def singleRun():
params = decision.Params()
params.decisionType = 'probabilistic'
params.memory = False
params.nSteps = 1000
params.nAgents = 1
params.output = 'output_dmp_0.csv'
decision.run(params)
def singleRun():
params = decision.Params()
params.decisionType = "probabilistic"
params.memory = False
params.nSteps = 1000
params.nAgents = 1
params.output = "output_dmp_0.csv"
decision.run(params)
def singleRun():
params = decision.Params()
params.nSteps = 100
params.nAgents = 10
params.output = 'output.csv'
params.maxEnergy = 10
params.energyCost = 1
params.resourceGrowthRate = 1
params.xDim = 10
params.yDim = 10
decision.run(params)
def equilibrium():
params = decision.Params()
params.nSteps = 5000
params.radius = 1
params.nAgents = 30
params.output = 'fig1.csv'
params.maxEnergy = 10
params.energyCost = 2
params.resourceGrowthRate = 2
params.xDim = 30
params.yDim = 30
decision.run(params)
def multifinality():
repetitions = 3
params = decision.Params()
params.nSteps = 300
params.radius = 3
params.output = 'fig2b.csv'
params.maxEnergy = 50
params.energyCost = 10
params.resourceGrowthRate = 10
params.nAgents = 50
params.xDim = 30
params.yDim = 30
params.oneFile = True
for i in range(repetitions):
print('run:',i,'from:',repetitions)
params.numRun = i
decision.run(params)
def equifinality():
repetitions = 10
params = decision.Params()
params.nSteps = 150
params.radius = 1
params.output = 'fig2a.csv'
params.maxEnergy = 10
params.energyCost = 3
params.resourceGrowthRate = 3
params.xDim = 30
params.yDim = 30
params.oneFile = True
for i in range(repetitions):
print('run:',i,'from:',repetitions)
params.numRun = i
params.nAgents = random.randint(1,200)
decision.run(params)
def all():
# experiment
numRuns = 10
decisionTypeSweep = ["greedy", "probabilistic"]
memoryMapSweep = [True, False]
params = decision.Params()
totalRuns = 0
for i in decisionTypeSweep:
for j in memoryMapSweep:
params.decisionType = i
params.memory = j
for run in range(0, numRuns):
print("run:", totalRuns + 1, "of:", numRuns * len(memoryMapSweep) * len(decisionTypeSweep))
params.numRun = totalRuns
params.output = "output_dmp_" + str(params.numRun) + ".csv"
totalRuns += 1
decision.run(params)
def all():
# experiment
numRuns = 10
decisionTypeSweep = ['greedy','probabilistic']
memoryMapSweep = [True, False]
params = decision.Params()
totalRuns = 0
for i in decisionTypeSweep:
for j in memoryMapSweep:
params.decisionType = i
params.memory = j
for run in range(0, numRuns):
print('run:',totalRuns+1,'of:',numRuns*len(memoryMapSweep)*len(decisionTypeSweep))
params.numRun = totalRuns
params.output = 'output_dmp_'+str(params.numRun)+'.csv'
totalRuns += 1
decision.run(params)
def convergence():
# experiment
numRuns = 10
decisionTypeSweep = ["greedy"]
memoryMapSweep = [False]
params = decision.Params()
params.nSteps = 500
totalRuns = 0
for i in decisionTypeSweep:
for j in memoryMapSweep:
params.decisionType = i
params.memory = j
for run in range(0, numRuns):
print("run:", totalRuns + 1, "of:", numRuns * len(memoryMapSweep) * len(decisionTypeSweep))
params.numRun = totalRuns
params.nAgents = random.randint(1, 200)
params.output = "output_dmp_" + str(params.numRun) + ".csv"
totalRuns += 1
decision.run(params)
def convergence():
# experiment
numRuns = 10
decisionTypeSweep = ['greedy']
memoryMapSweep = [False]
params = decision.Params()
params.nSteps = 500
totalRuns = 0
for i in decisionTypeSweep:
for j in memoryMapSweep:
params.decisionType = i
params.memory = j
for run in range(0, numRuns):
print('run:',totalRuns+1,'of:',numRuns*len(memoryMapSweep)*len(decisionTypeSweep))
params.numRun = totalRuns
params.nAgents = random.randint(1,200)
params.output = 'output_dmp_'+str(params.numRun)+'.csv'
totalRuns += 1
decision.run(params)
def exploreRadius():
repetitions = 1000
radiusValues = range(1,31)
numRuns = repetitions*len(radiusValues)
numRun = 0
for i in radiusValues:
for j in range(repetitions):
params = decision.Params()
params.oneFile = False
params.nSteps = 5001
params.nAgents = 50
params.maxEnergy = 100
params.energyCost = 25
params.resourceGrowthRate = 25
params.xDim = 30
params.yDim = 30
params.numRun = numRun
params.radius = i
params.output = 'output_dec_'+str(numRun).zfill(5)+'.csv'
print('run:',numRun+1,'of:',numRuns,'radius:',params.radius)
decision.run(params)
numRun += 1
