arXiv subjects

Xinxiang Guo

Publications and source records attributed to Xinxiang Guo.

3 recordsLinked to original sources

arXiv2026

Robust Aggregation of Calibrated Forecasts

Decision-makers often rely on multiple probabilistic forecasts that are individually calibrated but need not be fully informative. We develop a framework for aggregating such forecasts when the decision-maker knows only that experts satisfy calibration. We show that the joint distribution of calibrated forecasts can contain decision-relevant information that is unavailable from any single expert, so the standard optimal-in-hindsight (OIH) benchmark may substantially understate attainable performance. To formalize this idea, we introduce a robust max-min benchmark: the best payoff a decision-maker can guarantee against all profile-wise conditional-mean mappings compatible with calibration. This benchmark is tractable, admits a linear-programming formulation, and dominates the OIH benchmark up to calibration error. It can nevertheless be strictly below the Bayesian benchmark, clarifying the value of knowing experts' information structures. Finally, we provide online algorithms that attain the robust benchmark under forecast-only feedback and stronger contextual benchmarks under state feedback.

econ.TH↗

arXiv2024

Periodicity in Hedge-myopic system and an asymmetric NE-solving paradigm for two-player zero-sum games

In this paper, we consider the $n \times n$ two-payer zero-sum repeated game in which one player (player X) employs the popular Hedge (also called multiplicative weights update) learning algorithm while the other player (player Y) adopts the myopic best response. We investigate the dynamics of such Hedge-myopic system by defining a metric $Q(\textbf{x}_t)$, which measures the distance between the stage strategy $\textbf{x}_t$ and Nash Equilibrium (NE) strategy of player X. We analyze the trend of $Q(\textbf{x}_t)$ and prove that it is bounded and can only take finite values on the evolutionary path when the payoff matrix is rational and the game has an interior NE. Based on this, we prove that the stage strategy sequence of both players are periodic after finite stages and the time-averaged strategy of player Y within one period is an exact NE strategy. Accordingly, we propose an asymmetric paradigm for solving two-player zero-sum games. For the special game with rational payoff matrix and an interior NE, the paradigm can output the precise NE strategy; for any general games we prove that the time-averaged strategy can converge to an approximate NE. In comparison to the NE-solving method via Hedge self-play, this HBR paradigm exhibits faster computation/convergence, better stability and can attain precise NE convergence in most real cases.

math.DS↗

arXiv2023

The Optimal Strategy against Hedge Algorithm in Repeated Games

This paper aims to solve the optimal strategy against a well-known adaptive algorithm, the Hedge algorithm, in a finitely repeated $2\times 2$ zero-sum game. In the literature, related theoretical results are very rare. To this end, we make the evolution analysis for the resulting dynamical game system and build the action recurrence relation based on the Bellman optimality equation. First, we define the state and the State Transition Triangle Graph (STTG); then, we prove that the game system will behave in a periodic-like way when the opponent adopts the myopic best response. Further, based on the myopic path and the recurrence relation between the optimal actions at time-adjacent states, we can solve the optimal strategy of the opponent, which is proved to be periodic on the time interval truncated by a tiny segment and has the same period as the myopic path. Results in this paper are rigorous and inspiring, and the method might help solve the optimal strategy for general games and general algorithms.

math.OC↗