Repository: econ-ark/HARK (language: python)

Issue:
replace AgentType.sim_one_period with a generic Monte Carlo simulator that consumes equations
This can more explicitly take in equations, and be removed to a `HARK.simulation.monte_carlo` module

https://github.com/econ-ark/HARK/blob/master/HARK/core.py#L432-L479

This will help with the whiteboard problem #889 

cf. #1292
replace AgentType.sim_one_period with a generic Monte Carlo simulator that consumes equations
This can more explicitly take in equations, and be removed to a `HARK.simulation.monte_carlo` module

https://github.com/econ-ark/HARK/blob/master/HARK/core.py#L432-L479

This will help with the whiteboard problem #889 

cf. #1292


Discussion / hints:
The FrameAgentType was an attempt at this. The problem with it, noted by @mnwhite , is that the configuration of the FrameAgentType is too complex. 

https://github.com/econ-ark/HARK/blob/master/examples/FrameAgentType/FrameModels.ipynb

Something simpler, like a dictionary of lambdas, would be easier to read and write.
Here's the off-the-top-of-my-head draft of a format to specify the *mechanics* of ConsIndShock:

`inter = [aNrm, pLvl]`
`control = [cNrm]`
`discrete = []`
`exog = [psi, theta]`
`param = [gamma, omega, R, J]`

`dynamics = [`
`    G = gamma * psi,`
`    Rnrm = R / G,`
`    bNrm = Rnrm * aNrm,`
`    mNrm = bNrm + theta,`
`    cNrm = policy(mNrm),`
`    aNrm = mNrm - cNrm,`
`    die(omega),`
`    old = j >= J,`
`    die(old)`
`    ]`

`auxiliary = [`
`    pLvl = G * pLvl,`
`    mLvl = mNrm * pLvl,`
`    cLvl = cNrm * pLvl,`
`    aLvl = aNrm * pLvl,`
`    MPC = policy.der(mNrm)`
`    ]`

The simulator should be able to take a statement like this and implement it. The `inter` variables specify what *must* exist for an agent when it is initialized. The `control` variables specify the variables that the simulator should expect to exist, probably named like `cNrmFunc` or stored in `policy['cNrm']`.

The `discrete` variables are those that the code should expect to be whole numbers, not continuous values, which might matter for evaluating `policy(d, mNrm)` where d is discrete.

The `exog` variables name variables that come from some process exogenous to the agent-- maybe RVs, maybe chosen by someone else, maybe an aggregate. The code just needs to know to expect to be given some way to get or generate things named in exog, which will not be found within `dynamics`.

Variables named in `param` are parameters to be pulled from the agent's data, common to a "type". Whether or not each one is time/age-varying is *irrelevant* for the mechanics of the model. Each instance of an AgentType using this model can specify its own `vary` list to indicate how parameters should be looked up. In our standard ConsIndShock lifecycle model, omega and gamma would be in `vary`, while R and J would not.

Note what's not here: state variables. It might be that we need to name the variables that are used as "states" in the sense of "inputs to a policy function", but it might be enough to have them implicit in the `dynamics` statement.

The statements in `dynamics` should be self-explanatory. It's a start to finish description of what happens to each agent in one period. The parameter `J` is the maximum age beyond which an agent can't live.

The contents of `auxiliary` are *optional* variables that can be computed along the way, but don't *need* to be as a required part of simulating the model. Each of these lines would be executed if and only if the accompanying variable is requested in `track`, with dependencies automatically generated and handled.

There are some additional complications for things like jointly determined variables, but those don't appear in any of our models so far.
Oh, and `j` is used to mean "age". There would also be `t` for absolute time, if needed.
Ok, I think it's possible to move incrementally in this direction.

The hardest part is the Python object for equations, I think.

I've got a few aesthetic quibbles but let's start getting the functionality in.

I think I can get part of the way there with #1292 and refactoring the Frame code, as discussed.

I very much hope that @William Du ***@***.***> 's implementation of the
"shuffling" method is included in the generic Monte Carlo simulator.



On Thu, Jun 29, 2023 at 1:07 AM Sebastian Benthall ***@***.***>
wrote:

> Ok, I think it's possible to move incrementally in this direction.
>
> The hardest part is the Python object for equations, I think.
>
> I've got a few aesthetic quibbles but let's start getting the
> functionality in.
>
> I think I can get part of the way there with #1292
> <https://github.com/econ-ark/HARK/pull/1292> and refactoring the Frame
> code, as discussed.
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/econ-ark/HARK/issues/1295#issuecomment-1612223273>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AAKCK76WDRCLQDI4PIAIMMTXNS2K3ANCNFSM6AAAAAAZXOKJ5A>
> .
> You are receiving this because you are subscribed to this thread.Message
> ID: ***@***.***>
>


-- 
- Chris Carroll

> `dynamics = [`
> ` G = gamma * psi,`
> ` Rnrm = R / G,`
> ` bNrm = Rnrm * aNrm,`
> ` mNrm = bNrm + theta,`
> ` cNrm = policy(mNrm),`
> ` aNrm = mNrm - cNrm,`
> ` die(omega),`
> ` old = j >= J,`
> ` die(old)`
> ` ]`

As we've discussed, one tricky part about this proposal is the syntax for these sorts of 'equations'.
Without a symbolic library, it's hard to do this very cleanly in Python.

This commit is the most lightweight way I can think of doing this without a symbolic library:
https://github.com/econ-ark/HARK/pull/1292/commits/d09eb303ebc01d85cd8e5c00d875b4384830a6ee

It is possible to use `inspect.signature` to pull out the parameter names and bind them in simulation.
https://docs.python.org/3/library/inspect.html#inspect.signature
The FrameAgentType was an attempt at this. The problem with it, noted by @mnwhite , is that the configuration of the FrameAgentType is too complex. 

https://github.com/econ-ark/HARK/blob/master/examples/FrameAgentType/FrameModels.ipynb

Something simpler, like a dictionary of lambdas, would be easier to read and write.
Here's the off-the-top-of-my-head draft of a format to specify the *mechanics* of ConsIndShock:

`inter = [aNrm, pLvl]`
`control = [cNrm]`
`discrete = []`
`exog = [psi, theta]`
`param = [gamma, omega, R, J]`

`dynamics = [`
`    G = gamma * psi,`
`    Rnrm = R / G,`
`    bNrm = Rnrm * aNrm,`
`    mNrm = bNrm + theta,`
`    cNrm = policy(mNrm),`
`    aNrm = mNrm - cNrm,`
`    die(omega),`
`    old = j >= J,`
`    die(old)`
`    ]`

`auxiliary = [`
`    pLvl = G * pLvl,`
`    mLvl = mNrm * pLvl,`
`    cLvl = cNrm * pLvl,`
`    aLvl = aNrm * pLvl,`
`    MPC = policy.der(mNrm)`
`    ]`

The simulator should be able to take a statement like this and implement it. The `inter` variables specify what *must* exist for an agent when it is initialized. The `control` variables specify the variables that the simulator should expect to exist, probably named like `cNrmFunc` or stored in `policy['cNrm']`.

The `discrete` variables are those that the code should expect to be whole numbers, not continuous values, which might matter for evaluating `policy(d, mNrm)` where d is discrete.

The `exog` variables name variables that come from some process exogenous to the agent-- maybe RVs, maybe chosen by someone else, maybe an aggregate. The code just needs to know to expect to be given some way to get or generate things named in exog, which will not be found within `dynamics`.

Variables named in `param` are parameters to be pulled from the agent's data, common to a "type". Whether or not each one is time/age-varying is *irrelevant* for the mechanics of the model. Each instance of an AgentType using this model can specify its own `vary` list to indicate how parameters should be looked up. In our standard ConsIndShock lifecycle model, omega and gamma would be in `vary`, while R and J would not.

Note what's not here: state variables. It might be that we need to name the variables that are used as "states" in the sense of "inputs to a policy function", but it might be enough to have them implicit in the `dynamics` statement.

The statements in `dynamics` should be self-explanatory. It's a start to finish description of what happens to each agent in one period. The parameter `J` is the maximum age beyond which an agent can't live.

The contents of `auxiliary` are *optional* variables that can be computed along the way, but don't *need* to be as a required part of simulating the model. Each of these lines would be executed if and only if the accompanying variable is requested in `track`, with dependencies automatically generated and handled.

There are some additional complications for things like jointly determined variables, but those don't appear in any of our models so far.
Oh, and `j` is used to mean "age". There would also be `t` for absolute time, if needed.
Ok, I think it's possible to move incrementally in this direction.

The hardest part is the Python object for equations, I think.

I've got a few aesthetic quibbles but let's start getting the functionality in.

I think I can get part of the way there with #1292 and refactoring the Frame code, as discussed.

I very much hope that @William Du ***@***.***> 's implementation of the
"shuffling" method is included in the generic Monte Carlo simulator.



On Thu, Jun 29, 2023 at 1:07 AM Sebastian Benthall ***@***.***>
wrote:

> Ok, I think it's possible to move incrementally in this direction.
>
> The hardest part is the Python object for equations, I think.
>
> I've got a few aesthetic quibbles but let's start getting the
> functionality in.
>
> I think I can get part of the way there with #1292
> <https://github.com/econ-ark/HARK/pull/1292> and refactoring the Frame
> code, as discussed.
>
> —
> Reply to this email directly, view it on GitHub
> <https://github.com/econ-ark/HARK/issues/1295#issuecomment-1612223273>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AAKCK76WDRCLQDI4PIAIMMTXNS2K3ANCNFSM6AAAAAAZXOKJ5A>
> .
> You are receiving this because you are subscribed to this thread.Message
> ID: ***@***.***>
>


-- 
- Chris Carroll

> `dynamics = [`
> ` G = gamma * psi,`
> ` Rnrm = R / G,`
> ` bNrm = Rnrm * aNrm,`
> ` mNrm = bNrm + theta,`
> ` cNrm = policy(mNrm),`
> ` aNrm = mNrm - cNrm,`
> ` die(omega),`
> ` old = j >= J,`
> ` die(old)`
> ` ]`

As we've discussed, one tricky part about this proposal is the syntax for these sorts of 'equations'.
Without a symbolic library, it's hard to do this very cleanly in Python.

This commit is the most lightweight way I can think of doing this without a symbolic library:
https://github.com/econ-ark/HARK/pull/1292/commits/d09eb303ebc01d85cd8e5c00d875b4384830a6ee

It is possible to use `inspect.signature` to pull out the parameter names and bind them in simulation.
https://docs.python.org/3/library/inspect.html#inspect.signature

Fix patch:
diff --git a/Documentation/CHANGELOG.md b/Documentation/CHANGELOG.md
index 9ec54333c..d91d204f1 100644
--- a/Documentation/CHANGELOG.md
+++ b/Documentation/CHANGELOG.md
@@ -16,6 +16,7 @@ Release Date: TBD
 
 - Adds `HARK.core.AgentPopulation` class to represent a population of agents with ex-ante heterogeneous parametrizations as distributions. [#1237](https://github.com/econ-ark/HARK/pull/1237)
 - Adds `HARK.core.Parameters` class to represent a collection of time varying and time invariant parameters in a model. [#1240](https://github.com/econ-ark/HARK/pull/1240)
+- Adds `HARK.simulation.monte_carlo` module for generic Monte Carlo simulation functions using Python model configurations. [1296](https://github.com/econ-ark/HARK/pull/1296)
 
 ### Minor Changes
 
@@ -26,6 +27,8 @@ Release Date: TBD
 - Fixes bug that prevented risky-asset consumer types from working with time-varying interest rates `Rfree`. [1343](https://github.com/econ-ark/HARK/pull/1343)
 - Overhauls and expands condition checking for the ConsIndShock model [#1294](https://github.com/econ-ark/HARK/pull/1294). Condition values and a description of their interpretation is stored in the bilt dictionary of IndShockConsumerType.
 - Creates a `models/` directory with Python model configurations for perfect foresight and Fisher 2-period models. [1347](https://github.com/econ-ark/HARK/pull/1347)
+- Fixes bug in AgentType simulations where 'who_dies' for period t was being recorded in period t-1in the history Carlo simulation functions using Python model configurations.[1296](https://github.com/econ-ark/HARK/pull/1296)
+- Removes unused `simulation.py` .[1296](https://github.com/econ-ark/HARK/pull/1296)
 
 ### 0.13.0
 
diff --git a/Documentation/reference/index.rst b/Documentation/reference/index.rst
index 1d9cced2f..de65cad62 100644
--- a/Documentation/reference/index.rst
+++ b/Documentation/reference/index.rst
@@ -13,6 +13,7 @@ API Reference
    tools/frame
    tools/helpers
    tools/interpolation
+   tools/model
    tools/numba_tools
    tools/parallel
    tools/rewards
diff --git a/Documentation/reference/tools/model.rst b/Documentation/reference/tools/model.rst
new file mode 100644
index 000000000..141b2ac93
--- /dev/null
+++ b/Documentation/reference/tools/model.rst
@@ -0,0 +1,7 @@
+Model
+-------------
+
+.. automodule:: HARK.model
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/Documentation/reference/tools/simulation.rst b/Documentation/reference/tools/simulation.rst
index 55e17756f..a56040159 100644
--- a/Documentation/reference/tools/simulation.rst
+++ b/Documentation/reference/tools/simulation.rst
@@ -1,7 +1,7 @@
 Simulation
 ------------
 
-.. automodule:: HARK.simulation
+.. automodule:: HARK.simulation.monte_carlo
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/HARK/core.py b/HARK/core.py
index c2ec0ebd1..71a79e2a9 100644
--- a/HARK/core.py
+++ b/HARK/core.py
@@ -7,6 +7,7 @@
 problem by finding a general equilibrium dynamic rule.
 """
 # Set logging and define basic functions
+# Set logging and define basic functions
 import logging
 import sys
 from collections import defaultdict, namedtuple
@@ -1061,7 +1062,14 @@ def simulate(self, sim_periods=None):
                     elif var_name in self.controls:
                         self.history[var_name][self.t_sim, :] = self.controls[var_name]
                     else:
-                        self.history[var_name][self.t_sim, :] = getattr(self, var_name)
+                        if var_name == "who_dies" and self.t_sim > 1:
+                            self.history[var_name][self.t_sim - 1, :] = getattr(
+                                self, var_name
+                            )
+                        else:
+                            self.history[var_name][self.t_sim, :] = getattr(
+                                self, var_name
+                            )
                 self.t_sim += 1
 
             return self.history
diff --git a/HARK/model.py b/HARK/model.py
index b03734338..44e47261b 100644
--- a/HARK/model.py
+++ b/HARK/model.py
@@ -2,10 +2,28 @@
 Tools for crafting models.
 """
 
+from HARK.distribution import Distribution
+
+
+class Aggregate:
+    """
+    Used to designate a shock as an aggregate shock.
+    If so designated, draws from the shock will be scalar rather
+    than array valued.
+    """
+
+    def __init__(self, dist: Distribution):
+        self.dist = dist
+
 
 class Control:
     """
-    Should go in different model support module.
+    Used to designate a variabel that is a control variable.
+
+    Parameters
+    ----------
+    args : list of str
+        The labels of the variables that are in the information set of this control.
     """
 
