Publications associées à Effective Usage of Computational Trust Models in Rational Environments

Publications associées (27)

Breaking Blockchain Rationality with Out-of-Band Collusion

Bryan Alexander Ford, Haoqian Zhang, Louis-Henri Manuel Jakob Merino, Verónica del Carmen Estrada Galiñanes

Blockchain systems often rely on rationality assumptions for their security, expecting that nodes are motivated to maximize their profits. These systems thus design their protocols to incentivize nodes to execute the honest protocol but fail to consider ou ...

Springer International Publishing Ag2024

Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations

Zilu Tian

Agent-based simulations have been widely applied in many disciplines, by scientists and engineers alike. Scientists use agent-based simulations to tackle global problems, including alleviating poverty, reducing violence, and predicting the impact of pandem ...

EPFL2023

Scalable Multi-agent Coordination and Resource Sharing

Panayiotis Danassis

A plethora of real world problems consist of a number of agents that interact, learn, cooperate, coordinate, and compete with others in ever more complex environments. Examples include autonomous vehicles, robotic agents, intelligent infrastructure, IoT de ...

EPFL2022

Peer-Prediction in the Presence of Outcome Dependent Lying Incentives

Boi Faltings, Naman Goel, Aris Filos Ratsikas

We derive conditions under which a peer-consistency mechanism can be used to elicit truthful data from non-trusted rational agents when an aggregate statistic of the collected data affects the amount of their incentives to lie. Furthermore, we discuss the ...

2022

The DESI N-body Simulation Project - II. Suppressing sample variance with fast simulations

Yu Yu, Andrei Variu, Cheng Zhao

Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches similar to 20 h(-3) Gpc(3). It is a great challenge to prepare high-resolution simulations with a much ...

OXFORD UNIV PRESS2022

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

François Maréchal, Ivan Daniel Kantor, Julia Granacher

Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm fo ...

2021

Self-learning surrogate models in superstructure optimization

François Maréchal, Ivan Daniel Kantor, Julia Granacher, Michel Lopez

In this contribution, we propose an algorithm for replacing non-linear process simulation integrated in multi-level optimization of an energy system superstructure with surrogate models. With our approach, we demonstrate that surrogate models are a valid t ...

Elsevier2021

Computer Simulation of the Surface of Aqueous Ionic and Surfactant Solutions

Mária Lbadaoui-Darvas

The surface of aqueous solutions of simple salts was not the main focus of scientific attention for a long while. Considerable interest in studying such systems has only emerged in the past two decades, following the pioneering finding that large halide io ...

AMER CHEMICAL SOC2022

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

EPFL2020

Motivating Innovation: The Effect of Loss Aversion on the Willingness to Persist

Kenneth Younge

We investigate the willingness of individuals to persist at exploration in the face of failure. Prior research suggests that the organization's "tolerance for failure" may motivate greater exploration by the individual. Little is known, however, about how ...

MIT Press2020

Unfolding the prospects of computational (bio)materials modeling

Ignacio Pagonabarraga Mora

In this perspective communication, we briefly sketch the current state of computational (bio)material research and discuss possible solutions for the four challenges that have been increasingly identified within this community: (i) the desire to develop a ...

AMER INST PHYSICS2020

Atomistic modeling of the solid-liquid interface of metals and alloys

Edoardo Baldi

Solidification is a phase transformation of utmost importance in material science, for it largely controls materials' microstructure on which a wide range of mechanical properties depends. Almost every human artifact undergoes a transformation that leads t ...

EPFL2020

Computational characteristics and hardware implications of brain tissue simulations

Francesco Cremonesi

Understanding the link between the brain's anatomy and its function through computer simulations of neural tissue models is a widely used approach in computational neuroscience. This technique enables rapid prototyping and testing of hypotheses, allowing r ...

EPFL2019

Moving on: Is Existenzminimum Still Relevant?

Bruno Marchand

In the inter-war period, progressive architects confronted the building of mass housing with an analogy with rational and functional workplaces. At the 2nd CIAM (Congres Internationaux d'Architecture Moderne), held in Frankfurt in 1929, this was tested aga ...

2019

Explicit versus implicit front advancing schemes for the simulation of hydraulic fracture growth

Brice Tanguy Alphonse Lecampion, Haseeb Zia

The proper computation of the time evolution of the fracture front is the main challenge of three-dimensional (3D) hydraulic fracture growth simulation. We discuss explicit and implicit variants of a hydraulic fracture propagation scheme based on a level s ...

2019

Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks

Sebastian Gerlach, Aurelio Bay, Mikhail Chapochnikov, Guido Haefeli, Alexey Boyarsky, Sun Hee Kim, Alexandre Emmanuel Grandchamp, Oleg Ruchayskiy, Lesya Shchutska, Nikolaos Charitonidis, Jean-Loup Tastet, Ekaterina Kuznetsova, Inar Timiryasov, Maria Elena Stramaglia, Ana Bárbara Rodrigues Cavalcante, Federico Leo Redi, Elena Graverini

This paper presents a fast approach to simulating muons produced in interactions of the SPS proton beams with the target of the SHiP experiment. The SHIP experiment will be able to search for new long-lived particles produced in a 400 GeV/c SPS proton beam ...

2019

Data-Driven Inverse Optimization with Incomplete Information

Peyman Mohajerin Esfahani, Daniel Kuhn, Soroosh Shafieezadeh Abadeh, Grani Adiwena Hanasusanto

In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. Thus, the observer seeks the agent's objective function that best explains a historical ...

2018

Computational Design of Deployable Auxetic Shells

Mark Pauly, Mina Konakovic Lukovic

We propose an interactive computational design method for deployable auxetic shells. We realize deployable auxetics as triangular linkages that can be actuated with simple expansive mechanisms to assume a desired freeform target shape. The core feature of ...

2018

Entrepreneurial Behavior: A Reconceptualization and Extension Based on Identity Theory

Marc Gruber

Research summaryEntrepreneurial behavior is core to our understanding of entrepreneurship. Yet, research progress is hindered because most studies adopt a traditional perspective of the construct that is embedded in economic rationality and focused on for- ...

Wiley2017

Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a POD-Galerkin method and a vascular shape parametrization

Alfio Quarteroni, Gianluigi Rozza, Andrea Manzoni, Elena Faggiano

In this work a reduced-order computational framework for the study of haemodynamics in three-dimensional patient-specific configurations of coronary artery bypass grafts dealing with a wide range of scenarios is proposed. We combine several efficient algor ...

Elsevier2016