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Chapter 7 Applications to marketing

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1 Chapter 7 Applications to marketing
State Equation: Sale expressed in terms of advertising (which is a control variable) Objective: Profit maximization Constraints: Advertising levels to be nonnegative

2 The Nerlove-Arrow Advertising Model:
Let G(t)  0 denote the stock of goodwill at time t . where is the advertising effort at time t measured in dollars per unit time. Sale S is given by Assuming the rate of total production costs is c(S), we can write the total revenue net of production costs as the revenue net of advertising expenditure is therefore .

3 The firm wants to maximize the present value of net
revenue streams discounted at a fixed rate  , i.e., subject to (7.1). Since the only place that p occurs is in the integrand, we can maximize J by first maximizing R with to price p holding G fixed, and then maximize the result with respect to u. Thus, which implicitly gives the optimal price

4 Defining as the elasticity of demand
with respect to price, we can rewrite condition (7.5) as which is the usual price formula for a monopolist, known sometimes as the Amoroso-Robinson relation. In words, the formula means that the marginal revenue must equal the marginal cost See, e.g., Cohen and Cyert (1965, p.189). Defining , the objective function in (7.4) can be rewritten as

5 For convenience, we assume Z to be a given constant
and restate the optimal problem which we have just formulated:

6 Solution by the Maximum Principle
The adjoint variable (t) is the shadow price associated with the goodwill at time t . Thus, the Hamiltonian in (7.8) can be interpreted as the dynamic profit rate which consist of two terms: (i) the current net profit rate and (ii) the value of the new goodwill created by advertising at rate u.

7 Equation (7.9) corresponding to the usual
equilibrium relation for investment in capital goods: see Arrow and Kurz (1970) and Jacquemin (1973). It states that the marginal opportunity cost of investment in goodwill should equal the sum of the marginal profit from increased goodwill and the capital gain:

8 Defining as the elasticity of demand
with respect to goodwill and using (7.3), (7.5), and (7.9), we can derive ( see exercise 7.3) We use (3.74) to obtain the optimal long-run stationary equilibrium or turnpike That is, we obtain from (7.8) by using We then set and in (7.9). Finally, from (7.11) and (7.9), or also the singular level can be obtained as

9 The property of is that the optimal policy is to go to
as fast as possible. If , it is optimal to jump instantaneously to by applying an appropriate impulse at t =0 and then set for t >0. If , the optimal control u*(t)=0 until the stock of goodwill depreciates to the level , at which time the control switches to and stays at this level to maintain the level of goodwill. See Figure 7.1.

10 Figure 7.1: Optimal Policies in the Nerlove-Arrow Model

11 For a time-dependent Z, however, will be a
function of time. To maintain this level of , the required control is If is decreasing sufficiently fast, then may become negative and thus infeasible.If for all t, then the optimal policy is as before. However, suppose is infeasible in the interval [t1,t2] shown in Figure 7.2. In such a case, it is feasible to set for t < t1 ; at t = t1 ( which is point A in figure 7.2) we can no longer stay on the turnpike and must set u(t)=0 until we hit the turnpike again (at point B in figure 7.2). However, such a policy is not necessarily optimal.

12 Figure 7.2: A Case of a Time-Dependent Turnpike and the Nature of Optimal Control

13 For instance, suppose we leave the turnpike at point C
anticipating the infeasibility at point A. The new path CDEB may be better than the old path CAB. Roughly the reason this may happen is that path CDEB is “closer” to the turnpike than CAB. The picture in Figure 7.2 illustrates such a case. The optimal policy is the one that is “closer” to the turnpike. This discussion will become clearer in Section 7.2.2, when a similar situation arises in connection with the Vidale-Wolfe model. For further details, see Sethi (1977b) and Breakwell (1968).

