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# Stackelberg Plans¶

This notebook formulates and computes a plan that a **Stackelberg
leader** uses to manipulate forward-looking decisions of a **Stackelberg
follower** that depend on continuation sequences of decisions made once
and for all by the Stackelberg leader at time $ 0 $

To facilitate computation and interpretation, we formulate things in a context that allows us to apply linear optimal dynamic programming

From the beginning we carry along a linear-quadratic model of duopoly in which firms face adjustment costs that make them want to forecast actions of other firms that influence future prices

## Duopoly¶

Time is discrete and is indexed by $ t = 0, 1, \ldots $

Two firms produce a single good whose demand is governed by the linear inverse demand curve

$$ p_t = a_0 - a_1 (q_{1t}+ q_{2t} ) $$where $ q_{it} $ is output of firm $ i $ at time $ t $ and $ a_0 $ and $ a_1 $ are both positive

$ q_{10}, q_{20} $ are given numbers that serve as initial conditions at time $ 0 $

By incurring a cost of change

$$ \gamma v_{it}^2 $$where $ \gamma > 0 $, firm $ i $ can change its output according to

$$ q_{it+1} = q_{it} + v_{it} $$Firm $ i $‘s profits at time $ t $ equal

$$ \pi_{it} = p_t q_{it} - \gamma v_{it}^2 $$Firm $ i $ wants to maximize the present value of its profits

$$ \sum_{t=0}^\infty \beta^t \pi_{it} $$where $ \beta \in (0,1) $ is a time discount factor

### Stackelberg leader and follower¶

Each firm $ i=1,2 $ chooses a sequence $ \vec q_i \equiv \{q_{it+1}\}_{t=0}^\infty $ once and for all at time $ 0 $

We let firm 2 be a **Stackelberg leader** and firm 1 be a **Stackelberg
follower**

The leader firm 2 goes first and chooses $ \{q_{2t+1}\}_{t=0}^\infty $ once and for all at time $ 0 $

Knowing that firm 2 has chosen $ \{q_{2t+1}\}_{t=0}^\infty $, the follower firm 1 goes second and chooses $ \{q_{1t+1}\}_{t=0}^\infty $ once and for all at time $ 0 $

In choosing $ \vec q_2 $, firm 2 takes into account that firm 1 will base its choice of $ \vec q_1 $ on firm 2’s choice of $ \vec q_2 $

### Abstract statement of the leader’s and follower’s problems¶

We can express firm 1’s problem as

$$ \max_{\vec q_1} \Pi_1(\vec q_1; \vec q_2) $$where the appearance behind the semi-colon indicates thata $ \vec q_2 $ is given

Firm 1’s problem induces a best response mapping

$$ \vec q_1 = B(\vec q_2) $$(Here $ B $ maps a sequence into a sequence)

The Stackelberg leader’s problem is

$$ \max_{\vec q_2} \Pi_2 (B(\vec q_2), \vec q_2) $$whose maximizer is a sequence $ \vec q_2 $ that depends on the initial conditions $ q_{10}, q_{20} $ and the parameters of the model $ a_0, a_1, \gamma $

This formulation captures key features of the model

- Both firms make once-and-for-all choices at time $ 0 $
- This is true even though both firms are choosing sequences of
quantities that are indexed by
**time** - The Stackelberg leader chooses first
**within time**$ 0 $, knowing that the Stackelberg follower will choose second**within time**$ 0 $

While our abstract formulation reveals the timing protocol and equilibrium concept well, it obscures details that must be addressed when we want to compute and interpret a Stackelberg plan and the follower’s best response to it

To gain insights about these things, we study them in more detail

### Firms’ problems¶

Firm 1 acts as if firm 2’s sequence $ \{q_{2t+1}\}_{t=0}^\infty $ is given and beyond its control

Firm 2 knows that firm 1 chooses second and takes this into account in choosing $ \{q_{2t+1}\}_{t=0}^\infty $

In the spirit of *working backwards*, we study firm 1’s problem first,
taking $ \{q_{2t+1}\}_{t=0}^\infty $ as given

We can formulate firm 1’s optimum problem in terms of the Lagrangian

$$ L=\sum_{t=0}^{\infty}\beta^{t}\{a_{0}q_{1t}-a_{1}q_{1t}^{2}-a_{1}q_{1t}q_{2t}-\gamma v_{1t}^{2}+\lambda_{t}[q_{1t}+v_{1t}-q_{1t+1}]\} $$Firm 1 seeks a maximum with respect to $ \{q_{1t+1}, v_{1t} \}_{t=0}^\infty $ and a minimum with respect to $ \{ \lambda_t\}_{t=0}^\infty $

We approach this problem using methods described in Ljungqvist and Sargent RMT5 chapter 2, appendix A and Macroeconomic Theory, 2nd edition, chapter IX

First-order conditions for this problem are

$$ \eqalign{ \frac{\partial L}{\partial q_{1t}} & = a_0 - 2 a_1 q_{1t} - a_1 q_{2t} + \lambda_t - \beta^{-1} \lambda_{t-1} = 0 , \quad t \geq 1 \cr \frac{\partial L}{\partial v_{1t}} & = -2 \gamma v_{1t} + \lambda_t = 0 , \quad t \geq 0 \cr } $$These first-order conditions and the constraint $ q_{1t+1} = q_{1t} + v_{1t} $ can be rearranged to take the form

$$ \eqalign{ v_{1t} & = \beta v_{1t+1} + \frac{\beta a_0}{2 \gamma} - \frac{\beta a_1}{\gamma} q_{1t+1} - \frac{\beta a_1}{2 \gamma} q_{2t+1} \cr q_{t+1} & = q_{1t} + v_{1t} } $$We can substitute the second equation into the first equation to obtain

$$ (q_{1t+1} - q_{1t} ) = \beta (q_{1t+2} - q_{1t+1}) + c_0 - c_1 q_{1t+1} - c_2 q_{2t+1} $$where $ c_0 = \frac{\beta a_0}{2 \gamma}, c_1 = \frac{\beta a_1}{\gamma}, c_2 = \frac{\beta a_1}{2 \gamma} $

