arXiv research

This paper optimizes revenue and resource balance in network revenue management.

Improved algorithm reduces regret in NRM with unknown demand.

Mobile crowdsourcing has become easier thanks to the widespread of smartphones capable of seamlessly collecting and pushing the desired data to cloud services. However, the success of mobile crowdsourcing relies on balancing the supply and demand by first accurately forecasting spatially and temporally the supply-deman…

Proposes a new method for predicting uncertain net electricity demand.

This paper proposes an agent-based model that combines both spot and balancing electricity markets. From this model, we develop a multi-agent simulation to study the integration of the consumers' flexibility into the system. Our study identifies the conditions that real-time prices may lead to higher electricity costs,…

Proposes a pricing agent using reinforcement learning to balance renewable energy demand.

This paper shows how we can build a model for transactions when goods are given away in the expectation of a later settlement. In settings where people keep track of their social accounts we are able to redefine concepts like account balance, yield curve and the law of diminishing returns. The model provides us with a …

Less frequent retraining improves forecast accuracy in retail demand forecasting.

The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.

Study optimizes smart contract adoption under high demand variability using Negative Binomial models.

The paper analyzes trade dynamics among G7 countries, revealing unequal exchange and degenerate equilibrium states.

Paper develops a fair pricing algorithm for dynamic settings with uncertain demand.

Study develops smart contract framework for procurement under demand variability.

New algorithm controls large groups of devices to match energy demand signals.

This study improves electricity price forecasting in the Irish balancing market.

The paper analyzes systoles of complex projective spaces under various metrics.

MTAdam optimizes multiple loss terms in neural models, balancing gradients dynamically.

Paper explores balancing market dynamics and interpretable forecasting models for energy prices.

Predicts fine-grained OD matrices for ridesharing platforms to optimize supply-demand balance.

New method estimates consumer surplus from randomized pricing data.

This paper compares AMMs and LOBs in exchange mechanisms, formalizing complexity vs. expressiveness trade-offs.

The paper characterizes brain states and transitions using functional MRI data.

Overprocuring reserves can improve network efficiency by using excess reserves for congestion management.

Paper proposes a new model to prevent tariff wars by balancing trade balances.

Paper tackles online learning for DR management with incentives.

New method improves object detection models for long-tailed datasets.

This study considers a model of the income distribution of agents whose pairwise interaction is asymmetric and price-invariant. Asymmetric transactions are typical for chain-trading groups who arrange their business such that commodities move from senior to junior partners and money moves in the opposite direction. The…

Paper explores how analysts balance rule-based and situational aspects of data analytics.

Generative model creates frictional surfaces from friction laws.

New approach for pricing evaluation improves on existing methods.

As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration of the social consequences. How should we address this problem in a real-world system? How should we balance core performance and fairness me…

Scaling properties in financial fluctuations are reviewed from the standpoint of statistical physics. We firstly show theoretically that the balance of demand and supply enhances fluctuations due to the underlying phase transition mechanism. By analyzing tick data of yen-dollar exchange rates we confirm two fractal pro…

HLRC offers a new curvature metric for hypergraphs that balances interpretability and efficiency.

Understanding the functional architecture of the brain in terms of networks is becoming increasingly common. In most fMRI applications functional networks are assumed to be stationary, resulting in a single network estimated for the entire time course. However recent results suggest that the connectivity between brain …

In coming years residential consumers will face real-time electricity tariffs with energy prices varying day to day, and effective energy saving will require automation - a recommender system, which learns consumer's preferences from her actions. A consumer chooses a scenario of home appliance use to balance her comfor…

AER dynamically adjusts entropy regularization for better LLM reinforcement learning.

An agent-based model for financial markets has to incorporate two aspects: decision making and price formation. We introduce a simple decision model and consider its implications in two different pricing schemes. First, we study its parameter dependence within a supply-demand balance setting. We find realistic behavior…

SOOTT framework optimizes target tracking with robust and learning-augmented algorithms.

Study on price formation in a market with a major player and minor firms.

Demand variance can result in a mismatch between planned supply and actual demand. Demand shaping strategies such as pricing can be used to shift elastic demand to reduce the imbalance. In this work, we propose to consider elastic demand in the forecasting phase. We present a method to reallocate the historical elastic…

CROCS clusters consumer behaviour from smart meters, capturing variability and robustness.

Statistical inference methods are fundamentally important in machine learning. Most state-of-the-art inference algorithms are variants of Markov chain Monte Carlo (MCMC) or variational inference (VI). However, both methods struggle with limitations in practice: MCMC methods can be computationally demanding; VI methods …

We study the application of dynamic pricing to insurance. We view this as an online revenue management problem where the insurance company looks to set prices to optimize the long-run revenue from selling a new insurance product. We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive …

The paper proposes a new model to better estimate demand from censored data.

Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the general demand foreca…

In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible parameters of simulation programs that explain the observations. However, they demand large quantities of simulation calls. Critically, hyperpara…

Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available supply, historical transport data typically represents a biased, or censored, version of the true underlying demand pattern. Without explicitly…

Generative AI boosts analyst reports but increases forecast errors.