The Reflexivity Threshold: A Phase Transition for Multi-Agent Performative Prediction

Introduction

Many learning systems, once deployed, change the environment that later generates their training data. A credit scoring model can change how applicants behave; an algorithmic trading strategy can move the price data on which future strategies are trained. This feedback effect is the focus of performative prediction. Existing theory largely studies the case of one learner facing the consequences of its own deployment. Many real systems, however, contain multiple learners whose deployments jointly shape the environment.

The multi-agent setting is not simply a larger version of the single-agent problem; it introduces different difficulties. Feedback can travel across agents: one agent’s deployment changes the data distribution, which changes another agent’s retraining problem, which may itself affect the data distribution or other agents. Stability therefore depends not only on the overall strength of performativity, but also on how feedback is routed through the agent network.

We ask whether there is a general threshold separating stable multi-agent performative learning from unavoidable learning failure. When does repeated retraining converge to a stable equilibrium?

We answer this question through a reflexivity matrix $\Gamma$ with entries $\Gamma_{ij} := a_{ij}/\mu_i$, measuring how strongly agent $j$’s deployment can move agent $i$’s best response, after normalizing by the curvature of agent $i$’s objective. Thus $\Gamma$ records who affects whom, and how strongly, in the performative feedback network.

Our main result is a phase transition at $\rho(\Gamma) = 1$, where $\rho(\Gamma)$ is the spectral radius of $\Gamma$. This quantity captures the largest asymptotic amplification factor of the feedback network. If $\rho(\Gamma) < 1$, cross-agent feedback dissipates: repeated retraining is a contraction in a Perron–Frobenius weighted norm, converges geometrically to a unique performatively stable equilibrium, and incurs bounded cumulative excess loss. Above the threshold, this positive guarantee cannot be extended. We construct supercritical systems in which every decentralized local-information learner suffers cumulative loss that grows linearly with time, regardless of the retraining rule. Under sensitivity and curvature assumptions, the spectral radius of the reflexivity matrix gives a sharp boundary for general stability guarantees: below one, all systems satisfying our assumptions contract, while above one, there exist supercritical systems for which decentralized learning incurs linear cumulative loss.

Contributions. First, we introduce the reflexivity matrix $\Gamma$, a nonnegative matrix whose entry $\Gamma_{ij}$ records how strongly agent $j$’s deployment can move agent $i$’s best response, as determined by per-agent gradient sensitivities and curvature constants. We prove an entrywise best-response sensitivity bound that reduces nonlinear retraining dynamics with general strongly convex losses to a spectral comparison through $\Gamma$, making Perron–Frobenius theory available. Second, we prove the subcritical side of the threshold: if $\rho(\Gamma) < 1$, repeated retraining is a contraction in a Perron–Frobenius weighted norm, yielding a unique performatively stable equilibrium, geometric convergence from any initialization, and cumulative dynamic excess loss bounded uniformly in the time horizon. Third, we prove the supercritical side: for a rank-one family with $\rho(\Gamma) = 1 + \varepsilon$, every decentralized local-information learner incurs cumulative excess loss $\Omega(\Delta^2 T)$ over horizons $T = O(1/\varepsilon)$. Together, these results identify $\rho(\Gamma) = 1$ as a boundary for general stability guarantees under primitive gradient-sensitivity and curvature assumptions, and recover the single-agent scalar threshold from prior work when $k = 1$.

Related Work

This paper draws on three lines of research. The first is the single-agent theory of performative prediction, which introduces the framework we extend. The second is the multi-agent theory of performative prediction and decision-dependent games, which is where our contribution is most directly positioned. The third is the combined tradition of spectral methods for network games and statistical lower bound techniques, from which we import two standard technical tools.

Performative prediction. Perdono et al. [2020] introduced performative prediction in a single-agent setting, formalizing performative stability and convergence of repeated retraining under sensitivity conditions. Subsequent work largely retained this single-agent viewpoint while extending the framework to stochastic, causal, nonconvex, and sequential settings [11, 12, 4, 8, 13, 10]. Closest to our threshold result, Anagnostides et al. [2026] established hardness and lower bounds at the single-agent scalar threshold. We depart from this line by studying decentralized multi-agent retraining, where performative feedback is cross-agent and the scalar threshold is replaced by the spectral condition $\rho(\Gamma) = 1$.

Multi-agent performativity. The multi-agent extension is the most direct predecessor of our work, and the literature divides into three strands.

The first establishes scalar contraction conditions for convergence. Narang et al. [2023] formalized $n$-player performative prediction with a scalar aggregate threshold $\rho< 1$, and analogous scalar conditions appear in cooperative consensus learning [9], decentralized games with coupled constraints [21], and a Wasserstein-free variant [22]. We depart from this strand on two axes. We replace the scalar aggregate with a matrix $\Gamma$ that preserves per-agent-pair feedback, and we pair the upper bound with a worst-case information-theoretic lower bound above the threshold.