This equation can in turn be rearranged to become the second-order difference equation

- q_{1t} + (1+\beta + c
*1) q*{1t+1} - \beta q_{1t+2} = c_0 - c*2 q*{2t+1} \tag{1} $$

Equation (1) is a second-order difference equation in the sequence
$ \vec q_1 $ whose solution we want satisfies **two boundary
conditions:**

- an initial condition that $ q_{1,0} $ is given
- a terminal condition requiring that $ \lim_{T \rightarrow + \infty} \beta^T q_{1t}^2 < + \infty $

Using the lag operators described in chapter IX of *Macroeconomic
Theory, Second edition (1987)*, difference equation
(1) can be written as

The polynomial in the lag operator on the left side can be **factored**
as

$$ (1 - \frac{1+\beta + c_1}{\beta} L + \beta^{-1} L^2 ) = ( 1 - \delta_1 L ) (1 - \delta_2 L) \tag{2} $$

where $ 0 < \delta_1 < 1 < \frac{1}{\sqrt{\beta}} < \delta_2 $

Because $ \delta_2 > \frac{1}{\sqrt{\beta}} $ the operator
$ (1 - \delta_2 L) $ contributes an **unstable** component if solved
**backwards** but a **stable** component if solved **forwards**

Mechanically, write

$$ (1- \delta_2 L) = -\delta_{2} L (1 - \delta_2^{-1} L^{-1} ) $$and compute the following inverse operator

$$ \left[-\delta_{2} L (1 - \delta_2^{-1} L^{-1} )\right]^{-1} = - \delta_2 (1 - {\delta_2}^{-1} )^{-1} L^{-1} $$Operating on both sides of equation (2) with
$ \beta^{-1} $ times this inverse operator gives the follower’s
decision rule for setting $ q_{1t+1} $ in the
**feedback-feedforward** form

$$ q_{1t+1} = \delta_1 q_{1t} - c_0 \delta_2^{-1} \beta^{-1} \frac{1}{1 -\delta_2^{-1}} + c_2 \delta_2^{-1} \beta^{-1} \sum_{j=0}^\infty \delta_2^j q_{2t+j+1} , \quad t \geq 0 \tag{3} $$

The problem of the Stackelberg leader firm 2 is to choose the sequence $ \{q_{2t+1}\}_{t=0}^\infty $ to maximize its discounted profits

$$ \sum_{t=0}^\infty \beta^t \{ (a_0 - a_1 (q_{1t} + q_{2t}) ) q_{2t} - \gamma (q_{2t+1} - q_{2t})^2 \} $$subject to the sequence of constraints (3) for $ t \geq 0 $

We can put a sequence $ \{\theta_t\}_{t=0}^\infty $ of Lagrange multipliers on the sequence of equations (3) and formulate the following Lagrangian for the Stackelberg leader firm 2’s problem

$$ \eqalign{ \tilde L & = \sum_{t=0}^\infty \beta^t\{ (a_0 - a_1 (q_{1t} + q_{2t}) ) q_{2t} - \gamma (q_{2t+1} - q_{2t})^2 \} \cr & + \sum_{t=0}^\infty \beta^t \theta_t \{ \delta_1 q_{1t} - c_0 \delta_2^{-1} \beta^{-1} \frac{1}{1 -\delta_2^{-1}} + c_2 \delta_2^{-1} \beta^{-1} \sum_{j=0}^\infty \delta_2^{-j} q_{2t+j+1} - q_{1t+1} \} } \tag{4} $$

subject to initial conditions for $ q_{1t}, q_{2t} $ at $ t=0 $

**Comments:** We have formulated the Stackelberg problem in a space of
sequences

The max-min problem associated with Lagrangian (4) is unpleasant because the time $ t $ component of firm $ 1 $‘s payoff function depends on the entire future of its choices of $ \{q_{1t+j}\}_{j=0}^\infty $

This renders a direct attack on the problem cumbersome

Therefore, below, we will formulate the Stackelberg leader’s problem recursively

We’ll put our little duopoly model into a broader class of models with the same conceptual structure

## The Stackelberg Problem¶

We formulate a class of linear-quadratic Stackelberg leader-follower problems of which our duopoly model is an instance

We use the optimal linear regulator (a.k.a. the linear-quadratic dynamic programming problem described in LQ Dynamic Programming problems) to represent a Stackelberg leader’s problem recursively

Let $ z_t $ be an $ n_z \times 1 $ vector of **natural
state variables**

Let $ x_t $ be an $ n_x \times 1 $ vector of endogenous forward-looking variables that are physically free to jump at $ t $

In our duopoly example $ x_t = v_{1t} $, the time $ t $ decision
of the Stackelberg **follower**

Let $ u_t $ be a vector of decisions chosen by the Stackelberg leader at $ t $

The $ z_t $ vector is inherited physically from the past

But $ x_t $ is a decision made by the Stackelberg follower at time $ t $ that is the follower’s best response to the choice of an entire sequence of decisions made by the Stackelberg leader at time $ t=0 $

