arXiv subjects

Pin Gao

Publications and source records attributed to Pin Gao.

7 recordsLinked to original sources

arXiv2026

From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models

Economic World Models (EWMs) are generative economic models that simulate how economies evolve from within by modeling heterogeneous agents, their beliefs and actions, and the market and institutional mechanisms through which their interactions produce aggregate outcomes. This paper develops an implementation roadmap for building economic world models as generative engines in which heterogeneous agents act, interact, adapt, and co-evolve with markets and institutions, thereby producing economic dynamics from the inside. We organize EWM systems into a six-level capability ladder, from fixed rule-based agent worlds to adaptive and LLM-based agent worlds, self-evolving agents, evolving institutional worlds, and sim-to-real economic twins aligned with real observations. A systematic literature survey across these levels reveals that existing work remains concentrated in lower-level agent and simulation environments, while systems with self-evolving agents, endogenous institutions, persistent empirical alignment, and validated economic mechanisms remain rare. By translating the EWM agenda into an implementation blueprint, this paper aims to accelerate the development of the next generation of economic simulation environments that can serve as high-fidelity sandboxes for human decision-makers and as training, planning, evaluation, and safety substrates for AI agents. We release a curated paper list and related resources to support future research.

cs.AI↗

arXiv2025

From Small to Large: A Graph Convolutional Network Approach for Solving Assortment Optimization Problems

Assortment optimization seeks to select a subset of substitutable products, subject to constraints, to maximize expected revenue. The problem is NP-hard due to its combinatorial and nonlinear nature and arises frequently in industries such as e-commerce, where platforms must solve thousands of such problems each minute. We propose a graph convolutional network (GCN) framework to efficiently solve constrained assortment optimization problems. Our approach constructs a graph representation of the problem, trains a GCN to learn the mapping from problem parameters to optimal assortments, and develops three inference policies based on the GCN's output. Owing to the GCN's ability to generalize across instance sizes, patterns learned from small-scale samples can be transferred to large-scale problems. Theoretical results are established to show the expressive power of the proposed GCN, and explain the underlying mechanism of the size generalization ability. Numerical experiments show that a GCN trained on instances with 20 products achieves over 85% of the optimal revenue on problems with up to 2,000 products within seconds, outperforming existing heuristics in both accuracy and efficiency. We further extend the framework to settings with an unknown choice model using transaction data and demonstrate similar performance and scalability.

cs.LG↗

arXiv2024

Simultaneously search for multi-target Galactic binary gravitational waves

The search for Galactic binary gravitational waves is a critical challenge for future space-based gravitational wave detectors, such as LISA. We propose an innovative approach to simultaneously explore gravitational waves originating from Galactic binaries by developing a new Local Maxima Particle Swarm Optimization (LMPSO) algorithm. This new approach effectively addresses the inaccuracies often associated with signal subtraction contamination, a challenge for traditional iterative subtraction methods, particularly when dealing with low signal-to-noise ratio (SNR) signals (e.g., SNR $<$ 15). We also account for the effects of overlapping signals and degeneracy noise. To demonstrate the effectiveness of our approach, we use residuals from the LISA mock data challenge (LDC1-4), where 10,982 injected sources with SNR $\ge$ 15 have been removed. For the remaining sources with SNR $<$ 15, our method successfully identifies 6,508 signals, yielding a false alarm rate of $\text{FAS}_{0.8} = 36.8\%$. By focusing on a subset of sources-specifically, those with $f > 3$ mHz and those with $f \le 3$ mHz but SNR $\ge 13$-we identify 3,406 signals, with a reduced false alarm rate of $\text{FAS}_{0.8} = 22.5\%$. We further demonstrate that, within the same detection SNR range, our method achieves a comparable or lower $\text{FAS}$ than other existing methods.

gr-qc↗

arXiv2023

Adapting to climate change: Long-term impact of wind resource changes on China's power system resilience