The second develops spectral characterizations in special models. Wang et al. [2023] derived a necessary-and-sufficient spectral condition for a linear-quadratic mean-squared-error model on multiplex networks. Their spectral object is a weighted adjacency matrix with exogenous structure, and the condition is specific to the linear-quadratic model. Our $\Gamma$ is built from primitive per-agent quantities, applies to general strongly convex losses, and is paired with a lower bound.

The third identifies dynamical phase transitions. Piliouras and Yu [ [17] proved a transition from stability to Li–Yorke chaos as update aggressiveness grows under scalar aggregate influence. Their threshold is dynamical and concerns the long-run behavior of specific update rules; ours is information-theoretic and applies to every decentralized local-information learner. Farina and Perdomo [ [7] show that, for bounded losses, no-regret online learning yields approximate performative stability of a mixture over joint deployment histories, without regularity assumptions on the distribution maps. Our subcritical result guarantees geometric convergence of the last retraining iterate to a unique performatively stable equilibrium under curvature and sensitivity assumptions.

Spectral methods and lower bounds. Two technical traditions supply tools used in our analysis without serving as competing frameworks. Spectral conditions for equilibrium uniqueness and stability are well established in network games [2, 3, 15], where the spectral radius governs payoff coupling: agent $j$’s action enters agent $i$’s utility directly. In our setting, it governs distributional feedback through $D(w)$, and $\Gamma$ is built from gradient sensitivities and curvature constants rather than payoff coefficients. Our lower bound uses the two-point method [19]; recent extensions to interactive [5] and decision-dependent [6] settings do not cover our performative learning setting. Our construction is tailored to multi-agent performative feedback over a finite horizon.

Model

This section specifies the multi-agent performative prediction model used throughout the paper. We describe the agents, their objectives, the dynamics of repeated retraining, and the assumptions that underlie the reduction of § 12.1.

There are $k$ agents. Each agent $i \in\{1,\ldots,k\}$ chooses a deployment $w_i$ from a feasible set $\mathcal{W}_i = \mathbb{R}^{d_i}$. The joint deployment profile is $w := (w_1,\ldots,w_k) \in\mathcal{W} := \mathcal{W}_1 \times\cdots\times\mathcal{W}_k$. The deployed profile induces a shared endogenous distribution $D(w)$ over observations $z \in\mathcal{Z}$. This distribution represents the data-generating process that all agents face after the profile $w$ has been deployed. The dependence of $D$ on $w$ captures the performative nature of the system: the environment that agents observe is shaped by their own collective actions.

Each agent $i$ has a loss function $\ell_i:\mathcal{W}_i \times\mathcal{Z} \to\mathbb{R}$ and aims to minimize its performative risk

The two arguments of $F_i$ play distinct roles. The first, $u_i$, is the candidate action optimized by agent $i$. The second, $w$, is the deployed profile that determines the environment through $D(w)$. Retraining couples these roles by replacing the deployed profile with agents’ optimized actions.

For each agent $i$ and each deployed profile $w$, the best response is

Assumption 1 below ensures that this minimizer exists and is unique. The joint best-response map is $B(w) := (B_1(w),\ldots,B_k(w))$. An equilibrium of the system is a profile that is consistent with every agent’s best response.

At a performatively stable equilibrium, each agent’s deployed action is optimal for the distribution induced by the joint deployment. Existence of such an equilibrium is not assumed; it is derived in Section 5 as a consequence of the spectral condition on the reflexivity matrix.

We study the repeated retraining dynamics in which every agent simultaneously replaces its current deployment with its best response,

with initialization $w_0 \in\mathcal{W}$. The central question of the paper is when the sequence $\{w_t\}$ converges to a performatively stable equilibrium, and when convergence fails.

Main assumptions. Three assumptions are used throughout the paper. The first two are local to individual agents and enter the reduction of § 12.1. The third is a mild regularity condition on the feasible sets that allows application of Banach’s fixed-point theorem.

Assumption 1 (Strong convexity in own action). For each agent $i$ and every fixed deployed profile $w \in\mathcal{W}$, the map $u_i \mapsto F_i(u_i; w)$ is $\mu_i$-strongly convex on $\mathcal{W}_i$, with $\mu_i > 0$.

Strong convexity makes $B_i(w)$ unique and quantifies agent $i$’s resistance to gradient perturbations. Larger $\mu_i$ means that the best response is less sensitive to changes in the data distribution.

Assumption 2 (Gradient sensitivity to the deployed profile). For each $i$, $F_i$ is continuously differentiable in $u_i$, and there exist nonnegative constants $a_{ij} \ge0$ for all $i, j \in\{1, \ldots, k\}$ such that for every agent $i$, every candidate action $u_i \in\mathcal{W}_i$, and every pair of deployed profiles $w, w' \in\mathcal{W}$,

Gradient sensitivity quantifies how a change in the deployed profile affects each agent’s first-order condition. The coefficient $a_{ij}$ measures how much agent $j$’s deployment disturbs agent $i$’s gradient. If agent $j$ has no effect on agent $i$’s optimization problem, then $a_{ij} = 0$. Larger values of $a_{ij}$ indicate stronger cross-agent coupling. The diagonal coefficient $a_{ii}$ captures agent $i$’s own self-reflexive effect through the environment.

Assumptions 1 and 2 are the only two channels through which agents interact mathematically in this paper. Every downstream result is derived from these two assumptions and their combination in § 12.1. The reflexivity matrix $\Gamma$ of § 12.1 is built from exactly the parameters $\{\mu_i\}$ and $\{a_{ij}\}$ introduced here.

Assumption 3 (Unconstrained domains). Each feasible set is $\mathcal{W}_i = \mathbb{R}^{d_i}$.

This makes $\mathcal{W}$ complete under the norms used below, enabling Banach’s fixed-point theorem, and ensures that retraining stays in $\mathcal{W}$.

Throughout, $x_i$ denotes agent $i$’s block of a vector $x \in\mathbb{R}^{d_1} \times\cdots\times\mathbb{R}^{d_k}$, with Euclidean norm $\lVert\cdot\rVert$. The spectral radius of $M$ is $\rho(M)$, and for $q \in\mathbb{R}_{++}^{k}$ we write $\lVert x\rVert_q := \max_i \lVert x_i\rVert/q_i$.

The Reduction: From Retraining Dynamics to Gamma

The retraining dynamics $w_{t+1} = B(w_t)$ are nonlinear and couple all $k$ agents through the shared endogenous distribution $D(w)$. Direct analysis of these dynamics is difficult without additional structure. This section introduces the reduction used throughout the paper: under the assumptions of § 3, changes in the nonlinear map $B$ are bounded entrywise by a nonnegative matrix $\Gamma$. The spectral properties of $\Gamma$ can therefore be used to bound the dynamics of $B$.

The first step bounds the shift in any agent’s best response induced by a shift in the deployed profile. The bound is linear and decomposes cleanly across agents.

Proof. Fix an agent $i$ and let $u_i := B_i(w)$ and $u_i' := B_i(w')$. First-order optimality gives $\nabla_{u_i}F_i(u_i;w)=0$ and $\nabla_{u_i}F_i(u_i';w')=0$. Strong convexity of $F_i(\cdot;w)$ (Assumption 1) yields $\mu_i\lVert u_i-u_i'\rVert\le\lVert\nabla_{u_i}F_i(u_i';w)-\nabla_{u_i}F_i(u_i';w')\rVert$. Substituting the first optimality condition and then the second gives $\mu_i\lVert u_i-u_i'\rVert\le\lVert\nabla_{u_i}F_i(u_i';w)\rVert= \lVert\nabla_{u_i}F_i(u_i';w)-\nabla_{u_i}F_i(u_i';w')\rVert$. The right-hand side is a gradient difference at a fixed action $u_i'$ across two deployed profiles, which Assumption 2 bounds by $\sum_j a_{ij}\lVert w_j-w_j'\rVert$. Dividing by $\mu_i>0$ yields the claim. □

The proof uses each structural assumption once: gradient sensitivity bounds the performative change in agent $i$’s gradient, and strong convexity converts it into a best-response change. Their interaction appears only through $a_{ij}/\mu_i$, the ratio of agent $j$’s disturbance to agent $i$’s curvature, which measures how much $j$ can shift $i$’s best response.

The weights in Theorem 2 define the central object of the paper.

In terms of $\Gamma$, Theorem 2 states that for every $i$ and every $w,w'$,

The vector of best-response shifts is therefore dominated entrywise by the product of $\Gamma$ with the vector of action shifts.

The matrix $\Gamma$ is the central reduction object. It is built from primitive per-agent quantities rather than imposed as an aggregate Lipschitz constant, and its nonnegativity makes Perron–Frobenius theory available for the weighted-norm contraction argument below. The rank-one construction in Section 6 saturates (4) along the Perron direction, so the same spectral radius governs both the upper and lower sides of the threshold.

For a direct comparison with Narang et al. [ [14], consider scalar agents with quadratic losses $\ell_i(u_i;z_i)=\frac{1}{2}(u_i-z_i)^2$ and deterministic observations $z=Cw$, where $C\ge0$. Here $\mu_i=1$, the data-gradient Lipschitz constants are $\beta_i=1$, and the full-profile Wasserstein sensitivities are $\gamma_i=\lVert C_{i:}\rVert_2$. Their scalar aggregate condition becomes $\sqrt{\sum_i\gamma_i^2}=\lVert C\rVert_F<1$, whereas ours is $\rho(C)<1$, since $\Gamma=C$. Because $\rho(C)\le\lVert C\rVert_F$, our certificate is never more restrictive in this common subclass and can be strictly stronger. For example, if $C=cP$ with $P$ a cyclic permutation matrix, then $\rho(C)=c$ but $\lVert C\rVert_F=c\sqrt{k}$. Thus, for $k^{-1/2}<c<1$, the matrix condition certifies convergence while the scalar aggregate condition does not.

