We can think of this cake as a harvest that regrows if we save some seeds.

Specifically, if we save (invest) part of today's cake, it grows into next

period's cake according to a stochastic production process.

Specifically, if we save and invest part of today's harvest $x_t$, it grows into next

period's harvest $x_{t+1}$ according to a stochastic production process.

```{note}

The term "cake eating" is not such a good fit now that we have a stochastic and

import numpy as np

from scipy.interpolate import interp1d

from scipy.optimize import minimize_scalar

from typing import NamedTuple, Callable

```

## The Model

```{index} single: Stochastic Cake Eating; Model

```

Consider an agent who owns an amount $x_t \in \mathbb R_+ := [0, \infty)$ of a consumption good at time $t$.

Here we described the new model and the optimization problem.

This output can either be consumed or invested.

### Setup

When the good is invested, it is transformed one-for-one into capital.

Consider an agent who owns an amount $x_t \in \mathbb R_+ := [0, \infty)$ of a consumption good at time $t$.

The resulting capital stock, denoted here by $k_{t+1}$, will then be used for production.

This output can either be consumed or saved and used for production.

Production is stochastic, in that it also depends on a shock $\xi_{t+1}$ realized at the end of the current period.

Next period output is

$$

x_{t+1} := f(k_{t+1}) \xi_{t+1}

x_{t+1} := f(s_t) \xi_{t+1}

$$

where $f \colon \mathbb R_+ \to \mathbb R_+$ is called the production function.

The resource constraint is

where $f \colon \mathbb R_+ \to \mathbb R_+$ is the **production function** and

```{math}

:label: outcsdp0

k_{t+1} + c_t \leq x_t

s_t = x_t - c_t

```

and all variables are required to be nonnegative.

is **current savings**.

### Assumptions and Comments

and all variables are required to be nonnegative.

In what follows,

* The sequence $\{\xi_t\}$ is assumed to be IID.

* The common distribution of each $\xi_t$ will be denoted by $\phi$.

* The production function $f$ is assumed to be increasing and continuous.

* Depreciation of capital is not made explicit but can be incorporated into the production function.

While many other treatments of stochastic consumption-saving models use $k_t$ as the state variable, we will use $x_t$.

This will allow us to treat a stochastic model while maintaining only one state variable.

We consider alternative states and timing specifications in some of our other lectures.

### Optimization

* $u$ is a bounded, continuous and strictly increasing utility function and

* $\beta \in (0, 1)$ is a discount factor.

In {eq}`og_conse` we are assuming that the resource constraint {eq}`outcsdp0` holds with equality --- which is reasonable because $u$ is strictly increasing and no output will be wasted at the optimum.

In summary, the agent's aim is to select a path $c_0, c_1, c_2, \ldots$ for consumption that is

1. nonnegative,

1. feasible in the sense of {eq}`outcsdp0`,

1. feasible,

1. optimal, in the sense that it maximizes {eq}`texs0_og2` relative to all other feasible consumption sequences, and

1. **adapted**, in the sense that the action $c_t$ depends only on

observable outcomes, not on future outcomes such as $\xi_{t+1}$.

1. **adapted**, in the sense that the current action $c_t$ depends only on current and historical outcomes, not on future outcomes such as $\xi_{t+1}$.

In the present context

* $x_t$ is called the **state** variable --- it summarizes the "state of the world" at the start of each period.

* $c_t$ is called the **control** variable --- a value chosen by the agent each period after observing the state.

### The Policy Function Approach

```{index} single: Stochastic Cake Eating; Policy Function Approach

We'll be particularly interested in **Markov policies**, which are maps from the current state $x_t$ into a current action $c_t$.

For dynamic programming problems such as this one (in fact for any [Markov decision process](https://en.wikipedia.org/wiki/Markov_decision_process)), the optimal policy is always a Markov policy.

For dynamic programming problems such as this one, the optimal policy is always a Markov policy (see, e.g., [DP1](https://dp.quantecon.org/)).

In other words, the current state $x_t$ provides a [sufficient statistic](https://en.wikipedia.org/wiki/Sufficient_statistic)

for the history in terms of making an optimal decision today.

This is quite intuitive, but if you wish you can find proofs in texts such as {cite}`StokeyLucas1989` (section 4.1).

Hereafter we focus on finding the best Markov policy.

In other words, the current state $x_t$ provides a sufficient statistic for the history

in terms of making an optimal decision today.

In our context, a Markov policy is a function $\sigma \colon

\mathbb R_+ \to \mathbb R_+$, with the understanding that states are mapped to actions via

c_t = \sigma(x_t) \quad \text{for all } t

$$

In what follows, we will call $\sigma$ a *feasible consumption policy* if it satisfies

In what follows, we will call $\sigma$ a **feasible consumption policy** if it satisfies

```{math}

:label: idp_fp_og2

The next section covers these ideas more formally.

