MOANOFS tackles online feature selection for big data classification.
problem Online supervised feature selection for binary classification in big data.
method Hybrid of online learning and automated negotiation.
result MOANOFS achieves high accuracy with real-world applications.
Paper automates car negotiation in intersections using Q-learning.
problem Automated vehicles negotiate with human-driven cars in intersections.
method Deep Q-learning applied to simulated traffic with various driver behaviors.
result 98% success rate in avoiding collisions with other vehicles.
Paper proposes an intersection decision algorithm for autonomous vehicles.
problem Navigating intersections with non-automated vehicles.
method Combines reinforcement learning for high-level decisions and model predictive control for low-level planning.
result The proposed algorithm outperforms another controller in success rate and training episodes.
We present an effective technique for training deep learning agents capable of negotiating on a set of clauses in a contract agreement using a simple communication protocol. We use Multi Agent Reinforcement Learning to train both agents simultaneously as they negotiate with each other in the training environment. We al…
A framework for fair derivative contract pricing and risk-sharing between parties with funding differences.
problem Price asymmetry due to funding differences in bilateral contracts.
method Defines a negotiation problem that maximizes the sum of utilities for two parties, deriving optimal prices and collateral.
result Optimal negotiation price and collateral can be used to interpret margin requirements.
We consider two risk-averse financial agents who negotiate the price of an illiquid indivisible contingent claim in an incomplete semimartingale market environment. Under the assumption that the agents are exponential utility maximizers with non-traded random endowments, we provide necessary and sufficient conditions f…
The paper analyzes a game where players must balance short-term and long-term interests, leading to cooperative or competitive outcomes.
problem Analyzing time inconsistency in inter-personal decision-making under non-exponential discounting.
method Iterative procedures and Zorn's lemma to find Nash equilibria between players' intra-personal equilibria.
result Inter-personal equilibria exist and depend on the impatience levels of the players.
Deep RL mimics human driving for collision avoidance in self-driving cars.
problem Developing human-like driving policies for autonomous vehicles in mixed traffic environments.
method Model-free, deep reinforcement learning approach using a combination of rule-based and expert-driven data.
result Demonstrated human-like driving policies through Gaussian process modeling of track position and speed distributions.
We describe an agent-based simulation of a fictional (but feasible) information trading business. The Gas Price Information Trader (GPIT) buys information about real-time gas prices in a metropolitan area from drivers and resells the information to drivers who need to refuel their vehicles. Our simulation uses real wor…
Unified framework for complex financial networks using lattice theory.
problem Complex financial networks with multiple currencies and dependencies.
method Recast classical financial clearing model into lattice liability networks.
result Lattice-valued clearing sections form a complete lattice, enabling tractable analysis.
A rapid pattern-recognition approach to characterize driver's curve-negotiating behavior is proposed. To shorten the recognition time and improve the recognition of driving styles, a k-means clustering-based support vector machine ( kMC-SVM) method is developed and used for classifying drivers into two types: aggressiv…
This research improves debt collection strategies using advanced machine learning.
problem Accurate estimation of propensity to pay and cashflow for optimal debt collection.
method Developed a machine learning framework with pre-processing and model selection.
result The proposed model outperforms current industry strategies.
Optimal margin loan agreements for sophisticated gamblers and brokers.
problem Finding fair interest rates and loan sizes between gamblers and brokers.
method Derives formulas for optimal arrangements based on gamblers' risk preferences and market conditions.
result Gambler gains higher capital growth with lower interest rates, broker gains intermediary profit.
In an incomplete semimartingale model of a financial market, we consider several risk-averse financial agents who negotiate the price of a bundle of contingent claims. Assuming that the agents' risk preferences are modelled by convex capital requirements, we define and analyze their demand functions and propose a notio…
We show how to restructure the counterparty risk faced by the originator of a securitization or covered bond arising from an interest rate hedging swap assisted by a "one-way" collateral agreement. This risk emerges when the swap is negotiated between the special purpose vehicle and a third party that covers itself thr…
Money is a technology for promoting economic prosperity. Over history money has become increasingly abstract, it used to be hardware, gold coins and the like, now it is mostly software, data structures located in banks. Here I propose the logical conclusion of the abstraction of money: to use as money the most general …
The present paper analyses the formal parallelism existing between the laws of thermodynamics and some economic principles. Based on previous works, we shall show how the existence in Economics of principles analogous to those in thermodynamics involves the occurrence of economic events that remind of well-known phenom…
Siegel's paradox is a fundamental question in international finance about exchange rates for futures contracts and has puzzled many scholars for over forty years. The unorthodox approach presented in this article leads to an arbitrage-free solution which is invariant under currency re-denominations and is symmetric, as…
AGENTICAITA uses AI agents to autonomously trade markets without human intervention.