Let

$$ y_t = \begin{bmatrix} z_t \\ x_t \end{bmatrix} $$Represent the Stackelberg leader’s one-period loss function as

$$ r(y, u) = y' R y + u' Q u $$Subject to an initial condition for $ z_0 $, but not for $ x_0 $, the Stackelberg leader wants to maximize

$$ -\sum_{t=0}^\infty \beta^t r(y_t, u_t) \tag{5} $$

The Stackelberg leader faces the model

$$ \begin{bmatrix} I & 0 \\ G_{21} & G_{22} \end{bmatrix} \begin{bmatrix} z_{t+1} \\ x_{t+1} \end{bmatrix} = \begin{bmatrix} \hat A_{11} & \hat A_{12} \\ \hat A_{21} & \hat A_{22} \end{bmatrix} \begin{bmatrix} z_t \\ x_t \end{bmatrix} + \hat B u_t \tag{6} $$

We assume that the matrix $ \begin{bmatrix} I & 0 \\ G_{21} & G_{22} \end{bmatrix} $ on the left side of equation (6) is invertible, so that we can multiply both sides by its inverse to obtain

$$ \begin{bmatrix} z_{t+1} \\ x_{t+1} \end{bmatrix} = \begin{bmatrix} A_{11} & A_{12} \\ A_{21} & A_{22} \end{bmatrix} \begin{bmatrix} z_t \\ x_t \end{bmatrix} + B u_t \tag{7} $$

or

### Interpretation of the second block of equations¶

The Stackelberg follower’s best response mapping is summarized by the second block of equations of (7)

In particular, these equations are the first-order conditions of the Stackelberg follower’s optimization problem (i.e., its Euler equations)

These Euler equations summarize the forward-looking aspect of the follower’s behavior and express how its time $ t $ decision depends on the leader’s actions at times $ s \geq t $

When combined with a stability condition to be imposed below, the Euler equations summarize the follower’s best response to the sequence of actions by the leader

The Stackelberg leader maximizes (5) by choosing sequences $ {u_t, x_t, z_{t+1}}\_{t=0}^{\infty} $ subject to (8) and an initial condition for $ z_0 $

Note that we have an initial condition for $ z_0 $ but not for $ x_0 $

$ x_0 $ is among the variables to be chosen at time $ 0 $ by the Stackelberg leader

The Stackelberg leader uses its understanding of the responses restricted by (8) to manipulate the follower’s decisions

### More mechanical details¶

For any vector $ a_t $, define $ \vec a_t = [a_t, a\_{t+1} \ldots ] $

Define a feasible set of $ (\vec y_1, \vec u_0) $ sequences

$$ \Omega(y_0) = \left\{ (\vec y_1, \vec u_0) : y_{t+1} = A y_t + B u_t, \forall t \geq 0 \right\} $$Please remember that the follower’s Euler equation is embedded in the system of dynamic equations $ y_{t+1} = A y_t + B u_t $

Note that in the definition of $ \Omega(y_0) $, $ y_0 $ is taken as given

Although it is taken as given in $ \Omega(y_0) $, eventually, the $ x_0 $ component of $ y_0 $ will be chosen by the Stackelberg leader

### Two Subproblems¶

Once again we use backward induction

We express the Stackelberg problem in terms of **two subproblems**

Subproblem 1 is solved by a **continuation Stackelberg leader** at each
date $ t \geq 0 $

Subproblem 2 is solved the **Stackelberg leader** at $ t=0 $

The two subproblems are designed

- to respect the protocol in which the follower chooses $ \vec q_1 $ after seeing $ \vec q_2 $ chosen by the leader
- to make the leader choose $ \vec q_2 $ while respecting that $ \vec q_1 $ will be the follower’s best response to $ \vec q_2 $
- to represent the leader’s problem recursively by artfully choosing the state variables confronting and the control variables available to the leader

#### Subproblem 1¶

$$ v(y_0) = \max_{(\vec y_1, \vec u_0) \in \Omega(y_0)} - \sum_{t=0}^\infty \beta^t r(y_t, u_t) $$#### Subproblem 2¶

$$ w(z_0) = \max_{x_0} v(y_0) $$Subproblem 1 takes the vector of forward-looking variables $ x_0 $ as given

Subproblem 2 optimizes over $ x_0 $

The value function $ w(z_0) $ tells the value of the Stackelberg plan as a function of the vector of natural state variables at time $ 0 $, $ z_0 $

### Two Bellman equations¶

We now describe Bellman equations for $ v(y) $ and $ w(z_0) $

#### Subproblem 1¶

The value function $ v(y) $ in subproblem 1 satisfies the Bellman equation

$$ v(y) = \max_{u, y^*} \left\{ - r(y,u) + \beta v(y^*) \right\} \tag{9} $$

where the maximization is subject to

$$ y^* = A y + B u $$and $ y^* $ denotes next period’s value

Substituting $ v(y) = - y'P y $ into Bellman equation (9) gives

$$ -y' P y = {\rm max}_{ u, y^*} \left\{ - y' R y - u'Q u - \beta y^{* \prime} P y^* \right\} $$which as in lecture linear regulator gives rise to the algebraic matrix Riccati equation

$$ P = R + \beta A' P A - \beta^2 A' P B ( Q + \beta B' P B)^{-1} B' P A $$and the optimal decision rule coefficient vector

$$ F = \beta( Q + \beta B' P B)^{-1} B' P A $$where the optimal decision rule is

$$ u_t = - F y_t $$#### Subproblem 2¶

We find an optimal $ x_0 $ by equating to zero the gradient of $ v(y_0) $ with respect to $ x_0 $:

$$ -2 P_{21} z_0 - 2 P_{22} x_0 =0, $$which implies that

$$ x_0 = - P_{22}^{-1} P_{21} z_0 $$## Stackelberg plan¶

Now let’s map our duopoly model into the above setup.

