LFSC_Kai_final.pdf (436.38 kB)
Logit function in stochastic categorizations
journal contribution
posted on 2023-06-07, 07:52 authored by Kai WangBinary categorizations refer to the behaviour for which a decision maker puts the alternatives from a menu into binary categories (authorization category and rejection category). For example, a judge approves/denies parole for inmates, a doctor administers/withholds treatment to patients, and a teacher passes/fails essays. Categorizations are observed to exhibit randomness in various contexts, but the stochasticity in categorizations has received little attention in economic studies. We consider stochastic categorizations using a logit categorization function, which expresses the probability of authorizing an alternative from a menu in a ‘logit-like’ form. We characterize the model from a simple condition on authorization frequencies. Furthermore, we derive an empirical version of our model, which not only provides insight into applications of the model, but also gives an intuitive interpretation of the stochasticity in categorizations. We use simple examples to show that our model can bring new insights and research possibilities in the economic studies.
History
Publication status
- Published
File Version
- Accepted version
Journal
Oxford Economic PapersISSN
0030-7653Publisher
Oxford University PressExternal DOI
Page range
1-15Department affiliated with
- Economics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2021-10-13First Compliant Deposit (FCD) Date
2021-08-28Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC