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Bart Gajderowicz
Social Service Chain Modelling and Evaluation
Bart Gajderowicz Supervisors: Dr. Mark S. Fox and Dr. Michael Grüninger
Centre for Social Service Engineering
Enterprise Integration Laboratory Semantic Technologies Laboratory
Mechanical and Industrial Engineering Department
University of Toronto, Canada
February 6, 2015
Bart Gajderowicz
Vision and Goal Motivation Background Methodology Modelling Requirements Evaluation Method Summary
Outline
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Vision: Apply Industrial Engineering (IE) and Computer Science
(CS) theories and techniques to represent and analyze the social service process from the client’s perspective.
Goal: ① Create high fidelity models of social service clients.
② With the use of a simulation with these models, evaluate impact of intervention programs on the targeted clients.
E.g. Housing programs for the homeless.
Vision and Goal
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Research Question: Is it possible to create models and a simulation environment
which can be used to evaluate the impact of intervention programs?
Hypothesis: When factors are sufficiently quantified, a robust simulation
and analysis can be used to perform predictive analysis to evaluate the impact of such programs.
Factors: client behaviour and decision making.
Motivation
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We are dealing with people, and not just “materiel and machines”, whose behaviour is erratic and unique.
Humans are complex multi-layered, rational AND irrational decision makers
Begin by modeling Homeless Elderly Clients (HEC):
Motivation
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Vulnerable subpopulation. Rise in North America’s elderly living on the streets
[3, 4] Toronto: from 18% in 2006, to 29% in 2013 [4]. Homeless intervention studies often overlook the
vulnerabilities and unique needs of elderly people [4].
Bart Gajderowicz
IE techniques in the healthcare industry have traditionally focused on Operations Research (OR):
Optimizing service delivery procedures and resources [6]. Simulating epidemic outbreaks [7]. To identify the best strategies to address outstanding
healthcare problems, such as scheduling appointments [8]
This work: Relies on passive observations which can’t identify the
underlying causes or contributing behaviors [9]. Incorporating psychological or behavioral aspects has been
severely lacking.
Background
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Background
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Simulating client’s unique characteristics in a Social Service Chain (SSC).
A network of social services through which a client flows [1]. Based on a Case Management Plan (CMP).
Start
Goal
Social Housing
Faith Services
Health Services
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Background
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There are four key SSC components: ① Clients (main focus). ② Services. ③ Processes where clients and services interact. ④ Society: context and constraints [2].
Clients: e.g. impoverished, mental health issues, living with a physical disability.
Services: e.g. shelter, social worker
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Agent-based simulation (ABS) Model complex behavior of individual client agents, their
interactions, and organizational structures. Micro-simulation puts more focus on analysis of each agent.
Cognitive Architecture (CA):
“underlying infrastructure for an intelligent system” [10].
Reasoning Architecture (RA): Used by intelligent components of CAs. Memory used to store content about an agent’s beliefs, goals,
and overall knowledge.
Methodology
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Creating an agent architecture that has the following human behavioral characteristics:
Client Behavior Requirements
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Teleological (goal driven)
Constrained
Rational (bounded rationality)
Trainable
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Service Agent Requirements
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Service Delivery Specifications
Metrics and
Effectiveness
Constraints Accountability
Macro-level Service Organization
many clients many agents many services
Micro-level Service Agent
one or more services directly with clients
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Process Modelling Multiple treatment plans, made from an aggregate of client
plans. Model an aggregate of services. Analyze plans from an OR perspective.
Process Instrumentation Tracking resource consumption. Rationale behind decision making.
Process Modeling Requirements
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Society Requirements
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External Constraints Cultural norms, legal constraints.
Accountability: external factors influence service quality and client reasoning.
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Recall one of my goals: Evaluate impact of intervention programs on the targeted
clients. E.g. Housing programs for the homeless.
Methodology
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Methodology
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Open Government Data
Service Model Client Model
Micro-Simulation
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Model the Social Service Chain. Evaluate Social Service program interventions. Requirements: clients, services, processes, and social
context. Define an agent with bounded rationality. Build preliminary client models based on existing studies. Test model on agent configurations in a simulation. Verify and extend theories for various agent configurations
against existing housing intervention programs.
Summary
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[1] Beck, J. C., Chignell, M., Consens, M., Fox, M. S., and Grüninger, M. (2013). Optimizing the Social Services Chain, A Systems Engineering Perspective. Technical report, Information Engineering Group, Department of Mechanical and Industrial Engineering, University of Toronto.
[2] Gajderowicz, B., Fox, M. S., and Grüninger, M. (2014). Requirements for an Ontological Foundation for Modelling Social Service Chains. In Guan, Y. and Liao, J., editors, Proceedings of the 2014 Industrial and Systems Engineering Research Conference, Montreal, QC.
[3] Ng, S., Rizvi, S., and Kunik, M. (2013). Prevalence of Homeless Older Adults and Factors Causing Their Homelessness: A Review. The Internet Journal of Geriatrics and Gerontology, 8(1):1–12.[1] (2013). Street needs assessment 2013. Technical report, City of Toronto, Toronto.
[4] Ploeg, J., Hayward, L., Woodward, C., and Johnston, R. (2008). A case study of a Canadian homelessness intervention programme for elderly people. Health & social care in the community, 16(6):593–605.
[5] Duncan, I. and Curnow, R. (1978). Operational Research in the Health and Social Services. Journal of the Royal Statistical Society. Series A, 141(2):153–194.
References I
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[6] Beeler, M. F., Aleman, D. M., and Carter, M. W. (2012). A large simulation experiment to test influenza pandemic behavior. In Simulation Conference (WSC), pages 1–7.
[7] Vasilakis, C., Sobolev, B. G., Kuramoto, L., and Levy, A. R. (2006). A simulation study of scheduling clinic appointments in surgical care: individual surgeon versus pooled lists. Journal of the Operational Research Society, 58(2):202–211.
[8] Eveborn, P., Ronnqvist, M., Einarsdottir, H., Eklund, M., Liden, K., and Almroth, M. (2009). Operations Research Improves Quality and Efficiency in Home Care. Interfaces, 39(1):18–34.
[9] Langley, P., Laird, J. E., and Rogers, S. (2009). Cognitive architectures: Research issues and challenges. Cognitive Systems Research, 10(2):141–160.
References II
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