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Systems, Volume 7, Issue 4 (December 2019) – 9 articles

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20 pages, 1783 KiB  
Article
Theoretical Foundations for Preference Representation in Systems Engineering
by Hanumanthrao Kannan, Garima V. Bhatia, Bryan L. Mesmer and Benjamin Jantzen
Systems 2019, 7(4), 55; https://doi.org/10.3390/systems7040055 - 12 Dec 2019
Cited by 6 | Viewed by 5332
Abstract
The realization of large-scale complex engineered systems is contingent upon satisfaction of the preferences of the stakeholder. With numerous decisions being involved in all the aspects of the system lifecycle, from conception to disposal, it is critical to have an explicit and rigorous [...] Read more.
The realization of large-scale complex engineered systems is contingent upon satisfaction of the preferences of the stakeholder. With numerous decisions being involved in all the aspects of the system lifecycle, from conception to disposal, it is critical to have an explicit and rigorous representation of stakeholder preferences to be communicated to key personnel in the organizational hierarchy. Past work on stakeholder preference representation and communication in systems engineering has been primarily requirement-driven. More recent value-based approaches still do not offer a rigorous framework on how to represent stakeholder preferences but assume that an overarching value function that can precisely capture stakeholder preferences exists. This article provides a formalism based on modal preference logic to aid in rigorous representation and communication of stakeholder preferences. Formal definitions for the different types of stakeholder preferences encountered in a systems engineering context are provided in addition to multiple theorems that improve the understanding of the relationship between stakeholder preferences and the solution space. Full article
(This article belongs to the Collection Systems Engineering)
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<p>Absolute preference—Example.</p>
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<p>Comparative preference—example.</p>
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<p>Incomparable worlds.</p>
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15 pages, 665 KiB  
Article
A Governance Perspective for System-of-Systems
by Polinpapilinho F. Katina, Charles B. Keating, James A. Bobo and Tyrone S. Toland
Systems 2019, 7(4), 54; https://doi.org/10.3390/systems7040054 - 9 Dec 2019
Cited by 7 | Viewed by 7086
Abstract
The operating landscape of 21st century systems is characteristically ambiguous, emergent, and uncertain. These characteristics affect the capacity and performance of engineered systems/enterprises. In response, there are increasing calls for multidisciplinary approaches capable of confronting increasingly ambiguous, emergent, and uncertain systems. System of [...] Read more.
The operating landscape of 21st century systems is characteristically ambiguous, emergent, and uncertain. These characteristics affect the capacity and performance of engineered systems/enterprises. In response, there are increasing calls for multidisciplinary approaches capable of confronting increasingly ambiguous, emergent, and uncertain systems. System of Systems Engineering (SoSE) is an example of such an approach. A key aspect of SoSE is the coordination and the integration of systems to enable ‘system-of-systems’ capabilities greater than the sum of the capabilities of the constituent systems. However, there is a lack of qualitative studies exploring how coordination and integration are achieved. The objective of this research is to revisit SoSE utility as a potential multidisciplinary approach and to suggest ‘governance’ as the basis for enabling ‘system-of-systems’ coordination and integration. In this case, ‘governance’ is concerned with direction, oversight, and accountability of ‘system-of-systems.’ ‘Complex System Governance’ is a new and novel basis for improving ‘system-of-system’ performance through purposeful design, execution, and evolution of essential metasystem functions.’ Full article
(This article belongs to the Special Issue Advances in the Systems Sciences 2018)
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<p>Complex system governance (CSG)’s metasystem system functions.</p>
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33 pages, 8508 KiB  
Article
Simulating a Watershed-Scale Strategy to Mitigate Drought, Flooding, and Sediment Transport in Drylands
by Connie M. Maxwell, Saeed P. Langarudi and Alexander G. Fernald
Systems 2019, 7(4), 53; https://doi.org/10.3390/systems7040053 - 28 Nov 2019
Cited by 8 | Viewed by 6135
Abstract
Drylands today are facing a landscape-scale water storage problem. Throughout the increasingly arid Southwest of the United States, vegetation loss in upland watersheds is leading to floods that scour soils and transport sediment that clogs downstream riparian areas and agricultural infrastructure. The resulting [...] Read more.