problem Inability of traditional trading systems to adapt to market complexity.
method Introduces an agentic AI framework with specialized LLM agents reasoning, negotiating, and acting.
result Demonstrated operational correctness and non-trivial inter-agent negotiation in live market conditions.
Optimal reinsurance contracts for multiple dependent risks are derived without specific dependency assumptions.
problem Finding optimal reinsurance contracts for multiple dependent risks without assuming their dependency structure.
method Assumes maximal expected utility criterion and independent negotiation of reinsurance for each risk. Derives optimality conditions and shows that under mild assumptions, optimal contracts are classical (non-randomized) type.
result Optimal reinsurance contracts exist and can be classical (non-randomized) type under mild assumptions.
A fair reward system boosts participation in federated learning.
problem Fairness in federated learning among competitive agents with siloed data.
method Hierarchically fair federated learning (HFFL) framework with proportional rewards based on contribution levels.
result Efficacy of HFFL in maintaining fairness and facilitating federated learning in competitive settings.
Our previous results are extended to the case of the margin account, which may depend on the contract's value for the hedger and/or the counterparty. The present work generalizes also the papers by Bergman (1995), Mercurio (2013) and Piterbarg (2010). Using the comparison theorems for BSDEs, we derive inequalities for …
Prediction Factory automates predictive model development and evaluation.
problem Rapidly developing and sharing predictive models with domain experts.
method Data science automation system with three interfaces: baseline, full, and optional automation.
result Full automation interface generated reports funded 57.5% of the time, compared to 42.5% for baseline.
Automated theorem prover proves non-orderability of groups.
problem Proving non-orderability of groups.
method Generic automated theorem prover with tools like positive cones, torsions, generalised torsions, and cofinal elements.
result Demonstrated automated proof of non-orderability of groups.
Study identifies what healthcare tasks can and should be automated.
problem Undesired consequences of automation in healthcare.
method Quantitative analysis using machine learning models trained on practitioner and expert ratings.
result Developed an analytical tool (Automatability-Desirability Matrix) for policymakers and leaders.
Cross-dimensional neural networks improve AI in Catan game.
problem Challenging to build AI agents for Catan game using RL.
method Introduced cross-dimensional neural networks to handle game complexities.
result RL agent outperforms best heuristic agent in Catan.
This paper surveys scalable automated alignment methods for LLMs.
problem Scalability issues in traditional human-annotated alignment methods for LLMs.
method Categorizes and discusses various automated alignment methods.
result Emerging automated alignment methods are effective and scalable.
Game-theoretic models predict asset prices in financial markets.
problem Understanding price formation in financial markets with limited liquidity.
method Developed game-theoretic models for many-person and mean-field games, derived analytical formulas, and numerically assessed results.
result The derived price converges to the mean-field counterpart under specific conditions.
This paper uses machine learning to assist automation engineers in decision making.
problem Imperfect decision making by automation engineers leads to multiple iterations and increased time for software development.
method Defined challenges and proposed solutions using machine learning for automation engineering, including code classification, finding similar code snippets, and hardware selection.
result Paragraph embedding techniques achieved an F1-score of 72% for classifying automation using code snippets, and autoencoder models for hardware recommendation achieved p@3 and p@5 of 0.79 and 0.95, respectively.
Automated strategies improve model adaptation efficiency.
problem Manual adaptation strategies are time-consuming and costly.
method Flexible adaptive mechanism deployment for automated adaptation strategies.
result Automated strategies achieve better or comparable performance.
New architecture improves decision-making in dense traffic.
problem Designing accurate and compact learning architectures for autonomous vehicles in crowded conditions.
method Attention-based architecture that accounts for interactions between vehicles.
result Significant performance gains and interpretable interaction patterns.
Automated machine learning simplifies model selection and tuning.
problem Manual tuning of machine learning models by data scientists is time-consuming and requires extensive expertise.
method Review of AutoML techniques including automated feature engineering, model learning, and deep learning.
result Current AutoML techniques can significantly reduce the burden of manual tuning.
