Business Process Modeling

Díaz, Rafael; Behr, Joshua G.; Tulpule, Mandar

doi:10.1002/9781118241042.ch6

Cited by 27 publications

(3 citation statements)

References 53 publications

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“…Simulation (or “computational”) models are powerful, well‐established tools for forecasting and evaluating conservation actions, allowing managers to address some of the barriers to conservation innovation (Brum, Pressey, Bini, & Loyola, 2019; DeAngelis et al, 1998; Larson, Thompson III, Millspaugh, Dijak, & Shifley, 2004; Nilsson et al, 2005; Ravenscroft, Scheller, Mladenoff, & White, 2010; Tulloch, Hagger, & Greenville, 2020; Twilley, Rivera‐Monroy, Chen, & Botero, 1999). Simulation models and their associated visualizations inform many critical policy and decision‐making endeavors today (Börner, Rouse, Trunfio, & Stanley, 2018), including global climate change (Lempert & Groves, 2010; Neilson et al, 2005), pandemic spread and response (Giordano et al, 2020; Hall, Gani, Hughes, & Leach, 2007), transportation planning and operation (Robinson, 2012), homeland security risk assessment (Ezell, 2012), and business process efficiency and performance (Diaz, Behr, & Tulpule, 2012) among others. Forecasting has rapidly developed over the past decade thanks to improvements in software and hardware (Scheller, 2018) and, combined with the rise in availability of large ecological data sets that expand the scales of observation, is being increasingly used to examine pressing ecological problems (Cheruvelil & Soranno, 2018).…”

Section: The Role Of Forecastingmentioning

confidence: 99%

Forecasting for intended consequences

Mozelewski

Scheller

2021

Conservat Sci and Prac

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Restoration and conservation innovations face numerous challenges that often limit widespread adoption, including uncertainty of outcomes, risk averse or status quo biased management, and unknown trade‐offs. These barriers often result in cautious conservation that does not consider the true cost of impeding innovation, and overemphasizes the risks of unintended consequences versus the opportunities presented by proactive and innovative conservation, the intended consequences. Simulation models are powerful tools for forecasting and evaluating the potential outcomes of restoration or conservation innovations prior to on‐the‐ground deployment. These forecasts provide information about the potential trade‐offs among the risks and benefits of candidate management actions, elucidating the likelihood that an innovation will achieve its intended consequences and at what cost. They can also highlight when and where business‐as‐usual management may incur larger costs than alternative management approaches over the long‐term. Forecasts inform the decision‐making process prior to the implementation of emergent, proactive practices at broad scales, lending support for management decisions and reducing the barriers to innovation. Here we review the science, motivations, and challenges of forecasting for restoration and conservation innovations.

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Section: The Role Of Forecastingmentioning

confidence: 99%

Forecasting for intended consequences

Mozelewski

Scheller

2021

Conservat Sci and Prac

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“…System Dynamics has also been used to inform multiple health policy domains such as tobacco (Ahmad, 2005;Ahmad & Billimek, 2007) and HIV (Atun et al, 2007), among others. Diaz, Tulpule, and Behr (2011) Hisch, 2011).…”

Section: Research Approachmentioning

confidence: 99%

Modeling the Transition from Adverse to Healthy Sleep Behaviors among School Age Children

Díaz

Szklo-Coxe

Behr

et al. 2015

International Journal of Information Systems and Social Change

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This research models and simulates, by way of a System Dynamics, approach sleep behavior in the presence of intervention strategies. The authors draw upon the established compartmental Susceptible, Infection, and Recovery (SIR) model used in epidemiology to characterize the potential for children and adolescents to both develop adverse sleep behaviors and to recover healthy sleep behaviors as they progress through educational levels. The development of healthy sleep during childhood and adolescence is important to the sustainment of healthy behaviors into early adulthood. Interventions designed to alter unhealthy sleep-related behaviors adopted at an early age may have a salubrious impact upon later chronic disease development. Our initial analyses adequately reproduce the drift experienced by children and adolescents who develop adverse sleep behaviors as they mature and transition through school levels. The ability to evaluate the effectiveness of interventions is important to public health officials. Investments in intervention programs shown to have positive health outcomes are attractive to policy makers. Although such programs may not be cost effective in the near-term, the programs may be cost saving in the long-term. The System Dynamics approach simulates behavior over time and allows policymakers insight into both the short- and longer-term cost and benefits.

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“…Both hurricane and the associated severe surge pose potentially significant hazards to the Hampton Roads population and infrastructure. Especially vulnerable are medically fragile populations, such as those with mobility and sensory disabilities, and residents of its economically challenged communities (Diaz et al, 2012;Diaz, Behr, Jeng, & Tulpule, 2011) . Despite the fact that past catastrophic events have increased situational awareness as well as generated greater local and regional collaboration, undeveloped is an understanding of the immediate and longer-term vulnerability of many traditionally underserved communities and vulnerable population segments within Hampton Roads.…”

Section: Introductionmentioning

confidence: 99%

A Systems Framework for Modeling Health Disparities in the Prevalence in Chronic Conditions following a Natural Disaster Event

Díaz

Behr

Longo

et al. 2013

International Journal of Privacy and Health Information Management

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Natural disaster events impact both the short- and long-term health of a region’s population. Due to variation in the vulnerability among population segments, a severe storm event can be expected over time to have a greater public health impact upon traditionally underserved and medically fragile populations. This research illustrates the causal relationships leading to a change over time in the prevalence of chronic conditions among black and non-black populations within U.S. Hampton Roads. Using a system dynamics approach, the authors develop and integrate a macro model that captures change in regional economic and demographic profiles with a micro model that focuses on access to health services and the ability to respond within the context of the changing regional environment. The authors’ study finds that: (1) the disparity in the prevalence of chronic conditions increases over time following the event, (2) the growth in health disparity may be slowed by regional resiliency intervention policies, and (3) mitigation efforts result in greater reductions in growth of chronic conditions among the black population relative the non-black general population. Knowledge of the disparate impact that such an event will have on the long-term health of underserved and medically fragile populations may be used to inform mitigation investments.

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Business Process Modeling

Cited by 27 publications

References 53 publications

Forecasting for intended consequences

Forecasting for intended consequences

Modeling the Transition from Adverse to Healthy Sleep Behaviors among School Age Children

A Systems Framework for Modeling Health Disparities in the Prevalence in Chronic Conditions following a Natural Disaster Event

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