     def __init__(self, args):
diff --git a/HARK/models/__init__.py b/HARK/models/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/HARK/models/perfect_foresight.py b/HARK/models/perfect_foresight.py
index 4c3ceb3e3..38bedd97d 100644
--- a/HARK/models/perfect_foresight.py
+++ b/HARK/models/perfect_foresight.py
@@ -20,9 +20,9 @@
         "BoroCnstArt": None,
     },
     "dynamics": {
+        "y": lambda p: p,
         "m": lambda Rfree, a, y: Rfree * a + y,
         "c": Control(["m"]),
-        "y": lambda p: p,
         "p": lambda PermGroFac, p: PermGroFac * p,
         "a": lambda m, c: m - c,
     },
diff --git a/HARK/models/perfect_foresight_normalized.py b/HARK/models/perfect_foresight_normalized.py
new file mode 100644
index 000000000..79fab78dc
--- /dev/null
+++ b/HARK/models/perfect_foresight_normalized.py
@@ -0,0 +1,31 @@
+from HARK.distribution import Bernoulli
+from HARK.model import Control
+
+# This way of distributing parameters across the scope is clunky
+# Can be handled better if parsed from a YAML file, probably
+# But it would be better to have a more graceful Python version as well.
+CRRA = (2.0,)
+LivPrb = 0.98
+
+model = {
+    "shocks": {
+        "live": Bernoulli(p=LivPrb),
+    },
+    "parameters": {
+        "DiscFac": 0.96,
+        "CRRA": CRRA,
+        "Rfree": 1.03,
+        "LivPrb": LivPrb,
+        "PermGroFac": 1.01,
+        "BoroCnstArt": None,
+    },
+    "dynamics": {
+        "p": lambda PermGroFac, p: PermGroFac * p,
+        "r_eff": lambda Rfree, PermGroFac: Rfree / PermGroFac,
+        "b_nrm": lambda r_eff, a_nrm: r_eff * a_nrm,
+        "m_nrm": lambda b_nrm: b_nrm + 1,
+        "c_nrm": Control(["m_nrm"]),
+        "a_nrm": lambda m_nrm, c_nrm: m_nrm - c_nrm,
+    },
+    "reward": {"u": lambda c: c ** (1 - CRRA) / (1 - CRRA)},
+}
diff --git a/HARK/simulation.py b/HARK/simulation.py
deleted file mode 100644
index 918e4e377..000000000
--- a/HARK/simulation.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""
-Currently empty. Will be used for future simulation handling code.
-"""
-
-import warnings  # A library for runtime warnings
-
-import numpy as np  # Numerical Python
diff --git a/HARK/simulation/__init__.py b/HARK/simulation/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/HARK/simulation/monte_carlo.py b/HARK/simulation/monte_carlo.py
new file mode 100644
index 000000000..13a3e34af
--- /dev/null
+++ b/HARK/simulation/monte_carlo.py
@@ -0,0 +1,450 @@
+"""
+Functions to support Monte Carlo simulation of models.
+"""
+
+from copy import copy
+from inspect import signature
+from typing import Any, Callable, Mapping, Sequence, Union
+
+import numpy as np
+
+from HARK.distribution import (
+    Distribution,
+    IndexDistribution,
+    TimeVaryingDiscreteDistribution,
+)
+from HARK.model import Aggregate, Control
+
+
+def draw_shocks(shocks: Mapping[str, Distribution], conditions: Sequence[int]):
+    """
+    Draw from each shock distribution values, subject to given conditions.
+
+    Parameters
+    ------------
+    shocks Mapping[str, Distribution]
+        A dictionary-like mapping from shock names to distributions from which to draw
+
+    conditions: Sequence[int]
+        An array of conditions, one for each agent.
+        Typically these will be agent ages.
+
+    Parameters
+    ------------
+    draws : Mapping[str, Sequence]
+        A mapping from shock names to drawn shock values.
+    """
+    draws = {}
+
+    for shock_var in shocks:
+        shock = shocks[shock_var]
+
+        if isinstance(shock, (int, float)):
+            draws[shock_var] = np.ones(len(conditions)) * shock
+        elif isinstance(shock, Aggregate):
+            draws[shock_var] = shock.dist.draw(1)[0]
+        elif isinstance(shock, IndexDistribution) or isinstance(
+            shock, TimeVaryingDiscreteDistribution
+        ):
+            ## TODO  his type test is awkward. They should share a superclass.
+            draws[shock_var] = shock.draw(conditions)
+        else:
+            draws[shock_var] = shock.draw(len(conditions))
+
+    return draws
+
+
+def simulate_dynamics(
+    dynamics: Mapping[str, Union[Callable, Control]],
+    pre: Mapping[str, Any],
+    dr: Mapping[str, Callable],
+):
+    """
+    From the beginning-of-period state (pre), follow the dynamics,
+    including any decision rules, to compute the end-of-period state.
+
+    Parameters
+    ------------
+
+    dynamics: Mapping[str, Callable]
+        Maps variable names to functions from variables to values.
+        Can include Controls
+        ## TODO: Make collection of equations into a named type
+
+
+    pre : Mapping[str, Any]
+        Bound values for all variables that must be known before beginning the period's dynamics.
+
+
+    dr : Mapping[str, Callable]
+        Decision rules for all the Control variables in the dynamics.
+    """
+    vals = pre.copy()
+
+    for varn in dynamics:
+        # Using the fact that Python dictionaries are ordered
+
+        feq = dynamics[varn]
+
+        if isinstance(feq, Control):
+            # This tests if the decision rule is age varying.
+            # If it is, this will be a vector with the decision rule for each agent.
+            if isinstance(dr[varn], np.ndarray):
+                ## Now we have to loop through each agent, and apply the decision rule.
+                ## This is quite slow.
+                for i in range(dr[varn].size):
+                    vals_i = {
+                        var: vals[var][i]
+                        if isinstance(vals[var], np.ndarray)
+                        else vals[var]
+                        for var in vals
+                    }
+                    vals[varn][i] = dr[varn][i](
+                        *[vals_i[var] for var in signature(dr[varn][i]).parameters]
+                    )
+            else:
+                vals[varn] = dr[varn](
+                    *[vals[var] for var in signature(dr[varn]).parameters]
+                )  # TODO: test for signature match with Control
+        else:
+            vals[varn] = feq(*[vals[var] for var in signature(feq).parameters])
+
+    return vals
+
+
+def parameters_by_age(ages, parameters):
+    """
+    Returns parameters for this model, but with vectorized
+    values which map age-varying values to agent ages.
+
+    Parameters
+    ----------
+    ages: np.array
+        An array of agent ages.
+
+    parameters: dict
+        A parameters dictionary
+
+    Returns
+    --------
+    aged_parameters: dict
+        A dictionary of parameter values.
+        If a parameter is age-varying, the value is a vector
+        corresponding to the values for each input age.
+    """
+
+    def aged_param(ages, p_value):
+        if isinstance(p_value, (float, int)) or callable(p_value):
+            return p_value
+        elif isinstance(p_value, list) and len(p_value) > 1:
+            pv_array = np.array(p_value)
+
+            return np.apply_along_axis(lambda a: pv_array[a], 0, ages)
+        else:
+            return np.empty(ages.size)
+
+    return {p: aged_param(ages, parameters[p]) for p in parameters}
+
+
+class Simulator:
+    pass
+
+
+class AgentTypeMonteCarloSimulator(Simulator):
+    """
+    A Monte Carlo simulation engine based on the HARK.core.AgentType framework.
+
+    Unlike HARK.core.AgentType, this class does not do any model solving,
+    and depends on dynamic equations, shocks, and decision rules paased into it.
+
+    The purpose of this class is to provide a way to simulate models without
+    relying on inheritance from the AgentType class.
+
+    This simulator makes assumptions about population birth and mortality which
+    are not generic. All agents are replaced with newborns when they expire.
+
+    Parameters
+    ------------
+    parameters: Mapping[str, Any]
+
+    shocks: Mapping[str, Distribution]
+
+    dynamics: Mapping[str, Union[Callable, Control]]
+
+    dr: Mapping[str, Callable]
+
+    initial: dict
+
+    seed : int
+        A seed for this instance's random number generator.
+
+    Attributes
+    ----------
+    agent_count : int
+        The number of agents of this type to use in simulation.
+
+    T_sim : int
+        The number of periods to simulate.
+    """
+
+    state_vars = []
+
+    def __init__(
+        self, parameters, shocks, dynamics, dr, initial, seed=0, agent_count=1, T_sim=10
+    ):
+        super().__init__()
+
+        self.parameters = parameters
+        self.shocks = shocks
+        self.dynamics = dynamics
+        self.dr = dr
+        self.initial = initial
+
+        self.seed = seed  # NOQA
+        self.agent_count = agent_count
+        self.T_sim = T_sim
+
+        # changes here from HARK.core.AgentType
+        self.vars = list(shocks.keys()) + list(dynamics.keys())
+
+        self.vars_now = {v: None for v in self.vars}
+        self.vars_prev = self.vars_now.copy()
+
+        self.read_shocks = False  # NOQA
+        self.shock_history = {}
+        self.newborn_init_history = {}
+        self.history = {}
+
+        self.reset_rng()  # NOQA
+
+    def reset_rng(self):
+        """
+        Reset the random number generator for this type.
+        """
+        self.RNG = np.random.default_rng(self.seed)
+
+    def initialize_sim(self):
+        """
+        Prepares for a new simulation.  Resets the internal random number generator,
+        makes initial states for all agents (using sim_birth), clears histories of tracked variables.
+        """
+        if self.T_sim <= 0:
+            raise Exception(
+                "T_sim represents the largest number of observations "
+                + "that can be simulated for an agent, and must be a positive number."
+            )
+
+        self.reset_rng()
+        self.t_sim = 0
+        all_agents = np.ones(self.agent_count, dtype=bool)
+        blank_array = np.empty(self.agent_count)
+        blank_array[:] = np.nan
+        for var in self.vars:
+            if self.vars_now[var] is None:
+                self.vars_now[var] = copy(blank_array)
+
+        self.t_age = np.zeros(
+            self.agent_count, dtype=int
+        )  # Number of periods since agent entry
+        self.t_cycle = np.zeros(
+            self.agent_count, dtype=int
+        )  # Which cycle period each agent is on
+
+        # Get recorded newborn conditions or initialize blank history.
+        if self.read_shocks and bool(self.newborn_init_history):
+            for init_var_name in self.initial:
+                self.vars_now[init_var_name] = self.newborn_init_history[init_var_name][
+                    self.t_sim, :
+                ]
+        else:
+            for var_name in self.initial:
+                self.newborn_init_history[var_name] = (
+                    np.zeros((self.T_sim, self.agent_count)) + np.nan
+                )
+
+        self.sim_birth(all_agents)
+
+        self.clear_history()
+        return None
+
+    def sim_one_period(self):
+        """
+        Simulates one period for this type.  Calls the methods get_mortality(), get_shocks() or
+        read_shocks, get_states(), get_controls(), and get_poststates().  These should be defined for
+        AgentType subclasses, except get_mortality (define its components sim_death and sim_birth
+        instead) and read_shocks.
+        """
+        # Mortality adjusts the agent population
+        self.get_mortality()  # Replace some agents with "newborns"
+
+        # state_{t-1}
+        for var in self.vars:
+            self.vars_prev[var] = self.vars_now[var]
+
+            if isinstance(self.vars_now[var], np.ndarray):
+                self.vars_now[var] = np.empty(self.agent_count)
+                self.vars_now[var][:] = np.nan
+            else:
+                # Probably an aggregate variable. It may be getting set by the Market.
+                pass
+
+        shocks_now = {}
+
+        if self.read_shocks:  # If shock histories have been pre-specified, use those
+            for var_name in self.shocks:
+                shocks_now[var_name] = self.shock_history[var_name][self.t_sim, :]
+        else:
+            ### BIG CHANGES HERE from HARK.core.AgentType
+            shocks_now = draw_shocks(self.shocks, self.t_age)
+
+        pre = parameters_by_age(self.t_age, self.parameters)
+
+        pre.update(self.vars_prev)
+        pre.update(shocks_now)
+        # Won't work for 3.8: self.parameters | self.vars_prev | shocks_now
+
+        # Age-varying decision rules captured here
+        dr = parameters_by_age(self.t_age, self.dr)
