def __init__(
self,
cFuncAdj=None,
ShareFuncAdj=None,
vFuncAdj=None,
vPfuncAdj=None,
cFuncFxd=None,
ShareFuncFxd=None,
vFuncFxd=None,
dvdmFuncFxd=None,
dvdsFuncFxd=None,
aGrid=None,
Share_adj=None,
EndOfPrddvda_adj=None,
ShareGrid=None,
EndOfPrddvda_fxd=None,
AdjPrb=None,
):
# Change any missing function inputs to NullFunc
if cFuncAdj is None:
cFuncAdj = NullFunc()
if cFuncFxd is None:
cFuncFxd = NullFunc()
if ShareFuncAdj is None:
ShareFuncAdj = NullFunc()
if ShareFuncFxd is None:
ShareFuncFxd = NullFunc()
if vFuncAdj is None:
vFuncAdj = NullFunc()
if vFuncFxd is None:
vFuncFxd = NullFunc()
if vPfuncAdj is None:
vPfuncAdj = NullFunc()
if dvdmFuncFxd is None:
dvdmFuncFxd = NullFunc()
if dvdsFuncFxd is None:
dvdsFuncFxd = NullFunc()
# Set attributes of self
self.cFuncAdj = cFuncAdj
self.cFuncFxd = cFuncFxd
self.ShareFuncAdj = ShareFuncAdj
self.ShareFuncFxd = ShareFuncFxd
self.vFuncAdj = vFuncAdj
self.vFuncFxd = vFuncFxd
self.vPfuncAdj = vPfuncAdj
self.dvdmFuncFxd = dvdmFuncFxd
self.dvdsFuncFxd = dvdsFuncFxd
self.aGrid = aGrid
self.Share_adj = Share_adj
self.EndOfPrddvda_adj = EndOfPrddvda_adj
self.ShareGrid = ShareGrid
self.EndOfPrddvda_fxd = EndOfPrddvda_fxd
self.AdjPrb = AdjPrb
def __init__(self,
VfuncJock=None,
VfuncPunk=None,
switchFuncJock=None,
switchFuncPunk=None):
'''
Make a new instance of FashionSolution.
Parameters
----------
VfuncJock : function
Value when entering the period as a jock as a function of the proportion
of the population dressed as a punk.
VfuncPunk : function
Value when entering the period as a punk as a function of the proportion
of the population dressed as a punk.
switchFuncJock : function
Probability of switching styles (to punk) when entering the period
as a jock, as a function of the population punk proportion.
switchFuncPunk : function
Probability of switching styles (to jock) when entering the period
as a punkk, as a function of the population punk proportion.
Returns
-------
new instance of FashionSolution
'''
# Fill in missing function inputs with trivial defaults
if VfuncJock is None:
VfuncJock = NullFunc()
if VfuncPunk is None:
VfuncPunk = NullFunc()
if switchFuncJock is None:
switchFuncJock = NullFunc()
if switchFuncPunk is None:
switchFuncPunk = NullFunc()
self.VfuncJock = VfuncJock
self.VfuncPunk = VfuncPunk
self.switchFuncJock = switchFuncJock
self.switchFuncPunk = switchFuncPunk
self.distance_criteria = ['VfuncJock', 'VfuncPunk']
def __init__(
self,
Cpol=None,
cFuncs=None,
):
"""
The constructor for a new GLConsumerSolution object.
Parameters
----------
Cpol : Array
Consumption Policy from next period. 13 by 200 array
Cfuncs : Array
13 by 1 Array whose elements are objects of the 'LinearInterp' Class.
The ith element is the linearinterpolation between the Bond Grid and consumption for a certain level of productivity.
Returns
-------
None
"""
# Change any missing function inputs to NullFunc
self.Cpol = Cpol if Cpol is not None else NullFunc()
self.cFuncs = cFuncs if cFuncs is not None else NullFunc()
def __init__(
self,
cFuncAdj=None,
ShareFuncAdj=None,
vFuncAdj=None,
vPfuncAdj=None,
cFuncFxd=None,
ShareFuncFxd=None,
vFuncFxd=None,
dvdmFuncFxd=None,
dvdsFuncFxd=None,
):
# Change any missing function inputs to NullFunc
if cFuncAdj is None:
cFuncAdj = NullFunc()
if cFuncFxd is None:
cFuncFxd = NullFunc()
if ShareFuncAdj is None:
ShareFuncAdj = NullFunc()
if ShareFuncFxd is None:
ShareFuncFxd = NullFunc()
if vFuncAdj is None:
vFuncAdj = NullFunc()
if vFuncFxd is None:
vFuncFxd = NullFunc()
if vPfuncAdj is None:
vPfuncAdj = NullFunc()
if dvdmFuncFxd is None:
dvdmFuncFxd = NullFunc()
if dvdsFuncFxd is None:
dvdsFuncFxd = NullFunc()
# Set attributes of self
self.cFuncAdj = cFuncAdj
self.cFuncFxd = cFuncFxd
self.ShareFuncAdj = ShareFuncAdj
self.ShareFuncFxd = ShareFuncFxd
self.vFuncAdj = vFuncAdj
self.vFuncFxd = vFuncFxd
self.vPfuncAdj = vPfuncAdj
self.dvdmFuncFxd = dvdmFuncFxd
self.dvdsFuncFxd = dvdsFuncFxd
class FashionVictimType(AgentType):
'''
A class for representing types of agents in the fashion victim model. Agents
make a binary decision over which style to wear (jock or punk), subject to
switching costs. They receive utility directly from their chosen style and
from the proportion of the population that wears the same style.