New automated market makers for multi-asset trading.
problem Liquidity management in multi-asset trading.
method Derived from self-financing transactions and rebalancing principles.
result Constant product market maker as a special case.
Approach for assessing supply chain cyber risks using expert judgment and forecasting.
problem Supply chain managers face challenges in assessing cyber risks affecting business factors.
method Structured expert judgment and forecasting models to assess various attack techniques and impacts.
result Facilitates implementation of risk management activities and decision-making processes.
DriveML automates machine learning tasks in R.
problem Manual machine learning tasks are time-consuming and error-prone.
method Automated data preparation, feature engineering, model building, and model explanation.
result DriveML performs best across different parameters compared to other packages.
This paper reviews R packages for automating data analysis tasks.
problem Time-consuming Exploratory Data Analysis in large, noisy data sets.
method Systematic review of 12 R packages for autoEDA.
result Identifies automated tasks and areas for future development.
Automates size normalization for fashion items.
problem Reduce merchandise returns in e-commerce.
method Uses sales data to automate size mapping.
result Automated size mappings comparable to human-generated ones.
This study detects fake and automated accounts on Instagram.
problem Fake engagement on Instagram leads to financial loss and wrong audience targeting.
method Two datasets were created and machine learning algorithms like Naive Bayes, Logistic Regression, Support Vector Machines, Neural Networks, and cost-sensitive genetic algorithm were applied.
result 86% accuracy for automated accounts and 96% for fake accounts were achieved.
This paper explores SNNs for automated driving, promising low-power efficiency.
problem Power consumption and cost in embedded processors for automated driving.
method Overview of SNNs and their potential for automated driving.
result SNNs show potential for automated driving applications with low power consumption.
This research compiles knowledge on decentralized exchanges with AMM protocols.
problem Improving and developing AMM-based decentralized exchanges.
method Established a general AMM framework, compared mechanics, discussed security and privacy.
result Illustrated conservation and slippage functions of AMM protocols.
Fast, fully-automated histograms for large data sets.
problem Efficiently constructing histograms for large-scale data.
method G-Enum histograms using Minimum Description Length (MDL) principle for model selection and a greedy search heuristic.
result Construct histograms in linearithmic time, significantly faster than previous methods.
Automates infectious disease policy-making via inference in epidemiological models.
problem Improving policy-making for infectious diseases during pandemics.
method Performing inference in existing epidemiological models using a probabilistic programming language.
result Automated inference leads to better disease progression outcomes and policy prescriptions.
Automated market-making for CBDCs and stable coins on blockchain.
problem Creating fair exchange rates for digital assets on blockchain.
method Developed an innovative approach for generating fair exchange rates.
result Illustrated the approach's efficacy on G-10 currency exchange rates.
Study proposes automated framework for REM Sleep Behaviour Disorder detection.
problem Early detection of REM Sleep Behaviour Disorder (RBD) as a predictor of Parkinson's disease.
method Automated sleep staging followed by RBD identification using a Random Forest classifier and 156 features from EEG, EOG, and EMG channels.
result Automated RBD detection achieved 96% accuracy, surpassing individual established metrics.
New formula identifies and quantifies costs for automated market makers.
problem Adverse selection costs faced by liquidity providers in automated market makers.
method Derives a Black-Scholes-like formula for AMMs and identifies loss-versus-rebalancing cost.
result Closed-form expressions for LVR applicable to all automated market makers.
GPT-f uses language models to find new proofs in formal math.
problem Generating original mathematical terms for automated theorem proving.
method Transformer-based language model for automated theorem proving and proof assistant.
result GPT-f contributed new proofs to the Metamath library.
This paper improves transportation efficiency by teaching automated vehicles to cooperate.
problem Improving efficiency and safety of transportation systems with automated vehicles.
method Multi-agent graph reinforcement learning with attention mechanism.
result Automated vehicles can achieve better performance when learning to cooperate with each other.
New metric to measure liquidity position PNL, delta hedging algorithm for automated market makers.
problem Vulnerability of liquidity positions to price changes in underlying assets.
method Proposes a new metric for measuring PNL, delta hedging algorithm for various AMMs.
result New metric more accurately measures net value change due to price movement.