We we’ll formulate a state space system

$$ y_t = \begin{bmatrix} z_t \cr x_t \end{bmatrix} $$where in this instance $ x_t = v_{1t} $, the time $ t $ decision of the follower firm 1

### Calculations to prepare duopoly model¶

Now we’ll proceed to cast our duopoly model within the framework of the more general linear-quadratic structure described above

That will allow us to compute a Stackelberg plan simply by enlisting a Riccati equation to solve a linear-quadratic dynamic program

As emphasized above, firm 1 acts as if firm 2’s decisions $ \{q_{2t+1}, v_{2t}\}_{t=0}^\infty $ are given and beyond its control

### Firm 1’s problem¶

We again formulate firm 1’s optimum problem in terms of the Lagrangian

$$ L=\sum_{t=0}^{\infty}\beta^{t}\{a_{0}q_{1t}-a_{1}q_{1t}^{2}-a_{1}q_{1t}q_{2t}-\gamma v_{1t}^{2}+\lambda_{t}[q_{1t}+v_{1t}-q_{1t+1}]\} $$Firm 1 seeks a maximum with respect to $ \{q_{1t+1}, v_{1t} \}_{t=0}^\infty $ and a minimum with respect to $ \{ \lambda_t\}_{t=0}^\infty $

First-order conditions for this problem are

$$ \eqalign{ \frac{\partial L}{\partial q_{1t}} & = a_0 - 2 a_1 q_{1t} - a_1 q_{2t} + \lambda_t - \beta^{-1} \lambda_{t-1} = 0 , \quad t \geq 1 \cr \frac{\partial L}{\partial v_{1t}} & = -2 \gamma v_{1t} + \lambda_t = 0 , \quad t \geq 0 \cr } $$These first-order order conditions and the constraint $ q_{1t+1} = q_{1t} + v_{1t} $ can be rearranged to take the form

$$ \eqalign{ v_{1t} & = \beta v_{1t+1} + \frac{\beta a_0}{2 \gamma} - \frac{\beta a_1}{\gamma} q_{1t+1} - \frac{\beta a_1}{2 \gamma} q_{2t+1} \cr q_{t+1} & = q_{1t} + v_{1t} } $$We use these two equations as components of the following linear system that confronts a Stackelberg continuation leader at time $ t $

$$ \begin{bmatrix} 1 & 0 & 0 & 0 \cr 0 & 1 & 0 & 0 \cr 0 & 0 & 1 & 0 \cr \frac{\beta a_0}{2 \gamma} & - \frac{\beta a_1}{2 \gamma} & -\frac{\beta a_1}{\gamma} & \beta \end{bmatrix} \begin{bmatrix} 1 \cr q_{2t+1} \cr q_{1t+1} \cr v_{1t+1} \end{bmatrix} = \begin{bmatrix} 1 & 0 & 0 & 0 \cr 0 & 1 & 0 & 0 \cr 0 & 0 & 1 & 1 \cr 0 & 0 & 0 & 1 \end{bmatrix} \begin{bmatrix} 1 \cr q_{2t} \cr q_{1t} \cr v_{1t} \end{bmatrix} + \begin{bmatrix} 0 \cr 1 \cr 0 \cr 0 \end{bmatrix} v_{2t} $$Time $ t $ revenues of firm 2 are $ \pi_{2t} = a_0 q_{2t} - a_1 q_{2t}^2 - a_1 q_{1t} q_{2t} $ which evidently equal

$$ z_t' R_1 z_t \equiv \begin{bmatrix} 1 \cr q_{2t} \cr q_{1t} \end{bmatrix}' \begin{bmatrix} 0 & \frac{a_0}{2}& 0 \cr \frac{a_0}{2} & -a_1 & -\frac{a_1}{2}\cr 0 & -\frac{a_1}{2} & 0 \end{bmatrix} \begin{bmatrix} 1 \cr q_{2t} \cr q_{1t} \end{bmatrix} $$If we set $ Q = \gamma $, then firm 2’s period $ t $ profits can then be written

$$ y_t' R y_t - Q v_{2t}^2 $$where

$$ y_t = \begin{bmatrix} z_t \cr x_t \end{bmatrix} $$with $ x_t = v_{1t} $ and

$$ R = \begin{bmatrix} R_1 & 0 \cr 0 & 0 \end{bmatrix} $$We’ll report results of implementing this code soon

But first we want to represent the Stackelberg leader’s optimal choices recursively

It is important to do this for several reasons:

- properly to interpret a representation of the Stackelberg leaders’s choice as a sequence of history-dependent functions
- to formulate a recursive version of the follower’s choice problem

First let’s get a recursive representation of the Stackelberg leader’s choice of $ \vec q_2 $ for our duopoly model

## Recursive representation of Stackelberg plan¶

In order to attain an appropriate representation of the Stackelberg
leader’s history-dependent plan, we will employ what amounts to a
version of the **Big K, little k** device often used in
macroeconomics by distinguishing $ z_t $, which depends partly on
decisions $ x_t $ of the followers, from another vector
$ \check z_t $, which does not

We will use $ \check z_t $ and its history $ \check z^t = [\check z_t, \check z_{t-1}, \ldots, \check z_0] $ to describe the sequence of the Stackelberg leader’s decisions that the Stackelberg follower takes as given

Thus, we let $ \check y_t' = \begin{bmatrix}\check z_t' & \check x_t'\end{bmatrix} $ with initial condition $ \check z_0 = z_0 $ given

That we distinguish $ \check z_t $ from $ z_t $ is part and
parcel of the **Big K, little k** device in this
instance

We have demonstrated that a Stackelberg plan for $ \{u_t\}_{t=0}^\infty $ has a recursive representation

$$ \eqalign{ \check x_0 & = - P_{22}^{-1} P_{21} z_0 \cr u_t & = - F \check y_t, \quad t \geq 0 \cr \check y_{t+1} & = (A - BF) \check y_t, \quad t \geq 0 } $$From this representation we can deduce the sequence of functions $ \sigma = \{\sigma_t(\check z^t)\}_{t=0}^\infty $ that comprise a Stackelberg plan

