arXiv subjects

Razvan Ciuca

Publications and source records attributed to Razvan Ciuca.

6 recordsLinked to original sources

arXiv2026

Towards Sustainable Investment Policies Informed by Opponent Shaping

Addressing climate change requires global coordination, yet rational economic actors often prioritize immediate gains over collective welfare, resulting in social dilemmas. InvestESG is a recently proposed multi-agent simulation that captures the dynamic interplay between investors and companies under climate risk. We provide a formal characterization of the conditions under which InvestESG exhibits an intertemporal social dilemma, deriving theoretical thresholds at which individual incentives diverge from collective welfare. Building on this, we apply Advantage Alignment, a scalable opponent shaping algorithm shown to be effective in general-sum games, to influence agent learning in InvestESG. We offer theoretical insights into why Advantage Alignment systematically favors socially beneficial equilibria by biasing learning dynamics toward cooperative outcomes. Our results demonstrate that strategically shaping the learning processes of economic agents can result in better outcomes that could inform policy mechanisms to better align market incentives with long-term sustainability goals.

cs.LG↗

arXiv2024

Advantage Alignment Algorithms

Artificially intelligent agents are increasingly being integrated into human decision-making: from large language model (LLM) assistants to autonomous vehicles. These systems often optimize their individual objective, leading to conflicts, particularly in general-sum games where naive reinforcement learning agents empirically converge to Pareto-suboptimal Nash equilibria. To address this issue, opponent shaping has emerged as a paradigm for finding socially beneficial equilibria in general-sum games. In this work, we introduce Advantage Alignment, a family of algorithms derived from first principles that perform opponent shaping efficiently and intuitively. We achieve this by aligning the advantages of interacting agents, increasing the probability of mutually beneficial actions when their interaction has been positive. We prove that existing opponent shaping methods implicitly perform Advantage Alignment. Compared to these methods, Advantage Alignment simplifies the mathematical formulation of opponent shaping, reduces the computational burden and extends to continuous action domains. We demonstrate the effectiveness of our algorithms across a range of social dilemmas, achieving state-of-the-art cooperation and robustness against exploitation.

cs.LG↗

arXiv2021

Information Theoretic Bounds on Cosmic String Detection in CMB Maps with Noise

We use a convolutional neural network (CNN) to study cosmic string detection in cosmic microwave background (CMB) flat sky maps with Nambu-Goto strings. On noiseless maps we can measure string tensions down to order $10^{-9}$, however when noise is included we are unable to measure string tensions below $10^{-7}$. Motivated by this impasse, we derive an information theoretic bound on the detection of the cosmic string tension $Gμ$ from CMB maps. In particular we bound the information entropy of the posterior distribution of $Gμ$ in terms of the resolution, noise level and total survey area of the CMB map. We evaluate these bounds for the ACT, SPT-3G, Simons Observatory, Cosmic Origins Explorer, and CMB-S4 experiments. These bounds cannot be saturated by any method.

astro-ph.CO↗

arXiv2019

A Convolutional Neural Network For Cosmic String Detection in CMB Temperature Maps

We present in detail the convolutional neural network used in our previous work to detect cosmic strings in cosmic microwave background (CMB) temperature anisotropy maps. By training this neural network on numerically generated CMB temperature maps, with and without cosmic strings, the network can produce prediction maps that locate the position of the cosmic strings and provide a probabilistic estimate of the value of the string tension $Gμ$. Supplying noiseless simulations of CMB maps with arcmin resolution to the network resulted in the accurate determination both of string locations and string tension for sky maps having strings with string tension as low as $Gμ=5\times10^{-9}$. The code is publicly available online. Though we trained the network with a long straight string toy model, we show the network performs well with realistic Nambu-Goto simulations.

astro-ph.CO↗

arXiv2019

Inferring Cosmic String Tension through the Neural Network Prediction of String Locations in CMB Maps

In previous work, we constructed a convolutional neural network used to estimate the location of cosmic strings in simulated cosmic microwave background temperature anisotropy maps. We derived a connection between the estimates of cosmic string locations by this neural network and the posterior probability distribution of the cosmic string tension $Gμ$. Here, we significantly improve the calculation of the posterior distribution of the string tension $Gμ$. We also improve our previous plain convolutional neural network by using residual networks. We apply our new neural network and posterior calculation method to maps from the same simulation used in our previous work and quantify the improvement.

astro-ph.CO↗

arXiv2017

A Bayesian Framework for Cosmic String Searches in CMB Maps

There exists various proposals to detect cosmic strings from Cosmic Microwave Background (CMB) or 21 cm temperature maps. Current proposals do not aim to find the location of strings on sky maps, all of these approaches can be thought of as a statistic on a sky map. We propose a Bayesian interpretation of cosmic string detection and within that framework, we derive a connection between estimates of cosmic string locations and cosmic string tension $Gμ$. We use this Bayesian framework to develop a machine learning framework for detecting strings from sky maps and outline how to implement this framework with neural networks. The neural network we trained was able to detect and locate cosmic strings on noiseless CMB temperature map down to a string tension of $Gμ=5 \times10^{-9}$ and when analyzing a CMB temperature map that does not contain strings, the neural network gives a 0.95 probability that $Gμ\leq2.3\times10^{-9}$.

astro-ph.CO↗