Subcritical Regime: Contraction and Bounded Excess Loss

This section proves the positive side of the threshold. Under the assumptions of § 3, if $\rho(\Gamma)<1$, then the retraining map is a contraction in a Perron–Frobenius weighted norm. This yields a unique performatively stable equilibrium, geometric convergence from any initialization, and a cumulative excess-loss bound that does not grow with the time horizon.

Contraction via Perron–Frobenius. For two retraining trajectories $w_t$ and $w'_t$, let $d_{i,t}:=\lVert w_{i,t}-w'_{i,t}\rVert$. Inequality (4) gives $d_{t+1}\le\Gamma d_t$ entrywise, hence $d_t\le\Gamma^t d_0$. Since $\rho(\Gamma)<1$, the matrix powers vanish, directly explaining why deviations decay. Perron–Frobenius theory supplies positive weights $q$ with $\Gamma q\le\alpha q$ for some $\alpha<1$, converting this matrix comparison into a one-step contraction in $\lVert\cdot\rVert_q$ and allowing Banach’s theorem to establish existence and uniqueness of the equilibrium.

Proof. Since $\Gamma$ is nonnegative and $\rho(\Gamma)<1$, Perron–Frobenius theory guarantees the existence of a vector $q\in\mathbb{R}_{++}^{k}$ and a constant $\alpha\in(0,1)$ satisfying

If $\Gamma$ is reducible, apply the argument to the perturbed matrix $\Gamma_\eta:=\Gamma+\eta\mathbf{1}\mathbf{1}^{\mathsf T}$ for $\eta>0$ chosen small enough that $\rho(\Gamma_\eta)<1$; the strictly positive Perron eigenvector $q$ of $\Gamma_\eta$ then satisfies $\Gamma q\le\Gamma_\eta q=\rho(\Gamma_\eta)q$, and we set $\alpha:=\rho(\Gamma_\eta)\in(\rho(\Gamma),1)$. Fix $w$, $w' \in\mathcal{W}$ and an agent $i$. By the definition of the weighted norm, $\lVert w_j-w'_j\rVert\le q_j\lVert w-w'\rVert_q$ for every $j$. Substituting this bound into the best-response sensitivity inequality (4) and then applying (5) yields

Dividing by $q_i>0$ and taking the maximum over $i$ gives the claim.

Equivalently, for any $\alpha\in(\rho(\Gamma),1)$, one can choose positive weights $q$ such that $\Gamma q\le\alpha q$. The contraction factor can therefore be made arbitrarily close to $\rho(\Gamma)$. The proof uses only two facts: $\Gamma$ is nonnegative and $\rho(\Gamma)<1$.

The contraction theorem gives the standard fixed-point consequences. Since $B$ is a contraction on the complete space $(\mathcal{W},\lVert\cdot\rVert_q)$, Banach’s fixed-point theorem applies.

Proof. Assumption 3 ensures that $\mathcal{W}$ is complete under $\lVert\cdot\rVert_q$ and that $B$ maps $\mathcal{W}$ into itself. Theorem 4 shows that $B$ is an $\alpha$-contraction with $\alpha\in(0,1)$. Banach’s fixed-point theorem yields the unique fixed point $w^\star$. Iterating the contraction inequality with $w=w_t$ and $w'=w^\star$ gives the geometric convergence bound.

Corollary 5 gives the basic learnability result in the subcritical regime. The system has a unique performatively stable equilibrium, and repeated retraining reaches it at a geometric rate from any initialization. The condition is stated through $\Gamma$, which is constructed from primitive per-agent quantities.

If retraining instead satisfies $\lVert w_{t+1}-B(w_t)\rVert_q\le\eta_t$, then

Appendix C gives the resulting error bound and its interpretation for empirical retraining with controlled gradient error.

Contraction of the state dynamics alone does not give a performance bound. To control cumulative excess loss, we add smoothness in each agent’s own action.

The natural benchmark in a time-varying environment is the current best response. At round $t$, agent $i$ deploys $w_{i,t}$, while the action minimizing its risk under the current profile $w_t$ is $B_i(w_t)$. The gap between these two actions is the instantaneous excess loss, and summing over agents and rounds gives

This benchmark compares each deployment to the best response in the environment it actually faces. It also avoids assumptions on how objective values vary with the deployed profile. Bounds against the fixed equilibrium comparator require such assumptions and are therefore treated separately.

