Bellman equation

A Bellman equation, named after Richard E. Bellman, is a necessary condition for optimality associated with the mathematical optimization method known as dynamic programming. It writes the "value" of a decision problem at a certain point in time in terms of the payoff from some initial choices and the "value" of the remaining decision problem that results from those initial choices. This breaks a dynamic optimization problem into a sequence of simpler subproblems, as Bellman's “principle of optimality” prescribes.
The Bellman equation was first applied to engineering control theory and to other topics in applied mathematics, and subsequently became an important tool in economic theory; though the basic concepts of dynamic programming are prefigured in John von Neumann and Oskar Morgenstern's "Theory of Games and Economic Behavior" and Abraham Wald's "sequential analysis". The term 'Bellman equation' usually refers to the dynamic programming equation associated with discrete-time optimization problems. In continuous-time optimization problems, the analogous equation is a partial differential equation that is called the Hamilton–Jacobi–Bellman equation.
In discrete time any multi-stage optimization problem can be solved by analyzing the appropriate Bellman equation. The appropriate Bellman equation can be found by introducing new state variables (state augmentation). However, the resulting augmented-state multi-stage optimization problem has a higher dimensional state space than the original multi-stage optimization problem - an issue that can potentially render the augmented problem intractable due to the “curse of dimensionality”. Alternatively, it has been shown that if the cost function of the multi-stage optimization problem satisfies a "backward separable" structure then the appropriate Bellman equation can be found without state augmentation.
Analytical concepts in dynamic programming.
To understand the Bellman equation, several underlying concepts must be understood. First, any optimization problem has some objective: minimizing travel time, minimizing cost, maximizing profits, maximizing utility, etc. The mathematical function that describes this objective is called the "objective function".
Dynamic programming breaks a multi-period planning problem into simpler steps at different points in time. Therefore, it requires keeping track of how the decision situation is evolving over time. The information about the current situation that is needed to make a correct decision is called the "state". For example, to decide how much to consume and spend at each point in time, people would need to know (among other things) their initial wealth. Therefore, wealth formula_1 would be one of their "state variables", but there would probably be others.
The variables chosen at any given point in time are often called the "control variables". For instance, given their current wealth, people might decide how much to consume now. Choosing the control variables now may be equivalent to choosing the next state; more generally, the next state is affected by other factors in addition to the current control. For example, in the simplest case, today's wealth (the state) and consumption (the control) might exactly determine tomorrow's wealth (the new state), though typically other factors will affect tomorrow's wealth too.
The dynamic programming approach describes the optimal plan by finding a rule that tells what the controls should be, given any possible value of the state. For example, if consumption ("c") depends "only" on wealth ("W"), we would seek a rule formula_2 that gives consumption as a function of wealth. Such a rule, determining the controls as a function of the states, is called a "policy function" (See Bellman, 1957, Ch. III.2).
Finally, by definition, the optimal decision rule is the one that achieves the best possible value of the objective. For example, if someone chooses consumption, given wealth, in order to maximize happiness (assuming happiness "H" can be represented by a mathematical function, such as a utility function and is something defined by wealth), then each level of wealth will be associated with some highest possible level of happiness, formula_3. The best possible value of the objective, written as a function of the state, is called the "value function".
Bellman showed that a dynamic optimization problem in discrete time can be stated in a recursive, step-by-step form known as backward induction by writing down the relationship between the value function in one period and the value function in the next period. The relationship between these two value functions is called the "Bellman equation". In this approach, the optimal policy in the last time period is specified in advance as a function of the state variable's value at that time, and the resulting optimal value of the objective function is thus expressed in terms of that value of the state variable. Next, the next-to-last period's optimization involves maximizing the sum of that period's period-specific objective function and the optimal value of the future objective function, giving that period's optimal policy contingent upon the value of the state variable as of the next-to-last period decision. This logic continues recursively back in time, until the first period decision rule is derived, as a function of the initial state variable value, by optimizing the sum of the first-period-specific objective function and the value of the second period's value function, which gives the value for all the future periods. Thus, each period's decision is made by explicitly acknowledging that all future decisions will be optimally made.