Drylands today are facing a landscape-scale water storage problem. Throughout the increasingly arid Southwest of the United States, vegetation loss in upland watersheds is leading to floods that scour soils and transport sediment that clogs downstream riparian areas and agricultural infrastructure. The resulting higher flow energies and diminished capacity to infiltrate flood flows are depleting soil water storage across the landscape, negatively impacting agriculture and ecosystems. Land and water managers face challenges to reverse the trends due to the complex interacting social and biogeophysical root causes. Presented here is an integrative system dynamics model that simulates innovative and transformative management scenarios. These scenarios include the natural and hydro-social processes and feedback dynamics critical for achieving long-term mitigation of droughts, flooding, and sediment transport. This model is a component of the Flood Flow Connectivity to the Landscape framework, which integrates spatial and hydrologic process models. Scenarios of support and collaboration for land management innovations are simulated to connect flood flow to the floodplains throughout the watershed to replenish soil storage and shallow groundwater aquifers across regional scales. The results reveal the management policy levers and trade-off balances critical for restoring management and water storage capacity to the system for long-term resilience. Full article
(This article belongs to the Special Issue System Dynamics: Insights and Policy Innovation)
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<p>The dynamics of flood flow connections to the landscape are significant drivers of watershed conditions.</p>
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<p>Managing the water budget targets the processes that connect water inputs into the storage functions and, thus, reveals the mechanisms for increasing water availability.</p>
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<p>Outputs of the Flood Flow Connectivity to the Landscape (FlowCon) modeling framework that contributed to the generalizable and adaptable integrative socio-environmental system dynamics (SD) model addressed in this article. FlowCon identifies optimum locations and quantifies the resulting benefits and extent of management and collaborative support required for restoration of the critical landscape processes of reducing hydrologic energy through increasing water retention, recharge, and vegetative productivity. (<b>a</b>) Locations for reconnecting floodplains at varying priority levels; (<b>b</b>) effects on a synthetic hydrograph of executing the various levels. The top two priority levels were found to be the optimum levels for reversing degradation trends.</p>
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<p>The project area typifies the water storage challenges faced across Southwestern landscapes. The Hatch and Mesilla Valleys, the largest areas of agricultural valleys along the New Mexican Rio Grande, rely upon snowpack to fill the reservoirs, which is becoming increasingly variable in its supply. The occurrences of droughts and a general increase in aridity are drying upland soils, decreasing vegetation, and resulting in scouring floods. Agriculture has the potential to manage these landscapes by slowing and spreading flood flow, which can recharge soil stocks and refill aquifers.</p>
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<p>The upland section of the FlowCon SD model. See <a href="#systems-07-00053-f006" class="html-fig">Figure 6</a> for the downstream valley section. The SD model includes both the existing system and the scenarios of spreading flood flow and storm water, which are indicated in the model by variable shapes filled with dark-orange and bold connectors.</p>
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<p>The valley section of the FlowCon SD model to support farmers to increase water availability through recharge and stormwater spreading.</p>
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<p>Precipitation input is a pink-noise random variation based upon a mean of 10.41 inches (264 mm)/year with a standard deviation of 1.8 inches (45.72 mm). (Note that a common standard in system dynamics models and employed in several graphs in this work is to characterize the behavior in a graph more clearly by not starting the <span class="html-italic">y</span>-axis at zero.)</p>
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<p>Benefits realized by local land managers in the upland systems (<b>a</b>) and the valley systems (<b>b</b>). Three scenarios are shown in the results beginning in this Figure: “No interventions” which represents the base case scenario with no additional flow connectivity management, “Support 1—execution only” which represents outside financial support to execute the new practices on a wider scale quickly, and “Support 2—experimentation and execution” which represents additional support for a phase of experimentation for a better fit to the local conditions to realize productivity benefits more quickly and to a greater extent, which leads to a perception of greater legitimacy of the practices.</p>