14 A Nonlinear Extension Since , we can invert to solve (7.16) for u as a function of . Thus,

15 We note that which implies

16 Figure 7.3: Phase Diagram of System (7.18) for Problem (7.13)

17 Because of these conditions it is clear that for a given
G0 , a choice of 0 such that (0 ,G0 ) is in Regions II and III, will not lead to a path converging to the turnpike point On the other hand, the choice of (0 ,G0 ) in Region I when or (0 ,G0 ) in Region IV when , can give a path that converges to From a result in Coddington and Levinson(1955), it can be shown that at least in the neighborhood of , there exists a locus of optimum starting points Given , we choose 0 on the saddle point path in Region I of figure 7.3. Clearly, the initial control u*(0)=f1(0). Furthermore, (t) is increasing and by (7.17), u(t) is increasing, so that in this case the optimal policy is to advertise at a low rate initially and

18 and gradually increase advertising to the turnpike level
. If , it can be shown similarly that the optimal policy is to advertise most heavily in the beginning and gradually decrease it to the turnpike level as G approaches Note that the approach to the equilibrium is no longer via the bang-bang control as in the Nerlove- Arrow model. This, of course, is what one would expect when a model is made nonlinear with respect to the control variable u .

19 The Vidale-Wolfe Advertising Model
Now we can rewrite (7.19) as

20 The optimal control problem can be stated as

21 Solution Using Green’s Theorem when Q is Large
To make use of Green’s theorem, it is convenient to consider times and , where , and the problem: subject to To change the objective function in (7.24) into a line integral along any feasible arc from to in (t,x)-space as shown in figure 7.4, we multiply by dt and obtain the formal relation:

22 which we substitute into the objective function (7.24).
Thus, Consider another feasible arc from to lying above as shown in figure 7.4. Let ,where is a simple closed curve traversed in the counter- clockwise direction. That is, goes along in the direction of its arrow and along in the direction opposite its arrow. We now have

23 Figure 7.4: Feasible Arcs in (t,x) - Space

24 Since is a simple closed curve, we can use Green’s
theorem to express as an area integral over the region R enclosed by . Thus, treating x and t as independent variables, we can write Denote the term in brackets of the integrand of (7.28) by

25 Note that the sign of the integrand is the same as the
sign of I(x). Lemma 7.1(Comparison Lemma). Let and be the lower and upper feasible arcs as shown in figure 7.4. If I(x)  0 for all (x,t)R, then the lower arc is at least as profitable as the upper arc Analogously, if I(x) 0 for all (x,t)R, then is at least as profitable as . Proof. If I(x)  0 for all (x,t)R, then  0 from (7.28) and (7.29). Hence from (7.27), The proof of the other statement is similar.

26 To make use of this lemma to find the optimal control
for the problem stated in (7.23), we need to find regions where I(x) is positive and where it is negative. For this, note first that I(x) is an increasing function of x in [0,1]. Solving I(x)=0 will give that value of x, above which I(x) is positive and below which I(x) is negative. Since I(x) is quadratic in 1/(1-x), we can use the quadratic formula ( See Exercise 7.15) to get To keep x in the interval [0,1], we must choose the positive sign before the radical.

27 The optimal x must be nonnegative so we have
where the superscript s is used because this will turn out to be a singular trajectory.Since is nonnegative, the control Note that and if, and only if, Furthermore, the firm is better of with larger  and r , and smaller  and  . Thus  r/( +) represents a measure of favorable circumstances.

28 Figure 7.5: Optimal Trajectory for Case 1: x0<xs and xs  xT

29 Figure 7.6: Optimal Trajectory for Case 2: x0 xs and xs  xT

30 Figure 7.7: Optimal Trajectory for Case 3: x0 xs and xs  xT

31 Figure 7.8: Optimal Trajectory for Case 4: x0 xs and xs  xT

32 Figure 7.9: Optimal Trajectory (Solid Lines)

33 Theorem 7.1 Let T be large and let xT be reachable
From x0. For the Cases 1-4 of inequalities relating x0 and xT to xs , the optimal trajectories are given in figures , respectively. Proof. We give details for Case 1 only. The proofs for the other cases are similar. Figure 7.9 shows the optimal trajectory for figure 7.5 together with an arbitrarily chosen feasible trajectory, shown dotted. It should be clear that the dotted trajectory cannot cross the arc x0 to C ,since u=Q on that arc. Similarly the dotted trajectory cannot cross the arc G to xT,because u=0 on that arc.

34 We subdivide the interval [0,T ] into subintervals over
which the dotted arc is either above, below, or identical To the solid arc. In figure 7.9 these sub-intervals are [0,d],[d,e],[e,f], and [f,T]. Because I(x) is positive for and I(x) is negative for , the regions enclosed by the two trajectories have been marked with + or – sign depending on whether I(x) is positive or negative on the regions, respectively. By Lemma 7.1, the solid arc is better than the dotted arc in the subintervals [0,d],[d,e],and [f,T]; in interval [e,f], they have identical values. Hence the dotted trajectory is inferior to the solid trajectory. This proof can be extended to any (countable) number of crossing of the trajectories; see Sethi(1977b).