+
+        post = simulate_dynamics(self.dynamics, pre, dr)
+
+        self.vars_now = post
+        ### BIG CHANGES HERE
+
+        # Advance time for all agents
+        self.t_age = self.t_age + 1  # Age all consumers by one period
+
+        # What will we do with cycles?
+        # self.t_cycle = self.t_cycle + 1  # Age all consumers within their cycle
+        # self.t_cycle[
+        #    self.t_cycle == self.T_cycle
+        # ] = 0  # Resetting to zero for those who have reached the end
+
+    def make_shock_history(self):
+        """
+        Makes a pre-specified history of shocks for the simulation.  Shock variables should be named
+        in self.shock, a mapping from shock names to distributions.  This method runs a subset
+        of the standard simulation loop by simulating only mortality and shocks; each variable named
+        in shocks is stored in a T_sim x agent_count array in history dictionary self.history[X].
+        Automatically sets self.read_shocks to True so that these pre-specified shocks are used for
+        all subsequent calls to simulate().
+
+        Returns
+        -------
+        shock_history: dict
+            The subset of simulation history that are the shocks for each agent and time.
+        """
+        # Re-initialize the simulation
+        self.initialize_sim()
+        self.simulate()
+
+        for shock_name in self.shocks:
+            self.shock_history[shock_name] = self.history[shock_name]
+
+        # Flag that shocks can be read rather than simulated
+        self.read_shocks = True
+        self.clear_history()
+
+        return self.shock_history
+
+    def get_mortality(self):
+        """
+        Simulates mortality or agent turnover.
+        Agents die when their states `live` is less than or equal to zero.
+        """
+        who_dies = self.vars_now["live"] <= 0
+
+        self.sim_birth(who_dies)
+
+        self.who_dies = who_dies
+        return None
+
+    def sim_birth(self, which_agents):
+        """
+        Makes new agents for the simulation.  Takes a boolean array as an input, indicating which
+        agent indices are to be "born".  Does nothing by default, must be overwritten by a subclass.
+
+        Parameters
+        ----------
+        which_agents : np.array(Bool)
+            Boolean array of size self.agent_count indicating which agents should be "born".
+
+        Returns
+        -------
+        None
+        """
+        if self.read_shocks:
+            t = self.t_sim
+            initial_vals = {
+                init_var: self.newborn_init_history[init_var][t, which_agents]
+                for init_var in self.initial
+            }
+
+        else:
+            initial_vals = draw_shocks(self.initial, np.zeros(which_agents.sum()))
+
+        if np.sum(which_agents) > 0:
+            for varn in initial_vals:
+                self.vars_now[varn][which_agents] = initial_vals[varn]
+                self.newborn_init_history[varn][
+                    self.t_sim, which_agents
+                ] = initial_vals[varn]
+
+        self.t_age[which_agents] = 0
+        self.t_cycle[which_agents] = 0
+
+    def simulate(self, sim_periods=None):
+        """
+        Simulates this agent type for a given number of periods. Defaults to
+        self.T_sim if no input.
+        Records histories of attributes named in self.track_vars in
+        self.history[varname].
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        history : dict
+            The history tracked during the simulation.
+        """
+        if not hasattr(self, "t_sim"):
+            raise Exception(
+                "It seems that the simulation variables were not initialize before calling "
+                + "simulate(). Call initialize_sim() to initialize the variables before calling simulate() again."
+            )
+        if sim_periods is not None and self.T_sim < sim_periods:
+            raise Exception(
+                "To simulate, sim_periods has to be larger than the maximum data set size "
+                + "T_sim. Either increase the attribute T_sim of this agent type instance "
+                + "and call the initialize_sim() method again, or set sim_periods <= T_sim."
+            )
+
+        # Ignore floating point "errors". Numpy calls it "errors", but really it's excep-
+        # tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is
+        # -np.inf, np.inf/np.inf is np.nan and so on.
+        with np.errstate(
+            divide="ignore", over="ignore", under="ignore", invalid="ignore"
+        ):
+            if sim_periods is None:
+                sim_periods = self.T_sim
+
+            for t in range(sim_periods):
+                self.sim_one_period()
+
+                # track all the vars -- shocks and dynamics
+                for var_name in self.vars:
+                    self.history[var_name][self.t_sim, :] = self.vars_now[var_name]
+
+                self.t_sim += 1
+
+            return self.history
+
+    def clear_history(self):
+        """
+        Clears the histories.
+        """
+        for var_name in self.vars:
+            self.history[var_name] = np.empty((self.T_sim, self.agent_count))
+            self.history[var_name].fill(np.nan)
diff --git a/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb
new file mode 100644
index 000000000..2699e7125
--- /dev/null
+++ b/examples/MonteCarlo/Generic Monte Carlo Perfect Foresight.ipynb
@@ -0,0 +1,1933 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "be704ca8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType\n",
+    "from HARK.distribution import Bernoulli\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0698156",
+   "metadata": {},
+   "source": [
+    "## Original Perfect Foresight Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "83e6f76e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PFexample = PerfForesightConsumerType()\n",
+    "PFexample.cycles = 0\n",
+    "\n",
+    "SimulationParams = {\n",
+    "    \"AgentCount\": 3,  # Number of agents of this type\n",
+    "    \"T_sim\": 120,  # Number of periods to simulate\n",
+    "    \"aNrmInitMean\": -6.0,  # Mean of log initial assets\n",
+    "    \"aNrmInitStd\": 0,  # 1.0,  # Standard deviation of log initial assets\n",
+    "    \"pLvlInitMean\": 0.0,  # Mean of log initial permanent income\n",
+    "    \"pLvlInitStd\": 0.0,  # Standard deviation of log initial permanent income\n",
+    "    \"PermGroFacAgg\": 1.0,  # Aggregate permanent income growth factor\n",
+    "    \"T_age\": None,  # Age after which simulated agents are automatically killed,\n",
+    "    \"LivPrb\": [0.98],\n",
+    "}\n",
+    "\n",
+    "PFexample.assign_parameters(**SimulationParams)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e0f219ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PFexample.solve()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c3981c6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<HARK.ConsumptionSaving.ConsIndShockModel.PerfForesightConsumerType object at 0x7f6482eab190.\n",
+       "Parameters:\n",
+       "cycles: 1\n",
+       "CRRA: 2.0\n",
+       "Rfree: 1.03\n",
+       "DiscFac: 0.96\n",
+       "LivPrb: [0.98]\n",
+       "PermGroFac: [1.01]\n",
+       "BoroCnstArt: None\n",
+       "MaxKinks: 400\n",
+       "AgentCount: 3\n",
+       "aNrmInitMean: -6.0\n",
+       "aNrmInitStd: 0\n",
+       "pLvlInitMean: 0.0\n",
+       "pLvlInitStd: 0.0\n",
+       "PermGroFacAgg: 1.0\n",
+       "T_age: None\n",
+       "T_cycle: 1\n",
+       "PerfMITShk: False\n",
+       "T_sim: 120>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PFexample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "66cc08fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'who_dies': array([[ 0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  1.],\n",
+       "        [ 0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0.],\n",
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+       "        [ 0.,  0.,  0.],\n",
+       "        [nan, nan, nan]]),\n",
+       " 'mNrm': array([[  1.00252784,   1.00252784,   1.00252784],\n",
+       "        [ -0.30338194,  -0.30338194,  -0.30338194],\n",
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+       "        [-12.65752504, -12.65752504,  -2.81670212],\n",
+       "        [-13.61706682, -13.61706682,  -4.02577047],\n",
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+       "        [ -6.08371181,  -4.92818269, -21.05056386],\n",
+       "        [ -7.20994102,  -6.08371181, -21.79728952],\n",
+       "        [ -8.30761326,  -7.20994102, -22.525081  ],\n",
+       "        [ -9.37745262,  -8.30761326, -23.2344184 ],\n",
+       "        [-10.42016485,  -9.37745262, -23.92576966],\n",
+       "        [-11.43643779, -10.42016485, -24.59959082],\n",
+       "        [-12.42694183, -11.43643779, -25.25632639],\n",
+       "        [-13.39233038, -12.42694183, -25.89640959],\n",
+       "        [ -1.27807355, -13.39233038, -26.52026268],\n",
+       "        [ -2.52615583, -14.33324028, -27.12829717],\n",
+       "        [ -3.74259139, -15.25029222, -27.72091418],\n",
+       "        [ -4.92818269, -16.14409113, -28.29850463],\n",
+       "        [ -6.08371181, -17.01522664, -28.86144954],\n",
+       "        [ -7.20994102, -17.8642734 , -29.41012026],\n",
+       "        [ -8.30761326, -18.69179151, -29.94487875],\n",
+       "        [ -9.37745262, -19.49832683, -30.46607775],\n",
+       "        [-10.42016485, -20.28441144, -30.97406109],\n",
+       "        [-11.43643779, -21.05056386, -31.46916387],\n",
+       "        [-12.42694183, -21.79728952, -31.95171268],\n",
+       "        [-13.39233038, -22.525081  , -32.42202586],\n",
+       "        [-14.33324028, -23.2344184 , -32.88041365],\n",
+       "        [-15.25029222, -23.92576966, -33.32717843],\n",
+       "        [-16.14409113,  -1.27807355, -33.76261493],\n",
+       "        [-17.01522664,  -2.52615583, -34.18701038],\n",
+       "        [-17.8642734 ,  -3.74259139, -34.60064474],\n",
+       "        [-18.69179151,  -4.92818269, -35.00379088],\n",
+       "        [-19.49832683,  -6.08371181, -35.39671474],\n",
+       "        [-20.28441144,  -7.20994102, -35.77967551],\n",
+       "        [-21.05056386,  -8.30761326, -36.15292583],\n",
+       "        [-21.79728952,  -9.37745262, -36.51671191],\n",
+       "        [-22.525081  , -10.42016485, -36.87127373],\n",
+       "        [-23.2344184 , -11.43643779, -37.21684518],\n",
+       "        [-23.92576966, -12.42694183,  -1.27807355],\n",
+       "        [-24.59959082, -13.39233038,  -2.52615583]])}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PFexample.track_vars = [\"who_dies\", \"mNrm\", \"pLvl\", \"aNrm\"]\n",
+    "PFexample.make_shock_history()\n",
+    "\n",
+    "PFexample.initialize_sim()\n",
+    "PFexample.simulate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3b126cc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.mean(PFexample.history[\"mNrm\"], axis=1))\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mean normalized market resources\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "633034d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.mean(PFexample.history[\"mNrm\"] * PFexample.history[\"pLvl\"], axis=1))\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mean normalized market resources\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bb741c54",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_14820/947589964.py:1: RuntimeWarning: divide by zero encountered in log\n",
