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Computational social choice. Computational social choice is a field at the intersection of social choice theory, theoretical computer science, and the analysis of multi-agent systems. [1] It consists of the analysis of problems arising from the aggregation of preferences of a group of agents from a computational perspective.
Business and Economics portal. Money portal. v. t. e. Computational Economics is an interdisciplinary research discipline that involves computer science, economics, and management science. [1] This subject encompasses computational modeling of economic systems. Some of these areas are unique, while others established areas of economics by ...
Artificial economics. Artificial Economics can be defined as ″a research field that aims at improving our understanding of socioeconomic processes with the help of computer simulation ″. [1] Like in Theoretical Economics, the approach followed in Artificial Economics to gain understanding of socioeconomic processes involves building and ...
Behavioral economics is the study of the psychological, cognitive, emotional, cultural and social factors involved in the decisions of individuals or institutions, and how these decisions deviate from those implied by classical economic theory. [1] [2] Behavioral economics is primarily concerned with the bounds of rationality of economic agents.
Leigh Tesfatsion is a computational economist who taught at Iowa State University. She received her doctorate at the University of Minnesota, and taught at the University of Southern California before moving to Iowa State. She is known for promoting agent-based models as an alternative to rational expectations general equilibrium models for ...
All authors whose names appear on the submission. 1) made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work; 2) drafted the work or revised it critically for important intellectual content;
Jun 24, 2015 · This paper proposes a Bayesian network model to address censoring, class imbalance and real-time implementation issues in credit risk scoring. It shows that the Bayesian network model performs well against competing models (logistic regression model and neural network model) along several dimensions such as accuracy, sensitivity, precision and the receiver characteristic curve. Better ...
