9,322 research outputs found
Personality Assessment, Forced-Choice.
Instead of responding to questionnaire items one at a time, respondents may be forced to make a choice between two or more items measuring the same or different traits. The forced-choice format eliminates uniform response biases, although the research on its effectiveness in reducing the effects of impression management is inconclusive. Until recently, forced-choice questionnaires were scaled in relation to person means (ipsative data), providing information for intra-individual assessments only. Item response modeling enabled proper scaling of forced-choice data, so that inter-individual comparisons may be made. New forced-choice applications in personality assessment and directions for future research are discussed
Individualized Rank Aggregation using Nuclear Norm Regularization
In recent years rank aggregation has received significant attention from the
machine learning community. The goal of such a problem is to combine the
(partially revealed) preferences over objects of a large population into a
single, relatively consistent ordering of those objects. However, in many
cases, we might not want a single ranking and instead opt for individual
rankings. We study a version of the problem known as collaborative ranking. In
this problem we assume that individual users provide us with pairwise
preferences (for example purchasing one item over another). From those
preferences we wish to obtain rankings on items that the users have not had an
opportunity to explore. The results here have a very interesting connection to
the standard matrix completion problem. We provide a theoretical justification
for a nuclear norm regularized optimization procedure, and provide
high-dimensional scaling results that show how the error in estimating user
preferences behaves as the number of observations increase
A Voting-Based System for Ethical Decision Making
We present a general approach to automating ethical decisions, drawing on
machine learning and computational social choice. In a nutshell, we propose to
learn a model of societal preferences, and, when faced with a specific ethical
dilemma at runtime, efficiently aggregate those preferences to identify a
desirable choice. We provide a concrete algorithm that instantiates our
approach; some of its crucial steps are informed by a new theory of
swap-dominance efficient voting rules. Finally, we implement and evaluate a
system for ethical decision making in the autonomous vehicle domain, using
preference data collected from 1.3 million people through the Moral Machine
website.Comment: 25 pages; paper has been reorganized, related work and discussion
sections have been expande
Disentangling the effects of geographic and ecological isolation on genetic differentiation
Populations can be genetically isolated both by geographic distance and by
differences in their ecology or environment that decrease the rate of
successful migration. Empirical studies often seek to investigate the
relationship between genetic differentiation and some ecological variable(s)
while accounting for geographic distance, but common approaches to this problem
(such as the partial Mantel test) have a number of drawbacks. In this article,
we present a Bayesian method that enables users to quantify the relative
contributions of geographic distance and ecological distance to genetic
differentiation between sampled populations or individuals. We model the allele
frequencies in a set of populations at a set of unlinked loci as spatially
correlated Gaussian processes, in which the covariance structure is a
decreasing function of both geographic and ecological distance. Parameters of
the model are estimated using a Markov chain Monte Carlo algorithm. We call
this method Bayesian Estimation of Differentiation in Alleles by Spatial
Structure and Local Ecology (BEDASSLE), and have implemented it in a
user-friendly format in the statistical platform R. We demonstrate its utility
with a simulation study and empirical applications to human and teosinte
datasets
Can genetic algorithms explain experimental anomalies? An application to common property resources
It is common to find in experimental data persistent oscillations in the aggregate outcomes and high levels of heterogeneity in individual behavior. Furthermore, it is not unusual to find significant deviations from aggregate Nash equilibrium predictions. In this paper, we employ an evolutionary model with boundedly rational agents to explain these findings. We use data from common property resource experiments (Casari and Plott, 2003). Instead of positing individual-specific utility functions, we model decision makers as selfish and identical. Agent interaction is simulated using an individual learning genetic algorithm, where agents have constraints in their working memory, a limited ability to maximize, and experiment with new strategies. We show that the model replicates most of the patterns that can be found in common property resource experiments.Bounded rationality, Experiments, Common-pool resources, Genetic algorithms
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