+      "  plt.plot(np.log(np.mean(PFexample.history[\"mNrm\"], axis=1) - np.min(np.mean(PFexample.history[\"mNrm\"], axis=1))))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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YzgXPfM/23OAukBXMrn91Iac8/I1fN/+ocOQq4GAnItxySheeGNefHzfvZ8zE2azflV+vx9qy1zPts3ILoLLs9im8e8NwWiRGc9nUHKbP31Tna+zK96wCTmskLYAP8WwF+SHwBbAO+NifQRnnxg9rz5TxA9mwK59zn5rtl1kPxv+25x5k895Cxkz8ju/W+rfufW5RSaOfAeQPZ/fNZNrVQ8gtKuXcibOZt6HuexVv2eeZgVcxBlCVNilxvHndMAZ1SOHONxfz8H9X1mn8IVCrgMFZLaDe3oHa3qraBRgEfO/3yIxjJ3VvwX+uHUqZKuc//R2zQ2zjjGBQVFLGoPYptEyKYfzUubz7g/9WtOYWloZUC6CyAe2a8871w0iJi+LiyTl8VMfdyQ61AGpIAODppn3hikFcmN2ax79cwx3TF1Nc6myGUKDqAIGzFsBhVHUhnoFh04j0zEzmneuHk9kslsufnxuQJfHGdwqLy+iQFs+b1w1jQLvm3PrGIp75Zq1fVn/nFpWQHKIJAKBdajxvXTeM3lnJ3PDaQqbOWu/4Z7fsKyQ1PorYqNrHTyLDw/i/8/pw2yldeWvhZia8OI8DB2sfq6uoBNooxgBE5A+VjjtE5DWg5h0cjCsym8Xyn2uHcmzb5tzy+iKem7nO7ZCMQwXFpcRGhZMcG8mLVw5iVJ9W/Pvjn7j3/eU+L3ecW1gS1FNAnWgeH8WrVw3mtB4tue+D5fzvRyscJdvNewur7f+vSsX4wwPn9+G7tbsZO+n7Wrfg3H2gmIToiIAM0jtpASRWOqLxjAWc48+gTP1VvIGc1bsV//xoBf9y+Itt3FVUUn7oDz46IpzHx/ZnwnEdeOG7Ddz6xiLH3QdO5BaVVlsILpTERIYz8eIBXDKkLc/OXMcd0xfXupBry77CWrt/qnJBdhsmX5bN2h35nP/Md2zcXf0g9O78g6QE4NM/OKgFpKr3AohIgvd2/Se4moCIiQzniXH9SU2IYtLMdezNL+Z/x/T2y5ZypuFKy8opLisnttInvrAw4e6zjiE9MZp/f/wTewuKeeaSAcQ3sDrkob0AQrwFUCE8TPifc3qRnhDDI5+vYm9BMU9ddGyVXTyqytZ9hYzo1qJe1zqpewteu3owV74wj/Of+Z6XrhzEMa2SjrpfRSG4QHDSBdRLRH4AlgHLRGRBVVU7TeMSFibcO7ont5zchekLNnPdqwttg5lGqsj76T426vA/RxHh2t904v7z+jB7zS4unZLD/oKGbXxyaC+AEB4DOFJFN80/ftuLr1buYPzzc8krOvr/eXd+MUUl5XXqAjpS/7bNmX7tUMJF+N2z31e589quAwf9vhVkBScfCScBf1DVdqraDrjde840ciKevQXuHd2Tz5Zv56oX59uCsUaosNiTmGOrqfJ64cA2TLx4AEu35PI7B33INTlUB8haAEe5ZEg7Hhvbn4Ub9zLuuTlH7Y3sdAZQbTq3SOTN64aSmhDNxZNz+Hb14ft97M4v9vtWkBWcJIB4Vf2q4oaqfg3E+y0i43Pjh7XnwQv68t3aXVw6ZW7QbZ/X1FW0zGJrGPQ7o1cGUy7PZuPuAi58tv516A/VAbIxgCqN7pvJpMsGsHr7AcZOmsOOSosrt+yreRFYXbRuHsf0a4fSIS2BCS/M53Nvwbrycg1YKWhwlgDWichfRaS997gbz2Iw04ScP6A1T110LIs37+OSyTkNLlhlfKfQQQIAT23+V64axK68g1z47NEbqDtxaC8AawFUa0T3ljx/hafy7u8mzTmUbCtaAK2rKANRH2kJ0Uy7ejDHZCZx7SsL+GDxVvYXllBWro2qC+hKPBvCv+090rznTBNzZu9WTLo0m5Xb8xj3XM5RTVzjjoJDXUC1/zkOaJfCq1cPJq+olAuf/b7OJQ1+bQFYAqjJsE5pvDzh8GS7ZV8hCdERPm09NYuL4pUJgzi2bXNunvYDz8/2rEloNC0AVd2rqjer6rHAQOBv3n2BTRN0UvcWTL4sm3U7DzDuuTnssiTguooxAKfzvvu0bsa0q4dwsLScsZPqlgR+HQOwLqDaDGiXwmtXDyGvqJSxk+bww6Z9ZDWL9fk2jYkxkbxw5UAGd0jl8S/XAIGpAwTOZgG9JiJJIhIPLAGWi8id/g/N+MsJXdN5/oqB/LyngItDqCWwN7+YS6fk8NMvuW6HchgnYwBH6pGZxLSrh1BSpnVKAtYCqJverZN59arBHDhYyo+b9vmk/78qcVERTL18IMd1TgOgZVKMX65zJCddQD1UNRf4LZ4icB2AS/0ZlPG/YZ3SmDp+IBt253Px5NBIAnlFpazefoCLn8th9fbGUzTv0BiAg/IClXXLSDwsCdS0uKhCqOwF4Eu9sjxJoHlcJN0ynG9RWVexUeFMHp/Na1cNpnOLBL9dpzInCSBSRCLxJIAZqlqClYMOCsM6pzH18oGs35XvmR3UwDnmjV3b1DhevXowYWHCRZNzWNeATTt86dA00Hos/e+WkchrVw+muLSci57LYfPemgeGc4tKiYkMIzoi+PcC8KVeWcnMvmsEd5zWza/XiYkMZ5i3FRAIThLAs8AGPFM/Z4pIO6BxtaFNvQ3vnMaky7JZs+MAl1WzACaYdEpP4LWrBlNerlz0XE69ZtL4Wn1bABW6ZyTx8oTB5BWVcNFzOfyyv/p9IWwVcP3FRUUQHubb/n+3ORkEflxVs1R1pHpsBE4KQGwmQH7TNZ0nL+rPsi37mfDi/EOfSINVl5aJvHLVYAqKS7lkSs5hc73dUJ8xgCP1ykrmpQmD2ZNfzCVTqt/1KrcotDaDMTVzMgjcUkSmiMjH3ts9gPF+j8wE1Gk9M3jkd/2Yt2EPN7y2sNaiWE3dMa2SePHKQezMO8glU3LY24BtAhuqrrOAqtOvTTOmjM9m054Cxk+tujWXW1hqM4DMIU66gF4APgUyvbdXAbf6KR7jorP7ZvLP3/bmy592cMf0H31ehrix6d+2OZPHZ7NhdwFXvDDPtTIZBSVlRIYLkT4o1je4YypPX3IsK7blMuHF+UfVf7IWgKnMyW9cmqr+BygHUNVSILj7CELYRYPbcufp3Xhv0Vbu+2B50JeSHtYpjSfH9Wfx5n1c+8pCn5ZddqqwuMyntd9HdG/JQxf2Zd6GPdzy+g+UVUrkNgZgKnOSAPJFJBXvzB8RGQLs92tUxlXXn9jpUC36SSGwqcxpPTP417m9mblqJ3e+GfiWT1FJWYP6/6tyTr8s/jaqB58u287f3lt6KJHbXgCmMie/CX8AZgCdRGQ2nrIQ5/s1KuMqEeEvI49hR95B/vfjn2iZFMNv+2e5HZZfjR3Ult35xTzw6Uoym8XyxzO6B+zahSVl9Z4BVJMrhndge+5BnvlmLRlJMdw4orO1AMxhakwAIhIO/MZ7dAMEWOldC2CCWFiY8OAFfdiVd5A73/yRFonRAZ2f7IbrT+zEln2FPP31WrKaxXLJkHYBuW5hse9bABX+eEY3duQW8dBnq0hJiLK9AMxhauwCUtUyYJyqlqrqMlVdam/+oSM6IpxnLxtAh7R4rn1lAWt2NI6FU/4iItw3uicndUvnb+8t5cuftgfkuv5qAYDnOf37vD4M7ZjK3e8uBawSqPmVkzGA2SLypIgcLyLHVhx+j8w0CkkxkUwZP5CoiDCueGFu0JeMiAgP48mLjqVHZhI3vfZDQOoG+WMMoLKoiDCeuWQAHdM823jYGICp4CQB9AN6AvcBD3mPB/0Yk2lk2qTE8dxl2ezIPcg1Ly/gYGlwTwKLj45g8mUDiY+OYMIL8/1eMbXQzwkAIDkukheuGMQZPTM4tm1zv17LNB1OVgKfVMUxIhDBmcajf9vmPHRhXxZs3Mtf310a9NNDM5JjmDw+m935B7nmpfl+TXoFxWXE+KkLqLI2KXE8c+kAMhu4paEJHg1feWJCxqg+mdw0ojP/mb+ZF77b4HY4ftendTMeuqAfC3/ex9/eXea3pFfkx0FgY2piCcDUyW2ndOXUHi35x4cr+G7NLrfD8buz+rTixpM688b8TbyS87NfrhGILiBjquKkFtBRW9NUdc6EhrAw4ZHf9aNDWjw3Tvuh3puTNyW3ndqVk7qlc++MZczbsMfnj+/PWUDG1MRJC+B7h+dMiEiIjuCZSwZQXFrOda8uDPpB4fAw4dGx/WmTEsf1ry5kR57vqoeWlytFJeU+LQVhjFPVJgARyRCRAUCsiPSvNAX0RCAuUAGaxqlziwQevKAPP27ax33vL3c7HL9Ljo3k6UuOJa+ohFumLaLUR9VSD3prD8VZC8C4oKYWwOl4pnu2Bh7m1ymgtwF/buiFRaSNiHwlIstFZJmI3NLQxzSBdUavVvz+hI68mvMzM37c6nY4ftc9I4l//LY336/bzSOfr/LJYxb6YC8AY+qr2hUhqvoi8KKInKeqb/nh2qXA7aq6UEQSgQUi8pmqBv/HySByx+ndmLdhD39+ewl9spJp711sFKzOH9Caeev38NRXaxnYPoUTu7Vo0ONZAjBucroS+LANYURkQkMvrKrbVHWh9+s8YAUQ3BXHglBkeBhPXHQs4WHCjdOCfzwA4N5zetKtZSJ3TP+RnXkNWyRW6N2DIBDrAIw5kpME8Dx+3hBGRNoD/YGcKr53jYjMF5H5O3fu9OVljY9kNYvlwQv6snRLLvd/stLtcPwuJjKcx8f1J6+olNsbuHFOYbFnDMBaAMYNrm8IIyIJwFvArap6VOEVVZ2kqtmqmp2enu6ryxofO7VHSy4b2o4ps9Yza3Xwrw/olpHI3aN6MHPVTqbOXl/vx7EuIOMmVzeEEZFIPG/+r6rq2754TOOeP515DJ3S47l9+iL2Fbi3x26gXDK4Laf1aMn9n6ysd9G4QwkgytZkmsBz8lt35IYwLwE3NfTCIiLAFGCFqj7c0Mcz7ouNCuexsf3ZfaCYP7+zJOjrBYkI/zumN0mxEfzhjR/rtZ2krzaEN6Y+nCSAvXg2hBkG/B5PZVBfrAQeDlwKjBCRRd5jpA8e17ioV1Yyt53alY+W/MIHi7e5HY7fpSZE869ze7N8Wy5PfLm6zj9fZF1AxkVOEsCbQMuKDWGAocDUhl5YVWepqqhqH1Xt5z0+aujjGvf9/oSO9G2dzN9nLPN7KeXG4LSeGZw/oDUTv17Lok376vSzFV1AcVFWo98EnpMEcC3wrndl8EjgCcA+qZtqRYSH8cAFfTlQVMrf31vmdjgB8beze9AiMZo/vrm4Tl1BBcXWAjDucbIfwDzgZuC/wD3AKaq6yc9xmSaua8tEbjmlCx8u2cZHS4K/KygpJpJ//LYXK7fn8cw3ax3/XEUXUIwNAhsX1FQL6H0RmSEiM4A/4an/cxCY4j1nTI1+f0JHemUl8fcZy8gtCv6tpE8+piWj+rTiyS/XsGZHnqOfKSwuI0wgKtwSgAm8mn7rHuTX+j8PAROAuyvdNqZGEeFh/Ovc3uw+cJAHPw3+BWIA94zuSVx0OHe9tcTRArGKvQA8k+KMCaxqE4CqfgPMAu5R1W+OPAIXomnK+rRuxmVD2/PynI11HiBtitISovnLyGOYv3Evby3cXOv9bS8A46Ya252qWgaUi0hygOIxQej207rSIjGaP7+9xGdllBuz845tTXa75vz745/YX1Bz11dRcZmtATCucdLxeABY4i0I93jF4e/ATPBIjInkb6N6snxbLtPmBf/8gbAw4b5zerG3oJiHPqu568u2gzRucpIA3gb+CswEFlQ6jHFsZO8MhnRM4eH/rgyJMhE9MpO4bGh7XpmzkaVbqq+cUlhSZpvBGNc4mQb6YlVHIIIzwUNE+NuonuwvLOHRz+u+YrYpuu3UrjSPi+K+95dXWxajwLqAjIucbArfRUTe9O7cta7iCERwJrj0yExi3KC2vDxnI6u3O5sm2ZQlx0Zy66ldmbthD/9dvr3K+xTZILBxkdP9AJ7Gs4PXSXiKwb3iz6BM8Lr9tG7ER4Xzjw9XuB1KQIwb2IbOLRL498c/VblCuLDYxgCMe5wkgFhV/QIQVd2oqvcAZ/k3LBOsUuKjuOGkznyzaiffr93tdjh+FxEexp9Hdmf9rnxezdl41PdtENi4yUkCOCgiYcBqEblRRM4FEvwclwli44e1p1VyDP/3yU9BXzIa4KRuLRjeOZXHvlh91IroopIy2w7SuMZJArgFTxmIm4EBeEo4j/dnUCa4xUSGc+spXVi0aR+fLqu6bzyYiAh/OvMY9hWUMOXbw3cPsy4g4yZHxeBU9YCqblbVK1R1jKrOCURwJnidd2xrOqXH88CnP4XE4rBeWcmc3rMlU2etPzQNVlWtC8i4ysksoGwReUdEForI4oojEMGZ4BURHsadp3dj7c583lu01e1wAuK2U7tyoLiUSTM9k+iKy8opV2wWkHGNky6gV/HMBDoPOLvSYUyDnN4zg+4ZiTz11RrKHBROa+q6ZyRxdp9Mnp+9gV0HDh7aDtJaAMYtThLATlWdoarrvbOANqrq0dMZjKkjEeHmk7uwblc+H4bAngEAt5zShYOlZTz7zdpKG8JbAjDucJIA/i4ik0VknIiMqTj8HpkJCWf0zKBLiwSe/HK1o/LJTV2n9ARG983ktZyf2ba/CLAWgHGPkwRwBdAPOINfu39G+TEmE0LCwoQbR3Rm1fYD/Hf5L26HExDXntiJ/OIynvOOBVgpCOMWJztRD1TVbn6PxISsUX0yefTz1Tz51RpO75kR9JujdM9IYkT3Fny81JPwrAvIuMVJC+A7Eenh90hMyAoPE64+viNLt+Qyd/0et8MJiOtO7HToa+sCMm5xkgCGAItEZKV3CugSmwZqfG3MsVk0j4tk8qz1td85CAxsn8KAds0BSwDGPU4SwBlAF+A0fu3/t2mgxqdiIsO5ZEg7Pl+xnQ278t0OJyD+cGpXMpJiyGoe63YoJkQ5WQm8saojEMGZ0HLp0HZEhoXx/OzQaAUM75zGnD+fTEp8lNuhmBDlpAVgTEC0SIzh7L6Z/Gf+5lr30jXGNJwlANOoTDiuA4UlZUxfEPx7BxvjNksAplHpkZlE/7bNmDb355AoFW2Mm6pNACKSJyK51R2BDNKElnGD2rJ2Zz7zN+51OxRjglq1CUBVE1U1CXgMuAvIAloDfwQeDUh0JiSN6tOKxOgIpuX87HYoxgQ1J11Ao1V1oqrmqWquqj4NnOPvwEzoiouKYHS/TD5css0Gg43xIycJIF9ELhaRcBEJE5GLgdCYqG1cM25QWw6WlvPuoi1uh2JM0HKSAC4CLgS2e48LvOeM8ZteWcn0zkpm2lzrBjLGX5wsBNugqueoapqqpqvqb1V1QwBiMyHu/AGt+emXPFb+kud2KMYEJSdbQnYVkS9EZKn3dh8Rudv/oZlQN7J3K8IEZvxo3UDG+IOTLqDngD8BJQCquhgY64uLi8hUEdlRkVyMqSw9MZrhndN4/8dttibAGD9wkgDiVHXuEedKfXT9F/AUmzOmSmf3zeTnPQUs2rTP7VCMCTpOEsAuEekEKICInA/4ZANXVZ0JhEYBeFMvp/fMICo8jBk/bnU7FGOCjpMEcAPwLNBdRLYAtwLX+TOoykTkGhGZLyLzd+7cGajLmkYiOTaSE7ul88HibZSFwJ7BxgSSk1lA61T1FCAd6K6qxwVyFpCqTlLVbFXNTk9PD9RlTSMyul8mO/MOkrNut9uhGBNUnMwCKhORfwMFqprnPbfQ75EZ43Vy95bERobzybLQ2DTemEBx0gW0zHu//4pIivdccO/abRqV2KhwhndO44sVO2w2kDE+5CQBlKrq/wMmA9+KyAC8A8INJSLTgO+BbiKyWUQm+OJxTfA5+ZgWbNlXyKrtB9wOxZigEeHgPgKgqm+IyDLgNaCtLy6uquN88Tgm+J3UrQUAX/y0nW4ZiS5HY0xwcNICuKriC1VdChwP3Oy3iIypQkZyDL2ykvhyxQ63QzEmaFTbAhCREar6JdBORNod8W1rh5uAG9G9JU9+uZo9+cW2kboxPlBTC+A33n/PruIY5ee4jDnKyd1bUK7wzSprBRjjC9W2AFT1795/rwhcOMZUr3dWMmkJ0XyxYgfn9m/tdjjGNHk1dQH9oaYfVNWHfR+OMdULCxNGdE/n46W/UFpWTkS4kyEsY0x1avoLSqzlMCbgju+STl5RKcu25rodijFNXk1dQPcGMhBjnBjc0bMWMWf9bvq2aeZuMMY0cbWuAxCRGGAC0BOIqTivqlf6MS5jqtQiMYaOafHkrNvDNSd0cjscY5o0J52oLwMZwOnAN0BrwPboM64Z3DGFuRv2WHVQYxrISQLorKp/BfJV9UXgLGCwf8MypnqDO6SSV1TKim02DmBMQzhJACXef/eJSC8gGWjhv5CMqdmv4wC2l5AxDeEkAUwSkebAX4EZwHLgfr9GZUwNWiXH0jYlzvYHMKaBah0EVtXJ3i+/ATr6NxxjnBncIYXPVmynvFwJC7Pq5MbUh5NZQM2Ay4D2le+vqlYQzrhmcMdUpi/YzKodeXTPSHI7HGOaJCfloD8C5gBLgHL/hmOMM4M7eMcB1u2xBGBMPTlJADGqWmNZCGMCrU1KHBlJMSz8eS/jh7V3OxxjmiRH6wBE5GoRaSUiKRWH3yMzpha9spKtJIQxDeAkARQDD+DZunGB95jvz6CMcaJnZhJrdx6goLjU7VCMaZKcdAHdjmcx2C5/B2NMXfTKSkYVVmzLY0C75m6HY0yT46QFsAYo8HcgxtRVz0zP4O+yrftdjsSYpslJCyAfWCQiXwEHK07aNFDjtlbJMaTER7Fsi40DGFMfThLAu97DmEZFROiZmcRSawEYUy81JgARCQcuV9WTAhSPMXXSMzOZKbPWUVxaTlSE7RBmTF3U+BejqmVAuYgkBygeY+qkZ2YSJWXKqu1WodyYunLSBXQAWCIin+EZDwBsDMA0Dr2yPJ9Nlm/NPfS1McYZJwngbe9hTKPTLiWOhOgIlm7dz4W0cTscY5oUJ9VAXxSRKKCr99RKVS2p6WeMCZSwMKFHqyRbEWxMPdQ6aiYiJwKrgaeAicAqETnBv2EZ41yPzCSWb821LSKNqSMnXUAPAaep6koAEekKTAMG+DMwY5zqkZlEYUkZP+8poENavNvhGNNkOJk3F1nx5g+gqquASP+FZEzddPS+6W/YnV/LPY0xlTlpAcwXkcnAK97bF2PF4Ewj0jY1DoCNu/Khm8vBGNOEOEkA1wE3ABXTPr/FMxZgTKOQnhBNXFQ4G/dYySpj6sLJLKCDwMPew5hGR0RomxLHxt2WAIypCyd7Ag8H7gHacfiewLZBvGk02qfGs3qHrQY2pi6cdAFNAW7DsxFMmX/DMaZ+2qXF8eVPOygrV8LDxO1wjGkSnMwC2q+qH6vqDlXdXXH44uIicoaIrBSRNSJyly8e04SmdinxFJeV80tukduhGNNkOEkAX4nIAyIyVESOrTgaemFvpdGngDOBHsA4EenR0Mc1oal95ZlAxhhHnHQBDfb+m13pnAIjGnjtQcAaVV0HICKvA+cAyxv4uCYEHZoKuqeAYS7HYkxT4WQWkL/2AsgCNlW6vZlfk80hInINcA1A27Zt/RSKaepaJccSFR5mi8GMqYNGv4OGqk5S1WxVzU5PT3c7HNNIhYcJrVNi+dmmghrjmJsJYAscVr+3tfecMfXSPjWeDZYAjHHMzQQwD+giIh285abHAjNcjMc0cZ7FYPmoWlVQY5xwMgiMiAwD2nP4QrCXGnJhVS0VkRuBT4FwYKqqLmvIY5rQ1j41joLiMnYdKCY9MdrtcIxp9JysBH4Z6AQs4teFYAo0KAEAqOpHwEcNfRxjANqleqqCbtydbwnAGAectACygR5q7WrTyLWrmAq6u4Ds9ikuR2NM4+dkDGApkOHvQIxpqNbN4wgTTwvAGFM7Jy2ANGC5iMwFDlacVNXRfovKmHqIiggjs1mslYU2xiEnCeAefwdhjK9kJMWwI/dg7Xc0xjhaCfxNIAIxxhdS4qNsNbAxDtU6BiAiQ0RknogcEJFiESkTkdxABGdMXaUmRLEnv9jtMIxpEpwMAj8JjANWA7HAVXiqeBrT6KTER7G3oITycpu0ZkxtHK0EVtU1QLiqlqnq88AZ/g3LmPpJiY+mrFzZX1jidijGNHpOBoELvKUaFonI/cA2mkAROROaUuOjANidX0xz79fGmKo5eSO/1Hu/G4F8PAXczvNnUMbUV4r3Td/GAYypnZNZQBtFJBZopar3BiAmY+otNaEiAdhUUGNq42QW0Nl46gB94r3dT0SsaqdplFLjPTWAdlsLwJhaOekCugfP9o37AFR1EdDBbxEZ0wDN4yMB2HPAEoAxtXGSAEpUdf8R52yOnWmUoiPCSYyOsBaAMQ44mQW0TEQuAsJFpAtwM/Cdf8Mypv5SbDGYMY44aQHcBPTEUwhuGpAL3OrHmIxpkJR4SwDGOOFkFlAB8BfvYUyjlxofxZZ9RW6HYUyjV20CqG2mj5WDNo1VSnwUizcfOWxljDlSTS2AocAmPN0+OYAEJCJjGiglPpq9BcWoKiL2a2tMdWpKABnAqXgKwV0EfAhMs43bTWOXGh9FSZmSW1RKcmyk2+EY02hVOwjsLfz2iaqOB4YAa4CvReTGgEVnTD1YOQhjnKlxEFhEooGz8LQC2gOPA+/4Pyxj6i+lUjmIDmnxLkdjTONV0yDwS0Av4CPgXlVdGrCojGmAQxVBbTWwMTWqqQVwCZ7qn7cAN1caTBNAVTXJz7EZUy/WBWSMM9UmAFW1mv+mSbKCcMY4Y2/yJujERoUTFxVuLQBjamEJwAQlKwdhTO0sAZiglBofZV1AxtTCEoAJSp4WgO0KZkxNLAGYoJQSH22bwhhTC0sAJiilJkSxK99TD8gYUzVLACYopcRHUVxaTn5xmduhGNNoWQIwQenQYjDrBjKmWpYATFA6VA7CBoKNqZYlABOU2qXGMbJ3BnFRTra9NiY0uZIAROQCEVkmIuUiku1GDCa4dW6RyMSLB9AtI9HtUIxptNxqASwFxgAzXbq+McaEPFfax6q6ArDt+owxxkWNfgxARK4RkfkiMn/nzp1uh2OMMUHDby0AEfkcz77CR/qLqr7n9HFUdRIwCSA7O9tW9RhjjI/4LQGo6in+emxjjDEN1+i7gIwxxviHW9NAzxWRzcBQ4EMR+dSNOIwxJpS5NQvoHeAdN65tjDHGQ5pStUQR2QlsrOePpwG7fBiOm+y5NF7B9HzsuTRO9Xku7VQ1/ciTTSoBNISIzFfVoFh1bM+l8Qqm52PPpXHy5XOxQWBjjAlRlgCMMSZEhVICmOR2AD5kz6XxCqbnY8+lcfLZcwmZMQBjjDGHC6UWgDHGmEosARhjTIgKiQQgImeIyEoRWSMid7kdT12ISBsR+UpElns30bnFez5FRD4TkdXef5u7HatTIhIuIj+IyAfe2x1EJMf7+rwhIlFux+iEiDQTkTdF5CcRWSEiQ5vq6yIit3l/v5aKyDQRiWlKr4uITBWRHSKytNK5Kl8L8Xjc+7wWi8ix7kV+tGqeywPe37PFIvKOiDSr9L0/eZ/LShE5vS7XCvoEICLhwFPAmUAPYJyI9HA3qjopBW5X1R7AEOAGb/x3AV+oahfgC+/tpuIWYEWl2/8HPKKqnYG9wARXoqq7x4BPVLU70BfPc2pyr4uIZAE3A9mq2gsIB8bStF6XF4AzjjhX3WtxJtDFe1wDPB2gGJ16gaOfy2dAL1XtA6wC/gTgfS8YC/T0/sxE73ueI0GfAIBBwBpVXaeqxcDrwDkux+SYqm5T1YXer/PwvMlk4XkOL3rv9iLwW1cCrCMRaQ2cBUz23hZgBPCm9y5N4rmISDJwAjAFQFWLVXUfTfR1wVMWJlZEIoA4YBtN6HVR1ZnAniNOV/danAO8pB5zgGYi0ioggTpQ1XNR1f+qaqn35hygtffrc4DXVfWgqq4H1uB5z3MkFBJAFrCp0u3N3nNNjoi0B/oDOUBLVd3m/dYvQEu34qqjR4H/B5R7b6cC+yr9cjeV16cDsBN43tudNVlE4mmCr4uqbgEeBH7G88a/H1hA03xdKqvutWjq7wlXAh97v27QcwmFBBAURCQBeAu4VVVzK39PPXN5G/18XhEZBexQ1QVux+IDEcCxwNOq2h/I54junib0ujTH80myA5AJxHN0F0ST1lRei9qIyF/wdAu/6ovHC4UEsAVoU+l2a++5JkNEIvG8+b+qqm97T2+vaLZ6/93hVnx1MBwYLSIb8HTFjcDTj97M2/UATef12QxsVtUc7+038SSEpvi6nAKsV9WdqloCvI3ntWqKr0tl1b0WTfI9QUQuB0YBF+uvC7ga9FxCIQHMA7p4ZzRE4RkwmeFyTI55+8inACtU9eFK35oBjPd+PR5wvM2mW1T1T6raWlXb43kdvlTVi4GvgPO9d2sqz+UXYJOIdPOeOhlYThN8XfB0/QwRkTjv71vFc2lyr8sRqnstZgCXeWcDDQH2V+oqapRE5Aw8XaejVbWg0rdmAGNFJFpEOuAZ2J7r+IFVNegPYCSekfO1ePYkdj2mOsR+HJ6m62JgkfcYiafv/AtgNfA5kOJ2rHV8XicCH3i/7uj9pV0DTAei3Y7P4XPoB8z3vjbvAs2b6usC3Av8BCwFXgaim9LrAkzDM35Rgqd1NqG61wIQPDMD1wJL8Mx+cv051PJc1uDp6694D3im0v3/4n0uK4Ez63ItKwVhjDEhKhS6gIwxxlTBEoAxxoQoSwDGGBOiLAEYY0yIsgRgjDEhyhKAMVUQkVQRWeQ9fhGRLd6vD4jIRLfjM8YXbBqoMbUQkXuAA6r6oNuxGONL1gIwpg5E5MRK+xjcIyIvisi3IrJRRMaIyP0iskREPvGW8EBEBojINyKyQEQ+bUyVJ01oswRgTMN0wlPTaDTwCvCVqvYGCoGzvEngCeB8VR0ATAX+6VawxlQWUftdjDE1+FhVS0RkCZ6NVD7xnl8CtAe6Ab2AzzxldgjHs8zfGNdZAjCmYQ4CqGq5iJTor4Nq5Xj+vgRYpqpD3QrQmOpYF5Ax/rUSSBeRoeAp7S0iPV2OyRjAEoAxfqWebUjPB/5PRH7EU8lxmKtBGeNl00CNMSZEWQvAGGNClCUAY4wJUZYAjDEmRFkCMMaYEGUJwBhjQpQlAGOMCVGWAIwxJkT9fzLGhZIM/TZqAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(\n",
+    "    np.log(\n",
+    "        np.mean(PFexample.history[\"mNrm\"], axis=1)\n",
+    "        - np.min(np.mean(PFexample.history[\"mNrm\"], axis=1))\n",
+    "    )\n",
+    ")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mean normalized market resources\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "603ae6e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0,)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PFexample.newborn_init_history[\"pLvl\"][1, PFexample.history[\"who_dies\"][1, :] > 0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "567440dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0,)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PFexample.newborn_init_history[\"aNrm\"][2, PFexample.history[\"who_dies\"][2, :] > 0].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ead3ec8",
+   "metadata": {},
+   "source": [
+    "## Using the Generic Monte Carlo Simulator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "adfbe431",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from HARK.distribution import Lognormal\n",
+    "import HARK.models.perfect_foresight_normalized as pfn\n",
+    "from HARK.simulation.monte_carlo import AgentTypeMonteCarloSimulator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5a0c394b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'shocks': {'live': <HARK.distribution.Bernoulli at 0x7f6482cab160>},\n",
+       " 'parameters': {'DiscFac': 0.96,\n",
+       "  'CRRA': (2.0,),\n",
+       "  'Rfree': 1.03,\n",
+       "  'LivPrb': 0.98,\n",
+       "  'PermGroFac': 1.01,\n",
+       "  'BoroCnstArt': None},\n",
+       " 'dynamics': {'p': <function HARK.models.perfect_foresight_normalized.<lambda>(PermGroFac, p)>,\n",
+       "  'r_eff': <function HARK.models.perfect_foresight_normalized.<lambda>(Rfree, PermGroFac)>,\n",
+       "  'b_nrm': <function HARK.models.perfect_foresight_normalized.<lambda>(r_eff, a_nrm)>,\n",
+       "  'm_nrm': <function HARK.models.perfect_foresight_normalized.<lambda>(b_nrm)>,\n",
+       "  'c_nrm': <HARK.model.Control at 0x7f6482cc5b40>,\n",
+       "  'a_nrm': <function HARK.models.perfect_foresight_normalized.<lambda>(m_nrm, c_nrm)>},\n",
+       " 'reward': {'u': <function HARK.models.perfect_foresight_normalized.<lambda>(c)>}}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pfn.model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e9d068bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pfn_simulator = AgentTypeMonteCarloSimulator(\n",
+    "    pfn.model[\"parameters\"],\n",
+    "    pfn.model[\"shocks\"],\n",
+    "    pfn.model[\"dynamics\"],\n",
+    "    {\"c_nrm\": lambda m_nrm: PFexample.solution[0].cFunc(m_nrm)},\n",
+    "    {  # initial states\n",
+    "        \"a_nrm\": Lognormal(-6, 0),\n",
+    "        #'live' : 1,\n",
+    "        \"p\": 1.0,\n",
+    "    },\n",
+    "    agent_count=3,\n",
+    "    T_sim=120,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "36ba1dda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pfn_simulator.read_shocks = True\n",
+    "# pfn_simulator.shock_history['live'] = 1 - np.roll(PFexample.history[\"who_dies\"], -1)\n",
+    "\n",
+    "pfn_simulator.shock_history[\"live\"] = 1 - PFexample.history[\"who_dies\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bc84d3e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pfn_simulator.newborn_init_history[\"a_nrm\"] = PFexample.newborn_init_history[\"aNrm\"]\n",
+    "pfn_simulator.newborn_init_history[\"p\"] = PFexample.newborn_init_history[\"pLvl\"]\n",
+    "# pfn_simulator.newborn_init_history['live'] = PFexample.newborn_init_history['pLvl']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "65df3a7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "        [-14.33324028, -23.2344184 , -32.88041365],\n",
+       "        [-15.25029222, -23.92576966, -33.32717843],\n",
+       "        [-16.14409113,  -1.27807355, -33.76261493],\n",
+       "        [-17.01522664,  -2.52615583, -34.18701038],\n",
+       "        [-17.8642734 ,  -3.74259139, -34.60064474],\n",
+       "        [-18.69179151,  -4.92818269, -35.00379088],\n",
+       "        [-19.49832683,  -6.08371181, -35.39671474],\n",
+       "        [-20.28441144,  -7.20994102, -35.77967551],\n",
+       "        [-21.05056386,  -8.30761326, -36.15292583],\n",
+       "        [-21.79728952,  -9.37745262, -36.51671191],\n",
+       "        [-22.525081  , -10.42016485, -36.87127373],\n",
+       "        [-23.2344184 , -11.43643779, -37.21684518],\n",
+       "        [-23.92576966, -12.42694183,  -1.27807355],\n",
+       "        [-24.59959082, -13.39233038,  -2.52615583]])}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# pf_simulator.track_vars = [\"mNrm\"]\n",
+    "pfn_simulator.initialize_sim()\n",
+    "pfn_simulator.simulate(sim_periods=120)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9e2c7ad0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DRfbDyKmZq3fx7oItvDLb3R5XjY2DiHQUkaFAoogMrrKM9RighatRNTPRUcLlY7vx44Y9zFqzK9ThGFNv+4rLSYqLJipK6vW4E/p2ILtdEo/MWGU/jBwqKvMMpjz+1RrKKipdO09tPYeT8CxZ7QLcz8FlrL8B/uhaRM3U2UO70DYpjke/XB3qUIypt8I68irVJDpK+MXYbizctJdvrVKcI0WlnsZh054ipi/c4tp5amwcVPVpVT0WuOSwZayTVPUN1yJqphJio7lodFc+W7ad5VsLQh2OaaYqKpXPl2+vd2K8fTUU+nHizCHptGsZzyMzVjXo8c1NcVkF8TFR9GjfkkdmuDcU7XSH9CHFfkTkMleiaeYuGp1Fi7hoHrU/EhMis9fu4tKps3l1bv3Gsz0ZWWMbdM6E2GimjOnKVz/tYNEmS/hcl6KyClrERXPFUd1YuiWfL3/a4cp5nDQOU7FiP0HROimO80Zk8vaPm62cogmJ/CJP6rRHZ6yu1woiT/3ohqdBmzwyi5bxMTas6kBRaQWJsdGcPiidDinxTHUpu7MV+wkzl4/NJkrgia/sj8QEX0m5Z4Jz9Y7Cei2trqvQT11SE2OZPDKT9xZsZu2OQsePu+mNBfzprYUNPm8kKiqrICEumriYKB6aPIT7zxnkynms2E+Y6ZSayBmD03lp9gZ27CsJdTimmfE1DikJMTz0xUrH49meOYeGDSv5XDYmm5joKB790vmw6ozleTw/cz2r8vY16tyRpLjM03MAT+XJNklxrpzHiv2EoSuO6k5pRSX/+2ZtqEMxzUyxd5nk5WO7sWhTPl85HM8uKC5r1LASQPuUBM4Z1oXX525i6966E/KVlleyJb8YVXj4i+YzT1dUpXFwk5PGYTeHFfsBGrVD2tSuR/uWjO/Xkae/W0t+cSDKZxjjjK/ncN6ITDqkxPPQFyvrfIyqUlha0aClrIf75VHdqVB1NKy6ZW8RqtAhJZ635m1i4+7mMU9XVFpBYlx4NA6vAR18xX7wpLx4yt2wzFXH9qCguJxnLZ23CSJfzyE5IYYrjurO96t3MWdt7Rszi8sqqajURg8rAWS0acGkgZ15fuZ6dheW1nrfDbuKAPjDhN4APN5MJrOLyipJCJOew5XAW94d0ycD/wFOdjcsk5ueyrG90njiq9WWztsEja/nEB8TxXkjMmibFMeDn9feeygo8fRuA9FzAPjVMd0pKquos8a6r6cwvGsbzhzimafbXtD060MUh8uwkqrOBq4BPgJuA8apqqVRDIKrj8th9/4yXpi5PtShmGaipNyzwUpEaBEXw2Vjs/lieR4LN9a8BqWwxNPbaOycg09Oh2TG9+vI/75dy96imodVN+zeT0yU0DElgV8d04OyikqebAZld31LWd1WW26ld0RkmohMA27Ck0+pBHjSe51x2dCs1hzRvS2Pfbn6QHffGDeVlFUSH3Pwa+HCUVmkJMTw4Oc/1fiYgxlZGz+s5HP1cZ5h1ae/XVvjfTbuLqJTqwRioqPIbpfEqQM78+z369hVx3BUpCsqC/2cw70czKd0H3AZ8Kcql00QXH1sD7YXlPCK5bw3QVBSXkF8lV+lyQmxXHpkNh8u3sayrfl+H3NgWKkR+xwOl5ueyrg+7XnqmzXsq6EY0IZd+8lofTAH6NXH9qCorIInv27acw9FZRWhnXNQ1RnA18Btqjrj8MP1yAwAo7u3ZVhWax7+YhUl5dZ7MO4qLqskIfbQr4VLj+xKy/gY/vOZ/7mHg8NKgWscAH59XA579pfVuChj4+4iurROPHA5p0MyJ+d24ulv17Fnf9PsPVRUKqXllaGfc/AW2qkUkVTXIzF+iQjXjsthy95iXp1jpUSNuzxzDod+8bRqEcfFR2QxfeEWVmyrnhRyX4AnpH0GZrTiqJ7+F2UUl1WwvaDkkJ4DeIaj9pWUM7WJ7hHyDS8nxrlfxNPJGfYBC73J9/7tO9wOzBw0pkc7hmS24uEvVlFa7l7+dmP89RwALh/TjRax0fz70+pzDw2pAufUtcf3YGdhKc99f2jvYdMezzLWLm0SD7m+T6cUTuzbgae+WVPrZHak8tVyCHnPwesN4BbgS2BulcMEiYhwzfE5bNpTxGtzrfdg3OOv5wCepJAXH9GV9xZu4afDeg879nmGcJID3HMAT3qIsTnteHTGob0HX2LKw3sOANeOy6GguJynvm56K5d8tRzCYp+Dt65DtcP1yMwhju6ZxsCMVvz385XWezCuKamh5wCelBqJsdGHzD2oKu8t3MLgzFaufWFdNy6HnYWlh8w9bNzt7Tn4aRz6dU7lpH5Ns/dwcFgpDBoHEckRkddEZImIrPYdjTmpiPxMRBaLSKWIDKtyfVcRKRKR+d7jkcacpykREX4zztN7sJVLxi3FNfQcANp4ew/vLNh8oPcwd91uVm7fx3nDM12L6UDv4cuDvYcNu/cTFx1F+2T/mXyuPb4nBcXlPNnEeg/hNqw0FXgYKAeOxZN477lGnncRcCaeoarDrVLVQd7jykaep0k5umcaQzI9vQfb92DccPg+h8NdMdYz9/DPTzxzDy/MWk/L+BgmDuzkalzXjevJrsJSnvH2HjbuLiK9dWKNNav7dk5hfL+OTP16DXv3N53eg29YKVwah0RV/RQQVV2nqrcBpzTmpKq6VFWXN+Y5miMR4foTerFlbzEvz7begwm84vLa19C3Topjyphs3lu4hZmrdzJ94RZOG9SZFnGBn2+oamhWa47umcajM1ZRUFzGxl37D1nG6s+143IoKCnn8SZUG8XXc0gIh2EloEREooCfRORqETkDaOliTNkiMk9EZojI2JruJCJXiMgcEZmTl5fnYjjh5cgebRmR3cZ6D8YVdfUcwLNyKTkhhl88M4fiskpXh5SquuHEnuzeX8bUb9Z69zhUn2+oqk+nFE4Z0Imp36xhZxOpjVIcZsNK1+JJnXENMBS4ELi4rgeJyCcissjPMamWh20BMlV1MJ46Ei+ISIq/O6rqY6o6TFWHpaWlOXgZTYOn99CT7QUl1Zb3GdNYJeV1Z/xMbRHL5WO6kV9cTm56Cv27BGcb1IAurTihbwce/3I1OwtL6+w5APxmXA5FZRVNpvxoWM05qOpsVd2nqhtV9VJVPVNVv3fwuHGqmuvneLuWx5So6k7vv+cCq4Ce9XlBzcGobm0Z06MdD32xqsbUAsY0RHFZRZ09B4ApY7rSu2MyVx7dPQhRHXT9CT0p8H7mM9rU3nMA6NE+mdMHp/P0t2vZnu9+xtbrXprHB4ucl1etr6JSz0rFcFmtNExE3hSRH0Rkge9wIxgRSRORaO+/uwE5QNNo8gPstyf1YldhKVOb2GoMEzqqSkl55SG5lWqSnBDLB9cdxcQBnYMQ2UF9OqUwcYBn8ttJzwHg2uNzqKhU/ltH6vHGKi2v5K35m7l12iLXhnwPzDmEQ88BeB7PiqWzgFOrHA0mImeIyEY8hYPeE5EPvTcdBSwQkfl4igxdqaq1VxpppgZleLrYj321usnmkTHBVbWWQzi76eQ+TDkym9zOzoazstom8bNhGbwwa/2BzXNuKPBWbdyWX8Iz36115RzhNueQp6rTVHWNd7XSOlVt1GC3qr6pql1UNV5VO6jqSd7rX1fVft5lrENU9Z3GnKepu+HEnuwrKW8y46kmtCKlcUhvlcifT+1LXD3ivOb4HkSJ8MAnK1yLK9+bRiQhNoqHvljlSonfotIKoqOE2Gj/S3gDycn/3VtF5AkROU9EzvQdrkdm6tS7YwqnDezM1G/WBGU81TRtJUEcsgi2TqmJXHJEV96ct4nlW6snDwyEfO9u7CuP7s6e/WU84ULhoSJvFTiR8GgcLgUGAeM5OKQ00cWYTD1cf0JPyiuUf/lJiGZMfURKz6Ghrjy6Oy3jYrj3I3e2WPl6Ckf2aMfJ/Tvy5Fer2RHgJbTBquUAzhqH4d4loxd7VytdqqpTXI/MOJLVNonzR2by0uwNrNlRGOpwTATz1Qtpij0H8Gzg++XR3fh4yTZ+WL874M+fX+QZVkpJiOWGE3tRXF7JgzXUwGio4tKKoKTrBmeNw7ci0tf1SEyD/fq4HOJjolz7RWSah+Kypt1zALj0yGzatYzn7veXoaoBfW5fzyElMYbuaS05Z1gGz89cx7qdgfvR5htWCgYnn4JRwHwRWe5dxrrQraWspmHSkuO5fEw27y3YwoKNe0IdjolQTb3nAJAUH8O143KYtWYXny3bHtDn9s05pCZ6amlfNy6H6Cjhvo8CNwkebo3DeDz7DU7k4HxDo5aymsD7xVHdaJMU58ovItM8NIeeA8C5wzPIbpfE3e8vo7wicOnv84vLiImSA1/eHVISuGxMNtN+3MyiTXsDco6i0jCac6i6fDVQS1lN4CUnxHLNcT34dtVOvljRfHJNmcDx9RycbIKLZLHRUfzupF78tH0fr/8QuOJZ+UXlpCTGHrKS6JdHd6d1i9iA/WgrLqsIyu5ocNZzMBHi/JFZdG3bgrunL6Oi0noPpn5KvD2Hmor9NCUTcjsyKKMV93+84kAa7MbKLy4j5bBqeCkJsfz6uBy+XrmDGQH40RZuw0omQsTFRHHj+N4s31bA61ZO1NRTsa/nUEOxn6ZERPjjyX3Yll/CEwFK6Z1fVEaKd76hqgtGZZHVtgV3TV/a6GEsaxxMg03I7cjgzFbc9/HyQ2ruGlOX5tRzABiR3YYT+3bg4Rmr2F7Q+E2k+cXlpCRUbxziYqL4/fjerNi2r9E14ItKK4NSywFqaRxEpEBE8ms6ghKdqTcR4WbvL6LHLK2GqQdf3p7m0HPw+cOE3pSWV/LAx43fROrpOfgvejQhtyNDMltx38crKGxEJuXicOg5qGqyqqYA/wL+AKQDXYDfA/8MSnSmQYZ1bcMp/Tvx6IzVbN1raTWMM019h7Q/3dJacsGoLF6evb7RaTU8cw7Vew7g/dF2Sh/yCkp4dMaqBj2/qobdsNJpqvqQqhaoar6qPgzUVrDHhIHfj+9NRaXaxrgIt6+knM+WbQvK8uTmspT1cNcen0NSfAx3Tl/aqOfxrVaqydCsNkwc0InHvlrN5j1F9X7+sgqlolLDarVSoYhMFpFoEYkSkcmA5WkIc5ltW3DpkV15/YeNAVtjbYLvvQWbmfK/OXy4eJvr5yopryAmSoiJbl6NQ+ukOK49PocvV+TxeQM3xpWWV1JUVlFttdLh/jChN5UK93ywrN7nCGYtB3DWOJwPnANs8x4/815nwtxVx/WgdYs47nhniW2Mi1AF3jTQd01femAfgluclAhtqi4a3ZXsdkn85b0llDVgRVHBgdQZNfccALq0bsHlY7J5a/5m5m/YU69zBLOWAzjbBLdWVSepajtVTVPV01V1bRBiM42UkhDLb0/sxay1u5i+0L3ShcY9vi+E9bv289TXa10/V3MbUvKJi4ni5pP7sDqvsEG12X21HGqac6jq/47tQbuW8dzxzuJ6/Wjz7ccIm8R7ItJTRD4VkUXeywNE5E/uh2YC4efDM+jTKYW7pi91rXShcU9Rmae4y7g+7Xnws58CsuSyJs255wBwfJ/2jM1pxwMfr2B3Yf2qK/ryKtW0WqmqlvEx3HhSL35Yv4e35292fI6icOs5AI8DNwFlAKq6ADjXzaBM4ERHCX+e2JdNe4psaWsEKi6rJDE2mptP6UtpRSX/+MC9BQbNuecAnhVFfzqlL4WlFdz3cf3+Px/IyOqg5wBw9tAuDOiSyt/eX+p4aWs4zjm0UNVZh13XqN1VIvIPEVnmzfL6poi0qnLbTSKy0psF9qTGnMd4jO7elpP7d+ThL1Y1aJWECR1PcZcostslcemR2bw6d2O9x6qdKimvrFfpzaaoV8dkLhyVxQsz17N4s/OFHAdqOdQx5+ATFSXcemo/tuWX8N/PndV8KC4Nv57DDhHpDiiAiJwNbGnkeT8GclV1ALACT88Eb92Ic4F+eLLBPiQizbefG0A3TehDpSp3NXK5ngkuz695z5/Ar4/rQVpyPLe+vYhKF3JnNfdhJZ/fjOtJqxZx3D7N+UKO+vYcAIZmteaMwek88dUaRzUfDgwrhdFS1quAR4HeIrIJuA74VWNOqqofqaqv9/E9ns114Nk/8ZKqlqjqGmAlMKIx5zIeGW1a8H/H9ODdBVv4dtWOUIdjHCopqzzwZZCcEMtNE3rz48a9jU7D4E9zH1bySW0Ry+9O8izkmPajszmB+sw5VPWHCb2JjRbueGdJnfcNuzkHVV2tquOANKC3qo4J8GqlKcD73n+nAxuq3LbRe50JgF8e3Y2MNoncNm1xg5brmeDzDSv5nDE4nSGZrfj7B8vY6/1CChTrORx0zrAM+qenctf0pexzMCdweC0HpzqkJHDN8Tl8umw7ny6tfS+Lb7VS2Mw5iEiFiNwN7FfVAu91Pzh43CcissjPManKfW7GM3/xfH0DF5ErRGSOiMzJy7P6BU4kxEbz54n9WLFtH09/uzbU4RgHissqSKiS60hEuGNSLrv2l3J/gHe/l1jP4YDoKOGOSZ45gX9/WnfeJX+1HJy69Mhsuqclcfs7S2pdUVgchsNKi733+0hE2nivq/P/gKqOU9VcP8fbACJyCZ6qcpP14MDeJiCjytN08V7n7/kfU9VhqjosLS3NwcswAOP6tOeYXmn885Of2JZveZfCXZGf4i656alcMDKLZ79fF9Dd79ZzONTgzNacOzyDp75ew4pttedd8lfLwam4mCjumJTL+l37eXRGzSsKw25YCShX1RuBJ4CvRGQo3snphhKR8cCNePI27a9y0zTgXBGJF5FsPOVJD18pZRpBRLj9tH6UVlRy53s2OR3uissq/WZJ/e2JvWjdIo5bAjg5bXMO1d04vjdJ8TH8+e1FtU5O762hloNTR/Zoxyn9O/HQFytZv3O/3/sUlfpSqodP4yAAqvoy8HNgKtCtked9EEgGPhaR+SLyiPcci4FXgCXAB8BVqmo7twIsq20S/3dMd6b9uJlvVtrkdDgrqaEsZGqLWG46uQ/z1u/h1bkb/DyyAecqryS+mdRycKpNUhw3ju/F96t31bphLb+o5oysTt0ysS8xUcKt0/w3REVlFcTFRBEdVf+hq4Zw8km43PcPVV0EjAWuacxJVbWHqmao6iDvcWWV2+5U1e6q2ktV36/teUzDXXl0d7LatuCWtxe5nrPHNFxRWQUJNfyaP2tIOsO7tubu95exq547ev0pOWx+w3icOzyTgRmt+Ot7S9i73/8igPzi8nqvVDpcx9QEfnNCTz5fnseHi6unuwlmLQeovdjPcd5/ZonImb4DOB7YF5TojGsSYqO5/bR+rM4r5LFaxjlNaBWXVdQ4jCAi/PX0/hQUlwdk/0qx9Rz8io4S7jw9l12FpfzjI//ZVAPRcwC45Iiu9OmUwu3vLKm2c7qoNEwaB+Bo739P9XNMdDkuEwTH9GrPKQM68Z/PV7Jmh2VhD0f+JqSr6tUxmcvHduO1uRv5fvXOBp+nvKKSikq1nkMNctNTueSIbJ6fuZ5563dXuz2/uHFzDj4x0VH89fRctuwt5oGPVxxyW12fhUCrrRLcrd7/XurnmBK0CI2rbp3Yl/joKG55q/YJNxN8qkpxWWWNw0o+1x6fQ5fWifzprUWUljds/0qxrwqc9RxqdP2JPemQnMDNby6ivMo+oZLyCorLKhu8WulwQ7Nac/7ITJ76Zs0hq9GKaulFuqG2YaXrazuCFqFxVfuUBG6c0JuvV+6oV4ZI4z5f2c66CsonxkXzl0m5rNy+r8ElKEuCnNQtErWMj+G20/qxZEs+T3695sD1vpobgeg5+Px+fG/atoznpjcWHmiIPHMOwWu8aztTch2HaSImj8hkUEYr7nh3SUAmNk1g+DY9ORnqOba3d4jws5Wsyqv/lGBzrB/dEONzO3Ji3w488MmKA0tOD6TOCMCcg09qYiy3ntqXhZv28vR3nvoSRaXhM6x0e21H0CI0rouKEu4+qz/5RWX89b26c7yY4PDVdHb6a/7WU/uSEBvFTW8srPfeB19D5G9PhTnU7ZP6ERMVxc1vLURVDxb6aeRqpcOd0r8Tx/ZK476PlrNx937PnEM4DCv5iEiCiFwlIg+JyFO+IxjBmeDp3TGFXx3TnTd+2MSXKywdSTg4mC7B2a/59skJ3HxKH2at2cXLc+q39+HAEJbNOdSpU2oiN47vxVc/7eDNeZtc6TmAZzXaX07PBeDmNxdRVBomcw5VPAt0BE4CZuBJaVH7XnITka46tgfd0pL445sL2V/aqJIdJgCK6jGs5HPOsAxGdWvDXdOXsnWv8/Qo1nOon8kjsxia1Zo73l3Cau8wXiDnHHy6tG7BjSf1YsaKPFbvKAyvngPQQ1VvAQpV9WngFGCku2GZUEiIjebuMwewcXcR9364ou4HGFcVN2CSWES4+8wBlFVU8qd6rEArsdVK9RIdJfz9rP7sL6ngvo88fyuB7jn4XDi6K0MyWwHBS7oHzhoH35bAPSKSC6QC7d0LyYTSiOw2XDgqi6nfrmHuul2hDidofli/mx/8rF8PpYaWhezaLokbTujFJ0u38c4CZ3W5rOdQfz3aJ3PN8T0oKHFnzsHH0xANIC46ilYt4lw5hz9OGofHRKQ1cAuexHhLgHtcjcqE1O8n9KZzaiK/e21BrSmEm5K73lvKxU/NYsve8CmjWlLW8HmAKWOyGZjRitumLWbnvpK6z2VzDg3yy6O706dTCnHRUa4O+eR0SOaD68ZyxVGNTWvnnJNiP0+o6m5VnaGq3VS1vao+EozgTGi0jI/h7rP6szqvkH9+Uncu+6ZgX0k5BcXl/PGNhWGzGbAx+fujo4R/nD2AguIybp22uM77H1zKaj2H+oiNjuLxi4by0OQhDarlUB/d0lrSMt6d3ok/TlYrtRKRa0TkfhH5t+8IRnAmdMbmpPHzYRk89uUq1wrah5Pisgpaxsfw+fI8V0pwNkRDJqSr6tkhmWuPz+HdBVuYvrD24aWDw0rWc6ivLq1bMK5vh1CHEXBOPgnTga7AQmBulcM0cTdP7EPHlARueGV+kx9eKiqrYEJuR0Z0bcMd7y6p10oft9R3n4M/Vx7dnf7pqfzprUXsqGV46eCwkvUcjIeTxiFBVa9X1amq+rTvcD0yE3IpCbH8/ewBrMor5P6Pm/bqpaLSCpLiY7jn7AGUVyi/f31ByIeXAlH5KyY6ivvOGci+4vJaC9b40mfYaiXj42ifg4j8QkQ6iUgb3+F6ZCYsjM1J4/yRmTz+1WrmrG26q5eKvSUyu7ZL4o8n92bGijxemLU+tDEF6Au7Z4dkrjshh+kLtzLtR//5sw70HGzOwXg5+dSVAv8AvuPgkNIcN4My4eWPJ/ehS+tErn/lx2o55puCikqltLzywC/0ySOzGNOjHXe+t7TGko3BUFJWgUhg5gGuGNuNIZmtuOWtRX6HzHznio0OTpUxE/6cfOpuwLMRrquqZnuP4K2nMiHXMj6G+342iA279/PXJlh3+uBmM8+fQ1SUcM/ZA4gW4YZX51MRoBrN9VXkrcwWiFUwnuGlQZRVKDf6GTIrLq8M2LlM0+CkcVgJhO7nkwkLI7LbcMXYbrw4az2fLdsW6nACqsjPktHOrRK57bR+zF67m0e/bFga7MYqLqsM6L6D7HZJ/PGUPny5Io/nvl93yG0lZRU232AO4eTTUAjMF5FHA7WUVUT+ISLLRGSBiLwpIq2813cVkSIRme89bD9FGLn+xJ707pjMja8tdLSxKlIUlfrfiXzmkHRO7t+RBz5ecUjRlaDF5UIWzgtGZnJUzzTunL6UldsPpvYuLqu0ZazmEE4+DW8BdwLfErilrB8Duao6AFgB3FTltlWqOsh7XNnI85gAio+J5oGfDyK/qIzfvx4+m8Uaq6Tc/6ogEeHO0/vTukUc170c/OW8tdWPbigR4d6zB5AYG811L887UDmupDy4GT9N+Ku1cRCRaOCSqktYA7GUVVU/UlXfzOb3eDK9mgjQp1MKN4735O15cVb90kKHq6JSzxekv1/prZPiuPdnA1m5fR9/mx7c+ZbiskriXfjCbp+SwN1nDWDRpnwe+GTFwXNZz8FUUeunQVUrgEoRSXUxhinA+1UuZ4vIPBGZISJja3qQiFwhInNEZE5entUfCKYpR2YzNqcdd7y7+JChiUjlb86hqqN6pjHlyGye/m5dUOdb3CwLeVK/jpw7PINHZqzi21U7rOdgqnHyydsHLBSRJ+sz5yAin4jIIj/HpCr3uRkoB573XrUFyFTVwcD1wAsikuLv+VX1MVUdpqrD0tLSHLwMEyhRUcK9PxtIYmw0174078CwTKQqOmy1kj+/n9CLPp1S+O2rC9heEJzd024MK1V1y8S+ZLdN4jcvz2dbfon1HMwhnHwa3sCTkfVL6jHnoKrjVDXXz/E2gIhcAkwEJqt38FpVS1R1p/ffc4FVQM8GvC7jsg4pCdxz9kAWb87nng+WhzqcRqlpQrqq+Jho/n3uIPaXlnPDKz/Wuwxng+JyuSxkUnwM/z5vMLsKS1myJd+S7plDOMnK+jTwIgcbhRcaO+cgIuOBG4HTVHV/levTvPMciEg3IAdY3ZhzGfec0LcDlxzRlSe/XsPny7aHOpwGq2lC+nA5HZK5ZWJfvvppB4995f7H0u2eA0Bueiq/H98bsHTd5lBOsrIeA/wE/Bd4CFghIkc18rwPAsnAx4ctWT0KWCAi84HXgCtVtenmbGgC/jChN707JnPDqz+yLT/0yeoawtdzcJIa+/wRmZzcvyP3frjc9eJAnglp97+wpxyZzbnDMziqpw3PmoOcfPLuA05U1aNV9Sg8taQfaMxJVbWHqmYcvmRVVV9X1X7e64ao6juNOY9xX0JsNA+eP5ii0gqufWleyHYTN0Z9EtyJCH87cwAdUxO45sV57C0qq/MxDVXs8rCST1SUcPdZA7hodFfXz2Uih5PGIVZVDwwqq+oKwJ1iqSYi9WifzF9Pz+X71bv416eRVxyovuU4UxNj+c95g9m6t5gbX/vRtf0ewRhWMqYmThqHOSLyhIgc4z0exxLvmcOcNbQLZw/twn8++4lvVu4IdTj1Ulxa/wR3gzNb8/vxvflw8TamfrM24DGpqjdTrM0DmNBw8sn7FZ660dd4jyXe64w5xB2T+tEjrSXXvjQvLIrlONXQBHeXj83mhL4duGv6UuYFeP6hrEKpqNSgDCsZ44+T1Uolqnq/qp7pPR5Q1aaTWMcETIu4GB6+YAj7Syv49Ys/UFZRGeqQHCkuq2xQnWZPKoqBdExN4OoX5rG7sDRwMZXXb6jLmEBzslrpSBH5WERWiMhq3xGM4Ezk6dE+mb+d2Z/Za3dzzwfLQh2OI43ZT5DaIpb/nj+EvIISrns5cOm9ix3svTDGTU6GlZ4E7gfGAMOrHMb4NWlQOheOyuLxr9bwfh2F7cNBUVlFo8b2B2a04tbT+jJjRR7/+SwwE/KBqB9tTGM4+YvYq6rvq+p2Vd3pO1yPzES0P03sw8CMVvz21R9Zub0g1OHUqri08auCzh+RyVlDuvCvT3/i8+WN3xB4cFjJJqRNaDj55H3urb8wWkSG+A7XIzMRLT4mmkcuGEJCbDS/fHYu+8K4vGgg0lSICH89PZfeHVO49sV5rNtZ2LiYSp3vvTDGDU4ah5HAMOAuPBvi7gPudTMo0zR0Sk3kP+cPZs2OQn77inv7ARqruKyiQRPSh0uMi+bRC4YiIvzy2bnsL214g1hcz70XxgSak9VKx/o5jgtGcCbyHdG9HX88uQ8fLN7Kfz9fGepw/CoqqwzYl3Bm2xb857zBrNhWwO9eq16r2XlM1jiY0LIBTeO6y8Zkc8bgdO77eAWfLg2/+tOBTlNxVM80fndSb95bsIVHZjRsYd/BCWn7EzWhYZ884zpPPqL+9OucwrUvzQ+7AkFFpYHPYXTl0d2YOKAT93y4rEEFgkpsn4MJMWscTFAkxEbz6IXDSIiN4vKnZ7Nnf+A2jDVWY5ey+iMi/OPsgfTtlMK1L86v94otm5A2oeboL0JEjhCR80XkIt/hdmCm6UlvlcijFw5l855irnohfHZQF5VVkBCACenDJcZF89hFw4iPjeLyp+fUq0G0CWkTak52SD+LZ3VS1U1ww1yOyzRRQ7PacOcZuXyzcid/eXdJqMOhslIpLa907Rd61QbxV885bxCLbM7BhFiMg/sMA/pquK5DNBHnZ8MyWLGtgMe/WkP3tJZcfETXkMVS7LAKXGMMzWrD387szw2v/sif317MXWfk1pnk70DPwUp3mhBx0jgsAjoC4Z8HwUSMP0zow5od+7n9ncVktm3Bsb3ahySO+lSBa4yzhnZhZd4+Hv5iFd3Tkrh8bLda719cXkFcTBRRUfXLFGtMoDjps7YDlojIhyIyzXe4HZhp2qKjhH+dO4jeHVP49QvzWLY1PyRxBHM/we9O7MWE3I7cOX0pHy7eWut9i11YQWVMfThpHG4DTufQHdL3NfbEIvIXEVngrSH9kYh09l4vIvJvEVnpvd1SdTRRSfExPHnJMJLio5kydXZIalAHc+I3Kkq4/5xBDOjSiutems+CjXtqicsK/ZjQcrJDeoa/IwDn/oeqDlDVQcC7wJ+9108AcrzHFcDDATiXCVOdUhN56pLh7C0q49Kps4Oeg8m32SxYv9IT46J54qJhtG0Zx5T/zWHDrv3+4yq3EqEmtJysVholIrNFZJ+IlIpIhYg0egxAVas+RxLgm/CeBDyjHt8DrUSkU2PPZ8JXv86p/HfyEJZvK+Cq54O7xNU3rBTMIZy05Hj+d+lwyioquWTqLL9LXN3YmGdMfTjptz4InAf8BCQClwP/DcTJReROEdkATOZgzyEd2FDlbhu91x3+2CtEZI6IzMnLywtEOCaEjunVnr+ensuMFXnc/ObCoCXpOzghHdwhnB7tk3n8omFs2FXEL56Zc2B4y6e4vJJ4axxMCDn6i1DVlUC0qlao6lRgvJPHicgnIrLIzzHJ+7w3q2oG8DxwdX0CV9XHVHWYqg5LS0urz0NNmDpvRCbXHJ/DK3M2cv/HK4JyzlAmuBuR3Yb7fz6Q2Wt3c91Lh1aR80xI25yDCR0nS1n3i0gcMF9E7sGzpNVpozLOYRzPA9OBW4FNQEaV27p4rzPNwG/G5bBtbzH/+Wwl7VMSuHBUlqvnC/VO5IkDOrMtv4S/vLuEP7+9iL+e7tkDUVxeQZukuJDEZAw4+5K/0Hu/q4FCPF/cZzX2xCKSU+XiJMBXcHgacJF31dIoPJXobI9FMyEi3HlGLsf3bs+f317Euws2u3q+cMhhdNmYbK48ujvPz1zPvz71lBktLquwDXAmpOrsOajqOhFJBDqp6u0BPPfdItILqATWAVd6r58OnAysBPYDlwbwnCYCxERH8eD5Q7joqZn85uX5tEqMY0xOO1fOVRyCCWl/fj++F3kFJfzzk59okxTnqU7n8sY8Y2rjZLXSqcB84APv5UGB2ASnqmepaq53OeupqrrJe72q6lWq2l1V+6vqnMaey0SexLhonrh4ON3TWnLFs3OYt363K+fx5TAK9RexiPD3s/ozrk8H/vz2YrbsKbZ9DiaknG6CGwHsAVDV+UC2axEZ45WaGMszU0bQrmU8l0ydzdItgd9F7ZuQjo8J/Rexp8c0mFHd2lBeqcTbsJIJISd/EWWquvew6ywJnwmK9ikJPH/5SBJjo7nwyVms2VEY0Of3VYGrKxFesCTERvP4RcOYkNuRMT3cGUozxgknjcNiETkfiBaRHBH5D/Cty3EZc0BGmxY8d/kIKlW54ImZbNztf1dxQxSVBr7QT2MlJ8Ty8AVDGde3Q6hDMc2Yk7+KXwP9gBLgRSAfuM7FmIyppkf7ZJ6ZMoKC4jImPzGTrXsDk4cp0PWjjWkqnORW2u/drDbcu+nsZlUNfoY00+zlpqfy9JQR7NxXyuQnvievoKTRz+lWFThjIl2NS1nrWpGkqqcFPhxjajc4szVPXTKci5+axfmPf8+LV4yiXcv4Bj+f9RyM8a+2fQ6j8eQ4ehGYCYTHjJ1p9kZkt+GpS4Zz6f9mMfnxmbzwi5G0bWADUWSNgzF+1Tas1BH4I5AL/As4AdgRwJTdxjTY6O5teeri4azdWcj5j89kx76GDTEVldpmM2P8qbFx8CbZ+0BVLwZG4dmx/IWI1CtBnjFuOaJHO566ZDjrdhVy3mMNm4MoLqu0/QTG+FHrhLSIxIvImcBzwFXAv4E3gxGYMU4c2aMdUy8ZwcbdRZz72Hdsr2c1uWJLU2GMXzU2DiLyDPAdMAS43bta6S++NBfGhIvR3dvy9JQRbN1bzDmPfsemPUWOH+uZcwivfQ7GhIPa/iouwFOq81rgWxHJ9x4FgagEZ0wgjchuw7OXj2RnYSnnPPIdax3upLYJaWP8q23OIUpVk71HSpUjWVVTghmkMU4MyWzNi78Yxf7Scs559DuWby2o8zFFpbbPwRh/rD9tmpTc9FRe/uVoAM559Ltas7lWViol5ZVWN8EYP6xxME1Ozw7JvP6rI0hNjGXyEzP5ZuUOv/crKQ+PdN3GhCNrHEyTlNGmBa9dOZrMNi24dOps3ltQvZhgUZgU+jEmHFnjYJqs9ikJvHzFaAZmpHL1iz/w7HdrD7ndGgdjamaNg2nSUlvE8uxlIzm+d3tueXsx9364HFVPORJf/WibkDamOmscTJOXEBvNIxcM5bwRGTz4+Up+++oCyioqw6Z+tDHhqLbEe64Rkb8Ak4BKYDtwiapuFpFjgLeBNd67vqGqd4QiRtO0xERHcdcZ/emYksgDn6xge0ExU470VLsNt2I/xoSDkDQOwD9U9RYAEbkG+DNwpfe2r1R1YojiMk2YiHDtuBw6t0rgpjcWMn/DHsB6Dsb4E5KfTKpadYd1ElaT2gTRz4Zl8PSUEQc+dQnWOBhTTah6DojIncBFwF7g2Co3jRaRH4HNwG9VdXENj78CuAIgMzPT5WhNU3Nkj3a8/n9H8OKs9eR0aBnqcIwJO+JbuRHwJxb5BE9NiMPdrKpvV7nfTUCCqt4qIilAparuE5GTgX+pak5d5xo2bJjOmTMnYLEbY0xzICJzVXWYv9tc6zmo6jiHd30emA7cWnW4SVWni8hDItJOVf1vcTXGGOOKkMw5iEjV3sAkYJn3+o4iIt5/j8AT387gR2iMMc1bqOYc7haRXniWsq7j4Eqls4FfiUg5UAScq26NexljjKlRSBoHVT2rhusfBB4McjjGGGMOY7t/jDHGVGONgzHGmGqscTDGGFONNQ7GGGOqcW0TXDCJSB6eVU8N1Q5oKnsp7LWEJ3st4am5v5YsVU3zd0OTaBwaS0Tm1LRLMNLYawlP9lrCk72WmtmwkjHGmGqscTDGGFONNQ4ej4U6gACy1xKe7LWEJ3stNbA5B2OMMdVYz8EYY0w11jgYY4ypplk3DiIyXkSWi8hKEflDqOOpDxHJEJHPRWSJiCwWkWu917cRkY9F5Cfvf1uHOlanRCRaROaJyLvey9kiMtP7/rwsInGhjtEJEWklIq+JyDIRWSoioyP8ffmN9zO2SEReFJGESHlvROQpEdkuIouqXOf3vRCPf3tf0wIRGRK6yKur4bX8w/s5WyAib4pIqyq33eR9LctF5KT6nq/ZNg4iEg38F5gA9AXOE5G+oY2qXsqBG1S1LzAKuMob/x+AT70V9D71Xo4U1wJLq1z+O/CAqvYAdgOXhSSq+vsX8IGq9gYG4nlNEfm+iEg6cA0wTFVzgWjgXCLnvfkfMP6w62p6LyYAOd7jCuDhIMXo1P+o/lo+BnJVdQCwArgJwPtdcC7Qz/uYh7zfeY4128YBGAGsVNXVqloKvISn8FBEUNUtqvqD998FeL6A0vG8hqe9d3saOD0kAdaTiHQBTgGe8F4W4DjgNe9dIuK1iEgqcBTwJICqlqrqHiL0ffGKARJFJAZoAWwhQt4bVf0S2HXY1TW9F5OAZ9Tje6CViHQKSqAO+HstqvqRqpZ7L34PdPH+exLwkqqWqOoaYCWe7zzHmnPjkA5sqHJ5o/e6iCMiXYHBwEygg6pu8d60FegQqrjq6Z/AjXgKQAG0BfZU+eBHyvuTDeQBU71DZE+ISBIR+r6o6ibgXmA9nkZhLzCXyHxvfGp6LyL9O2EK8L73341+Lc25cWgSRKQl8DpwXdUa3ADeKnphv1ZZRCYC21V1bqhjCYAYYAjwsKoOBgo5bAgpUt4XAO94/CQ8jV5nIInqQxsRK5Lei9qIyM14hpqfD9RzNufGYROQUeVyF+91EUNEYvE0DM+r6hveq7f5usLe/24PVXz1cCRwmoisxTO8dxyecftW3qEMiJz3ZyOwUVVnei+/hqexiMT3BWAcsEZV81S1DHgDz/sVie+NT03vRUR+J4jIJcBEYHKVssqNfi3NuXGYDeR4V13E4Zm8mRbimBzzjsk/CSxV1fur3DQNuNj774uBt4MdW32p6k2q2kVVu+J5Hz5T1cnA53jqikPkvJatwAZvjXSA44ElROD74rUeGCUiLbyfOd/ribj3poqa3otpwEXeVUujgL1Vhp/CkoiMxzMce5qq7q9y0zTgXBGJF5FsPJPss+r15KrabA/gZDwz/KuAm0MdTz1jH4OnO7wAmO89TsYzVv8p8BPwCdAm1LHW83UdA7zr/Xc37wd6JfAqEB/q+By+hkHAHO978xbQOpLfF+B2YBmwCHgWiI+U9wZ4Ec9cSRmeXt1lNb0XgOBZwbgKWIhnhVbIX0Mdr2UlnrkF33fAI1Xuf7P3tSwHJtT3fJY+wxhjTDXNeVjJGGNMDaxxMMYYU401DsYYY6qxxsEYY0w11jgYY4ypxhoHY+pBRNqKyHzvsVVENnn/vU9EHgp1fMYEii1lNaaBROQ2YJ+q3hvqWIwJNOs5GBMAInJMlToUt4nI0yLylYisE5EzReQeEVkoIh94054gIkNFZIaIzBWRD8MpA6gx1jgY447ueHJEnQY8B3yuqv2BIuAUbwPxH+BsVR0KPAXcGapgjTlcTN13McY0wPuqWiYiC/EUyPnAe/1CoCvQC8gFPvakLCIaT2oEY8KCNQ7GuKMEQFUrRaRMD07uVeL5uxNgsaqODlWAxtTGhpWMCY3lQJqIjAZP+nUR6RfimIw5wBoHY0JAPaVpzwb+LiI/4smoeURIgzKmClvKaowxphrrORhjjKnGGgdjjDHVWONgjDGmGmscjDHGVGONgzHGmGqscTDGGFONNQ7GGGOq+X9Zj8YvSXvG2QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mean normalized market resources\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2b471cf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_14820/2889722531.py:1: RuntimeWarning: divide by zero encountered in log\n",
+      "  plt.plot(np.log(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1) - np.min(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(\n",
+    "    np.log(\n",
+    "        np.mean(pfn_simulator.history[\"m_nrm\"], axis=1)\n",
+    "        - np.min(np.mean(pfn_simulator.history[\"m_nrm\"], axis=1))\n",
+    "    )\n",
+    ")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mean normalized market resources\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "464f19e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f6482c7c670>]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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MOvPccA699kAYFJ12OH+bsKiwaNC0RSI6AqUxv5SZL49s8ioAl3PJQQBfA/D4/oYpQ+xkpzB9N+IsDIom4qEPERSdrRkUBRZ46ApBUbcfYPhrN8/j1w2KToVtTJcsFwJwCYCbmfkdCza7DcBzq+0fDuBxAL7a1yCliHWAS2H6bsQJg6LSnevWIX4vye2vllzWkAyWaeizQla6CrNKBl/gog4qy6bAtu4LllUBJh22ORvA+QBuIKLrqs/eCGAvADDzRQD+DYD3E9ENAAjA65n5zv6H2y/huoJWWJQ2oYfu4h8pzKpibQkk20jMJZfukkEYg/CZFYVs6f+CVgXDdVtcdP76HU+so6voeV61ATN/DqWRXrbNdwD8Ql+D0iK2rmAKxsGI09LQEw6KSntiYwyKAu1CnqGDou7FohUUlU6N3uoymti6gilM340482l7JbkkVFiURTV0wf2FWRodJINQx/YpWFb+mOv3sqX2Xakll1mg6fctuURSo0XPs9hfHgF53p4mpxBgM+LMp+3l91LT5E2YRHp2SLaRmGwgGdRe8qxt0KfSkkvu5J6w1H5YyaUIg7QCkku4SPSgQdHdTE7NdQVNQ0+bRV5pCrOq2FJjktmUWc9B0UI6KBp66ANfuzCoLFXBmVGZrukvFWgeuhDhuoLSucPGzmi1PE0qbRHNmgbxbotNDbiLhxlmmvhIS0RtDV12AZBVZBTOGIRK/yP3rGnoQoTl2tLVfcbOaHfIqx7CJAqLstbUWnSBi5aEsdqgh5kmPlNhiajOsKk162FjHxMlyWWeWaRz3Ntt0MNpshn0pFnUIS8ZD13xXlokYSz10GmxQS9np32Pck4rq2TgNhuhZCVt0AulmclWG/QsErBIwTgYccL+G9Kd69YhbA8r321x/ZdbaFx8pKWA2OIkQ163Vi8XIU2/fc+ahy5Gq5fLzNIWU6ZlxAZuweqTZ1kje6QQzmZoT+W7Sy6xhaKls3LCazd0vMpJVtKFRa3jlp65if3lEdDq5WKFRUnTmr4OvEiCT55Bteq4PZUfW1B0WIOeUXs8/ud90ZqZFIUFRaWI9t8wg54srf4bA/cD8QnX65TuC7SJZBDq2D7Sla2hfi8tSa0izMmXKlLLWi8y4QIusb88AiZWWDQq5os6zI1CKjOqaC8XweybtufXvbBoGiksmkn3GIlID0Neu3DGUPdn7/n5j/WwscIiITJq999IxUAYbWJB0RS8c6AdFJ3xMEHRLoVFoYfOzOpBUem0zq7jKTxDSyQfFJVec2GrDXqeNfXEQng6ZOyMLCK5pDKjyrLmAg7SGnGoAXeRDELJyuG+FTU0Ec1fMAa7knkrZtmCn3nfdVT/C1cQy/3p9MmzLOiEJhuwMHZGWAySmuQC6AX9FmVpLE9bbEpWDo21WSeB3CPdXngVsaCyxPUKV4mSPu7tNuitDnBpVB0acWKLOqQyowozSKRnD7Gg6CrJIPwdh0ZPnFZWCcsuALKKaEm+wIC0ZybbbdAjq5aYh54uYabE0DqsT5hBIt1GImYoVt27oWTlkAoI+rSWDxy4sKjVy0Xo2Y8tFWh56EKELTSns2EbBhnLCVvGDp3L7BMW+kgXzsQkjFX7CyUrh1TZu0+4WlJp2IYzP06yasQ8BGbnreMWLuDacoNe/j+ftqZRdWjEqftvrOGVahFmTYgHRTfQgMP73aGxUEioWRfChUwrxxMpLBLx0MOZiQVF5Yi10DSDni5t3TOdF3AsL1yzsKiLZLCoOddUw6AH0oP0UmyraMU8hFJgY0sFmuQixCTiVaWwPqURp23Q05HIYj07tHu5rJIMQsnKIbXivc/8/BT1Podtnxu8gGcy12u+GLXOcW+1QW89FFZYlDTanevWIZzCSwdFoxJPx6BouGKRRhviuUEvvx+622KrFbNQIVh0ZmIGXQZfh3TVcqmkwRltYl5wKpdLawrvCF8gXfYXnj9HXWUq+gKq9sWyBrTzeMK0T7G0RbT2I7rUn9hfHgH+tMvd46kE2Yw2YWCtXNg4jVs4nMJL90bJMgLRepLBKoMuKxEF0oNwr5vV42nPqCSOv5WZZb1c5PCDRPVNbYVFyRJbziuVGZVvLJlLB0HaA/VX3Ori8S7qtqixUEhMekiq26JSUFR6zYWtNuh5w0Nf3dzIGJYwS0N62bR1yDxjqZHXDVQte7m7ZLCo26JUL3AfN5EqGtcuAclFOG0xXCpQungxkcdhGPxp1zx1a8gRGcsIs5KkFzZeB381IK2l8SYZ1R5mF8lgUbdFnV4u5XWaKklSq3CSlXTdQPS4BVWANJ6GgfA99LlXtdWnJGla1ZjCRRrrkEXvJQXJZSRB0VhR2NCz4ZxIvAYlthi1BUWF8CPddbVcIgbCaENUeVWJLGPmM2/0ppMGCJQGeK20xaCQzqGStthqhDf8tfMlqxnLvNBiyyZa2qIQvseiUS1n7Bx/ZaAUjILDFfVMi6Iu89aQXNbxMEPJylFLRILeTEt6SKCx2qTxQiyEslyCQL5w/r0ZdDSDoia5pE0WZHYMbRQcfgaJa8SkERRdr5dLMxDoKBQ89DAomsLLuCW5KARFpbo61vtbtQERnUREVxHRTUR0IxFduGC7ZxPRddU2f97/UPvHX8B1rnsOOSJjFXngoQ+twzr8SkhnMKVTKptpi6v35ySrQXq5BC+TFBqr+ZJVuVpZ//vQ6rvumHTYZgrgdcx8LREdA+AaIvokM9/kNiCiYwG8B8A5zHwbEf20zHD7xZ+CaqRuGTsn93XPgTMlfPw2qU5ykR5b3pBcukkG4WLWgG63RenKzHWYePfStChw1KSLOVwPbYO+8p3EzLcz87XV1/cCuBnACcFm/wjA5cx8W7XdHX0PVAJ/XUErLBoHoYc+tFFw+G1Sa8lFurBozaAo0F4YHdAuLNLpddOFLFtvhrMJrZlJSkFRItoH4AwAVwc/OhXAcUT0Z0R0DRH92oLfv4CIDhDRgUOHDm004D7JfA/dCotGgS8zFAP3A/Hxl1hzHrq45JIRXI1QV8kg93LXHVOF2WnLQxfuddOFxr0klAIbZtKxcAVxZ4NOREcDuAzAa5n5nuDHEwBPBXAugOcB+JdEdGr4N5j5Ymbez8z79+zZs4Nh94O/sK9W7rCxM3IvEDgduB+Iz7yXS1E7BxqSi+uN0rWvjS9ZOZyXLzleIkJGTelhaLmsPH/l11JFao1aF4X7opNoRERHoDTmlzLz5ZFNvgXge8z8IwA/IqLPAjgdwK29jVQA5yE0JBcz6EmTZ1SXrkt3rluHRlC0MrKpBUWBpkzjUCuEamjWw187/4UotYJSrHhx0F4uREQALgFwMzO/Y8FmHwXwTCKaENFRAJ6OUmtPGguKjo9GUDSh/vXNB7f8TMdDL7/uuqCGn7vu0DTohSdxDH3tJp5kVd5Lch56wayykEgXD/1sAOcDuIGIrqs+eyOAvQDAzBcx881E9HEA1wMoALyPmb8kMN5eiXVbNMklbXyjIN25bh2inphCUHQuuXTzeDOiQbotAkHedwI1BGGlrWRQdOoVLw4quTDz5wCsHAEzvx3A2/sYlBYxfWvom8xYjm8UpDvXrYMfFNXttlh+3VUy8LOEHFovoEZWiZABXYfyXirq8UiEY7IgKOp/JsFWl9H4QVGNXFxj5/hBUenOdevQkO+UgqKN0vWOkkG+RHJRGW9CNQS+ZDUTCorGZF3rtiiE3yGvrpZLxOMz4qReKeoH2DWCor6HOYag6LTQWwCky3iarRNk9gEE94V56DJEuy2ah540qfZyySKemHy3RdQ5710lg7LlbvOzenEXaYmIqHl+EtDQpTV9v0Oohqy73QbdNPTRMcnTrBQdoqZhkmXN3ihd89DdW6BCI1jn/n5Kz5ovWUm2IqiPW+G+MIMO1yFPx0sxdkbmeZgpNHhy+L3GNdLTgMDD3EFQVCNYB8yDolrthVfh5/FPBe8lN6vUmLmZQUf1EFph0SjwPcyUPPTck1y0ljPMKWjL2uFclMal+Zl2YZFWr5tVZFmzWZiUM5ebh66D/xBaYdE48D3MoRca9mn01q8fXNnHK8+ytbsXlpJVXHJRMegJeegNyUqwSM29yDRmbttt0K2waHTkRLVBSKFjnyO6Pq14YRHWlgyyJUFRlTVQE9LQ/bz4qYKHrvHi3GqD3ujlopQ7bOyMMvWtALN857p1qJ2DRjxGeJ9eG4SukkE8bbH6mUpl61xyGTpelVPQn13o+PNAQ7e0RSHqpH/Wyx02doZrGZtaM7XYcoYSvUGa+8yCwqJuBn0aSC4zpSXzXN631gIgq8czl6ykJRet495qgx5L+h86UGMsxwVFU8tK8g26ZlB0uqZk4EtWjpmSpu0Ki1IJijrJqu5TLmjQpzOdtWa32qD7C7iahj4OXJaGlu7bFTcMrZ4dQFMD7ioZxPqhzxd36X+MPi59T2sBkFW4c1Fr+pJpi15QdND2ubuZ+aIEeqlbxs5wxSBaxTBdIaJWepq05OKvDzrt2Bsl3sulQJ4RSPgFFBYWDX3tnGRVP/tCPVZcMZxGW+WtNujzXi5FMpF3YzlOA9bygtchrzyxeTxGeH8uHW4N+WlRUFRD/sjqF146QVENudUFRetgsAVF5QgfCjPoaZNlhILTnFGVvVX00vLq3ihrSAZ+LxxHub6nyBAb5FUvdq2smlVkmU4aZRYERU1DF8S10LRui+NAsy/GukyyrOnxKfRGma4pGfgyjWM6k1mtp7XvnFTPz8rxVIbWLZot2ctlOrPmXCqU06HC0hZHQpZYcYpPRrqFRW7FnXX2F10kmlk8IAqk123RxRPEPfR6ZmJZLuI4D71IJFBjLMelmqWYZuryjdV6o1CQpdGll0vMQ6+CotK0skoGtj7uBSMdj5kHy6vvTUOXwz2EWv0sjJ2RV/03UvHyfPJKclErpXcSxhqSQUxymRXyfWeAeT72/NoNa34mGTUqeyULi7SkJjPoCWdNGG1aHnpSBj3stijvoa8bhI0GRQuZ1XpC5kHRNGZXWUaYzVhcbtWcuW29QXeFKho5osbOcSlgWqvsrINmzw4gkrbYSUNHRHLR6Ssf5ukP7KDXklWxRpbQxvspdKSmrTforlAlldxYYzmuGCS1wiKglED8SlHpseUZgRk4vMb+cq9lrKNgFiuqae67+TLWyKxZOp682QVRavHmedtgealp6w26H+lOafpuxMkzvQV318V5fJqSCwAcnnZ3Rpws5KO18lOehedHfJfLx0NNQysdFNVIjd56g55lqIsdzKCnT1bLDOX3KV2zzPNAiSBeSu8M+P2z7s2ucoqV/sv1AvfJqsZgqcSr2lk3coVFM/aO2yQXOSZVC81ZUQwepDFWM+/lUtTfp4Jf9KQxLreP+ysPvYtk4LfcdWiOd+rVfAwuuVSS1VShsMhvLyJ53Ftv0OfFIGkZByOO8zBTDIr6iwFreJ954KF3DopGui1qjDcLqrJTCIoC681wNsElXmgc99YbdD9Qk5JxMOK4a3R4Jq9HroufnqYhBTkj7Dz0roVFMclFp7ColDdTaX2cZeufv01wcYvCNHR5XNRfq1rO2BmhzJDSNat7qygF2J3Ess65cJKVj57kkmE68yWX4Xu5APL3UtnjR0dqMoOezSWXlIyDEWfuoadn0OftYXU99MNrBkWjvVy0gqJep8wUgqKA/L0Udgg1yUUQP3Uppem7ESdvyQxDjqaJXwmplQYIrC+5MDdTF6czrfGmVeUbSlZiaYsuTmfdFuXxe7kMfYMZqwkDgUP3A/FxvUoKJcmlfS66SS5AMzCqJRG5RZlT6ZRZS1az7llCm1Afdwql/0R0EhFdRUQ3EdGNRHThkm3PJKIpEZ3X7zDl0H4IjZ3R8koTmlXVzsFMyUBuGBQFmuX/hWJQNKXGamoeumKH0EmHbaYAXsfM1xLRMQCuIaJPMvNN/kZElAN4G4BPCIxTDLeAq5buaeyMVqpeOg468ozwk8Oslga4UdoitQ36tGA8SOkFlFLr401mOJvuR+tFtvJxYObbmfna6ut7AdwM4ITIpv8EwGUA7uh1hMJM8nnTfbPn6RN66EMXp/j4PTukpu/h/oB1C4vakoueRFReq1QC2u17Sbb039kYyQritZ4GItoH4AwAVwefnwDglwC8d8XvX0BEB4jowKFDh9YcqgwZzXsVp2QcjDh5mNmR0CXze7loBkUPr1VYVG7jB0X1grjl/8kYdFr//G26Hxc7EF9ntuuGRHQ0Sg/8tcx8T/DjdwJ4PTMXy/4GM1/MzPuZef+ePXvWHqwEdTGIFRaNgrAYZOjUNx9XCamZBgisp6G7baaBQVcZb3jthjboSnnobqnAQqGCuIuGDiI6AqUxv5SZL49ssh/Ah6qpxPEAXkBEU2b+SF8DlcLvv5GSt2fEafUvSWhWVffsGKqXyxoGPfTQUx2vJFoG3RWcTRXO80qDTqWVvgTAzcz8jtg2zHyyt/37AXxsDMYcaPbfSCkFzogzD2Sl0Q/Exy8s0g2KrrHAhQuKBmmLqjOKNcYrSXj+ZLstsspMqIuHfjaA8wHcQETXVZ+9EcBeAGDmi2SGpoPfy0UhjmXskE1kBi1yrxJSpVJ0w8IiYN5hENDthw6kc+1a95Kghl4UOqnRKw06M38OQOdRMPMrdzIgberm80q5w8bOqKftsxmA4Y2Cj2sPq1Wktsm5cL9T8ICSixvvwB56azxCHp2m5JLQhHUY/Cb3KRkHI07dy2WaRi6zTxn80ksDrHu5TLtLBnXaYlBYpBkUPTwtFwBJJSgqfS/VxVwzeSnODLqXO2wGPX3mXlWqQVG9IrVW6fqGBl3DcwSa127ogCigV1ikedzpPA0D4XJEp0qBLGNntFPfhhxNE9drXEuTbpWudzHokaDoEGmWKTxrmmmLbj/S5zmhx2EY/KBoCl6DsZzWKjMJXbNGt0WVysvgXHQwkrFeLupB0Vkaaw9k4b0kGBR1+0mmsGi3or3KjLEzUl7goi7xZp1xbXIuJhGDrhXE9T3iFK5ba4EQsW6LesdtBt2bJqcwDTSW00rVS+iazXt26BisTZegA4bqtpiWQVdLW/SP24KisviFRRoNlYydobXKzCbMDbpO0UzrXKxRWFQM0Q/d652Swou41QtHyBr6+zEPXRi3xqJWy1NjZ/g6bEaynevWxaXAFkpZIy0NvYND4sbVKixSHm8KL2KtjCnN4956g+5LLincZMZy/EUdUrte84wpnbFtsthHLbnwsJWiKVy79gIXMvvRvGe33qBnFhQdFU0PPa3r5Xq5FKxTNBNm/HRxMOfNucrvmfXGmyV27bRme5rHvfUG3RWD2CLR48D38lJLM3XjOaxUOJMHWRpdJIN5+9zyd1xwVLvbYgrxqua9JGcKJ4r37NYb9KxqqHTYPPRR4D+EQ5eOhzTGpiFhbCAZhEFRzQWbG9JDAs5T817S2o8ZdFFqjyWRQI2xnMzPlEjsejXHprA/bwWgrpLBvPS//N5JLxovoMybwaTwMtbKuskUs3vMoCcWqDGWozl9XZdJ416Sf7ScTLCOZDA36KUld9KLuuSSwLXzJSvJZ19TajKDnligxlhOytfLD35peOh+ULTr+2Ohh76NQVGlknwLiioyn3ZZL5cxMJ+2pxfzcM7X4ZlSc65acum+P2dQZqGGrnAq/WcthWvXOH+C49E8bjPo3glO4SYzljNJ+HrlnluuKbmU++t2LiYLJJdcYUqR2rO2yfnbBL/gywy6MP4JTiFQYyzHn7KmYBR88sbY5PfnvzO6notFkotmYVH49VA0rpfg8WvtBzCD3jDiJrmkT8MoJKDD+vhGXLOwqNx3R8llkYeuofkndu0ypeul+SLbeoPuG/EUAjXGclLz8nx8mUWzl0v49TImizx0BYkotWs3UbpeZtAV2cTLMYYjNaPg43u5Gh4oEdXFRDsOiipm5ZT7G/7amYe+C8kSNhBGG98opDaj8sejFY9x92zX/dUa+syV/rsqU90ZRQrxKi1tO1O8Z7feoKecNWG08a9RCv1AfLSm8D7ufHTdX23Qq2aLTnrRWGy7ce0SeNa0POeJ4nFvvUFveOiJeXxGm4aXl9j10g6KAvN7dl0PvaiacrnmXNpB0RSuXUOyUpJcrJeLMKnpesZy/EuU2vXKFNPT6n1W56Dr/tx208Cgq/RyUU7r7IK7h0QrRRWPO5HTOhwpB9mMNkSk8hBuwkSxgKTe55rnovbQg6CohnzVlB7SMD0a95J/bqWPO42zOiCpBWqM1TgvMzWJbIiip3UN0rywaAAPPcFnTeNesqCoIv4UKIVAjbEa5+Sk5qEPMdtzBqJzYVG1WSi5aC6ZB+j0jumCe7HopS2K7QaAGfRGQUVqHp8Rx01bzaCvL7k4yaodFNWVXDQKmbrgxiTpzGkedxpndUDyAXKHjZ2hkZmwCZo9OxzrBkXdtk47L+puixYUlSJLyUMnopOI6CoiuomIbiSiCyPbvJyIrieiG4jo80R0usxw+yczyWV01MU0ic2ohojHrFtYVG4798ynQ0kuiTxr7h6SvJc0X/STDttMAbyOma8lomMAXENEn2Tmm7xtvgbgWcz8fSJ6PoCLATxdYLy940edzUMfB27amtoLeIjCmXULi8pts9qgF4oGPcWUUw3JJU9JcmHm25n52urrewHcDOCEYJvPM/P3q2+/AODEvgcqhXb/DWPn5BYUne9nzaAoUBrWMMtFY7yNlNNEnrWtDooS0T4AZwC4eslmrwZw5YLfv4CIDhDRgUOHDq2zazFS7q9txFm3OlILzfQ0xybyU55RS3JRG29i107jBaMZp+ts0InoaACXAXgtM9+zYJufR2nQXx/7OTNfzMz7mXn/nj17Nhlv72itWmL0h1sBJjXJpVlAkq7kkmdZKyiq1Rdnk/FKUht0wePPFe+LLho6iOgIlMb8Uma+fME2TwLwPgDPZ+bv9TdEWZqrvgw3DqM7uUIgaxOGyJjaJCiaZ5FeLtozilQMukJhkWaH0C5ZLgTgEgA3M/M7FmyzF8DlAM5n5lv7HaIsmgELox/qVL3ELtcQjd6yDQxSTtTu5aJWCDUfQwropC229ydFFw/9bADnA7iBiK6rPnsjgL0AwMwXAXgzgIcBeE9p/zFl5v29j1aAyQAPobEz5sU0aVn0xr2kJGFMNpAM8rxdWKQlgUzytDKUVHq5KLZVXmnQmflzAJaOgpl/A8Bv9DUoTZqLEgw4EKMz83L3gQcSMIZui27bcMUiLfkq28KgqH+oyQRFdyvN3OGtPx2jILXUN8do0haztuSi10ys+j+Ra6fxgtlkqcBN2XoLppkjavTDKCQXNQljfYM+ifRyUZNcXB+eRLpzaRQWlX9fp/9QWk/EAKS2ioqxmlEERbW7La5x72Y0z0N3aYtqQdHUPHQFDb3cD3T2I/rXR4BJLuPDPPT2PtfxeCe5V1g0G8hDT0RDX7db5eb7MQ9dhdyCoqMj2aDoEGmLPXVbVE9bTMSga61+pXXciT0S+qTYAc5YTrJB0SFWLNowKNrq5aJcWJTKs6bRbRHQu2fNoA/QIc/YGXmikssgWS4bBkWHaJ9b7idNyUX62dc67rSeiAGwoOj4mBv0gQcSMJq0RT8ouu1pi0qtCLQ6hCb2SOhj3RbHR2od+xyaPTvqfW7YbbEICovUJJfErp3WguNax731Bn2IzARjZ6TWsc+RZQRnF9LuttgsLCLSbyaWyrWrO3cK58VrdQjdeoNuQdHxkeoSdMBmEkgf+1uv22KzsEhT/kgtKKrVuVNrJrD1Bp1o7lWlousZy9EqBtkELU023N9Oerloyh9aWSVd0YrHaN0XW2/QAb3iAqMftDITNkF7bPX+1um2mFFdUDSbsep5nCS2OIlWxpTWfWEGHX6hSho3mbGc1AJrPtqLb/QRFNWcmSbXbbGWQmT3o3XcZtDhPRSJ3GTGcjaRGbTQloM2qZr1C4uKQldySa0oTOt6WWGRIqlF3o3lpCyRaY9t3m2x+6McFhapSi4b9J6RRKsvkNZ9YQYdaWdNGG3GEBTV77bY/XfCXi5DBEVT8dC1g6Jm0BXQTjUzdkbK10srPa3e3wYViFlGKIry621PW1Tr5aJ0z5pBR3q6nrGc1IyCj/bY5j1C1pNcppVFnxaseh5Tu3YTrcIi89D1yKsKPwuKjoOUX8DqBn3joGj5dbHlBt26Le5CMqIkjYMRJ+WspHk8Rmt/qPa3ZmHRUB56coVF7n/z0HcNk5yS8RiM1aSclZRnVM34dCWXdQuL/CXohvDQU7l29fkzg757yMkM+phIvbBIN8hY/r9+YVH59bYHRdV7uZhBlyfLTHIZE6kXFmmuu7FJlXPuBUVnyoVFdU+TRK6dWp9ypeM2g45yupVKoYOxmtR7uWguNr7JuciDtMUhCouks0q6orWSkPVyUcSCouMi5aBolpFaQBTYsJdLo9ui7nlMr9ui+1/JQzeDLk+eUZLGwYiTcjO1nHTHtUkFouvlwsyYFYV4Yyqf1DR0rcpV09AVyTNKcvpuxEm7l0sm3hekub/1z4X7nYKd5JK2RCSJ1r1kkosieUbJTAGN1aQdFJXvC9LY34ZBUaA05kUB3SBuYnKZWjqhBUX1sLTFceEkghSvWa6cMbVJBaIzKrOCMS2KQQqLUnkZa71gTHJRxCSXcZHnOpkJm5BnmWrG1CYeprvXZ8yYsXzrWJ/UNHQtKcTdE9KHvfJKEtFJRHQVEd1ERDcS0YWRbYiI/hMRHSSi64noKTLDlcGCouMi7W6Lut7nJgYya0gubEFRyEshTgWQriCedNhmCuB1zHwtER0D4Boi+iQz3+Rt83wAp1T/ng7gvdX/o8AKi8bFJtWRWmg7B5tUzToDXkouAzXnSuTaab1gtKS4lR46M9/OzNdWX98L4GYAJwSbvRjAf+eSLwA4loge2ftohZhkpqGPCa3+G5ugLd9t0hvFSVbnvffz+Os7fqj6YqwNaDKFRUqSi5KN6eKh1xDRPgBnALg6+NEJAL7pff+t6rPbg9+/AMAFALB37941hyrHK56xD/f+5PDQwzA68qxT9+A1P/8Y7H3oUUMPpcX5Z+3D9350n9r+znrMw/Cbz3o0Hv+In+r8O886ZQ9e/ORH4fCswOMfeQxe8tSTBEfY5JwnPAKzgnH0kWuZHjF+9pQ9+MfPfgwevedo0f380hknqNyvxFXF2MoNiY4G8OcA/h0zXx787GMA3srMn6u+/zSA1zPzgUV/b//+/XzgwMIfG4ZhGBGI6Bpm3h/7WafwNhEdAeAyAJeGxrzi2wD81/yJ1WeGYRiGEl2yXAjAJQBuZuZ3LNjsCgC/VmW7nAXgB8x8+4JtDcMwDAG6CFlnAzgfwA1EdF312RsB7AUAZr4IwP8G8AIABwH8LYBX9T5SwzAMYykrDXqliy8Nz3IpxL+mr0EZhmEY62OVooZhGLsEM+iGYRi7BDPohmEYuwQz6IZhGLuEzoVFve+Y6BCAb2z468cDuLPH4QyNHU+67KZjAXbX8eymYwG6H8/fYeY9sR8MZtB3AhEdWFQpNUbseNJlNx0LsLuOZzcdC9DP8ZjkYhiGsUswg24YhrFLGKtBv3joAfSMHU+67KZjAXbX8eymYwF6OJ5RauiGYRhGm7F66IZhGEaAGXTDMIxdwugMOhGdQ0Rfrhak/u2hx7MOixbcJqKHEtEniegr1f/HDT3WdSCinIj+X7XQCYjoZCK6urpGf0REDxh6jF0homOJ6MNEdAsR3UxEPzPW60NE/6y6z75ERH9IRA8c07Uhov9KRHcQ0Ze8z6LXYgwL1S84nrdX99r1RPSnRHSs97M3VMfzZSJ6Xpd9jMqgE1EO4N0oF6U+DcA/JKLThh3VWrgFt08DcBaA11Tj/20An2bmUwB8uvp+TFyIcq1Zx9sA/D4zPxbA9wG8epBRbcYfAPg4Mz8ewOkoj2t014eITgDwTwHsZ+YnAsgBvAzjujbvB3BO8Nmia+EvVH8ByoXqU+P9aB/PJwE8kZmfBOBWAG8AgMouvAzAE6rfeU9l/5YyKoMO4GkADjLzV5n5fgAfQrlA9ShYsuD2iwF8oNrsAwB+cZABbgARnQjgXADvq74nAM8B8OFqk9EcDxE9BMDPoVzQBcx8PzPfjfFenwmABxHRBMBRKNf4Hc21YebPArgr+HjRtUh+ofrY8TDzJ5h5Wn37BZSrvQHl8XyIme9j5q+hXGviaav2MTaDvmgx6tERLLj9cG+Fp+8CePhQ49qAdwL4FwCK6vuHAbjbu0nHdI1OBnAIwH+rJKT3EdGDMcLrw8zfBvAfANyG0pD/AMA1GO+1cSy6FrvBNvw6gCurrzc6nrEZ9F1BteD2ZQBey8z3+D+rFgsZRS4pEb0QwB3MfM3QY+mJCYCnAHgvM58B4EcI5JWxXJ9KW34xypfUowA8GO3p/qgZy7XoAhG9CaUke+lO/s7YDProF6NesOD237jpYfX/HUONb03OBvAiIvo6SvnrOSg16GOraT4wrmv0LQDfYuarq+8/jNLAj/H6/H0AX2PmQ8x8GMDlKK/XWK+NY9G1GK1tIKJXAnghgJfzvDBoo+MZm0H/SwCnVJH6B6AMGlwx8Jg6s2TB7SsAvKL6+hUAPqo9tk1g5jcw84nMvA/ltfgMM78cwFUAzqs2G9PxfBfAN4nocdVHzwVwE8Z5fW4DcBYRHVXdd+5YRnltPBZdi1EuVE9E56CULF/EzH/r/egKAC8joiOJ6GSUwd4vrvyDzDyqfygXo74VwF8DeNPQ41lz7M9EOUW8HsB11b8XoNSdPw3gKwA+BeChQ491g2N7NoCPVV8/urr5DgL4EwBHDj2+NY7jyQAOVNfoIwCOG+v1AfAWALcA+BKADwI4ckzXBsAfotT/D6OcPb160bVAue7xuyu7cAPK7J7Bj6HD8RxEqZU7e3CRt/2bquP5MoDnd9mHlf4bhmHsEsYmuRiGYRgLMINuGIaxSzCDbhiGsUswg24YhrFLMINuGIaxSzCDbhiGsUswg24YhrFL+P+05QcCzQBCjQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(pfn_simulator.history[\"live\"].sum(axis=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1cc1dc83",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.mean(pfn_simulator.history[\"a_nrm\"], axis=1), label=\"Generic monte carlo\")\n",
+    "plt.plot(\n",
+    "    np.mean(PFexample.history[\"aNrm\"], axis=1),\n",
+    "    label=\"HARK 0.13 PerfForesightConsumerType\",\n",
+    ")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"Mean normalized market resources\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "dcff94ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0.])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(pfn_simulator.history[\"a_nrm\"], axis=1) - np.mean(\n",
+    "    PFexample.history[\"aNrm\"], axis=1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "70de1058",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  8,  50,  65,  76,  81,  82,  93, 107, 117]),\n",
+       " array([2, 1, 2, 0, 0, 1, 0, 1, 2]))"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.where(pfn_simulator.history[\"live\"] < 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0e37528d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  8,  50,  65,  76,  81,  82,  93, 107, 117]),\n",
+       " array([2, 1, 2, 0, 0, 1, 0, 1, 2]))"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.where(1 - PFexample.history[\"who_dies\"] < 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e5cf6a1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hark-env",
+   "language": "python",
+   "name": "hark-env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}