For convenience, let $ \check A \equiv A - BF $ and partition $ \check A $ conformably to the partition $ y_t = \begin{bmatrix}\check z_t \cr \check x_t \end{bmatrix} $ as

$$ \begin{bmatrix}\check A_{11} & \check A_{12} \cr \check A_{21} & \check A_{22} \end{bmatrix} $$Let $ H^0_0 \equiv - P_{22}^{-1} P_{21} $ so that $ \check x_0 = H^0_0 \check z_0 $

Then iterations on $ \check y_{t+1} = \check A \check y_t $ starting from initial condition $ \check y_0 = \begin{bmatrix}\check z_0 \cr H^0_0 \check z_0\end{bmatrix} $ imply that for $ t \geq 1 $

$$ x_t = \sum_{j=1}^t H_j^t \check z_{t-j} $$where

$$ \eqalign{ H^t_1 & = \check A_{21} \cr H^t_2 & = \check A_{22} \check A_{21} \cr \ \ \vdots \ \ & \ \ \quad \vdots \cr H^t_{t-1} & = \check A_{22}^{t-2} \check A_{21} \cr H^t_t & = \check A_{22}^{t-1}(\check A_{21} + \check A_{22} H^0_0 ) } $$An optimal decision rule for the Stackelberg’s choice of $ u_t $ is

$$ u_t = - F \check y_t \equiv - \begin{bmatrix} F_z & F_x \cr \end{bmatrix} \begin{bmatrix}\check z_t \cr x_t \cr \end{bmatrix} $$or

$$ u_t = - F_z \check z_t - F_x \sum_{j=1}^t H^t_j z_{t-j} = \sigma_t(\check z^t) \tag{10} $$

Representation (10) confirms that whenever
$ F_x \neq 0 $, the typical situation, the time $ t $ component
$ \sigma_t $ of a Stackelberg plan is **history dependent**, meaning
that the Stackelberg leader’s choice $ u_t $ depends not just on
$ \check z_t $ but on components of $ \check z^{t-1} $

### Comments and interpretations¶

After all, at the end of the day, it will turn out that because we set $ \check z_0 = z_0 $, it will be true that $ z_t = \check z_t $ for all $ t \geq 0 $

Then why did we distinguish $ \check z_t $ from $ z_t $?

The answer is that if we want to present to the Stackelberg **follower**
a history-dependent representation of the Stackelberg **leader’s**
sequence $ \vec q_2 $, we must use representation
(10) cast in terms of the history
$ \check z^t $ and **not** a corresponding representation cast in
terms of $ z^t $

### Dynamic programming and time consistency of **follower’s** problem¶

Given the sequence $ \vec q_2 $ chosen by the Stackelberg leader in
our duopoly model, it turns out that the Stackelberg **follower’s**
problem is recursive in the *natural* state variables that confront a
follower at any time $ t \geq 0 $

This means that the follower’s plan is time consistent

To verify these claims, we’ll formulate a recursive version of a
follower’s problem that builds on our recursive representation of the
Stackelberg leader’s plan and our use of the **Big K, little k** idea

### Recursive formulation of a follower’s problem¶

We now use what amounts to another “Big $ K $, little $ k $” trick (see rational expectations equilibrium) to formulate a recursive version of a follower’s problem cast in terms of an ordinary Bellman equation

Firm 1, the follower, faces $ \{q_{2t}\}_{t=0}^\infty $ as a given quantity sequence chosen by the leader and believes that its output price at $ t $ satisfies

$$ p_t = a_0 - a_1 ( q_{1t} + q_{2t}) , \quad t \geq 0 $$Our challenge is to represent $ \{q_{2t}\}_{t=0}^\infty $ as a given sequence

To do so, recall that under the Stackelberg plan, firm 2 sets output according to the $ q_{2t} $ component of

$$ y_{t+1} = \begin{bmatrix} 1 \cr q_{2t} \cr q_{1t} \cr x_t \end{bmatrix} $$which is governed by

$$ y_{t+1} = (A - BF) y_t $$To obtain a recursive representation of a $ \{q_{2t}\} $ sequence that is exogenous to firm 1, we define a state $ \tilde y_t $

$$ \tilde y_t = \begin{bmatrix} 1 \cr q_{2t} \cr \tilde q_{1t} \cr \tilde x_t \end{bmatrix} $$that evolves according to

$$ \tilde y_{t+1} = (A - BF) \tilde y_t $$subject to the initial condition $ \tilde q_{10} = q_{10} $ and $ \tilde x_0 = x_0 $ where $ x_0 = - P_{22}^{-1} P_{21} $ as stated above

Firm 1’s state vector is

$$ X_t = \begin{bmatrix} \tilde y_t \cr q_{1t} \end{bmatrix} $$It follows that the follower firm 1 faces law of motion

$$ \begin{bmatrix} \tilde y_{t+1} \\ q_{1t+1} \end{bmatrix} = \begin{bmatrix} A - BF & 0 \\ 0 & 1 \end{bmatrix} \begin{bmatrix} \tilde y_{t} \\ q_{1t} \end{bmatrix} + \begin{bmatrix} 0 \cr 1 \end{bmatrix} x_t \tag{11} $$

This specfification assures that from the point of the view of a firm 1, $ q_{2t} $ is an exogenous process

Here

- $ \tilde q_{1t}, \tilde x_t $ play the role of
**Big K** - $ q_{1t}, x_t $ play the role of
**little k**

