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In economic applications, model averaging has found principal use examining the validity of various theories related to observed heterogeneity in outcomes such as growth, development, and trade.Though often easy to articulate, these…

Applications · Statistics 2019-10-16 Alex Lenkoski , Fredrik Lohne Aanes

In financial terms, an implied volatility surface can be described by its term structure, its skewness and its overall volatility level. We use a PCA variational auto-encoder model to perfectly represent these descriptors into a latent…

Pricing of Securities · Quantitative Finance 2023-06-09 Zheng Gong , Wojciech Frys , Renzo Tiranti , Carmine Ventre , John O'Hara , Yingbo Bai

The forecasting of the credit default risk has been an important research field for several decades. Traditionally, logistic regression has been widely recognized as a solution due to its accuracy and interpretability. As a recent trend,…

Computational Finance · Quantitative Finance 2022-09-22 Dangxing Chen , Weicheng Ye , Jiahui Ye

We review the literature on models that try to explain human behavior in social interactions described by normal-form games with monetary payoffs. We start by covering social and moral preferences. We then focus on the growing body of…

Computer Science and Game Theory · Computer Science 2024-03-18 Valerio Capraro , Joseph Y. Halpern , Matjaz Perc

The concept of reputation is widely used as a measure of trustworthiness based on ratings from members in a community. The adoption of reputation systems, however, relies on their ability to capture the actual trustworthiness of a target.…

Distributed, Parallel, and Cluster Computing · Computer Science 2015-02-25 Boris Skoric , Sebastiaan J. A. de Hoogh , Nicola Zannone

We introduce normalized nonnegative models (NNM) for explorative data analysis. NNMs are partial convexifications of models from probability theory. We demonstrate their value at the example of item recommendation. We show that NNM-based…

Machine Learning · Computer Science 2015-11-17 Cyril Stark

This paper explores improvements in prediction accuracy and inference capability when allowing for potential correlation in team-level random effects across multiple game-level responses from different assumed distributions. First-order and…

Applications · Statistics 2019-03-01 Jennifer E. Broatch , Andrew T. Karl

This paper axiomatizes, in a two-stage setup, a new theory for decision under risk and ambiguity. The axiomatized preference relation $\succeq$ on the space $\tilde{V}$ of random variables induces an ambiguity index $c$ on the space…

Optimization and Control · Mathematics 2026-03-24 Roger J. A. Laeven , Mitja Stadje

We review and illustrate how the volatility smile translates into a probability distribution, the market-implied probability distribution representing believes priced in. The effects of changes in the smile are examined. Special attention…

Pricing of Securities · Quantitative Finance 2009-11-05 Ulrich Kirchner

A quadratic discrete time probabilistic model, for optimal portfolio selection in (re-)insurance is studied. For positive values of underwriting levels, the expected value of the accumulated result is optimized, under constraints on its…

Optimization and Control · Mathematics 2007-05-23 Erik Taflin

We propose a general approach for supervised learning with structured output spaces, such as combinatorial and polyhedral sets, that is based on minimizing estimated conditional risk functions. Given a loss function defined over pairs of…

Machine Learning · Statistics 2017-02-28 Chong Yang Goh , Patrick Jaillet

The expectation is an example of a descriptive statistic that is monotone with respect to stochastic dominance, and additive for sums of independent random variables. We provide a complete characterization of such statistics, and explore a…

Theoretical Economics · Economics 2024-08-06 Xiaosheng Mu , Luciano Pomatto , Philipp Strack , Omer Tamuz

This paper describes a method for estimating conditional probability distributions over the parses of ``unification-based'' grammars which can utilize auxiliary distributions that are estimated by other means. We show how this can be used…

Computation and Language · Computer Science 2007-05-23 Mark Johnson , Stefan Riezler

A recent body of work addresses safety constraints in explore-and-exploit systems. Such constraints arise where, for example, exploration is carried out by individuals whose welfare should be balanced with overall welfare. In this paper, we…

Computer Science and Game Theory · Computer Science 2020-06-09 Gal Bahar , Omer Ben-Porat , Kevin Leyton-Brown , Moshe Tennenholtz

Probabilistic risk aversion, defined through quasi-convexity in probabilistic mixtures, is a common useful property in decision analysis. We study a general class of non-monotone mappings, called the generalized rank-dependent functions,…

Theoretical Economics · Economics 2024-09-30 Ruodu Wang , Qinyu Wu

As large language models (LLMs) like GPT become increasingly prevalent, it is essential that we assess their capabilities beyond language processing. This paper examines the economic rationality of GPT by instructing it to make budgetary…

General Economics · Economics 2023-11-07 Yiting Chen , Tracy Xiao Liu , You Shan , Songfa Zhong

Standard policy gradients weight each sampled action by advantage alone, regardless of how likely that action was under the current policy. This creates two pathologies: within a single decision context (e.g. one image or prompt), a rare…

Machine Learning · Computer Science 2026-03-17 Ian Osband

We introduce a way to compare actions in decision problems. One action is safer than another if the set of beliefs at which the decision-maker prefers the safer action expands as the decision-maker becomes more risk averse. We provide a…

Theoretical Economics · Economics 2026-02-19 Marilyn Pease , Mark Whitmeyer

Estimating consumer preferences is central to many problems in economics and marketing. This paper develops a flexible framework for learning individual preferences from partial ranking information by interpreting observed rankings as…

Machine Learning · Statistics 2026-02-19 Yu-Chang Chen , Chen Chian Fuh , Shang En Tsai

We propose an estimator and confidence interval for computing the value of a policy from off-policy data in the contextual bandit setting. To this end we apply empirical likelihood techniques to formulate our estimator and confidence…

Machine Learning · Computer Science 2020-10-20 Nikos Karampatziakis , John Langford , Paul Mineiro