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<p>Surface spreading in the uplands (SSa) begins with an optimal target identified by the FlowCon framework for this system (which would be different in other systems) to reduce the runoff quantity by 35%, as that target correlates to sufficient reductions in the peak flows. SSa (<b>a</b>) reduces stormwater runoff (Qr) (<b>b</b>). Approximately 15% of the diverted flow on average is infiltrated and results in reduction of runoff; therefore, the initial targeted surface spreading is approximately 120,000 AF (Acre Feet), as is shown achieved by the Support 2 scenario (<b>a</b>). The reduction of 35% of Qr is also shown achieved by the Support 2 scenario (<b>b</b>).</p>
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<p>This system is structured with a trend of modest groundwater declines to mimic likely site and common aquifer dynamics, as can be seen in the blue line and noting the fine scale of the <span class="html-italic">y</span>-axis starting at 19 million AF. While stormwater runoff (Qr) is substantially reduced, as shown in <a href="#systems-07-00053-f009" class="html-fig">Figure 9</a>, the combined intervention effects considered legitimate are still enough to quickly reverse the trend of shallow groundwater aquifer decline. While the differences might appear insignificant, typically only a ratio of the groundwater aquifer storage is usable and, as levels decline, the corresponding water quality generally also declines, decreasing that ratio. Each system would have different base conditions and different targets.</p>
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<p>Stormwater (SW) out of the valley (Qout) is reduced in a scenarios resulting from management targets of reduction of higher-energy storm events that would carry sediment transport. The reduction does not impact the downstream compact allocation (Cd) quantity (679,000 AFY).</p>
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<p>In the upland system, the additional infiltration in the floodplains of the valley bottoms results in additional vegetation coverage (VCa) (<b>a</b>). This in turn fuels surface spreading (SSa) which produces infiltration additional (AIup) (<b>b</b>) to the normal infiltration (Iu) in the valley (as shown in <a href="#systems-07-00053-f005" class="html-fig">Figure 5</a>).</p>
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<p>The valley farmers’ production is directly related to water availability. Withdrawals (W) (<b>a</b>) increase with the stormwater spreading (SWa) interventions (<b>c</b>) reducing any surface water supply compact allocation gap (<b>d</b>) and thus the need for groundwater pumping (GWout) (<b>b</b>), known as “in-lieu recharge”. The recovery policy ratio (Rp) for farmers able to capture and reuse groundwater is 0.75. This resulting effect of increasing groundwater levels and storage benefits the valley farmers with greater water availability in the form of W. Both support scenarios shown in red and orange produce similar results.</p>
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<p>The reference modes of behavior of productivity benefit uplands (PBu) (<b>a</b>) and productivity benefit valley (PBv) (<b>b</b>). For both figures, the support scenario 2 experimentation to fit the practice to the local conditions would yield productivity benefits more quickly and to a greater extent, leading to a perception of greater legitimacy of the practices. Additionally, it would provide local collaboration, which would lead to increased innovation and greater adoption. The support scenario 1 of outside support to simply shoulder the management burden would experience a longer learning curve and greater adaptive management inputs to correct unintended consequences, yielding slower and less robust benefit yields over time.</p>
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<p>Compact allocations determine the distribution of surface water upstream from the system (<b>a</b>), and they are affected by upstream precipitation/snowpack variability (<b>b</b>), where any quantity below a mean set by the compact agreement diminishes the allocation.</p>
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<p>Full model structure.</p>
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17 pages, 2236 KiB  
Communication
Sustainable Feedbacks of Colombian Paramos Involving Livestock, Agricultural Activities, and Sustainable Development Goals of the Agenda 2030
by Raúl Andrés Molina Benavides, Rómulo Campos Gaona, Hugo Sánchez Guerrero, Leonidas Giraldo Patiño and Alberto Stanislao Atzori
Systems 2019, 7(4), 52; https://doi.org/10.3390/systems7040052 - 28 Nov 2019
Cited by 10 | Viewed by 6337
Abstract
Colombian mountain Páramos are considered natural areas with a very important role for human life. Páramos provide, both in mountain and lowland areas, a multitude of ecosystem services which start from vegetation to soil sustainability. The sustainability of Páramos is however impaired by [...] Read more.
Colombian mountain Páramos are considered natural areas with a very important role for human life. Páramos provide, both in mountain and lowland areas, a multitude of ecosystem services which start from vegetation to soil sustainability. The sustainability of Páramos is however impaired by several anthropogenic activities, including agricultural and livestock practices. A system thinking approach was applied in this work to improve the systemic understanding of factors affecting sustainability and resilience of Páramos agro-ecosystems. Interdisciplinary literature evidences were summarized and conceptually analyzed in order to develop causal loop diagrams of Páramo system structures allowing describing the main feedback loops involving (involved in/connecting) the Páramo ecosystem and driving its sustainability. From the causal diagram analysis few insights to maintain the human presence in Páramos arose. The system analysis highlights that human presence in Páramos should be stimulated, avoiding agriculture and livestock activities as the main income source. Particularly, social interactions, education on the Páramos environmental and relevance of agricultural practices to foster ecosystem services and multiple rentable economic activities should be enhanced. The study also includes the role of the government in providing the Páramo inhabitants with payments for ecosystem services and environmental education aimed to boost sustainability. Sustainable Páramo management will apply specific leverages on the system to reach Sustainable Development Goals 6 (water), 8 (economic growth, employment and work), 13 (climate change), and 15 (sustainable use of terrestrial ecosystems) of the Agenda 2030. Full article
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<p>Ecological description of Páramo zonations based on altitude, soils, and vegetation (adapted from [<a href="#B8-systems-07-00052" class="html-bibr">8</a>]).</p>
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<p>Geographic location of the different Páramo ecosystems of Colombia. Data source: elaboration of original figures from [<a href="#B5-systems-07-00052" class="html-bibr">5</a>].</p>
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<p>Examples of landscape, ecosystem services (collection, regulation and water supply, and ecotourism), and anthropic activity (livestock) representative of the Páramo ecosystems of Colombia. Páramo de las Hermosas, Palmira-Valle del Cauca.</p>
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<p>Causal loop diagram showing the relationship between the population dynamics of the Páramos and their agricultural activities.</p>
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<p>Causal loop diagram showing the relationship between the population dynamics of the Páramos and their agricultural activities.</p>
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<p>Causal loop diagram showing the benefit of ecosystem services to low-lying areas and the connection between the populations of the Páramos and the cities.</p>
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<p>Causal loop diagram showing the effect of the proposed policies on Páramo populations, their agricultural activities and ecosystem services.</p>
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<p>Causal loop diagram showing the effect of reforestation on the water cycle and rainfall in the Páramo area.</p>
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35 pages, 466 KiB  
Article
Nonclassical Systemics of Quasicoherence: From Formal Properties to Representations of Generative Mechanisms. A Conceptual Introduction to a Paradigm-Shift
by Gianfranco Minati
Systems 2019, 7(4), 51; https://doi.org/10.3390/systems7040051 - 28 Nov 2019
Cited by 4 | Viewed by 4505
Abstract
In this article, we consider how formal models and properties of emergence, e.g., long-range correlations, power laws, and self-similarity are usually platonically considered to represent the essence of the phenomenon, more specifically, their acquired properties, e.g., coherence, and not their generative mechanisms. Properties [...] Read more.
In this article, we consider how formal models and properties of emergence, e.g., long-range correlations, power laws, and self-similarity are usually platonically considered to represent the essence of the phenomenon, more specifically, their acquired properties, e.g., coherence, and not their generative mechanisms. Properties are assumed to explain, rather than represent, real processes of emergence. Conversely, real phenomenological processes are intended to be approximations or degenerations of their essence. By contrast, here, we consider the essence as a simplification of the phenomenological complexity. It is assumed to be acceptable that such simplification neglects several aspects (e.g., incompleteness, inhomogeneities, instabilities, irregularities, and variations) of real phenomena in return for analytical tractability. Within this context, such a trade-off is a kind of reductionism when dealing with complex phenomena. Methodologically, we propose a paradigmatic change for systems science equivalent to the one that occurred in Physics from object to field, namely, a change from interactional entities to domains intended as extensions of fields, or multiple fields, as it were. The reason to introduce such a paradigm shift is to make nonidealist approaches suitable for dealing with more realistic quasicoherence, when the coherence does not consistently apply to all the composing entities, but rather, different forms of coherence apply. As a typical general interdisciplinary case, we focus on so-called collective behaviors. The goal of this paper is to introduce the concepts of domain and selection mechanisms which are suitable to represent the generative mechanisms of quasicoherence of collective behavior. Domains are established by self-tracking entities such as financial or are effectively GPS-detectable. Such domains allow the profiling of collective behavior. Selection mechanisms are based on learning techniques or cognitive approaches for social systems. Full article
22 pages, 2192 KiB  
Article
Dominant Factors for an Effective Selection System: An Australian Education Sector Perspective
by Sophia Diana Rozario, Sitalakshmi Venkatraman, Mei-Tai Chu and Adil Abbas
Systems 2019, 7(4), 50; https://doi.org/10.3390/systems7040050 - 1 Nov 2019
Cited by 4 | Viewed by 6643
Abstract
With the latest advancements in information technologies, many organisations expect systems to provide effective support in the recruitment process and decision making. However, there is a lack of clarity on the dominant factors required for an effective recruitment system which can influence business [...] Read more.
With the latest advancements in information technologies, many organisations expect systems to provide effective support in the recruitment process and decision making. However, there is a lack of clarity on the dominant factors required for an effective recruitment system which can influence business outcomes. This paper aimed to identify the predominant factors in the employee selection process and the use of a management system for decision support. The empirical study consisted of a qualitative survey of 74 samples and a quantitative survey of 204 individual participants from 17 organisations coming from technical and further education (TAFE)/dual education (higher education and vocational education) sector members of the Victorian TAFE Association in Australia. Using commonly adopted exploratory factor analysis (EFA) of 38 variables, the data triangulation of the qualitative and quantitative analysis resulted in conformance of five dominant factors under three themes. We believe the results of the study offer actionable suggestions in developing an effective recruitment system and furthers the research in this field of study. Full article
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<p>Theoretical framework using Applicant Attribution-Reaction Theory (AART); adapted from Ployhart and Harold [<a href="#B24-systems-07-00050" class="html-bibr">24</a>].</p>
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<p>Research design overview.</p>
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<p>Scree plot for 38 variables processed.</p>
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<p>Word frequency for the use of recruitment systems in HR departments.</p>
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<p>Mapping the qualitative and quantitative findings.</p>
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25 pages, 5722 KiB  
Article
A System Dynamics Model Examining Alternative Wildfire Response Policies
by Matthew P. Thompson, Yu Wei, Christopher J. Dunn and Christopher D. O’Connor
Systems 2019, 7(4), 49; https://doi.org/10.3390/systems7040049 - 4 Oct 2019
Cited by 4 | Viewed by 6552
Abstract
In this paper, we develop a systems dynamics model of a coupled human and natural fire-prone system to evaluate changes in wildfire response policy. A primary motivation is exploring the implications of expanding the pace and scale of using wildfires as a forest [...] Read more.
