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Because human preferences are too complex to codify, AIs operate with misspecified objectives. Optimizing such objectives often produces undesirable outcomes; this phenomenon is known as reward hacking. Such outcomes are not necessarily…

Artificial Intelligence · Computer Science 2026-04-27 Henrik Marklund , Alex Infanger , Benjamin Van Roy

Large deviations for fat tailed distributions, i.e. those that decay slower than exponential, are not only relatively likely, but they also occur in a rather peculiar way where a finite fraction of the whole sample deviation is concentrated…

Statistical Mechanics · Physics 2015-06-03 Mario Filiasi , Giacomo Livan , Matteo Marsili , Maria Peressi , Erik Vesselli , Elia Zarinelli

We examine the feasibility of predicting and subsequently managing the future evolution of a Complex Adaptive System. Our archetypal system mimics a competitive population of mechanical, biological, informational or human objects. We show…

Disordered Systems and Neural Networks · Physics 2007-05-23 David M. D. Smith , Neil F. Johnson

This conference shows the impressive rate of advances in the observations and theoretical interpretations of large-scale structure. But to explain my feeling that we may still have a lot to learn I offer some comments on our sociology,…

Astrophysics · Physics 2007-05-23 P. J. E. Peebles

What do binary (or probabilistic) forecasting abilities have to do with overall performance? We map the difference between (univariate) binary predictions, bets and "beliefs" (expressed as a specific "event" will happen/will not happen) and…

General Finance · Quantitative Finance 2020-04-10 Nassim Nicholas Taleb

We have studied the time lags between commercial line airplane disasters and their occurrence frequency till 2002, as obtained from a freely available website. We show that the time lags seem to be well described by Poisson random events,…

Physics and Society · Physics 2007-06-13 M. Ausloos , R. Lambiotte

In many prediction problems, the predictive model affects the distribution of the prediction target. This phenomenon is known as performativity and is often caused by the behavior of individuals with vested interests in the outcome of the…

Machine Learning · Statistics 2024-06-03 Seamus Somerstep , Ya'acov Ritov , Yuekai Sun

The transport of platelets in blood is commonly assumed to obey an advection-diffusion equation. Here we propose a disruptive view, by showing that the random part of their velocity is governed by a fat-tailed probability distribution,…

Computational Physics · Physics 2021-09-17 Christos Kotsalos , Karim Zouaoui Boudjeltia , Ritabrata Dutta , Jonas Latt , Bastien Chopard

Models for wildfires must be stochastic if their ability to represent wildfires is to be objectively assessed. The need for models to be stochastic emerges naturally from the physics of the fire, and methods for assessing fit are…

Computational Physics · Physics 2009-11-03 Jeffrey Picka

Normalizing flows are a flexible class of probability distributions, expressed as transformations of a simple base distribution. A limitation of standard normalizing flows is representing distributions with heavy tails, which arise in…

Machine Learning · Statistics 2025-06-13 Tennessee Hickling , Dennis Prangle

We consider the effects of noise on a model of epidemic outbreaks, where the outbreaks appear. randomly. Using a constructive transition approach that predicts large outbreaks, prior to their occurrence, we derive an adaptive control.…

Chaotic Dynamics · Physics 2009-11-11 I. B. Schwartz , L. Billings , E. M. Bollt

Many have dedicated their time trying to determine the ideal conditions for a cylinder to have equal probabilities of falling with one of its faces facing upwards or on its side. However, to this day, there is no concrete analysis of what…

Classical Physics · Physics 2023-11-30 M. N. C. Brustelo , M. M. Vivaldi , F. Marques

Many studies investigated the application of statistical mechanics to damage phenomena. However, so far the association of damage with statistical mechanics is far from completely developed. One of the most successful approaches maps the…

Statistical Mechanics · Physics 2009-12-28 S. G. Abaimov

Catastrophic transitions, where a system shifts abruptly between alternate steady states, are a generic feature of many nonlinear systems. Recently these regime shift were suggested as the mechanism underlies many ecological catastrophes,…

Populations and Evolution · Quantitative Biology 2015-06-11 Haim Weissmann , Nadav M. Shnerb

If we assume that earthquakes are chaotic, and influenced locally then chaos theory suggests that there should be a temporal association between earthquakes in a local region that should be revealed with statistical examination. To date no…

Applications · Statistics 2019-02-14 Parsa Rastin , Michael LuValle

Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two ways. We present a new theoretically tractable variant of…

Machine Learning · Statistics 2013-10-08 Misha Denil , David Matheson , Nando de Freitas

Connectivity - or the lack thereof - is crucial for the function of many man-made systems, from financial and economic networks over epidemic spreading in social networks to technical infrastructure. Often, connections are deliberately…

Disordered Systems and Neural Networks · Physics 2017-12-13 Malte Schröder , Nuno A. M. Araújo , Didier Sornette , Jan Nagler

Extreme events have an important role which is sometime catastrophic in a variety of natural phenomena including climate, earthquakes and turbulence, as well as in man-made environments like financial markets. Statistical analysis and…

Chaotic Dynamics · Physics 2009-10-31 Victor S. L'vov , Anna Pomyalov , Itamar Procaccia

This paper shows that one cannot learn the probability of rare events without imposing further structural assumptions. The event of interest is that of obtaining an outcome outside the coverage of an i.i.d. sample from a discrete…

Machine Learning · Statistics 2015-03-13 Elchanan Mossel , Mesrob I. Ohannessian

Turbulence is defined as an eddy-like state of fluid motion where the inertial-vortex forces of the eddies are larger than any other forces that tend to damp the eddies out. By this definition, turbulence always cascades from small scales…

General Physics · Physics 2012-12-21 Carl H Gibson