR
#===============
# A decentralized price discovery model with random matching
#===============
# SETUP
#===============
from helipad import Helipad
from helipad.utility import CobbDouglas
from math import sqrt, exp, floor
import random
heli = Helipad()
heli.name = 'Price Discover'
heli.order = 'match'
heli.addParameter('ratio',
'Log Endowment Ratio',
'slider',
dflt=0,
opts={
'low': -3,
'high': 3,
'step': 0.5
},
runtime=False)
heli.params['num_agent'].opts['step'] = 2 #Make sure we don't get stray agents
heli.params['num_agent'].opts['low'] = 2 #Make sure we don't get stray agents
heli.addGood('shmoo', '#11CC00', (1, 1000))
heli.addGood('soma', '#CC0000', lambda breed:
(1, floor(exp(heli.param('ratio')) * 1000)))
# A model of the long-run cyclical dynamics of urbanization and human capital.
from collections import namedtuple
import pandas
from helipad import Helipad
from math import sqrt, log
from numpy import *
import random as rand2
heli = Helipad()
#================
# CONFIGURATION
#================
heli.addBreed('urban', 'CC0000')
heli.addBreed('rural', '00CC00')
heli.addGood('consumption', '000000')
heli.addParameter('breedThresh',
'Breeding Threshold (φ)',
'slider',
dflt=20,
opts={
'low': 5,
'high': 500,
'step': 5
},
desc='Proportional to the minimum wealth necessary to breed')
heli.addParameter('movecost',
'Moving Cost (ω)',
# A group-selection model of the evolution of cooperation with deme extinction
# Loosely based on chapter 7.1 of Bowles and Gintis, "A Cooperative Species"
#===============
# SETUP
#===============
from helipad import Helipad, MultiLevel, Agent
from random import choice
import numpy.random as nprand
from math import exp, sqrt
heli = Helipad()
heli.name = 'Deme Selection'
heli.order = ['linear', 'linear', 'match']
heli.stages = 3 #Stage 1 for intra-demic competition, stage 2 for reproduction, stage 3 for war
heli.addPrimitive('deme', MultiLevel, dflt=20, priority=1)
heli.removePrimitive('agent')
heli.addParameter('b',
'Benefit conferred',
'slider',
dflt=6,
opts={
'low': 0,
'high': 10,
'step': 1
})
heli.addParameter('c',
'Cost incurred',
'slider',
#Registers parameters of every available type in order to test the rendering of the control panel, and runs a dummy model to test the Charts visualizer.
#===============
# PREAMBLE
#===============
from helipad import Helipad
heli = Helipad()
heli.name = 'Test'
#A handful of breeds and goods
breeds = [('hobbit', 'jam', '#D73229'), ('dwarf', 'axe', '#2D8DBE'),
('elf', 'lembas', '#CCBB22')]
AgentGoods = {}
for b in breeds:
heli.addBreed(b[0], b[2], prim='agent')
heli.addGood(b[1], b[2])
def gcallback(model, name, val):
print(name, '=', val)
def icallback(model, name, item, val):
print(name, '/', item, '=', val)
#===============
# ADD PARAMETERS
#===============
#A reconstruction of Hurford (1991), "The Evolution of the Critical Period for Language Acquisition"
#https://www.sciencedirect.com/science/article/abs/pii/001002779190024X
from helipad import Helipad, Agent
import random
from numpy.random import choice
from numpy import mean
heli = Helipad()
heli.name = 'Critical Period'
heli.order = 'random'
heli.addParameter('pleio', 'Pleiotropy', 'slider', dflt=7, opts={'low': 0, 'high': 10, 'step': 1}, runtime=False)
heli.addParameter('acq', 'Acquisition from', 'menu', dflt='mother', opts={'mother': 'Mother', 'wholepop': 'Whole Population'})
heli.addParameter('condition', 'Language affects', 'menu', dflt='rep', opts={'rep': 'Reproduction', 'srv': 'Survival'})
heli.params['num_agent'].opts['step'] = 10 #Need it to be divisible by the number of life stages
heli.params['num_agent'].opts['low'] = 10
heli.param('num_agent', 30)
#Returns language capacity
def capacity(self, gene=None, age=None):
if gene is None: gene = self.dominantLap
if age is None: age=self.age
start = (10-self.model.param('pleio'))*age
l = sum(gene[start:start+10])
return 0 if l<0 else l
Agent.capacity = capacity
@heli.hook
# An implementation of Conway's Game Of Life
# https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life
from helipad import Helipad
heli = Helipad()
heli.name = 'Game of Life'
heli.stages = 2
heli.param('refresh', 1)
heli.removePrimitive('agent')
mapPlot = heli.spatial(x=30, square=True, wrap=True, diag=True)
mapPlot.config('patchProperty', 'active')
@heli.hook
def patchInit(patch, model):
patch.active = False
@heli.hook
def patchStep(patch, model, stage):
if stage == 1: #Calculate neighbors *before* dying or coming to life
patch.activeNeighbors = len([n for n in patch.neighbors if n.active])
elif stage == 2:
if patch.active and not 1 < patch.activeNeighbors < 4:
patch.active = False
elif not patch.active and patch.activeNeighbors == 3:
patch.active = True