Proof. Fix $t \ge1$ and $i$. The best response $B_i(w_t)$ minimizes $u_i \mapsto F_i(u_i;w_t)$, which is $L_i$-smooth by Assumption 4. Standard smoothness gives

For $t \ge1$, the dynamics (3) give $w_{i,t}=B_i(w_{t-1})$, so

where the inequality is (4). Using $\lVert w_{j,t}-w_{j,t-1}\rVert\le q_j\lVert w_t-w_{t-1}\rVert_q$ gives

Substituting into (7) and summing over $i$ gives

for a constant $C'$ absorbing the $L_i$ and the row-sum factors $\left(\sum_j\Gamma_{ij}q_j\right)^2$. By Corollary 5 and the triangle inequality,

so the per-round excess loss decays as $\alpha^{2(t-1)}$. Summing the geometric series from $t=1$ to $T-1$ gives a contribution of order $\lVert w_0-w^\star\rVert_q^2/(1-\alpha^2)$. For $t=0$, $\lVert w_0-B(w_0)\rVert_q\le\lVert w_0-w^\star\rVert_q+\lVert B(w^\star)-B(w_0)\rVert_q\le(1+\alpha)\lVert w_0-w^\star\rVert_q$, which fits the same geometric series. Combining yields the claim. □

The bound depends on the initialization and the contraction factor but not on the time horizon, with the $1/(1-\alpha^2)$ factor arising from the geometric decay of the one-step gap together with smoothness. Below the threshold, cumulative excess loss is thus $O(1)$; § 6 constructs supercritical instances with $\Omega(T)$ cumulative excess loss over a critical horizon.

The geometric bound gives iteration complexity $O(\log(\lVert w_0-w^\star\rVert_q/\delta)/(-\log\alpha))$ to accuracy $\delta$. This bound and the factor $1/(1-\alpha^2)$ deteriorate as $\alpha\to1^-$. Figure 1 illustrates the slowdown in a rescaled linear family as $\rho(\Gamma)$ approaches one.

Supercritical Regime

Section 5 established that $\rho(\Gamma)<1$ is sufficient for geometric convergence and bounded cumulative excess loss. Whether the supercritical regime produces an intrinsic learning obstruction is delicate: failure of contraction does not by itself imply information-theoretic impossibility, since with enough observations a learner may recover the correct equilibrium even when repeated retraining diverges. We show that the threshold has a sharp negative side in the worst case. For every $\varepsilon>0$, we construct a supercritical family with $\rho(\Gamma)=1+\varepsilon$ in which any decentralized local-information learner—one that at each round uses only its own history of observations and actions—incurs linear cumulative excess loss for $T=O(1/\varepsilon)$ rounds.

All agents know the model parameters $(\Gamma_\varepsilon,\Delta,\sigma_\varepsilon^2,v)$; only the hidden parameter $\nu$ is unknown.

No agent observes the full system state: each sees only its own signal, without access to other agents’ actions or observations. The lower bound applies to any learner satisfying this constraint, regardless of the algorithm used within each agent.

Hard family. The construction places all endogenous amplification along a single direction, which simplifies the analysis and makes the separation between the two environments explicit.

Fix $\varepsilon\in(0,1]$ and a unit vector $v \in\mathbb{R}^{k}$ with nonnegative entries. Define the reflexivity matrix

The matrix $\Gamma_{\varepsilon}$ is nonnegative, symmetric, and rank one, with single nonzero eigenvalue $1+\varepsilon$, so $\rho(\Gamma_{\varepsilon})=1+\varepsilon$. The system is therefore supercritical by construction, with $\varepsilon$ measuring the distance above the threshold. Each agent has $d_i=1$ and $W_i=\mathbb{R}$, and the quadratic loss gives $\mu_i=1$, so the abstract reflexivity matrix of § 12.1 coincides with $\Gamma_{\varepsilon}$ in this construction.

The environment contains a hidden binary parameter $\nu\in\{-1,+1\}$ that determines the true state of the world. Fix an amplitude $\Delta>0$ and let the noise variance be $\sigma_{\varepsilon}^{2}:=8\Delta^{2}/\varepsilon$. The observation model is

where each agent $i$ observes only the coordinate $Y_{i,t}$.

Each agent uses quadratic loss $\ell_i(a;y):=\frac{1}{2}(a-y)^2$, which gives the performative risk

The additive constant $\sigma_{\varepsilon}^{2}/2$ cancels in differences and is dropped below. Minimizing over $a$ gives the best-response map $B(w;\nu)=\nu\Delta v+\Gamma_{\varepsilon}w$. The fixed-point equation $w^{\star,\nu}=B(w^{\star,\nu};\nu)$ has the explicit solution

The last equality uses the identity $(I-\Gamma_{\varepsilon})v=-\varepsilon v$, which follows from the definition (8).

