Overconfidence and Financial Fraud : Application of Structural-Choice Model of Victimization

2020 ◽  
Vol 31 (3) ◽  
pp. 91-115
Author(s):  
A Reum Kim ◽  
Hae Kyung Yang
Keyword(s):  
Choice Model ◽  
Financial Fraud ◽  
Structural Choice

Victim Selection

2017 ◽  
Author(s):  
Margit Averdijk
Keyword(s):  
Final Section ◽  
Choice Model ◽  
Methodological Innovation ◽  
Future Directions ◽  
Victim Selection ◽  
Theoretical Perspectives ◽  
Structural Choice ◽  
Crime Types ◽  
Social Interactionism

This chapter reviews what is known about victim selection—that is, the question of why offenders select some people, but not others, to be victims of crime. It first addresses theoretical perspectives on victim selection, namely the structural–choice model of victim selection, social interactionism, and target congruence. It then describes three data sources that have been used in prior research to study victim selection: police reports, victimization surveys, and offender interviews. Subsequently, empirical findings on victim selection are reviewed and organized into nine subsections: victims’ demographic characteristics, psychological characteristics, physical characteristics, behavior, biological characteristics, prior victimization, relationship to the offender, behavior during the offense, and the role of randomness. The final section discusses research gaps and potential future directions in the field, including an emphasis on theoretical explanations and mediators, cross-cultural studies, methodological innovation and diversity, interactions between victim and offender characteristics, and generality across crime types and subpopulations.

Are More Profiles Better Than Fewer?: Searching for Parsimony and Relevance in Stated Choice Experiments

2000 ◽  
Vol 1719 (1) ◽  
pp. 165-174 ◽  
Author(s):  
Peter R. Stopher ◽  
David A. Hensher
Keyword(s):  
New Zealand ◽  
Degrees Of Freedom ◽  
Choice Model ◽  
Fractional Factorial ◽  
Sources Of Information ◽  
Stated Choice ◽  
Complex Design ◽  
Data Collection Instrument ◽  
Main Effects ◽  
Better Than

Transportation planners increasingly include a stated choice (SC) experiment as part of the armory of empirical sources of information on how individuals respond to current and potential travel contexts. The accumulated experience with SC data has been heavily conditioned on analyst prejudices about the acceptable complexity of the data collection instrument, especially the number of profiles (or treatments) given to each sampled individual (and the number of attributes and alternatives to be processed). It is not uncommon for transport demand modelers to impose stringent limitations on the complexity of an SC experiment. A review of the marketing and transport literature suggests that little is known about the basis for rejecting complex designs or accepting simple designs. Although more complex designs provide the analyst with increasing degrees of freedom in the estimation of models, facilitating nonlinearity in main effects and independent two-way interactions, it is not clear what the overall behavioral gains are in increasing the number of treatments. A complex design is developed as the basis for a stated choice study, producing a fractional factorial of 32 rows. The fraction is then truncated by administering 4, 8, 16, 24, and 32 profiles to a sample of 166 individuals (producing 1, 016 treatments) in Australia and New Zealand faced with the decision to fly (or not to fly) between Australia and New Zealand by either Qantas or Ansett under alternative fare regimes. Statistical comparisons of elasticities (an appropriate behavioral basis for comparisons) suggest that the empirical gains within the context of a linear specification of the utility expression associated with each alternative in a discrete choice model may be quite marginal.

An Economic Choice Model for Lexicographic Preferences

2015 ◽  
Author(s):  
Hyung Soo Lim ◽  
Duk Bin Jun ◽  
Dong Soo Kim ◽  
Yun Shin Lee
Keyword(s):  
Choice Model ◽  
Lexicographic Preferences ◽  
Economic Choice
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