### Optimality

The $\sigma$ associated with a given policy $\sigma$ is the mapping defined by

The lifetime value $v_{\sigma}$ associated with a given policy $\sigma$ is the mapping defined by

```{math}

:label: vfcsdp00

when $\{x_t\}$ is given by {eq}`firstp0_og2` with $x_0 = x$.

In other words, it is the lifetime value of following policy $\sigma$

starting at initial condition $x$.

In other words, it is the lifetime value of following policy $\sigma$ forever, starting at initial condition $x$.

The **value function** is then defined as

v^*(x) := \sup_{\sigma \in \Sigma} \; v_{\sigma}(x)

```

The value function gives the maximal value that can be obtained from state $x$, after considering all feasible policies.

The value function gives the maximal value that can be obtained from state $x$,

after considering all feasible policies.

A policy $\sigma \in \Sigma$ is called **optimal** if it attains the supremum in {eq}`vfcsdp0` for all $x \in \mathbb R_+$.

A policy $\sigma \in \Sigma$ is called **optimal** if it attains the supremum in

{eq}`vfcsdp0` for all $x \in \mathbb R_+$.

### The Bellman Equation

With our assumptions on utility and production functions, the value function as defined in {eq}`vfcsdp0` also satisfies a **Bellman equation**.

### The Bellman Equation

For this problem, the Bellman equation takes the form

The following equation is called the **Bellman equation** associated with this

dynamic programming problem.

```{math}

:label: fpb30

\qquad (x \in \mathbb R_+)

```

This is a *functional equation in* $v$.

This is a *functional equation in* $v$, in the sense that a given $v$ can either

satisfy it or not satisfy it.

The term $\int v(f(x - c) z) \phi(dz)$ can be understood as the expected next period value when

* $v$ is used to measure value

* the state is $x$

* consumption is set to $c$

As shown in [EDTC](https://johnstachurski.net/edtc.html), theorem 10.1.11 and a range of other texts,

As shown in [EDTC](https://johnstachurski.net/edtc.html), Theorem 10.1.11 and a range of other texts,

the value function $v^*$ satisfies the Bellman equation.

In other words, {eq}`fpb30` holds when $v=v^*$.

* current reward from a given action, vs

* expected discounted future value of the state resulting from that action

The Bellman equation is important because it gives us more information about the value function.

The Bellman equation is important because it

1. gives us more information about the value function and

2. suggests a way of computing the value function, which we discuss below.

It also suggests a way of computing the value function, which we discuss below.

### Greedy Policies

The primary importance of the value function is that we can use it to compute optimal policies.

The details are as follows.

### Greedy Policies

The value function can be used to compute optimal policies.

Given a continuous function $v$ on $\mathbb R_+$, we say that

$\sigma \in \Sigma$ is $v$-**greedy** if $\sigma(x)$ is a solution to

The intuition is similar to the intuition for the Bellman equation, which was

provided after {eq}`fpb30`.

See, for example, theorem 10.1.11 of [EDTC](https://johnstachurski.net/edtc.html).

See, for example, Theorem 10.1.11 of [EDTC](https://johnstachurski.net/edtc.html).

Hence, once we have a good approximation to $v^*$, we can compute the

(approximately) optimal policy by computing the corresponding greedy policy.

The advantage is that we are now solving a much lower dimensional optimization

problem.

### The Bellman Operator

How, then, should we compute the value function?

One way is to use the so-called **Bellman operator**.

(An operator is a map that sends functions into functions.)

(The term **operator** is usually reserved for functions that send functions into functions!)