Test patch:
diff --git a/HARK/simulation/test_monte_carlo.py b/HARK/simulation/test_monte_carlo.py
new file mode 100644
index 000000000..bb1620c3c
--- /dev/null
+++ b/HARK/simulation/test_monte_carlo.py
@@ -0,0 +1,160 @@
+"""
+This file implements unit tests for the Monte Carlo simulation module
+"""
+import unittest
+
+from HARK.distribution import Bernoulli, IndexDistribution, MeanOneLogNormal
+from HARK.model import Aggregate, Control
+from HARK.simulation.monte_carlo import *
+
+cons_shocks = {
+    "agg_gro": Aggregate(MeanOneLogNormal(1)),
+    "psi": IndexDistribution(MeanOneLogNormal, {"sigma": [1.0, 1.1]}),
+    "theta": MeanOneLogNormal(1),
+    "live": Bernoulli(p=0.98),
+}
+
+cons_pre = {
+    "R": 1.05,
+    "aNrm": 1,
+    "gamma": 1.1,
+    "psi": 1.1,  # TODO: draw this from a shock,
+    "theta": 1.1,  # TODO: draw this from a shock
+}
+
+cons_dynamics = {
+    "G": lambda gamma, psi: gamma * psi,
+    "Rnrm": lambda R, G: R / G,
+    "bNrm": lambda Rnrm, aNrm: Rnrm * aNrm,
+    "mNrm": lambda bNrm, theta: bNrm + theta,
+    "cNrm": Control(["mNrm"]),
+    "aNrm": lambda mNrm, cNrm: mNrm - cNrm,
+}
+
+cons_dr = {"cNrm": lambda mNrm: mNrm / 2}
+
+
+class test_draw_shocks(unittest.TestCase):
+    def test_draw_shocks(self):
+        drawn = draw_shocks(cons_shocks, np.array([0, 1]))
+
+        self.assertEqual(len(drawn["theta"]), 2)
+        self.assertEqual(len(drawn["psi"]), 2)
+        self.assertTrue(isinstance(drawn["agg_gro"], float))
+
+
+class test_simulate_dynamics(unittest.TestCase):
+    def test_simulate_dynamics(self):
+        post = simulate_dynamics(cons_dynamics, cons_pre, cons_dr)
+
+        self.assertAlmostEqual(post["cNrm"], 0.98388429)
+
+
+class test_AgentTypeMonteCarloSimulator(unittest.TestCase):
+    def setUp(self):
+        self.shocks = {
+            "theta": MeanOneLogNormal(1),
+            "agg_R": Aggregate(MeanOneLogNormal(1)),
+            "live": Bernoulli(p=0.98),
+        }
+
+        self.initial = {"a": MeanOneLogNormal(1), "live": 1}
+
+        self.parameters = {  # TODO
+            "G": 1.05,
+        }
+
+        self.dynamics = {
+            "b": lambda agg_R, G, a: agg_R * G * a,
+            "m": lambda b, theta: b + theta,
+            "c": Control(["m"]),
+            "a": lambda m, c: m - c,
+        }
+
+        self.dr = {"c": lambda m: m / 2}
+
+    def test_simulate(self):
+        self.simulator = AgentTypeMonteCarloSimulator(
+            self.parameters,
+            self.shocks,
+            self.dynamics,
+            self.dr,
+            self.initial,
+            agent_count=3,
+        )
+
+        self.simulator.initialize_sim()
+        history = self.simulator.simulate()
+
+        a1 = history["a"][5]
+        b1 = (
+            history["a"][4] * history["agg_R"][5] * self.parameters["G"]
+            + history["theta"][5]
+            - history["c"][5]
+        )
+
+        self.assertTrue((a1 == b1).all())
+
+    def test_make_shock_history(self):
+        self.simulator = AgentTypeMonteCarloSimulator(
+            self.parameters,
+            self.shocks,
+            self.dynamics,
+            self.dr,
+            self.initial,
+            agent_count=3,
+        )
+
+        self.simulator.make_shock_history()
+
+        newborn_init_1 = self.simulator.newborn_init_history.copy()
+        shocks_1 = self.simulator.shock_history.copy()
+
+        self.simulator.initialize_sim()
+        self.simulator.simulate()
+
+        self.assertEqual(newborn_init_1, self.simulator.newborn_init_history)
+        self.assertTrue(np.all(self.simulator.history["theta"] == shocks_1["theta"]))
+
+
+class test_AgentTypeMonteCarloSimulatorAgeVariance(unittest.TestCase):
+    def setUp(self):
+        self.shocks = {
+            "theta": MeanOneLogNormal(1),
+            "agg_R": Aggregate(MeanOneLogNormal(1)),
+            "live": Bernoulli(p=0.98),
+            "psi": IndexDistribution(MeanOneLogNormal, {"sigma": [1.0, 1.1]}),
+        }
+
+        self.initial = {"a": MeanOneLogNormal(1), "live": 1}
+
+        self.parameters = {  # TODO
+            "G": 1.05,
+        }
+
+        self.dynamics = {
+            "b": lambda agg_R, G, a: agg_R * G * a,
+            "m": lambda b, theta: b + theta,
+            "c": Control(["m"]),
+            "a": lambda m, c: m - c,
+        }
+
+        self.dr = {"c": [lambda m: m * 0.5, lambda m: m * 0.9]}
+
+    def test_simulate(self):
+        self.simulator = AgentTypeMonteCarloSimulator(
+            self.parameters,
+            self.shocks,
+            self.dynamics,
+            self.dr,
+            self.initial,
+            agent_count=3,
+        )
+
+        self.simulator.initialize_sim()
+        history = self.simulator.simulate(sim_periods=2)
+
+        a1 = history["a"][1]
+        b1 = history["m"][1] - self.dr["c"][1](history["m"][1])
+
+        self.assertTrue((a1 == b1).all())