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Many of the chemical reactions that take place within a living cell are irreversible. Due to evolutionary pressures, the number of allowable reactions within these systems are highly constrained and thus the resulting metabolic networks…

Molecular Networks · Quantitative Biology 2013-03-04 Jonathan J Crofts , Ernesto Estrada

Many complex systems satisfy a set of constraints on their degrees of freedom, and at the same time, they are able to work and adapt to different conditions. Here, we describe the emergence of this ability in a simplified model in which the…

Disordered Systems and Neural Networks · Physics 2007-05-23 Ginestra Bianconi , Roberto Mulet

Many natural and technological systems fail to adapt to changing external conditions and move to a different state if the conditions vary too fast. Such "non-adiabatic" processes are ubiquitous, but little understood. We identify these…

Dynamical Systems · Mathematics 2015-06-18 Clare Perryman , Sebastian Wieczorek

In this paper, we consider a mean-reverting stochastic volatility equation with regime switching, and present some sufficient conditions for the existence of global positive solution, asymptotic boundedness in pth moment, positive…

Probability · Mathematics 2019-12-16 Yanling Zhu , Kai Wang , Yong Ren

Credit Valuation Adjustment is a balance sheet item which is nowadays subject to active risk management by specialized traders. However, one of the most important risk factors, which is the vector of default intensities of the counterparty,…

Computational Finance · Quantitative Finance 2024-09-24 Roberto Daluiso

We consider a dynamic model of interconnected banks. New banks can emerge, and existing banks can default, creating a birth-and-death setup. Microscopically, banks evolve as independent geometric Brownian motions. Systemic effects are…

Probability · Mathematics 2019-05-28 Tomoyuki Ichiba , Michael Ludkovski , Andrey Sarantsev

We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component. Such systems…

Systems and Control · Electrical Eng. & Systems 2022-12-05 Rohan Sinha , James Harrison , Spencer M. Richards , Marco Pavone

A model of an organism as an autonomous intelligent system has been proposed. This model was used to analyze learning of an organism in various environmental conditions. Processes of learning were divided into two types: strong and weak…

Artificial Intelligence · Computer Science 2007-05-23 Alexey V. Melkikh

From behavioral sciences to biology to quantum mechanics, one encounters situations where (i) a system outputs several random variables in response to several inputs, (ii) for each of these responses only some of the inputs may "directly"…

Probability · Mathematics 2015-09-01 E. N. Dzhafarov , J. V. Kujala

Motivated by the random Lorentz gas, we study deterministic walks in random environment and show that (in simple, yet relevant, cases) they can be reduced to a class of random walks in random environment where the jump probability depends…

Probability · Mathematics 2020-01-23 Romain Aimino , Carlangelo Liverani

A risk analyst assesses potential financial losses based on multiple sources of information. Often, the assessment does not only depend on the specification of the loss random variable but also various economic scenarios. Motivated by this…

Risk Management · Quantitative Finance 2023-10-02 Tolulope Fadina , Yang Liu , Ruodu Wang

We develop a generalized stability framework for stochastic discrete-time systems, where the generality pertains to the ways in which the distribution of the state energy can be characterized. We use tools from finance and operations…

Systems and Control · Electrical Eng. & Systems 2022-11-23 Margaret P. Chapman , Dionysios S. Kalogerias

We study an optimal investment problem under default risk where related information such as loss or recovery at default is considered as an exogenous random mark added at default time. Two types of agents who have different levels of…

Pricing of Securities · Quantitative Finance 2017-03-02 Ying Jiao , Idris Kharroubi

Robustness to genetic or environmental disturbances is often considered as a key property of living systems. Yet, in spite of being discussed since the 1950s, how robustness emerges from the complexity of genetic architectures and how it…

Populations and Evolution · Quantitative Biology 2022-03-31 Arnaud Le Rouzic

Randomly-assembled dynamical systems are theoretically predicted to be unstable upon crossing a critical threshold of complexity, as first shown by May. Yet, empirical complex systems exhibit remarkable stability, indicating the presence of…

Disordered Systems and Neural Networks · Physics 2026-03-31 Francesco Ferraro , Christian Grilletta , Amos Maritan , Samir Suweis , Sandro Azaele

Individual risk models need to capture possible correlations as failing to do so typically results in an underestimation of extreme quantiles of the aggregate loss. Such dependence modelling is particularly important for managing credit…

Methodology · Statistics 2014-12-11 Michel Denuit , Anna Kiriliouk , Johan Segers

In natural phenomena, data distributions often deviate from normality. One can think of cataclysms as a self-explanatory example: events that occur almost never, and at the same time are many standard deviations away from the common…

Machine Learning · Computer Science 2022-12-16 Nuno Costa , Nuno Moniz

The recent interest in human dynamics has led researchers to investigate the stochastic processes that explain human behaviour in different contexts. Here we propose a generative model to capture the essential dynamics of survival analysis,…

Physics and Society · Physics 2015-06-18 Trevor Fenner , Mark Levene , George Loizou

The interconnectedness of financial institutions affects instability and credit crises. To quantify systemic risk we introduce here the PD model, a dynamic model that combines credit risk techniques with a contagion mechanism on the network…

Computational Finance · Quantitative Finance 2018-04-10 Daniele Petrone , Vito Latora

In robust optimization, the uncertainty set is used to model all possible outcomes of uncertain parameters. In the classic setting, one assumes that this set is provided by the decision maker based on the data available to her. Only…

Optimization and Control · Mathematics 2019-01-23 Trivikram Dokka , Marc Goerigk , Rahul Roy
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