The two equilibria $w^{\star,+}$ and $w^{\star,-}$ are therefore located at distance $2\Delta/\varepsilon$ along $v$. As $\varepsilon$ decreases, these equilibria move apart, and the geometric penalty for getting the wrong sign grows. This divergence is the mechanism that converts slow information acquisition into large cumulative excess loss.

The instantaneous excess performative loss at round $t$ is

and the cumulative excess loss over $T$ rounds is $\operatorname{Reg}_T(\mathcal{A}):=\sum_{t=0}^{T-1}r_t^\nu$.

The subcritical result uses a dynamic comparator (the current best response $B_i(w_t)$), whereas (12) uses the fixed equilibrium comparator $w^{\star,\nu}$: the dynamic comparator measures one-step adaptation to the current caused environment, while the equilibrium comparator measures whether the learner identifies the correct stable equilibrium. In the supercritical construction these goals decouple, so the equilibrium comparator is the appropriate benchmark for the two-environment testing argument. For the quadratic family in (10) the two comparators in fact yield identical instantaneous excess loss, so the $\Omega(\Delta^2 T)$ bound holds under either benchmark.

The proof proceeds in three steps. Step 1 shows that the two environments $\nu=+1$ and $\nu=-1$ remain statistically indistinguishable over the critical horizon. Step 2 shows that misidentifying the environment forces the learner into a region of large excess loss. Step 3 combines these two facts.

Proof sketch; full proof in Appendix § 9. The argument has two parts.

Indistinguishability. We prove the KL bound under a stronger centralized observation model that reveals the full profile $w_t$ and vector observation $Y_t$; the bound therefore also applies to decentralized learners. Conditional on the realized pre-observation history $\mathcal{F}_t$, the action $w_t$ is fixed by the learner’s policy. Under the two hypotheses, the next observation is Gaussian with common covariance $\sigma_\epsilon^2 I_k$ and conditional means differing by $2\Delta v$. The per-round Kullback–Leibler divergence is therefore

using $\lVert v\rVert=1$ and $\sigma_\epsilon^2=8\Delta^2/\epsilon$. By the chain rule for KL divergence and Pinsker’s inequality, the total variation distance remains bounded away from one for all $t\le c_0/\epsilon$, so the optimal equal-prior testing error is bounded below by an absolute constant $p_0>0$.

Geometric cost of misidentification. The two equilibria (11) lie at $\pm(\Delta/\epsilon)v$, so on the event that the midpoint decoder picks the wrong sign, $\left|\langle w_t-w^{\star,\nu},v\rangle\right|\ge\Delta/\epsilon$. The closed form $r_t^\nu=\frac{1}{2}\left\|(I-\Gamma_\epsilon)(w_t-w^{\star,\nu})\right\|^2$ together with $(I-\Gamma_\epsilon)v=-\epsilon v$ then gives $r_t^\nu\ge\Delta^2/2$ on that event. The factor $\epsilon$ from the contraction of $(I-\Gamma_\epsilon)$ along $v$ cancels exactly the $1/\epsilon$ from the equilibrium separation, which is why the obstruction survives the limit $\epsilon\to0$ and produces a horizon of order $1/\epsilon$.

Combining the two parts: averaging over $\nu$ and using equal-prior testing error at least $p_0$ gives $\frac{1}{2}\mathbb{E}_{+}[r_t^{+}]+\frac{1}{2}\mathbb{E}_{-}[r_t^{-}]\ge p_0\Delta^2/2$ for every $t\le c_0/\epsilon$, and summing yields the claim with $c_1=p_0/2$.

The positive theorem assumes exact population best responses, whereas the lower bound concerns noisy observations over a finite horizon, with $\sigma_\epsilon^2=8\Delta^2/\epsilon$. The proof bounds indistinguishability even for a centralized learner observing the same signals, so communication alone does not remove this obstruction in the constructed family.

Discussion

Reflexivity matrix and interpretation. The framework replaces a scalar Lipschitz constant with the reflexivity matrix $\Gamma$, whose entries $a_{ij}/\mu_i$ retain the per-pair structure of performative feedback. This matters because two systems with the same scalar bound can have different stability behavior if their feedback is routed through different agent networks. The Perron weights $q$ in Theorem 4 encode this heterogeneity by rescaling agents according to their role in the collective retraining dynamics. When $k=1$, $\Gamma$ reduces to the scalar ratio $a_{11}/\mu_1$, recovering the single-agent contraction threshold of Perdomo et al. [2020]. More generally, $\rho(\Gamma)=1$ separates dissipative feedback, where repeated retraining contracts, from supercritical feedback, where uniform stability guarantees fail. We use reflexivity for this deployment-to-data channel, following the financial-market terminology of Soros [1987]; we do not formalize the broader economic concept.