Derivation.
A dynamic decision problem.
Let the state at time formula_4 be formula_5. For a decision that begins at time 0, we take as given the initial state formula_6. At any time, the set of possible actions depends on the current state; we can write this as formula_7, where the action formula_8 represents one or more control variables. We also assume that the state changes from formula_9 to a new state formula_10 when action formula_11 is taken, and that the current payoff from taking action formula_11 in state formula_9 is formula_14. Finally, we assume impatience, represented by a discount factor formula_15.
Under these assumptions, an infinite-horizon decision problem takes the following form:
subject to the constraints
Notice that we have defined notation formula_18 to denote the optimal value that can be obtained by maximizing this objective function subject to the assumed constraints. This function is the "value function". It is a function of the initial state variable formula_6, since the best value obtainable depends on the initial situation.
Bellman's principle of optimality.
The dynamic programming method breaks this decision problem into smaller subproblems. Bellman's "principle of optimality" describes how to do this:Principle of Optimality: An optimal policy has the property that whatever the initial state and initial decision are, the remaining decisions must constitute an optimal policy with regard to the state resulting from the first decision. (See Bellman, 1957, Chap. III.3.)
In computer science, a problem that can be broken apart like this is said to have optimal substructure. In the context of dynamic game theory, this principle is analogous to the concept of subgame perfect equilibrium, although what constitutes an optimal policy in this case is conditioned on the decision-maker's opponents choosing similarly optimal policies from their points of view.
As suggested by the "principle of optimality", we will consider the first decision separately, setting aside all future decisions (we will start afresh from time 1 with the new state formula_20). Collecting the future decisions in brackets on the right, the above infinite-horizon decision problem is equivalent to:
subject to the constraints
Here we are choosing formula_23, knowing that our choice will cause the time 1 state to be formula_24. That new state will then affect the decision problem from time 1 on. The whole future decision problem appears inside the square brackets on the right.
The Bellman equation.
So far it seems we have only made the problem uglier by separating today's decision from future decisions. But we can simplify by noticing that what is inside the square brackets on the right is "the value" of the time 1 decision problem, starting from state formula_24.
Therefore, we can rewrite the problem as a recursive definition of the value function:
This is the Bellman equation. It can be simplified even further if we drop time subscripts and plug in the value of the next state:
The Bellman equation is classified as a functional equation, because solving it means finding the unknown function formula_29, which is the "value function". Recall that the value function describes the best possible value of the objective, as a function of the state formula_9. By calculating the value function, we will also find the function formula_31 that describes the optimal action as a function of the state; this is called the "policy function".
In a stochastic problem.
In the deterministic setting, other techniques besides dynamic programming can be used to tackle the above optimal control problem. However, the Bellman Equation is often the most convenient method of solving "stochastic" optimal control problems.
For a specific example from economics, consider an infinitely-lived consumer with initial wealth endowment formula_32 at period formula_33. They have an instantaneous utility function formula_34 where formula_35 denotes consumption and discounts the next period utility at a rate of formula_36. Assume that what is not consumed in period formula_4 carries over to the next period with interest rate formula_38. Then the consumer's utility maximization problem is to choose a consumption plan formula_39
and
The first constraint is the capital accumulation/law of motion specified by the problem, while the second constraint is a transversality condition that the consumer does not carry debt at the end of their life. The Bellman equation is
Alternatively, one can treat the sequence problem directly using, for example, the Hamiltonian equations.
Now, if the interest rate varies from period to period, the consumer is faced with a stochastic optimization problem. Let the interest "r" follow a Markov process with probability transition function formula_42 where formula_43 denotes the probability measure governing the distribution of interest rate next period if current interest rate is formula_38. In this model the consumer decides their current period consumption after the current period interest rate is announced.
Rather than simply choosing a single sequence <math>\