35 Theorem 7.2 Let T be small, i.e., T < t1+t2 , and let xT
be reachable from x0 . For the two possible Case1 and 2 of inequalities relating x0 to xT and xs , the optimal trajectories are given in figure 7.10 and 7.11, respectively. Proof. The requirement of feasibility when T is small rules out cases where x0 and xT are on opposite sides of or equal to xs . The proofs of optimality of the trajectories shown in figures 7.10 and 7.11 are similar to proofs of the parts of theorem 7.1, and are left as exercise In figures 7.10 and 7.11, it is possible to have either t1  T or t2  T. Try sketching some of these special cases.

36 Figure 7.10: Optimal Trajectory When T is Small in Case 1: x0< xs and xT > xs

37 Figure 7.11: Optimal Trajectory When T is Small in Case 2: x0> xs and xT < xs

38 Figure 7.12: Optimal Trajectory for Case 2 of Theorem 7.1 for Q = 

39 Solution When Q is Small
where (T) is a constant, as in Row 2 of Table 3.1, that must be determined. Furthermore, the Lagrange multiplier u in (7.34) must satisfy

40 where From (7.33) we notice that the Hamiltonian is linear in
the control. So the optimal control is where

41 Solution When T is Infinite
We now formulate the infinite horizon version of (7.23) When Q is small, i.e., Q< us , it is not possible to follow the turnpike x = xs , because that would require u = us , which is not a feasible control. Intuitively, it seems clear that the “closest” stationary path which we can follow is the path obtained by setting and u=Q, the largest possible control, in the state equation of (7.39) .

42 This gives by setting u = Q, and in (7.35) and solving for  . More specifically, we state the following theorems which give the turnpike and optimal control when Q is small. To prove these theorems we need to define two more quantities, namely,

43 Theorem 7.3 when Q is small, the following quantities
form a turnpike. Proof. We show that the conditions in (3.73) hold for (7.44). The first two are obvious. By exercise 7.28 we know , which, from definitions (7.42) and (7.43), implies Furthermore , so (7.36) holds and the third condition of (3.73) also holds. Finally because from (7.38) and (7.43), it follows that W  0 , so the Hamiltonian maximizing condition of (3.73) holds with , and the proof is complete.

44 Figure 7.13: Optimal Trajectory x0<

45 Figure 7.14: Optimal Trajectory x0<

46 Theorem 7.4 When Q is small, the optimal control at
time is given by: Proof. (a) we set for all and note that  satisfies the adjoint equation (7.35) and the transversality condition (3.70). By Exercise 7.28 and the assumption that , we know that for all t . the proof that (7.36) and (7.37) hold for all relies on the fact that and on an argument similar to the proof of the previous theorem.

47 Figure 7.13 shows the optimal trajectories for
and two different starting values x(0), one above and the other below Note that in this figure we are always in Case (a) since for all (b) Assume For this case we will show that the optimal trajectory is as shown in figure 7.14, which is obtained by applying u=0 until and thereafter. Using this policy we can find the time t1 at which , by solving the state equation in (7.39) with u = 0. This gives

48 Clearly for t  t1 , the policy u=Q is optimal because
Case (a) applies. We now consider the interval [0, t1). Let be any time in this interval as shown in figure 7.14, and let be the corresponding value of the sate variable. Consider the following two-point boundary value problem in the interval In Exercise 7.31 you are asked to show that the switching function W(t) defined in (7.38) is negative in the interval and W(t1)=0.

49 Therefore by (7.37), the policy u=0 used in deriving
(7.46) satisfies the maximum principle. This policy “joins” the optimal policy after t1 because In this book the sufficiency of the transversality condition (3.70) was stated under the hypothesis that the derived Hamiltonian was concave; see Theorem 2.1. In the present example, this hypothesis does not hold. However, as mentioned in Section 7.2.3, for this simple bilinear problem, it can be shown that (3.70) is sufficient for optimality. Because of the technical nature of this issue we omit the details.

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