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Econ-659-A2 · revision r1 · on Econ-659

GPT 6 Astra Ultra attempt on Econ-659

Full solution claimed · LLM-generated · gpt 6 astra ultra ·

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Contents
  1. Exact Nash computation in extensive-form games: resolved classification
  2. Scope and submitted result
  3. Polynomial counterfactual quantities without conditioning
  4. The fixed-point map and its local consequence
  5. Why a fixed point is a Nash equilibrium
  6. The circuit domain and the upper bound
  7. Hardness, a diagnostic example, and limits
  8. Completion Estimate
  9. References and attribution

Exact Nash computation in extensive-form games: resolved classification

Scope and submitted result

The historical classification is already known: exact Nash computation in explicitly represented finite perfect-recall extensive-form games is FIXP-complete. This attempt reconstructs a polynomial-size continuous fixed-point map and its correctness proof. Normal-form FIXP-hardness is used as a cited external theorem. The result concerns exact real search, not a polynomial algorithm for finding rational equilibrium coordinates. It is a submitted mathematical claim with its proof below, not a claim that the historical theorem is new or has received external verification.

Let $b_{I,a}$ be the behavioral probability of action $a$ at information set $I$. Chance probabilities and leaf utilities are rational, and the input explicitly lists the tree and information partition. Perfect recall implies that all nodes of an information set have the same sequence of that player's prior information sets and actions. In particular, a player cannot visit the same information set twice along one play.

Polynomial counterfactual quantities without conditioning

Fix player $i$, its information set $I$, and action $a$ there. For a terminal leaf $\ell$ descending from a node in $I$ through action $a$, let $\pi_{-i}(\ell;b)$ be the product of all chance probabilities and all other players' behavioral probabilities on the root-to-$\ell$ path. Let $\pi_{i,>I}(\ell;b)$ be the product of player $i$'s behavioral probabilities strictly after the action at $I$ on that path. Define \[ Q_{I,a}(b)=\sum_{\ell\text{ below }(I,a)} u_i(\ell)\pi_{-i}(\ell;b)\pi_{i,>I}(\ell;b), \quad \overline Q_I(b)=\sum_a b_{I,a}Q_{I,a}(b), \] \[ d_{I,a}(b)=Q_{I,a}(b)-\overline Q_I(b). \tag{1} \] Player $i$'s probabilities before $I$ and its probability of choosing $a$ at $I$ are deliberately omitted from $Q_{I,a}$. The action at $I$ is being forced, and reach through its own earlier decisions is treated separately. Each expression is polynomial in the behavioral coordinates. There is at most one relevant visit to $I$ per leaf by perfect recall. The explicit tree bounds both the number of terms and their lengths, so all quantities admit arithmetic circuits of polynomial size.

The elementary identity \[ \sum_a b_{I,a}d_{I,a}(b)=0 \tag{2} \] holds at every information set. If opponents and chance make $I$ unreachable under every strategy of $i$, every $Q_{I,a}$ is zero. No division by an information-set reach probability is necessary. This also makes (1) continuous as such reach probabilities approach zero.

The fixed-point map and its local consequence

On the product $P$ of the information-set probability simplexes, put \[ F_{I,a}(b)= \frac{b_{I,a}+\max\{0,d_{I,a}(b)\}} {1+\sum_c\max\{0,d_{I,c}(b)\}}. \tag{3} \] The denominator is at least one. For each $I$ the coordinates are nonnegative and sum to one, so $F$ maps $P$ into itself continuously.
Lemma: At every fixed point of $F$, $d_{I,a}(b)\leq0$ for all $I,a$.
Proof: Fix $I$ and abbreviate $S=\sum_a\max\{0,d_{I,a}\}$. If $S>0$, the fixed-point equation implies $b_{I,a}S=\max\{0,d_{I,a}\}$ for every $a$. Consequently every action with $b_{I,a}>0$ has $d_{I,a}=b_{I,a}S>0$. But then $\sum_a b_{I,a}d_{I,a}=S\sum_a b_{I,a}^2>0$, contradicting (2). Thus $S=0$, which is the desired conclusion. ∎

These inequalities express optimality for counterfactual decisions even when $i$'s current earlier strategy assigns zero probability to reaching $I$. Checking only the actual payoff effect of changing one information set would lose precisely this information.

Why a fixed point is a Nash equilibrium

Fix the opponents' behavior and any alternative full strategy $b'_i$. Order $i$'s information sets so that any predecessor in an own-action history appears before its successors. Such an order exists because the game tree is finite and perfect recall rules out cycles in own histories. Transform $b_i$ into $b'_i$ by changing the information sets one at a time in that order.