The time $ t $ component of firm 1’s objective is

$$ \tilde X_t' \tilde R x_t - x_t^2 \tilde Q = \begin{bmatrix} 1 \cr q_{2t} \cr \tilde q_{1t} \cr \tilde x_t \cr q_{1t} \end{bmatrix}' \begin{bmatrix} 0 & 0 & 0 & 0 & \frac{a_0}{2} \cr 0 & 0 & 0 & 0 & - \frac{a_1}{2} \cr 0 & 0 & 0 & 0 & 0 \cr 0 & 0 & 0 & 0 & 0 \cr \frac{a_0}{2} & -\frac{a_1}{2} & 0 & 0 & - a_1 \end{bmatrix} \begin{bmatrix} 1 \cr q_{2t} \cr \tilde q_{1t} \cr \tilde x_t \cr q_{1t} \end{bmatrix} - \gamma x_t^2 $$Firm 1’s optimal decision rule is

$$ x_t = - \tilde F X_t $$and it’s state evolves according to

$$ \tilde X_{t+1} = (\tilde A - \tilde B \tilde F) X_t $$under its optimal decision rule

Later we shall compute $ \tilde F $ and verify that when we set

$$ X_0 = \begin{bmatrix} 1 \cr q_{20} \cr q_{10} \cr x_0 \cr q_{10} \end{bmatrix} $$we recover

$$ x_0 = - \tilde F \tilde X_0 $$which will verify that we have properly set up a recursive representation of the follower’s problem facing the Stackelberg leader’s $ \vec q_2 $

### Time consistency of follower’s plan¶

Since the follower can solve its problem using dynamic programming its
problem is recursive in what for it are the **natural state variables**,
namely

It follows that the follower’s plan is time consistent

## Computing the Stackelberg Plan¶

Here is our code to compute a Stackelberg plan via a linear-quadratic dynamic program as outlined above

```
import numpy as np
import numpy.linalg as la
import quantecon as qe
from quantecon import LQ
import matplotlib.pyplot as plt
%matplotlib inline
```

```
# == Parameters == #
a0 = 10
a1 = 2
β = 0.96
γ = 120
n = 300
tol0 = 1e-8
tol1 = 1e-16
tol2 = 1e-2
βs = np.ones(n)
βs[1:] = β
βs = βs.cumprod()
```

```
# == In LQ form == #
Alhs = np.eye(4)
# Euler equation coefficients
Alhs[3, :] = β * a0 / (2 * γ), -β * a1 / (2 * γ), -β * a1 / γ, β
Arhs = np.eye(4)
Arhs[2, 3] = 1
Alhsinv = la.inv(Alhs)
A = Alhsinv @ Arhs
B = Alhsinv @ np.array([[0, 1, 0, 0]]).T
R = np.array([[0, -a0 / 2, 0, 0],
[-a0 / 2, a1, a1 / 2, 0],
[0, a1 / 2, 0, 0],
[0, 0, 0, 0]])
Q = np.array([[γ]])
# == Solve using QE's LQ class == #
# LQ solves minimization problems which is why the sign of R and Q was changed
lq = LQ(Q, R, A, B, beta=β)
P, F, d = lq.stationary_values(method='doubling')
P22 = P[3:, 3:]
P21 = P[3:, :3]
P22inv = la.inv(P22)
H_0_0 = -P22inv @ P21
# == Simulate forward == #
π_leader = np.zeros(n)
z0 = np.array([[1, 1, 1]]).T
x0 = H_0_0 @ z0
y0 = np.vstack((z0, x0))
yt, ut = lq.compute_sequence(y0, ts_length=n)[:2]
π_matrix = (R + F. T @ Q @ F)
for t in range(n):
π_leader[t] = -(yt[:, t].T @ π_matrix @ yt[:, t])
# == Display policies == #
print("Computed policy for Stackelberg leader\n")
print(f"F = {F}")
```

### Implied time series for price and quantities¶

The following code plots the price and quantities

```
q_leader = yt[1, :-1]
q_follower = yt[2, :-1]
q = q_leader + q_follower # Total output, Stackelberg
p = a0 - a1 * q # Price, Stackelberg
fig, ax = plt.subplots(figsize=(9, 5.8))
ax.plot(range(n), q_leader, 'b-', lw=2, label='leader output')
ax.plot(range(n), q_follower, 'r-', lw=2, label='follower output')
ax.plot(range(n), p, 'g-', lw=2, label='price')
ax.set_title('Output and prices, Stackelberg duopoly')
ax.legend(frameon=False)
plt.xlabel('t')
plt.show()
```

### Value of Stackelberg leader¶

We’ll compute the present value earned by the Stackelberg leader

We’ll compute it two ways (they give identical answers – just a check on coding and thinking)

```
v_leader_forward = np.sum(βs * π_leader)
v_leader_direct = -yt[:, 0].T @ P @ yt[:, 0]
# == Display values == #
print("Computed values for the Stackelberg leader at t=0:\n")
print(f"v_leader_forward(forward sim) = {v_leader_forward:.4f}")
print(f"v_leader_direct (direct) = {v_leader_direct:.4f}")
```

```
# Manually checks whether P is approximately a fixed point
P_next = (R + F.T @ Q @ F + β * (A - B @ F).T @ P @ (A - B @ F))
(P - P_next < tol0).all()
```

```
# Manually checks whether two different ways of computing the
# value function give approximately the same answer
v_expanded = -((y0.T @ R @ y0 + ut[:, 0].T @ Q @ ut[:, 0] +
β * (y0.T @ (A - B @ F).T @ P @ (A - B @ F) @ y0)))
(v_leader_direct - v_expanded < tol0)[0, 0]
```

## Exhibiting time inconsistency of Stackelberg plan¶

In the code below we compare two values

- the continuation value $ - y_t P y_t $ earned by a continuation Stackelberg leader who inherits state $ y_t $ at $ t $
- the value of a
**reborn Stackelberg leader**who inherits state $ z_t $ at $ t $ and sets $ x_t = - P_{22}^{-1} P_{21} $

