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I am working through the book "Foundations of Stochastic Inventory Theory".

One of the results in the book is Theorem 11.2. The background to this theorem is as follows.

Given finite state space $\mathcal{S}=\{1,2,...,S\}$, policy (which provides a mapping of what action to take in each state $s$ in teach time period $t$:) $\mathcal{\pi}=\{\pi_1,\pi_2,...,\pi_t,...\}$, and a value space $\mathbb{R}^S$. Then, to obtain period $t$'s value function, $v_t(\pi)$ (which is an $S$ vector of reals), we can solve the following recursion:

$$v_t(\pi)=H(\pi_t)v_{t+1}(\pi) $$

In plain language, this means that given an $S$ vector of period $t+1$'s values of being in different states, the value of starting in different states in period $t$ can be obtained by applying operator $H(\pi_t)$ to the next period's values. It is known that $H(\pi_t)$ is an affine map and is also a contracting operator. That is, given a suitable metric, for all $t$, $d(H(\pi_t)u,H(\pi_t)v)<d(u,v)$. As a result, it is known that a unique fixed point exists. Operator $H(\delta)v=H_{\delta}v=r_\delta+Q_\delta v$. Here, $r_\delta$ is an $S$-vector of immediate payoffs in the corresponding states. $Q_\delta=\alpha P_\delta$, where $0\leq \alpha<1$ can be thought of as a discount factor, and $P_\delta=[p_\delta^{ij}]$ is the $S\times S$ matrix of transitioning from state $i$ in one period to state $j$ in the next period in following decision rule prescribed by $\delta$.

Now, the theorem states:

The infinite horizon values exist for every policy: $v_t(\pi)$ exists and is finite for every $t$.

However, it is not clear to me how this can be established. What the fixed point result seems to state is that for arbitrary $\epsilon>0$, and an arbitrary value function $v$, there exists a sufficiently large index $M$ such that if we have $v_M:=v\in \mathbb{R}^S$, we can recursively obtain $v_{M-1}=H(\pi_{M-1})v_M$, and then sequentially iteratively obtain previous time period's value function until we get $v_0$ and this $v_0$ is going to be $\epsilon$-close to the fixed point in metric $d()$.

This theorem, however, seems to want one to prove the result in the other direction, beginning from a particular $t$ and then going to $t+1, t+2,...$. Any pointers would be appreciated.

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  • $\begingroup$ There seems to be a lot missing. Is there a discount rate? How are payoffs determined? $\endgroup$ Commented Jan 31, 2021 at 19:16
  • $\begingroup$ @MichaelGreinecker I described operator $H$ in greater clarify to specify the discount rate and the payoffs. $\endgroup$
    – Tryer
    Commented Feb 1, 2021 at 1:19
  • $\begingroup$ @MichaelGreinecker Since you focus on game theory, I can add that Porteus specifies in his book that this theorem was stated/proven by Shapley in his 1953 paper on "Stochastic Games". He further states that he himself proved this in Porteus' 1982 paper "Conditions for characterizing the structure of optimal strategies in infinite horizon dynamic programs" that appearead in JOTA. I have both these paper, but this theorem does not seem to be stated / proven exactly in these works in my reading. $\endgroup$
    – Tryer
    Commented Feb 1, 2021 at 1:23
  • $\begingroup$ Are you sure $H$ is correctly written down? Usually, this should involve a maximization part. The fixed-point gives you then the optimal-value function and you can use it to find optimal policies.You can take a look at the Wiki entry for the Bellman equation. $\endgroup$ Commented Feb 1, 2021 at 4:39
  • $\begingroup$ @MichaelGreinecker There is a separate operator $A$, which is defined as $Av:=\sup_{\delta \in \Delta}H_\delta v$. This theorem, however, does not talk about an optimal policy or $A$. It talks about any policy even if it is suboptimal. You can see the precise statement of the theorem at imgur.com/XGL8OAv $\endgroup$
    – Tryer
    Commented Feb 1, 2021 at 6:06

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