Beyond the spectral radius. The threshold $\rho(\Gamma) = 1$ governs asymptotic behavior, but finite-horizon dynamics can depend on finer matrix properties and initial conditions. One important case is non-normality: $\lVert\Gamma^t \rVert$ may grow transiently even if $\rho(\Gamma) < 1$, as illustrated by the non-normal subcritical trajectory in Panel 1b. Thus the spectral radius identifies the asymptotic threshold, while quantities such as the numerical range or pseudospectra may better capture finite-horizon amplification. We leave this refinement to future work.

Empirical measurement. The framework identifies a stability quantity, $\rho(\Gamma)$, that is measurable in principle but difficult to identify empirically. Each entry $a_{ij}$ is counterfactual: it describes how agent $i$’s gradient would respond to alternative deployments by agent $j$, while $\mu_i$ is also not directly observed. Estimation is therefore a causal identification problem, since common system shocks may affect both agent $j$’s deployment and agent $i$’s gradient. Testing whether estimated values of $\rho(\Gamma)$ predict observed instability is a concrete direction for future work. The primitive bounds are useful when $B$ is defined implicitly by a nonlinear optimizer: curvature and pairwise gradient sensitivity can be bounded without solving or differentiating the full best-response map. The certificate can be conservative and depends on the chosen block geometry; failure of $\rho(\Gamma) < 1$ alone does not diagnose instability. As a design lever, adding a fixed-reference quadratic penalty $\frac{\lambda_i}{2}\lVert u_i-u_i^{\mathrm{ref}}\rVert^2$ replaces $\Gamma_{ij}$ by $a_{ij}/(\mu_i+\lambda_i)$, potentially restoring the certificate, while changing the objective and generally the equilibrium.

Limitations and extensions. Several modeling choices delimit our results. The main guarantees assume strong convexity in each agent’s own action and exact best-response retraining; Appendix C controls approximate retraining under explicit error bounds. Sharper finite-sample rates, weaker curvature assumptions, and gradient updates remain directions for future work. We also treat the reflexivity matrix as fixed across rounds. If the interaction structure evolves, a natural extension is to study time-varying matrices $\Gamma_t$, where tools such as joint spectral radii may be needed to characterize stability. The lower bound establishes that no universal positive guarantee can extend above the threshold, since at least one supercritical family forces linear cumulative excess loss; it does not assert that every supercritical $\Gamma$ is unlearnable. Characterizing which supercritical systems remain learnable is open for future work. Coordination among agents may shift the threshold, while damped retraining is a natural design lever for stabilizing otherwise unstable systems.

Acknowledgments and Disclosure of Funding

This work received no funding. The authors declare no competing interests.

Full Proof of Theorem 8

This appendix gives the full proof of Theorem 8, expanding the sketch in § 6. We retain the same notation: $P_{\nu}$ denotes the law of the full system trajectory under learner $A$ in environment $\nu\in\{-1,+1\}$, and $\mathcal{F}_t$ denotes the history up to and including the action $w_t$ but excluding the observation $Y_t$.

Proof of Theorem 8. Step 1: Bounding the Kullback–Leibler divergence. The KL bound is proved under a stronger centralized observation model in which the learner sees the full profile $w_t$ and vector observation $Y_t$. Since decentralized learners observe only part of this information, the same indistinguishability bound applies to the learners in Theorem 8. Conditional on the realized pre-observation history $\mathcal{F}_t$, the action $w_t$ is fixed by the learner’s policy. The observation $Y_t$ is then drawn from a Gaussian whose mean differs between the two environments only through the term $\nu\Delta v$. Specifically,

The means differ by $2\Delta v$, and the covariance matrices are identical. The Kullback–Leibler divergence between two Gaussians with common covariance $\sigma^2 I$ and mean difference $\mu$ equals $\lVert\mu\rVert^2/(2\sigma^2)$. Applying this formula and using $\sigma_\varepsilon^2=8\Delta^2/\varepsilon$,

By the chain rule for Kullback–Leibler divergence, the total divergence after $t$ rounds is

Choose $c_0:=1/2$. For every $t\le c_0/\varepsilon$, Pinsker’s inequality gives

The minimax testing error for deciding $\nu$ from the observed history is therefore bounded below by an absolute constant $p_0>0$, independent of $\varepsilon$ and $t$.

Step 2: Geometric cost of misidentification. Define the midpoint decoder

On the event $\{\widehat{\nu}_t\ne\nu\}$, the action $w_t$ lies at least as close to the incorrect equilibrium as to the correct one. Using (11), the two equilibria lie on opposite sides of the origin along $v$ at distance $\Delta/\varepsilon$ each. The midpoint between them is the origin, so $\widehat{\nu}_t\ne\nu$ implies $\nu\langle w_t,v\rangle\ge0$. Consequently,

For the quadratic family, the equilibrium identity $w^{\star,\nu}=\nu\Delta v+\Gamma_\varepsilon w^{\star,\nu}$ implies $F_i(w_i^{\star,\nu};w^{\star,\nu},\nu)=\sigma_\varepsilon^2/2$. Substituting into (12) and summing over $i$ gives

where the second equality uses $\nu\Delta v=(I-\Gamma_\varepsilon)w^{\star,\nu}$.