The difference between these two values is a tell-tale time of the time inconsistency of the Stackelberg plan

```
# Compute value function over time with reset at time t
vt_leader = np.zeros(n)
vt_reset_leader = np.empty_like(vt_leader)
yt_reset = yt.copy()
yt_reset[-1, :] = (H_0_0 @ yt[:3, :])
for t in range(n):
vt_leader[t] = -yt[:, t].T @ P @ yt[:, t]
vt_reset_leader[t] = -yt_reset[:, t].T @ P @ yt_reset[:, t]
```

```
fig, axes = plt.subplots(3, 1, figsize=(10, 7))
axes[0].plot(range(n+1), (- F @ yt).flatten(), 'bo', label='Stackelberg leader', ms=2)
axes[0].plot(range(n+1), (- F @ yt_reset).flatten(), 'ro', label='continuation leader at t', ms=2)
axes[0].set(title=r'Leader control variable $u_{t}$', xlabel='t')
axes[0].legend()
axes[1].plot(range(n+1), yt[3, :], 'bo', ms=2)
axes[1].plot(range(n+1), yt_reset[3, :], 'ro', ms=2)
axes[1].set(title=r'Follower control variable $x_{t}$', xlabel='t')
axes[2].plot(range(n), vt_leader, 'bo', ms=2)
axes[2].plot(range(n), vt_reset_leader, 'ro', ms=2)
axes[2].set(title=r'Leader value function $v(y_{t})$', xlabel='t')
plt.tight_layout()
plt.show()
```

## Recursive formulation of the follower’s problem¶

We now formulate and compute the recursive version of the follower’s problem

We check that the recursive **Big** $ K $ **, little** $ k $ formulation of the follower’s problem produces the same output path
$ \vec q_1 $ that we computed when we solved the Stackelberg problem

```
A_tilde = np.eye(5)
A_tilde[:4, :4] = A - B @ F
R_tilde = np.array([[0, 0, 0, 0, -a0 / 2],
[0, 0, 0, 0, a1 / 2],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[-a0 / 2, a1 / 2, 0, 0, a1]])
Q_tilde = Q
B_tilde = np.array([[0, 0, 0, 0, 1]]).T
lq_tilde = LQ(Q_tilde, R_tilde, A_tilde, B_tilde, beta=β)
P_tilde, F_tilde, d_tilde = lq_tilde.stationary_values(method='doubling')
y0_tilde = np.vstack((y0, y0[2]))
yt_tilde = lq_tilde.compute_sequence(y0_tilde, ts_length=n)[0]
```

```
# Checks that the recursive formulation of the follower's problem gives
# the same solution as the original Stackelberg problem
plt.plot(yt_tilde[4], 'r', label="q_tilde")
plt.plot(yt_tilde[2], 'b', label="q")
plt.legend()
plt.show()
```

Note: Variables with `_tilde`

are obtained from solving the follower’s
problem – those without are from the Stackelberg problem

```
# Maximum absolute difference in quantities over time between the first and second solution methods
np.max(np.abs(yt_tilde[4] - yt_tilde[2]))
```

```
# x0 == x0_tilde
yt[:, 0][-1] - (yt_tilde[:, 1] - yt_tilde[:, 0])[-1] < tol0
```

### Explanation of alignment¶

If we inspect the coefficients in the decision rule $ - \tilde F $, we can spot the reason that the follower chooses to set $ x_t = \tilde x_t $ when it sets $ x_t = - \tilde F X_t $ in the recursive formulation of the follower problem

Can you spot what features of $ \tilde F $ imply this?

Hint: remember the components of $ X_t $

```
# Policy function in the follower's problem
F_tilde.round(4)
```

```
# Value function in the Stackelberg problem
P
```

```
# Value function in the follower's problem
P_tilde
```

```
# Manually check that P is an approximate fixed point
(P - ((R + F.T @ Q @ F) + β * (A - B @ F).T @ P @ (A - B @ F)) < tol0).all()
```

```
# Compute `P_guess` using `F_tilde_star`
F_tilde_star = -np.array([[0, 0, 0, 1, 0]])
P_guess = np.zeros((5, 5))
for i in range(1000):
P_guess = ((R_tilde + F_tilde_star.T @ Q @ F_tilde_star) +
β * (A_tilde - B_tilde @ F_tilde_star).T @ P_guess
@ (A_tilde - B_tilde @ F_tilde_star))
```

```
# Value function in the follower's problem
-(y0_tilde.T @ P_tilde @ y0_tilde)[0, 0]
```

```
# Value function with `P_guess`
-(y0_tilde.T @ P_guess @ y0_tilde)[0, 0]
```

```
# Compute policy using policy iteration algorithm
F_iter = (β * la.inv(Q + β * B_tilde.T @ P_guess @ B_tilde)
@ B_tilde.T @ P_guess @ A_tilde)
for i in range(100):
# Compute P_iter
P_iter = np.zeros((5, 5))
for j in range(1000):
P_iter = ((R_tilde + F_iter.T @ Q @ F_iter) + β *
(A_tilde - B_tilde @ F_iter).T @ P_iter @
(A_tilde - B_tilde @ F_iter))
# Update F_iter
F_iter = (β * la.inv(Q + β * B_tilde.T @ P_iter @ B_tilde)
@ B_tilde.T @ P_iter @ A_tilde)
dist_vec = (P_iter - ((R_tilde + F_iter.T @ Q @ F_iter) +
β * (A_tilde - B_tilde @ F_iter).T @ P_iter @
(A_tilde - B_tilde @ F_iter)))
if np.max(np.abs(dist_vec)) < 1e-8:
dist_vec2 = (F_iter - (β * la.inv(Q + β * B_tilde.T @ P_iter @ B_tilde)
@ B_tilde.T @ P_iter @ A_tilde))
if np.max(np.abs(dist_vec)) < 1e-8:
F_iter
else:
print("The policy didn't converge: try increasing the number of outer loop iterations")
else:
print("`P_iter` didn't converge: try increasing the number of inner loop iterations")
```

```
# Simulate the system using `F_tilde_star` and check that it gives the same result as the original solution
yt_tilde_star = np.zeros((n, 5))
yt_tilde_star[0, :] = y0_tilde.flatten()
for t in range(n-1):
yt_tilde_star[t+1, :] = (A_tilde - B_tilde @ F_tilde_star) @ yt_tilde_star[t, :]
plt.plot(yt_tilde_star[:, 4], 'r', label="q_tilde")
plt.plot(yt_tilde[2], 'b', label="q")
plt.legend()
plt.show()
```

```
# Maximum absolute difference
np.max(np.abs(yt_tilde_star[:, 4] - yt_tilde[2, :-1]))
```

