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Data-driven, model-free analytics are natural choices for discovery and forecasting of complex, nonlinear systems. Methods that operate in the system state-space require either an explicit multidimensional state-space, or, one approximated…

Machine Learning · Statistics 2021-03-15 Joseph Park , Gerald M Pao , Erik Stabenau , George Sugihara , Thomas Lorimer

We focus on the influence of external sources of information upon financial markets. In particular, we develop a stochastic agent-based market model characterized by a certain herding behavior as well as allowing traders to be influenced by…

General Finance · Quantitative Finance 2015-07-28 Adrián Carro , Raúl Toral , Maxi San Miguel

In this paper we investigate a dynamic pricing model for constant demand elasticity where customers have a probability distribution on the number of items they order. This is a generalization from standard models which restrict customers to…

Optimization and Control · Mathematics 2018-03-01 Nyles Breecher , Richard Stockbridge

This paper analyzes single-item continuous-review inventory models with random supplies in which the inventory dynamic between orders is described by a diffusion process, and a long-term average cost criterion is used to evaluate decisions.…

Optimization and Control · Mathematics 2024-02-07 K. L. Helmes , R. H. Stockbridge , C. Zhu

Choo-Siow (2006) proposed a model for the marriage market which allows for random identically distributed noise in the preferences of each of the participants. The randomness is McFadden-type, which permits an explicit resolution of the…

Optimization and Control · Mathematics 2011-09-06 Colin Decker , Elliott H. Lieb , Robert J. McCann , Benjamin K. Stephens

The Unbiased Learning-to-Rank framework has been recently proposed as a general approach to systematically remove biases, such as position bias, from learning-to-rank models. The method takes two steps - estimating click propensities and…

Information Retrieval · Computer Science 2019-10-23 Grigor Aslanyan , Utkarsh Porwal

Structural equation models (SEMs) are widely used in sciences, ranging from economics to psychology, to uncover causal relationships underlying a complex system under consideration and estimate structural parameters of interest. We study…

Machine Learning · Statistics 2020-10-21 Luofeng Liao , You-Lin Chen , Zhuoran Yang , Bo Dai , Zhaoran Wang , Mladen Kolar

The Expectation Maximization (EM) algorithm is a versatile tool for model parameter estimation in latent data models. When processing large data sets or data stream however, EM becomes intractable since it requires the whole data set to be…

Statistics Theory · Mathematics 2012-10-18 Sylvain Le Corff , Gersende Fort

Sound Event Detection (SED) aims to predict the temporal boundaries of all the events of interest and their class labels, given an unconstrained audio sample. Taking either the splitand-classify (i.e., frame-level) strategy or the more…

Sound · Computer Science 2023-08-21 Swapnil Bhosale , Sauradip Nag , Diptesh Kanojia , Jiankang Deng , Xiatian Zhu

We propose a heterogeneous agent market model (HAM) in continuous time. The market is populated by fundamental traders and chartists, who both use simple linear trading rules. Most of the related literature explores stability, price…

General Economics · Economics 2019-02-27 Zsolt Bihary , Attila András Víg

We study Bayesian inverse problems with mixed noise, modeled as a combination of additive and multiplicative Gaussian components. While traditional inference methods often assume fixed or known noise characteristics, real-world…

Machine Learning · Computer Science 2025-10-17 Paul Hagemann , Robert Gruhlke , Bernhard Stankewitz , Claudia Schillings , Gabriele Steidl

We present a methodology for representing probabilistic relationships in a general-equilibrium economic model. Specifically, we define a precise mapping from a Bayesian network with binary nodes to a market price system where consumers and…

Computer Science and Game Theory · Computer Science 2013-02-18 David M. Pennock , Michael P. Wellman

We introduce a data-driven learning framework that assimilates two powerful ideas: ideal large eddy simulation (LES) from turbulence closure modeling and neural stochastic differential equations (SDE) for stochastic modeling. The ideal LES…

This article addresses the problem of average consensus in a multi-agent system when the desired consensus quantity is a time varying signal. Although this problem has been addressed in existing literature by linear schemes, only bounded…

Systems and Control · Electrical Eng. & Systems 2022-05-26 Rodrigo Aldana-López , Rosario Aragüés , Carlos Sagüés

We describe a bottom-up framework, based on the identification of appropriate order parameters and determination of phase diagrams, for understanding progressively refined agent-based models and simulations of financial markets. We…

Trading and Market Microstructure · Quantitative Finance 2015-06-04 Ribin Lye , James Peng Lung Tan , Siew Ann Cheong

We consider data-driven inventory and pricing decisions in the feature-based newsvendor problem, where demand is influenced by both price and contextual features and is modeled without any structural assumptions. The unknown demand…

Machine Learning · Statistics 2024-11-14 Shijin Gong , Huihang Liu , Xinyu Zhang

The thesis of this essay is that, in heterogeneous agent macroeconomics, the assumption of rational expectations about equilibrium prices is unrealistic and should be replaced. Rational expectations imply that decision makers forecast…

General Economics · Economics 2025-08-29 Benjamin Moll

This paper studies the economic role of persistent dispersion in allocations across agents. We develop a tractable model in which firms allocate resources under imperfect information and behavioral updating, generating sustained…

Theoretical Economics · Economics 2026-04-02 Nazaria Solferino

We investigate the non-equilibrium dynamics of a three state kinetic exchange model of opinion formation, where switches between extreme states are possible, depending on the value of a parameter $q$. The mean field dynamical equations are…

Statistical Mechanics · Physics 2023-02-28 Kathakali Biswas , Parongama Sen

The selection of an equilibrium state by maximising the entropy of a system, subject to certain constraints, is often powerfully motivated as an exercise in logical inference, a procedure where conclusions are reached on the basis of…

Statistical Mechanics · Physics 2015-12-03 Ian J. Ford
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