In this paper, we develop a systems dynamics model of a coupled human and natural fire-prone system to evaluate changes in wildfire response policy. A primary motivation is exploring the implications of expanding the pace and scale of using wildfires as a forest restoration tool. We implement a model of a forested system composed of multiple successional classes, each with different structural characteristics and propensities for burning at high severity. We then simulate a range of alternative wildfire response policies, which are defined as the combination of a target burn rate (or inversely, the mean fire return interval) and a predefined transition period to reach the target return interval. We quantify time paths of forest successional stage distributions, burn severity, and ecological departure, and use departure thresholds to calculate how long it would take various policies to restore forest conditions. Furthermore, we explore policy resistance where excessive rates of high burn severity in the policy transition period lead to a reversion to fire exclusion policies. Establishing higher burn rate targets shifted vegetation structural and successional classes towards reference conditions and suggests that it may be possible to expand the application of wildfires as a restoration tool. The results also suggest that managers may be best served by adopting strategies that define aggressive burn rate targets but by implementing policy changes slowly over time. Full article
(This article belongs to the Special Issue System Dynamics: Insights and Policy Innovation)
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<p>Stylized causal loop diagram of coupled human and natural fire-prone system, highlighting three key drivers of system behavior considered here: forest conditions (density), burn severity, and wildfire response policy (expressed through burn rate). The clockwise arrow with the “R” corresponds to a reinforcing feedback loop, and the clockwise arrow with the “B” to a balancing feedback loop. Polarity signs indicate the nature of the relationship; for instance, the “-“ on the link between burn rate and forest density indicates that as the burn rate increases, forest density decreases. The color of the polarity signs indicates ecological (black) or management (red) effects.</p>
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<p>Large wildfires managed in designated “restore” or “maintain” Strategic Response Zones with pre-identified control opportunities on the Tonto National Forest 2017–2019. Fire panels display background strategic wildfire response designation overlaid with Burned Area Reflectance Classification (BARC) initial soil burn severity classes. Clockwise from top, the 2017 Brooklyn Fire burned during monsoon season with no direct containment actions taken; the 2018 Bears Fire also during monsoon season was contained with burn out operations along the southern and eastern POD boundaries; the 2019 Woodbury fire burned during the hotter drier pre-monsoon season and with uncharacteristic herbaceous fuels generated from an extremely wet winter. The fire was contained along POD boundaries to the west and eventually to the north only after breaching the first set of pre-identified potential control opportunities. The 2017 Pinal fire also during the pre-monsoon was actively managed from ignition as a resource benefit fire with a final fire footprint which was nearly identical to the pre-identified POD boundary.</p>
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<p>Simplified diagram of the stock-and-flow model. Square boxes represent the standard S-Class stocks in the original LANDFIRE model (A-E), and the grey hexagons represent the added unnatural S-Class stocks (UNB and UNE). Flows are labeled by type and the corresponding “from to” stocks, and the types are color coded as follows: NS (green), UN (black), high-severity fire (red), moderate severity fire (orange), alternate succession (purple), and insect/disease (brown). For example, “NS A B” represents a natural succession flow from S-Class A to S-Class B. An auxiliary Burn Rate variable, determined by the Fire Response Policy, acts on UN/NS flows as well as fire-related flows. Here for simplicity, we only display relationships between the Burn Rate and S-Class C. The amount of S-Class A influences the Fire Response Policy, as described later.</p>
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<p>Distribution of area in each S-Class (ha) on the hypothetical 1000 ha landscape, as it varies with fire management policy. All S-Classes are as defined in <a href="#systems-07-00049-t001" class="html-table">Table 1</a> and <a href="#systems-07-00049-t002" class="html-table">Table 2</a>. SS = steady state solution after 2000 years of a simulated natural fire regime., and all other wildfire response policies are defined as in <a href="#systems-07-00049-t004" class="html-table">Table 4</a>.</p>
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<p>Behavior over time graph of high-severity fire area burned, for the 10-10 and 40-40 policies. Starting from year 150, alternative fire management policies are implemented and compared. High-severity fire area burned peaks sharply with the 10-10 policy, and then rapidly depletes to reach nearly equivalent levels to the 40-40 policy.</p>
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<p>Behavior over time graph of high-severity fire percentage, for the 10-10 and 40-40 policies. Starting from year 150, alternative fire management policies are implemented and compared. High-severity fire percentage drops less steeply and persists for longer with the 40-40 policy.</p>