The Bellman operator is denoted by $T$ and defined by

\qquad (x \in \mathbb R_+)

```

In other words, $T$ sends the function $v$ into the new function

$Tv$ defined by {eq}`fcbell20_optgrowth`.

In other words, $T$ sends the function $v$ into the new function $Tv$ defined by {eq}`fcbell20_optgrowth`.

By construction, the set of solutions to the Bellman equation

{eq}`fpb30` *exactly coincides with* the set of fixed points of $T$.

By construction, the set of solutions to the Bellman equation {eq}`fpb30`

*exactly coincides with* the set of fixed points of $T$.

For example, if $Tv = v$, then, for any $x \geq 0$,

It follows that $v^*$ is a fixed point of $T$.

### Review of Theoretical Results

```{index} single: Dynamic Programming; Theory

```{index} single: Dynamic Programming; Unbounded Utility

```

The results stated above assume that the utility function is bounded.

The results stated above assume that $u$ is bounded.

In practice economists often work with unbounded utility functions --- and so will we.

Our implementation in this lecture will focus on clarity and

flexibility.

Both of these things are helpful, but they do cost us some speed --- as you

will see when you run the code.

(In subsequent lectures we will focus on efficiency and speed.)

The algorithm we will use is fitted value function iteration, which was

described in earlier lectures {doc}`the McCall model <mccall_fitted_vfi>` and

{doc}`cake eating <cake_eating_numerical>`.

We will use fitted value function iteration, which was

already described in {doc}`cake eating <cake_eating_numerical>`.

The algorithm will be

(fvi_alg)=

1. Begin with an array of values $\{ v_1, \ldots, v_I \}$ representing

the values of some initial function $v$ on the grid points $\{ x_1, \ldots, x_I \}$.

1. Build a function $\hat v$ on the state space $\mathbb R_+$ by

linear interpolation, based on these data points.

1. Obtain and record the value $T \hat v(x_i)$ on each grid point

$x_i$ by repeatedly solving {eq}`fcbell20_optgrowth`.

1. Unless some stopping condition is satisfied, set

$\{ v_1, \ldots, v_I \} = \{ T \hat v(x_1), \ldots, T \hat v(x_I) \}$ and go to step 2.

### Scalar Maximization

To maximize the right hand side of the Bellman equation {eq}`fpb30`, we are going to use

the `minimize_scalar` routine from SciPy.

Since we are maximizing rather than minimizing, we will use the fact that the

maximizer of $g$ on the interval $[a, b]$ is the minimizer of

$-g$ on the same interval.

To this end, and to keep the interface tidy, we will wrap `minimize_scalar`

in an outer function as follows:

To keep the interface tidy, we will wrap `minimize_scalar` in an outer function as follows:

```{code-cell} python3

def maximize(g, a, b, args):

return maximizer, maximum

```

### Stochastic Cake Eating Model

We will assume for now that $\phi$ is the distribution of $\xi := \exp(\mu + s \zeta)$ where

### Model

We will assume for now that $\phi$ is the distribution of $\xi := \exp(\mu + \nu \zeta)$ where

* $\zeta$ is standard normal,

* $\mu$ is a shock location parameter and

* $s$ is a shock scale parameter.

* $\nu$ is a shock scale parameter.

We will store the primitives of the model in a `NamedTuple`.

```{code-cell} python3

from typing import NamedTuple, Callable

class Model(NamedTuple):

u: Callable # utility function

f: Callable # production function

β: float # discount factor

μ: float # shock location parameter

s: float # shock scale parameter

ν: float # shock scale parameter

grid: np.ndarray # state grid

shocks: np.ndarray # shock draws

f: Callable,

β: float = 0.96,

μ: float = 0.0,

s: float = 0.1,

ν: float = 0.1,

grid_max: float = 4.0,

grid_size: int = 120,

shock_size: int = 250,

# Store shocks (with a seed, so results are reproducible)

np.random.seed(seed)

shocks = np.exp(μ + s * np.random.randn(shock_size))

shocks = np.exp(μ + ν * np.random.randn(shock_size))

return Model(u=u, f=f, β=β, μ=μ, s=s, grid=grid, shocks=shocks)

return Model(u, f, β, μ, ν, grid, shocks)

def state_action_value(c: float,

Let's suppose now that

$$

f(k) = k^{\alpha}

f(x-c) = (x-c)^{\alpha}

\quad \text{and} \quad

u(c) = \ln c

$$

For this particular problem, an exact analytical solution is available (see {cite}`Ljungqvist2012`, section 3.1.2), with

For this particular problem, an exact analytical solution is available (see

{cite}`Ljungqvist2012`, section 3.1.2), with

```{math}

:label: dpi_tv

```{code-cell} python3

α = 0.4

def fcd(k):

return k**α

def fcd(s):

return s**α

model = create_model(u=np.log, f=fcd)

```