'''
_solution_terminal = FashionSolution(VfuncJock=LinearInterp(np.array([0.0, 1.0]),np.array([0.0,0.0])),
VfuncPunk=LinearInterp(np.array([0.0, 1.0]),np.array([0.0,0.0])),
switchFuncJock=NullFunc(),
switchFuncPunk=NullFunc())
def __init__(self,**kwds):
'''
Instantiate a new FashionVictim with given data.
Parameters
----------
**kwds : keyword arguments
Any number of keyword arguments key=value; each value will be assigned
to the attribute key in the new instance.
Returns
-------
new instance of FashionVictimType
'''
# Initialize a basic AgentType
AgentType.__init__(self,solution_terminal=FashionVictimType._solution_terminal,cycles=0,pseudo_terminal=True,**kwds)
# Add class-specific features
self.time_inv = ['DiscFac','conformUtilityFunc','punk_utility','jock_utility','switchcost_J2P','switchcost_P2J','pGrid','pEvolution','pref_shock_mag']
self.time_vary = []
self.solveOnePeriod = solveFashion
self.update()
def updateEvolution(self):
'''
Updates the "population punk proportion" evolution array. Fasion victims
believe that the proportion of punks in the subsequent period is a linear
function of the proportion of punks this period, subject to a uniform
shock. Given attributes of self pNextIntercept, pNextSlope, pNextCount,
pNextWidth, and pGrid, this method generates a new array for the attri-
bute pEvolution, representing a discrete approximation of next period
states for each current period state in pGrid.
Parameters
----------
none
Returns
-------
none
'''
self.pEvolution = np.zeros((self.pCount,self.pNextCount))
for j in range(self.pCount):
pNow = self.pGrid[j]
pNextMean = self.pNextIntercept + self.pNextSlope*pNow
dist = approxUniform(N=self.pNextCount,bot=pNextMean-self.pNextWidth,top=pNextMean+self.pNextWidth)[1]
self.pEvolution[j,:] = dist
def update(self):
'''
Updates the non-primitive objects needed for solution, using primitive
attributes. This includes defining a "utility from conformity" function
conformUtilityFunc, a grid of population punk proportions, and an array
of future punk proportions (for each value in the grid). Results are
stored as attributes of self.
Parameters
----------
none
Returns
-------
none
'''
self.conformUtilityFunc = lambda x : stats.beta.pdf(x,self.uParamA,self.uParamB)
self.pGrid = np.linspace(0.0001,0.9999,self.pCount)
self.updateEvolution()
def reset(self):
'''
Resets this agent type to prepare it for a new simulation run. This
includes resetting the random number generator and initializing the style
of each agent of this type.
'''
self.resetRNG()
sNow = np.zeros(self.pop_size)
Shk = self.RNG.rand(self.pop_size)
sNow[Shk < self.p_init] = 1
self.sNow = sNow
def preSolve(self):
'''
Updates the punk proportion evolution array by calling self.updateEvolution().
Parameters
----------
none
Returns
-------
none
'''
# This step is necessary in the general equilibrium framework, where a
# new evolution rule is calculated after each simulation run, but only
# the sufficient statistics describing it are sent back to agents.
self.updateEvolution()
def postSolve(self):
'''
Unpack the behavioral and value functions for more parsimonious access.
Parameters
----------
none
Returns
-------
none
'''
self.switchFuncPunk = self.solution[0].switchFuncPunk
self.switchFuncJock = self.solution[0].switchFuncJock
self.VfuncPunk = self.solution[0].VfuncPunk
self.VfuncJock = self.solution[0].VfuncJock
def simOnePrd(self):
'''
Simulate one period of the fashion victom model for this type. Each
agent receives an idiosyncratic preference shock and chooses whether to
change styles (using the optimal decision rule).
Parameters
----------
none
Returns
-------
none
'''
pNow = self.pNow
sPrev = self.sNow
J2Pprob = self.switchFuncJock(pNow)
P2Jprob = self.switchFuncPunk(pNow)
Shks = self.RNG.rand(self.pop_size)
J2P = np.logical_and(sPrev == 0,Shks < J2Pprob)
P2J = np.logical_and(sPrev == 1,Shks < P2Jprob)
sNow = copy(sPrev)
sNow[J2P] = 1
sNow[P2J] = 0
self.sNow = sNow
def marketAction(self):
'''
The "market action" for a FashionType in the general equilibrium setting.
Simulates a single period using self.simOnePrd().
Parameters
----------
none
Returns
-------
none
'''
self.simOnePrd()
