This paper describes a process by which anthropologists, computer scientists, and social welfare case managers collaborated to build a stochastic model of welfare advising in Kentucky. In the process of collaboration, the research team rethought the Bayesian network model of Markov decision processes and designed a new knowledge elicitation format. We expect that this model will have wide applicability in other domains.
|Number of pages||13|
|Journal||International Journal of Approximate Reasoning|
|State||Published - Mar 2009|
Bibliographical noteFunding Information:
This work was partly supported by NSF Grant ITR-0325063. The opinions expressed here are those of the authors and do not represent the Foundation, the University, or any social welfare offices. We thank Russell Almond for enabling communication between the computer scientists and social scientists about bowties, and we thank Joan Mazur for her work on the design of the HLE interface and her discussions of SCOT theory with several of the coauthors.
- Bayesian networks
- Human-centered design
- Knowledge elicitation
- Social construction of technology
ASJC Scopus subject areas
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics