scholarly journals A Finite-Life Private-Information Theory of Unsecured Consumer Debt

2007 ◽  
Author(s):  
Satyajit Chatterjee ◽  
Philip Dean Corbae ◽  
José-Victor Rios-Rull
Keyword(s):  
Information Theory ◽  
Private Information ◽  
Consumer Debt

scholarly journals A finite-life private-information theory of unsecured consumer debt

2008 ◽  
Vol 142 (1) ◽  
pp. 149-177 ◽  
Author(s):  
Satyajit Chatterjee ◽  
Dean Corbae ◽  
José-Víctor Ríos-Rull
Keyword(s):  
Information Theory ◽  
Private Information ◽  
Consumer Debt

scholarly journals Modeling the Revolving Revolution: The Debt Collection Channel

2017 ◽  
Vol 107 (3) ◽  
pp. 897-930 ◽  
Author(s):  
Lukasz A. Drozd ◽  
Ricardo Serrano-Padial
Keyword(s):  
Information Technology ◽  
Private Information ◽  
Credit Cards ◽  
Consumer Debt ◽  
Widespread Adoption ◽  
Debt Collection ◽  
Role Of Information

We investigate the role of information technology (IT) in the collection of delinquent consumer debt. We argue that the widespread adoption of IT by the debt collection industry in the 1990s contributed to the observed expansion of unsecured risky lending such as credit cards. Our model stresses the importance of delinquency and private information about borrower solvency. The prevalence of delinquency implies that the costs of debt collection must be borne by lenders to sustain incentives to repay debt. IT mitigates informational asymmetries, allowing lenders to concentrate collection efforts on delinquent borrowers who are more likely to repay. (JEL D14, D82, G21, L84, M15, O33)

Constructing and Deconstructing Concepts

2016 ◽  
Vol 63 (5) ◽  
pp. 249-262 ◽  
Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo
Keyword(s):  
Information Theory ◽  
Model Performance ◽  
Unsupervised Classification ◽  
Family Resemblance ◽  
Category Membership ◽  
Formal Models ◽  
Human Beings ◽  
One Dimensional ◽  
Relational Patterns ◽  
Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

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