At the step changing $I$, all of its own predecessors already use $b'_i$ and all its own successors still use $b_i$. Let $r'_i(I)$ be the product of the new probabilities of the actions in the common own history before $I$. The payoff change at this step is exactly \[ \begin{aligned} \Delta_I &=r'_i(I)\sum_a (b'_{I,a}-b_{I,a})Q_{I,a}(b)\\ &=r'_i(I)\sum_a b'_{I,a}d_{I,a}(b). \end{aligned}\tag{4} \] To verify the first equality, expand the expected utility as a sum over terminal leaves. Leaves not descending through $I$ do not change. For the others, the factors before $I$ involving player $i$ are the common product $r'_i(I)$; the action factor changes from $b_{I,a}$ to $b'_{I,a}$; all later own factors and every opponent/chance factor are exactly those in $Q_{I,a}(b)$. Information sets incomparable with $I$ do not occur on a root-to-leaf path through $I$, so previous modifications there have no effect on this equality. The second equality uses that both action distributions sum to one.

At a fixed point every term on the second line of (4) is nonpositive. Summing the hybrid payoff changes therefore gives \[ U_i(b'_i,b_{-i})-U_i(b)\leq0. \] The alternative strategy and player were arbitrary. Hence every fixed point is a Nash equilibrium. This proof handles arbitrary simultaneous changes in one player's contingent plan, including changes that make previously unreached information sets reachable.

The circuit domain and the upper bound

If the definition of FIXP uses the unit cube rather than a product of simplexes, first retract cube coordinates $x$ to $P$. For a block of $r$ actions with sum $s$, define \[ R_a(x)=\frac{x_a+\max\{0,1-s\}/r}{\max\{1,s\}}. \tag{5} \] This is continuous, maps each block into its simplex, and is the identity on that simplex. The circuit $F\circ R$ maps the cube into itself. Every fixed point lies in $P$, because its output lies there; hence $R(x)=x$ and it is a fixed point of $F$. Brouwer supplies existence. Equations (1), (3), and (5) use rational constants and the permitted arithmetic and maximum gates, with nonzero denominators throughout. Their total size is polynomial in the explicit input. Returning their fixed point therefore solves exact Any-Nash, establishing FIXP membership.

Hardness, a diagnostic example, and limits

Use the established FIXP-hardness of exact three-player normal-form Nash. Represent a three-player normal-form payoff table by a three-level game tree: each player acts once, and later players are not told earlier actions. All nodes at a player's level belong to one information set. Perfect recall holds because each player has no earlier own action to forget. The leaves correspond exactly to entries of the input table; the tree has polynomial size in that table. Behavioral profiles and mixed normal-form profiles have identical payoff formulas and unilateral deviations. An exact extensive-form equilibrium thus returns an exact normal-form equilibrium. Together with the upper bound, this proves the claimed FIXP-completeness relative to the cited hardness theorem.

A small example checks why counterfactual rather than ordinary local gains are needed. One player first chooses Stop, obtaining $1$, or Enter, then chooses Bad for $0$ or Good for $2$. At Stop/Bad, changing only the root action hurts, and changing only the off-path later action changes nothing. Nevertheless Enter/Good is a profitable full deviation. In (1) the later Good action has positive counterfactual gain, so this profile is correctly excluded as a fixed point.

This construction does not prove that every Nash equilibrium is a fixed point; the search reduction needs only that every returned fixed point is an equilibrium. It does not yield the same result for imperfect recall, where the common-history factorization in (4) can fail, or for succinct trees whose leaves cannot be enumerated in polynomial input size.

Completion Estimate

100%

This is a post hoc, heuristic estimate for the full catalogue target. The attempt reconstructs a polynomial-size continuous fixed-point map and proves every fixed point is a behavioral Nash equilibrium through perfect-recall counterfactual deviations. Together with its polynomial reduction from the standard cited normal-form hardness theorem, this establishes the full exact-search classification under the canonical explicit-tree assumptions.

References and attribution

The already reported extensive-form classification is Theorem 5 of Vincent Cheval, Florian Horn, Soumyajit Paul, and Mahsa Shirmohammadi, On the Complexity of the Optimal Correlated Equilibria in Extensive-Form Games, arXiv:2507.11509v2 (2026). https://arxiv.org/pdf/2507.11509v2. The external normal-form hardness theorem is due to Kousha Etessami and Mihalis Yannakakis, On the Complexity of Nash Equilibria and Other Fixed Points, SIAM Journal on Computing (2010). https://epubs.siam.org/doi/10.1137/080720826. The unnormalized formulas and hybrid proof above provide the explicit verification made in this attempt; they are not a novelty claim.