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<p>Behavior over time graph of S-Class A levels, for seven wildfire response policies in <a href="#systems-07-00049-t004" class="html-table">Table 4</a>. Starting from year 150, alternative policies are implemented. The higher burn rates increase the stock levels of S-Class A, in some cases to surplus status due to high rates of high-severity fire in other S-Classes.</p>
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<p>Behavior over time graph of S-Class D levels, for seven wildfire response policies in <a href="#systems-07-00049-t004" class="html-table">Table 4</a>. Starting from year 150, alternative policies are implemented. The higher rates of burning increase (restore) stocks of S-Class D.</p>
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<p>Behavior over time graph of S-Class E levels, for seven wildfire response policies in <a href="#systems-07-00049-t004" class="html-table">Table 4</a>. Starting from year 150, alternative fire management policies are implemented. The higher rates of burning decrease (restore) stocks of S-Class E.</p>
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<p>Behavior over time graph of high-severity area burned levels, for two policy resistance scenarios (10-10-SQ and 10-40-CS). The x-axis is shortened to display only years 150–250 (i.e., the years over which the policy resistance scenario is active), to better compare behavior over this time period. The 10-10-SQ policy results in more oscillations, over a longer duration, with higher swings in high-severity area burned.</p>
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<p>Illustration of influence of annual burn rate (x-axis) and fuel accumulation parameter (fa) on the flow rates for uncharacteristic (UN) and natural succession (NS). Note that identical line colors and symbols indicate the same fuel accumulation rates, with solid and dashed lines representing NS and UN flows, respectively.</p>
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<p>Conditional burn severity probability distributions for S-Classes B, UNB, E, and UNE.</p>
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<p>Steady state analysis illustrating influence of burn rate on high-severity fire percentage, across five modeling formulations.</p>
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<p>Steady state analysis illustrating influence of burn rate on area of S-Class D, across five modeling formulations.</p>
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20 pages, 2003 KiB  
Article
Systems Thinking Using SSM and TRIZ for Stakeholder Engagement in Infrastructure Megaprojects
by Johan Ninan, Ibukun Phillips, Shankar Sankaran and Swaminathan Natarajan
Systems 2019, 7(4), 48; https://doi.org/10.3390/systems7040048 - 26 Sep 2019
Cited by 12 | Viewed by 8367
Abstract
Infrastructure megaprojects straddle multiple stakeholder boundaries who have an interest in the project and are affected by the project. Multiple papers in the literature stress the need for holistic approaches to stakeholder engagement, as existing approaches only address the concerns of the noisy [...] Read more.
Infrastructure megaprojects straddle multiple stakeholder boundaries who have an interest in the project and are affected by the project. Multiple papers in the literature stress the need for holistic approaches to stakeholder engagement, as existing approaches only address the concerns of the noisy stakeholders. This research proposes an innovative approach in which Soft Systems Methodology (SSM) is used for understanding stakeholder concerns, complemented by the use of Theory of Inventive Problem Solving (TRIZ) for identifying innovative solutions to address conflicting stakeholder goals. The researchers simulated the stakeholder engagement of the Coimbatore metro rail project, in India, through a workshop setting in a classroom to check the feasibility of this approach for stakeholder engagement. The 15 participants of the workshop were divided into four groups representing different stakeholders of the project. Data was collected through participant observations by the authors and oral feedback from the participants. The results show that while SSM helped to capture the concerns and goals of each stakeholder, TRIZ helped to identify and dissolve conflicts among these goals through innovative solutions. The theoretical, practical and pedagogical contributions are highlighted. Full article
(This article belongs to the Special Issue Systems Thinking in Project Management)
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<p>Theory of Inventive Problem Solving (TRIZ) problem-solving model. Source: Chai et al. [<a href="#B56-systems-07-00048" class="html-bibr">56</a>].</p>
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<p>Methodology through which SSM and TRIZ were applied to the stakeholder engagement context.</p>
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<p>Rich pictures developed by the participant groups (<b>a</b>) Government; (<b>b</b>) Main contractor; (<b>c</b>) Chamber and travelling public; (<b>d</b>) Owners and residents.</p>
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<p>Rich pictures developed by the participant groups (<b>a</b>) Government; (<b>b</b>) Main contractor; (<b>c</b>) Chamber and travelling public; (<b>d</b>) Owners and residents.</p>
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19 pages, 2026 KiB  
Article
A System Dynamics Modeling Support System Based on Computational Intelligence
by Hassan Abdelbari and Kamran Shafi
Systems 2019, 7(4), 47; https://doi.org/10.3390/systems7040047 - 25 Sep 2019
Cited by 6 | Viewed by 6735
Abstract
System dynamics (SD) is a complex systems modeling and simulation approach with wide ranging applications in various science and engineering disciplines. While subject matter experts lead most of the model building, recent advances have attempted to bring system dynamics closer to fast growing [...] Read more.
System dynamics (SD) is a complex systems modeling and simulation approach with wide ranging applications in various science and engineering disciplines. While subject matter experts lead most of the model building, recent advances have attempted to bring system dynamics closer to fast growing fields such as data sciences. This may prove promising for the development of novel support methods that augment human cognition and improve efficiencies in the model building process. A few different directions have been explored recently to support individual modeling stages, such as the generation of model structure, model calibration and policy optimization. However, an integrated approach that supports across the board modeling process is still missing. In this paper, a prototype integrated modeling support system is presented for the purpose of supporting the modelers at each stage of the process. The proposed support system facilitates data-driven inferring of causal loop diagrams (CLDs), stock-flow diagrams (SFDs), model equations and the estimation of model parameters using computational intelligence (CI) techniques. The ultimate goal of the proposed system is to support the construction of complex models, where the human power is not enough. With this goal in mind, we demonstrate the working and utility of the proposed support system. We have used two well-known synthetic reality case studies with small models from the system dynamics literature, in order to verify the support system performance. The experimental results showed the effectiveness of the proposed support system to infer close model structures to target models directly from system time-series observations. Future work will focus on improving the support system so that it can generate complex models on a large scale. Full article
(This article belongs to the Collection System Dynamics)
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<p>The proposed system dynamics modeling support system.</p>
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<p>The main components of the support system learning engine.</p>
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<p>Experimental setup.</p>
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<p>Case studies system dynamics models and behaviors.</p>
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<p>Illustration of Case 1 target behaviors, healthy people (<math display="inline"><semantics> <mrow> <mi>H</mi> <mi>P</mi> </mrow> </semantics></math>) and sick people (<math display="inline"><semantics> <mrow> <mi>S</mi> <mi>P</mi> </mrow> </semantics></math>), and the generated behaviors from the corresponding inferred models for Setup 1, with and without applying genetic programming depth controller.</p>
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<p>Illustration of Case 2 target outputs, susceptible population (<math display="inline"><semantics> <mrow> <mi>S</mi> <mi>P</mi> </mrow> </semantics></math>), infected population (<math display="inline"><semantics> <mrow> <mi>I</mi> <mi>P</mi> </mrow> </semantics></math>) and recovery population (<math display="inline"><semantics> <mrow> <mi>R</mi> <mi>P</mi> </mrow> </semantics></math>), and the two generated outputs from the corresponding inferred models for Setup 1, with and without applying genetic programming depth controller.</p>
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<p>Illustration of Case 1 target outputs, healthy people (<math display="inline"><semantics> <mrow> <mi>H</mi> <mi>P</mi> </mrow> </semantics></math>) and sick people (<math display="inline"><semantics> <mrow> <mi>S</mi> <mi>P</mi> </mrow> </semantics></math>), and the two generated outputs from the corresponding inferred models for Setup 2 with genetic programming depth controller.</p>
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<p>Case 1 inferred CLD by showing the correctly predicted links (<b>b</b>), additional links (<b>c</b>) and missing links (<b>d</b>) compared to the target CLD (<b>a</b>)—Setup 2.</p>
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<p>Case 1 inferred SFD vs. target one—Setup 2.</p>
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<p>Illustration of Case 2 target outputs, susceptible population (<math display="inline"><semantics> <mrow> <mi>S</mi> <mi>P</mi> </mrow> </semantics></math>), infected population (<math display="inline"><semantics> <mrow> <mi>I</mi> <mi>P</mi> </mrow> </semantics></math>) and recovery population (<math display="inline"><semantics> <mrow> <mi>R</mi> <mi>P</mi> </mrow> </semantics></math>), and the two generated outputs from the corresponding inferred models for Setup 2 with genetic programming depth controller.</p>
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<p>Case 2 inferred CLD by showing the correctly predicted links (<b>b</b>); additional links (<b>c</b>); and missing links (<b>d</b>) compared to the target CLD (<b>a</b>)—Setup 2.</p>
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<p>Case 2 inferred SFD vs. target one—Setup 2.</p>
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