## Markov perfect equilibrium¶

The **state** vector is

and the state transition dynamics are

$$ z_{t+1} = A z_t + B_1 v_{1t} + B_2 v_{2t} $$where $ A $ is a $ 3 \times 3 $ identity matrix and

$$ B_1 = \begin{bmatrix} 0 \cr 0 \cr 1 \end{bmatrix} , \quad B_2 = \begin{bmatrix} 0 \cr 1 \cr 0 \end{bmatrix} $$The Markov perfect decision rules are

$$ v_{1t} = - F_1 z_t , \quad v_{2t} = - F_2 z_t $$and in the Markov perfect equilibrium the state evolves according to

$$ z_{t+1} = (A - B_1 F_1 - B_2 F_2) z_t $$```
# == In LQ form == #
A = np.eye(3)
B1 = np.array([[0], [0], [1]])
B2 = np.array([[0], [1], [0]])
R1 = np.array([[0, 0, -a0 / 2],
[0, 0, a1 / 2],
[-a0 / 2, a1 / 2, a1]])
R2 = np.array([[0, -a0 / 2, 0],
[-a0 / 2, a1, a1 / 2],
[0, a1 / 2, 0]])
Q1 = Q2 = γ
S1 = S2 = W1 = W2 = M1 = M2 = 0.0
# == Solve using QE's nnash function == #
F1, F2, P1, P2 = qe.nnash(A, B1, B2, R1, R2, Q1,
Q2, S1, S2, W1, W2, M1,
M2, beta=β, tol=tol1)
# == Simulate forward == #
AF = A - B1 @ F1 - B2 @ F2
z = np.empty((3, n))
z[:, 0] = 1, 1, 1
for t in range(n-1):
z[:, t+1] = AF @ z[:, t]
# == Display policies == #
print("Computed policies for firm 1 and firm 2:\n")
print(f"F1 = {F1}")
print(f"F2 = {F2}")
```

```
q1 = z[1, :]
q2 = z[2, :]
q = q1 + q2 # Total output, MPE
p = a0 - a1 * q # Price, MPE
fig, ax = plt.subplots(figsize=(9, 5.8))
ax.plot(range(n), q, 'b-', lw=2, label='total output')
ax.plot(range(n), p, 'g-', lw=2, label='price')
ax.set_title('Output and prices, duopoly MPE')
ax.legend(frameon=False)
plt.xlabel('t')
plt.show()
```

```
# Computes the maximum difference between the two quantities of the two firms
np.max(np.abs(q1 - q2))
```

```
# Compute values
u1 = (- F1 @ z).flatten()
u2 = (- F2 @ z).flatten()
π_1 = p * q1 - γ * (u1) ** 2
π_2 = p * q2 - γ * (u2) ** 2
v1_forward = np.sum(βs * π_1)
v2_forward = np.sum(βs * π_2)
v1_direct = (- z[:, 0].T @ P1 @ z[:, 0])
v2_direct = (- z[:, 0].T @ P2 @ z[:, 0])
# == Display values == #
print("Computed values for firm 1 and firm 2:\n")
print(f"v1(forward sim) = {v1_forward:.4f}; v1 (direct) = {v1_direct:.4f}")
print(f"v2 (forward sim) = {v2_forward:.4f}; v2 (direct) = {v2_direct:.4f}")
```

```
# Sanity check
Λ1 = A - B2 @ F2
lq1 = qe.LQ(Q1, R1, Λ1, B1, beta=β)
P1_ih, F1_ih, d = lq1.stationary_values()
v2_direct_alt = - z[:, 0].T @ lq1.P @ z[:, 0] + lq1.d
(np.abs(v2_direct - v2_direct_alt) < tol2).all()
```

## MPE vs. Stackelberg¶

```
vt_MPE = np.zeros(n)
vt_follower = np.zeros(n)
for t in range(n):
vt_MPE[t] = -z[:, t].T @ P1 @ z[:, t]
vt_follower[t] = -yt_tilde[:, t].T @ P_tilde @ yt_tilde[:, t]
plt.plot(vt_MPE, 'b', label='MPE')
plt.plot(vt_leader, 'r', label='Stackelberg leader')
plt.plot(vt_follower, 'g', label='Stackelberg follower')
plt.title(r'MPE vs. Stackelberg Value Function')
plt.xlabel('t')
plt.legend(loc=(1.05, 0))
plt.show()
```

```
# == Display values == #
print("Computed values:\n")
print(f"vt_leader(y0) = {vt_leader[0]:.4f}")
print(f"vt_follower(y0) = {vt_follower[0]:.4f}")
print(f"vt_MPE(y0) = {vt_MPE[0]:.4f}")
```

```
# Compute the difference in total value between the Stackelberg and the MPE
vt_leader[0] + vt_follower[0] - 2 * vt_MPE[0]
```