def simBirth(self,which_agents):
        '''
        Makes new consumers for the given indices.  Initialized variables include aNrm and pLvl, as
        well as time variables t_age and t_cycle.  Normalized assets and permanent income levels
        are drawn from lognormal distributions given by aNrmInitMean and aNrmInitStd (etc).

        Parameters
        ----------
        which_agents : np.array(Bool)
            Boolean array of size self.AgentCount indicating which agents should be "born".

        Returns
        -------
        None
        '''
        # Get and store states for newly born agents
        N = np.sum(which_agents) # Number of new consumers to make
        self.aNrmNow[which_agents] = drawLognormal(N,mu=self.aNrmInitMean,sigma=self.aNrmInitStd,seed=self.RNG.randint(0,2**31-1))
        pLvlInitMeanNow = self.pLvlInitMean + np.log(self.PlvlAggNow) # Account for newer cohorts having higher permanent income
        self.pLvlNow[which_agents] = drawLognormal(N,mu=pLvlInitMeanNow,sigma=self.pLvlInitStd,seed=self.RNG.randint(0,2**31-1))
        self.t_age[which_agents]   = 0 # How many periods since each agent was born
        self.t_cycle[which_agents] = 0 # Which period of the cycle each agent is currently in
        return None
Makes new consumers for the given indices.  Initialized variables include aNrm and pLvl, as
        well as time variables t_age and t_cycle.  Normalized assets and permanent income levels
        are drawn from lognormal distributions given by aNrmInitMean and aNrmInitStd (etc).

        Parameters
        ----------
        which_agents : np.array(Bool)
            Boolean array of size self.AgentCount indicating which agents should be "born".

        Returns
        -------
        None