The identity $(I-\Gamma_\varepsilon)v=-\varepsilon v$ implies that $(I-\Gamma_\varepsilon)$ contracts the direction $v$ by the factor $\varepsilon$. Combining (14) with the lower bound

and substituting (14),

Substituting into (15) yields

The factor $\varepsilon$ from the contraction of $(I-\Gamma_\varepsilon)$ and the factor $1/\varepsilon$ from the equilibrium separation cancel exactly. This cancellation is why the lower bound survives the limit $\varepsilon\to0$ and produces a horizon of order $1/\varepsilon$ rather than a faster-vanishing horizon.

Step 3: Combining the bounds. Taking expectation of (16) under environment $\nu$ gives

Averaging over $\nu\in\{-1,+1\}$,

The bracketed quantity is twice this decoder’s equal-prior error, which is at least the optimal equal-prior testing error and hence at least $p_0$ by Step 1 for every $t \le c_0/\varepsilon$. Therefore,

for every $t \le c_0/\varepsilon$.

Summing over $t = 0,\ldots,T-1$ gives the first claim with $c_1 := p_0/2$. The supremum bound follows because the maximum over $\nu$ dominates the average. $\square$

Simulation Details for Figure 1

All instances have $k = 100$. The sparse matrices have zero diagonal; each off-diagonal position is independently selected with probability 0.15 and assigned a uniform $[0,1]$ weight, before rescaling to the target spectral radius. The non-normal instance is constructed separately as described below. Code, seeds, and commands to reproduce the figures are provided in the supplementary simulation artifact.

Panel 1a (phase diagram). With seed 0, we generate 20 sparse base matrices with spectral radius one. Each is paired with 5 Gaussian initial states normalized to $\lVert w_0\rVert_q = 1$ using its positive right Perron vector $q$. The dynamics are $w_{t+1} = \Gamma w_t$ with $w^\star= 0$. For each $\rho$ on a grid of 200 values in $[0.5,1.5]$, densely sampled near one, we rescale the base to $\Gamma= \rho\Gamma_{\mathrm{base}}$ and record the first iteration at which $\lVert w_t\rVert_q \le10^{-3}$, with computational cap $T_{\max} = 10{,}000$. The curve reports the median and interquartile band across the 100 trajectories. Only $\rho< 1$ values are evaluated for hitting times and plotted; the vertical display is limited to 1,000. The same Perron weights are used across rescalings of each base.

Panel 1b (trajectories). With seed 20260439, each sparse baseline at $\rho\in\{0.50,0.99,1.01,1.50\}$ uses 100 independent draws of a matrix and of vectors $b,w_0$ whose coordinates are Gaussian with standard deviation $1/\sqrt{k}$. We simulate $w_{t+1} = b + \Gamma w_t$ for 200 steps and compute $w^\star= (I-\Gamma)^{-1}b$. The non-normal instance uses a fixed dense upper-triangular $\Gamma= 0.5I + sU$, where $U$ has ones strictly above the diagonal and $s$ is chosen so that $\lVert\Gamma\rVert_2/\rho(\Gamma) = 10$. It uses $b = 0$ and 100 Gaussian initial states normalized to Euclidean unit norm. Each curve reports the mean of $\log_{10}(\lVert w_t-w^\star\rVert_2/\lVert w_0-w^\star\rVert_2 + 10^{-12})$, with pointwise interquartile bands. The horizontal zero line marks the initial normalized distance.

Since the experiments are synthetic simulations, they can be reproduced with standard CPU resources.

Approximate and Empirical Retraining

Proof. The triangle inequality and Theorem 4 give

Iterating this inequality proves the bound. For bounded errors, the geometric sum is at most $\bar{\eta}/(1-\alpha)$. For vanishing errors, split the sum into a finite prefix, which vanishes as $t\to\infty$, and a tail bounded by $\sup_{s\ge S}\eta_s/(1-\alpha)$, then let $S\to\infty$.

For empirical retraining, first-order optimality gives $\nabla\widehat{F}_{i,t}(w_{i,t+1})=0$. Strong convexity of $F_i(\cdot;w_t)$ and $\nabla_{u_i}F_i(B_i(w_t);w_t)=0$ imply

Dividing by $\mu_iq_i$ and maximizing over agents gives the claimed $\eta_t$.

This is a conditional stability statement: obtaining probabilities or sample-size rates for the gradient-error event requires additional statistical assumptions on the losses and samples.

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