21 papers · ranked by Valyu relevance
Bin Yan, Feng Shi, Rui-qiang Yu
In order to seek a new method of book evaluation and realize book resources sharing among the regional university libraries, we think that library should collect books of the high utility value in the case of limited funds. We proposed a changing Bellman equation as a utility function and used the explicit functions of…
William R. Stauffer, Armin Lak, Wolfram Schultz
Title: Summary Background Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that…
Andrés Muñoz Medina, Jenny Gillenwater
We propose and analyze a general-purpose dataset-distance-based utility function family (Duff) for differential privacy's exponential mechanism. Given a particular dataset and a statistic (e.g., median, mode), this function family assigns utility to a possible output o based on the number of individuals whose data…
Tom Everitt, Daniel Filan, Mayank Daswani, Marcus Hütter
Any agent that is part of the environment it interacts with and has versatile actuators (such as arms and fingers), will in principle have the ability to self-modify – for example by changing its own source code. As we continue to create more and more intelligent agents, chances increase that they will learn about this…
Philipe M. Bujold, Simone Ferrari-Toniolo, Wolfram Schultz
This study investigated the influence of experienced reward distributions on the shape of utility functions inferred from economic choice. Utility is the hypothetical variable that appears to be maximized by the choice. Despite the generally accepted notion that utility functions are not insensitive to external…
Urszula Chajewska, Daphne Koller
Decision theory does not traditionally include uncertainty over utility functions. We argue that the a person's utility value for a given outcome can be treated as we treat other domain attributes: as a random variable with a density function over its possible values. We show that we can apply statistical density…
Philipe M. Bujold, Leo Chi U. Seak, Wolfram Schultz, Simone Ferrari-Toniolo
'Simone Ferrari-Toniolo'] Decisions can be risky or riskless, depending on the outcomes of the choice. Expected utility theory describes risky choices as a utility maximization process: we choose the option with the highest subjective value (utility), which we compute considering both the option’s value and its…
Rebekka Wohlrab, David Garlan
[Context and motivation:] For realistic self-adaptive systems, multiple quality attributes need to be considered and traded off against each other. These quality attributes are commonly encoded in a utility function, for instance, a weighted sum of relevant objectives. [Question/problem:] The research agenda for…
Lukas D. Sauer, Alexander Ritz, Meinhard Kieser, Michael Brimacombe
Basket trial designs are a type of master protocol in which the same therapy is tested in several strata of the patient cohort. Many basket trial designs implement borrowing mechanisms. These allow sharing information between similar strata with the goal of increasing power in responsive strata while at the same time…
Yoav Shoham
We introduce a new interpretation of two related notions - conditional utility and utility independence. Unlike the traditional interpretation, the new interpretation render the notions the direct analogues of their probabilistic counterparts. To capture these notions formally, we appeal to the notion of utility…
Arthur E. Attema, Han Bleichrodt, Olivier L'Haridon
In most medical decisions, probabilities are ambiguous and not objectively known. Empirical evidence suggests that people's preferences are affected by ambiguity. Health economic analyses generally ignore ambiguity preferences and assume that they are the same as preferences under risk. We show how health preferences…
Christopher P. Chambers, Peng Liu, Ruodu Wang
In this paper, we establish a mathematical duality between utility transforms and probability distortions. These transforms play a central role in decision under risk by forming the foundation for the classic theories of expected utility, dual utility, and rank-dependent utility. Our main results establish that…
Authors not listed
We present an open source collection of scripts and programs for the setup, management and evaluation of calculations with the Vienna ab-initio simulation package (VASP), called utils4VASP. It contains 20 independent Python scripts and Fortran programs, all with a unified and intuitive handling concept based on command…
Milosz Wieczor, Jacek Czub
Despite the increasing automation of workflows for the preparation of systems for molecular dynamics simulations, the custom editing of molecular topologies to accommodate non-standard modifications remains a daunting task even for experienced users. To alleviate this issue, we created Gromologist, a utility library…
Christian Engwer, Carsten Gräser, Steffen Müthing, Oliver Sander
The dune-functions Dune module introduces a new programmer interface for discrete and non-discrete functions. Unlike the previous interfaces considered in the existing Dune modules, it is based on overloading operator(), and returning values by-value. This makes user code much more readable, and allows the…
M.Z. Naser, Mohammad Khaled al-Bashiti, Arash Teymori Gharah Tapeh, Armin Dadras Eslamlou + 6 more
Optimization Algorithms and Metaheuristics with Mathematical and Visual Descriptions Authors: ['M.Z. Naser' '\u202c\u202c\u202cMohammad Khaled al-Bashiti' 'Arash Teymori Gharah Tapeh' 'Armin Dadras Eslamlou' 'Ahmed Z. Naser' 'Venkatesh Kodur' 'Rami Hawileeh' 'Jamal A. Abdalla' 'Nima Khodadadi' 'Amir H. Gandomi'] In the…
Angela Jones, Eric Schulz, Björn Meder, Azzurra Ruggeri
How do people actively explore to learn about functional rules, that is, how continuous inputs map onto continuous outputs? We introduce a novel paradigm to investigate information search in continuous, multi-feature function learning scenarios. Participants either actively selected or passively observed information to…
Charley M. Wu, Eric Schulz, Samuel J Gershman
How do people learn functions on structured spaces? And how do they use this knowledge to guide their search for rewards in situations where the number of options is large? We study human behavior on structures with graph-correlated values and propose a Bayesian model of function learning to describe and predict their…
Charley M. Wu, Eric Schulz, Samuel J. Gershman
From social networks to public transportation, graph structures are a ubiquitous feature of life. Yet little is known about how humans learn functions on graphs, where relationships are defined by the connectivity structure. We adapt a Bayesian framework for function learning to graph structures, and propose that…
Eric Schulz, Joshua B. Tenenbaum, David Duvenaud, Maarten Speekenbrink + 1 more
How do people recognize and learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is achieved by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian…
Carlos P. Carmona, Nicola Pavanetto, Giacomo Puglielli
Functional trait space analyses are pivotal to define species’ ecological strategies across the tree of life. Yet, there is no single application that streamlines the many sometimes-troublesome steps needed to build and analyze functional trait spaces. To fill this gap, we propose funspace, an R package to easily…