Modern society's reliance on power systems is at risk from the escalating effects of wind-related climate change. Yet, failure to identify the intricate relationship between wind-related climate risks and power systems could lead to serious short- and long-term issues, including partial or complete blackouts. Here, we develop a comprehensive framework to assess China's power system resilience across various climate change scenarios, enabling a holistic evaluation of the repercussions induced by wind-related climate change. Our findings indicate that China's current wind projects and planning strategies could be jeopardized by wind-related climate change, with up to a 12\% decline in regional wind power availability. Moreover, our results underscore a pronounced vulnerability of power system resilience amidst the rigors of hastened climate change, unveiling a potential amplification of resilience deterioration, even approaching fourfold by 2060 under the most severe scenario, relative to the 2020 benchmark. This work advocates for strategic financial deployment within the power sector aimed at climate adaptation, enhancing power system resilience to avert profound losses from long-term, wind-influenced climatic fluctuations.

eess.SY↗

arXiv2023

Fast resolving Galactic binaries in LISA data and its ability to study the Milky Way

Resolving individual gravitational waves from tens of millions of double white dwarf (DWD) binaries in the Milky Way is a challenge for future space-based gravitational wave detection programs. By using previous data to define the priors for the next search, we propose an accelerated approach of searching the DWD binaries and demonstrate its efficiency based on the GBSIEVER detection pipeline. Compared to the traditional GBSIEVER method, our method can obtain $\sim 50\%$ of sources with 2.5\% of the searching time for LDC1-4 data. In addition, we find that both methods have a similar ability to detect the Milky Way structure by their confirmed sources. The relative error of distance and chirp mass is about 20\% for DWD binaries whose gravitational wave frequency is higher than $4\times10^{-3}$ Hz, even if they are close to the Galactic center. Finally, we propose a signal-to-noise ratio (SNR) threshold for LISA to confirm the detection of DWD binaries. The threshold should be 16 when the gravitational wave frequency is lower than $4\times10^{-3}$ Hz and 9 when the frequency range is from $4\times10^{-3}$ Hz to $1.5\times10^{-2}$ Hz.

astro-ph.HE↗

arXiv2020

A Distributed Multi-GPU System for Large-Scale Node Embedding at Tencent

Real-world node embedding applications often contain hundreds of billions of edges with high-dimension node features. Scaling node embedding systems to efficiently support these applications remains a challenging problem. In this paper we present a high-performance multi-GPU node embedding system. It uses model parallelism to split node embeddings onto each GPU's local parameter server, and data parallelism to train these embeddings on different edge samples in parallel. We propose a hierarchical data partitioning strategy and an embedding training pipeline to optimize both communication and memory usage on a GPU cluster. With the decoupled design of CPU tasks (random walk) and GPU tasks (embedding training), our system is highly flexible and can fully utilize all computing resources on a GPU cluster. Comparing with the current state-of-the-art multi-GPU single-node embedding system, our system achieves 5.9x-14.4x speedup on average with competitive or better accuracy on open datasets. Using 40 NVIDIA V100 GPUs on a network with almost three hundred billion edges and more than one billion nodes, our implementation requires only 3 minutes to finish one training epoch.

cs.DC↗

arXiv2016

Symmetry-protected non-Abelian braiding of Majorana Kramers' pairs

We develop the complete theory for non-Abelian braiding of Majorana Kramers' pairs (MKPs) in time-reversal (TR) invariant topological superconductors. By introducing an effective Hamiltonian approach to describe the braiding of MKPs, we show that the non-Abelian braiding is protected when the effective Hamiltonian exhibits a new TR like anti-unitary symmetry, which is satisfied if the system is free of dynamical noise. Importantly, even the dynamical noise may not cause error in braiding, unless the noise correlation function breaks a dynamical TR symmetry, which generalizes the TR symmetry protection of MKPs to dynamical regime. Moreover, the resulted error by noise is shown to be a higher order effect, compared with the decoherence of Majorana qubits without TR symmetry protection. These results show that the non-Abelian braiding of MKPs is observable and may have versatile applications to future quantum computation technologies.

cond-mat.supr-con↗