Set starting aNrm for Lucas population near equilibrium

[sbenthall]

Related to #204

solve_distributed_agents calls solve() on each AgentType

SHARKFin/sharkfin/population.py

Lines 258 to 262 in cd70a7a

	def solve_distributed_agents(self):
	# see Market class for an example of how to solve distributed agents in parallel
	for agent in self.agents:
	agent.solve()

When we have multiple AgentTypes 'per type' (i.e. num_per_type), this introduces unnecessary computation, because the same solution can be used across different AgentTypes.

[alanlujan91]

SHARKFin/simulate/parameters.py

Lines 10 to 32 in 9140b17

	def build_population(agent_type, parameters, rng=None, dphm=1500):
	num_per_type = parameters.get("num_per_type", 1)
	pop = AgentPopulation(
	agent_type(), parameters, rng=rng, dollars_per_hark_money_unit=dphm
	)
	if "approx_params" in parameters:
	pop.approx_distributions(parameters["approx_params"])
	pop.parse_params()
	pop.create_distributed_agents()
	pop.create_database()
	pop.solve_distributed_agents()
	pop.solve(merge_by=parameters["ex_post"])
	pop.explode_agents(num_per_type)
	# initialize population model
	pop.init_simulation()
	return pop

This depends on the ordering of the methods. If you see the method above, solve happens before explode_agents, so this should only be solving the unique agents with ex_ante heterogeneity. In the Lucas case, this should be 1 agent.

However, I notice that there are more than 1 build_population methods on the codebase, we should probably get rid of those and just use the one above.

[sbenthall]

Hmm.

I think 'solve' got called 10 times when I ran:

$ python run_any_simulation.py --quarters 1 --dphm 500000 ../output/20230321_o

(I got 10 copies of the Thorn, etc., output)

I think that's using the build_population in parameters.py

I wonder if this is slowing down the tests as well.

[alanlujan91]

Are we solving anywhere else after creating the population?

[alanlujan91]

SHARKFin/sharkfin/population.py

Lines 280 to 299 in 9140b17

	def init_simulation(self, T_sim=1000):
	"""
	Sets up the agents with their state for the state of the simulation
	"""
	for agent in self.agents:
	agent.track_vars += ["pLvl", "mNrm", "cNrm", "Share", "Risky"]
	agent.T_sim = T_sim
	agent.initialize_sim()
	if self.stored_class_stats is None:
	## build an IndShockConsumerType "double" of this agent, with the same parameters
	parameters = agent.parameters.copy()
	if "Rfree" in parameters and isinstance(parameters["Rfree"], list):
	# patch potential bug until HARK is updated
	parameters["Rfree"] = parameters["Rfree"][0]
	ind_shock_double = cism.IndShockConsumerType(**parameters)
	## solve to get the mNrmStE value
	## that is, the Steady-state Equilibrium value of mNrm, for the IndShockModel
	ind_shock_double.solve()

Seems like we are solving the equivalent ind_shock agent for every agent, this probably shouldn't be happening

[sbenthall]

Aha. That could be it.

I think we're doing this because @llorracc was interested in the mNrmStE value for some reason.
But we haven't used it in any analysis and it adds a lot of overhead....

I guess it could be done once for each agent class/type, if needed.

[sbenthall]

I've tested commenting out that 'solve' use, and that's indeed it.
Thanks for catching that!

For G.G. Shark I'm going to hackily disable this code. We should figure out how to really fix this for FIRE SHARK.

[ccarrollATjhuecon]

Yes, I am interested in mNrmStE. But there's a unique answer to that which gets constructed automatically when the agent's problem is solved. I can't see any reason to recompute it. Why doesn't it just automatically transfer when the agent is duplicated?