Systemic Leverage and Homogeneity: Assessing Multifaceted Amplifying Mechanism of Systemic Risk

Kim, Myeong Hyeon; Kim, Baeho

doi:10.2139/ssrn.2647846

Cited by 4 publications

(7 citation statements)

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“…The purpose of this modelling is not to predict future oil prices or levels of production or consumption, rather it is to use SD in order to assess the types (termed 'archetypes' in SD discourse) of market dynamics that might be expected to occur in markets under stress and to consider the resilience of markets and the SD modelling technique. (Systems archetypes are a class of tools offered by Systems Thinking methodology for diagnosing problems and to capture 'common stories' or dynamic phenomenon occurring repeatedly in diverse settings, for details see (Kim, 1992))). The SD model is sufficiently robust and seen to function well into a scenario space of market failure.…”

Section: Section 4 Analysis Of Model Behaviour and Simulation Resultsmentioning

confidence: 99%

Modern global oil market under stress: system dynamics and scenarios

Madhavi

Nuttall

2023

Proceedings of the Institution of Civil Engineers - Energy

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Modelling techniques developed 40 years ago to gain insight into the issues of energy supply security remain powerful even as the focus of energy technology policy analysis has shifted to consider new problems and research methods more closely linked to pressing environmental challenges. Dynamic modelling continues to be of merit and indeed it has the potential to provide helpful perspectives on contemporary problems such as climate change and the Net Zero transition. This work seeks to help revive system dynamics modelling of the global oil market. The SD-based analysis used in this paper facilitates a rational representation of physical stocks and flows as well as causal linkages that drive decision-making in the global oil system. The use of SD provides insights (intuitive and counter-intuitive) into the macro-level non-linear dynamics of global oil exchanges and permits assessment of the potential impacts of future changes in system behaviour. The model exhibits damped oscillations in oil prices, broadly consistent with real market behaviours. The results further illustrate the resilience of market dynamics in the phase of extreme supply side shocks and reveal the ability of System Dynamics to simulate and model such effects even beyond the point of market failure.

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Section: Section 4 Analysis Of Model Behaviour and Simulation Resultsmentioning

confidence: 99%

Modern global oil market under stress: system dynamics and scenarios

Madhavi

Nuttall

2023

Proceedings of the Institution of Civil Engineers - Energy

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“…The ST approach aims mainly to interpret these behaviours, conceptualized as a synthesis of cause–effect relationships and feedback mechanisms that could often generate specific archetypes (i.e., ‘system structure level’ ). In particular, archetypes constitute a toolset of templates that can be used to explain common long‐term problematic behaviours from a systemic perspective to assist in identifying interventions that could create a pivotal change in the system's state (Kim, 2000; Senge, 1990). Indicatively, several archetypes have been already employed to describe production patterns in complex agricultural systems (Banson et al, 2016; Nyam et al, 2020).…”

Section: Methodological Approachmentioning

confidence: 99%

“…Focusing on the societal perspective, human–robot synergetic technologies in agriculture are expected to tackle the limits to growth (Kim, 2000; Meadows, 2009) in the traditional automated and robotic systems; instead of posing pressure on rural communities by eliminating the human factor from farming activities, robots can work collaboratively with workers for promoting sustainable agriculture (Vasconez et al, 2019). In particular, high ‘Society's reluctance due to perceived AR threats’ of automation emphasizes the ‘Need for socially viable solutions’ ; the communication of the benefits of automated solutions' utilization could reduce workers' negative feelings, hence supporting the development and implementation of human–robot collaboration (Meissner et al, 2021).…”

Section: Conceptual Model Buildingmentioning

confidence: 99%

“…Notably, the combination of the counterbalancing feedback loops R1 (reinforcing) and B3 (balancing) generates the so‐called ‘limits to growth (or success)’ archetype (Kim, 2000; Meadows, 2009); this system archetype reflects how an initial improvement of performance due to systematic efforts can be decelerated or even declined due to eventual limiting factors (Nyam et al, 2020). Thus, the cumulative effect of continuous growth may induce future problems.…”

Section: Conceptual Model Buildingmentioning

confidence: 99%

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Transitioning towards human–robot synergy in agriculture: A systems thinking perspective

Aivazidou

Tsolakis

2022

Syst Res Behav Sci

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Digital transformation has unveiled new prospects for increased performance and productivity in the agricultural sector to meet rising food security needs. Continuous industrialization and unexpected disruptions (e.g., workforce mobility restrictions due to the COVID‐19 pandemic) call for the adoption of agricultural robots. However, automated solutions could be associated with societal challenges in rural areas; unemployment growth has been perceived as a major threat that jeopardizes societal welfare, potentially hindering the implementation of digital technologies. In this context, human–robot synergistic systems could act as a promising socially viable alternative. Through systems thinking, this research investigates the complex interconnections and key feedback mechanisms of automation diffusion (conventional and human–robot interactive) under the socio‐economic perceptions (drivers and barriers) of agribusinesses and rural communities. Overall, this study contributes towards eliciting the mental models that underpin the transition from agricultural robots to human–robot collaboration by transforming automation‐related societal risks into opportunities for sustainable rural development.

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“…Systems archetypes are common patterns that develop in organizations or systems. 33 They are so common in fact, that they occur over and over again in different systems with different functions—that is why they are called archetypes. Understanding these recurrent patterns of behaviour gives us insight into the drivers and dynamics of systems and how to mitigate against the dysfunctional manifestations that may arise.…”

Section: Now What?mentioning

confidence: 99%

The impact of cognitive biases, mental models, and mindsets on leadership and change in the health system

Petrie,

Lindstrom,

Campbell

2023

Healthc Manage Forum

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Understanding how cognitive biases, mental models, and mindsets impact leadership in health systems is essential. This article supports the notion of cognitive biases as flawed thinking or cognitive traps which negatively influence leadership. Mental models that do not fit with current evidence limit our ability to comprehend and respond to system issues. Resulting mindsets affect cognition, behaviour, and decision-making. Metacognition is critical. The wicked problems in today’s complex health system require leaders and everyone involved to elevate their personal, organizational, and disciplinary perspectives to a systems level. Three examples of mental models/mindsets are reviewed. They do not change simply because we wish or will them to. The first step is being aware of what they are and how they impact our thinking and decision-making. Some tips for managing these traps are offered as examples of how to challenge our leadership approach in the health system.

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Systemic Leverage and Homogeneity: Assessing Multifaceted Amplifying Mechanism of Systemic Risk

Cited by 4 publications

References 0 publications

Modern global oil market under stress: system dynamics and scenarios

Modern global oil market under stress: system dynamics and scenarios

Transitioning towards human–robot synergy in agriculture: A systems thinking perspective

The impact of cognitive biases, mental models, and mindsets on leadership and change in the health system

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