Planning for success: The interdisciplinary approach to building Bayesian models

Alex Dekhtyar; Judy Goldsmith; Beth Goldstein; Krol Kevin Mathias; Cynthia Isenhour

doi:10.1016/j.ijar.2008.05.005

Planning for success: The interdisciplinary approach to building Bayesian models

Alex Dekhtyar
, Judy Goldsmith
, Beth Goldstein
, Krol Kevin Mathias
, Cynthia Isenhour

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	416-428
Number of pages	13
Journal	International Journal of Approximate Reasoning
Volume	50
Issue number	3
DOIs	https://doi.org/10.1016/j.ijar.2008.05.005
State	Published - Mar 2009

Bibliographical note

Funding 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.

Funding

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.

Funders	Funder number
U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China	ITR-0325063

Keywords

Bayesian networks
Human-centered design
Knowledge elicitation
Planning
Social construction of technology

ASJC Scopus subject areas

Software
Theoretical Computer Science
Applied Mathematics
Artificial Intelligence

Access to Document

10.1016/j.ijar.2008.05.005

Cite this

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title = "Planning for success: The interdisciplinary approach to building Bayesian models",

abstract = "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.",

keywords = "Bayesian networks, Human-centered design, Knowledge elicitation, Planning, Social construction of technology",

author = "Alex Dekhtyar and Judy Goldsmith and Beth Goldstein and Mathias, \{Krol Kevin\} and Cynthia Isenhour",

note = "Funding 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.",

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doi = "10.1016/j.ijar.2008.05.005",

language = "English",

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T2 - The interdisciplinary approach to building Bayesian models

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AU - Goldsmith, Judy

AU - Goldstein, Beth

AU - Mathias, Krol Kevin

AU - Isenhour, Cynthia

N1 - Funding 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.

PY - 2009/3

Y1 - 2009/3

N2 - 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.

AB - 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.

KW - Bayesian networks

KW - Human-centered design

KW - Knowledge elicitation

KW - Planning

KW - Social construction of technology

UR - https://www.scopus.com/pages/publications/60849119587

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DO - 10.1016/j.ijar.2008.05.005

M3 - Article

AN - SCOPUS:60849119587

SN - 0888-613X

VL - 50

SP - 416

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JO - International Journal of Approximate Reasoning

JF - International Journal of Approximate Reasoning

IS - 3

ER -

Planning for success: The interdisciplinary approach to building Bayesian models

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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