System Dynamics Research Articles

Values‐based food procurement: Black farmers and structural racism in New York State

AbstractResponding to growing calls for system dynamics (SD) research to engage with structural racism, this article examines the barriers that Black farmers in New York State face and explores values‐based public institutional food procurement (VBFP) as one possible solution for overcoming these barriers. Combining a qualitative data analysis with causal loop diagrams, this analysis reveals that ongoing structural racism generates (1) structural disconnects between Black farmers' and public institutions' supply chains that inhibit their integration; and (2) procurement policies that create new, rather than addressing existing, barriers for Black farmers. VBFP initiatives' potential to support Black farmers is thus dependent upon dismantling structural racism. In addition, this article advances SD supply chain research by examining one effort to align supply chains with social and environmental values and demonstrates how causal loop diagrams, with their emphasis on system structure, reveal why values‐based supply chain efforts in general often struggle. © 2024 System Dynamics Society.

Q-learning based tracking control with novel finite-horizon performance index

A data-driven method is designed to realize the model-free finite-horizon optimal tracking control (FHOTC) of unknown linear discrete-time systems based on Q-learning in this paper. First, a novel finite-horizon performance index (FHPI) that only depends on the next-step tracking error is introduced. Then, an augmented system is formulated, which incorporates with the system model and the trajectory model. Based on the novel FHPI, a derivation of the augmented time-varying Riccati equation (ATVRE) is provided. We present a data-driven FHOTC method that uses Q-learning to optimize the defined time-varying Q-function. This allows us to estimate the solutions of the ATVRE without the system dynamics. Finally, the validity and features of the proposed Q-learning-based FHOTC method are demonstrated by means of conducting comparative simulation studies.

Minding the abstraction gap: approaches supporting implementation

AbstractRegardless of insights gained from building and analyzing dynamic models, the only strategies people can act on are those in their heads. The strategies people internalize are related to their perceived capacities to act—the verbs they believe they can do. If we want others to implement model‐informed policies, then we must connect model abstractions with new situated, concrete actions stakeholders can take. We can emphasize opportunities to act with SD representations, navigating levels of abstraction cleanly, identifying flows as verbs, and choosing variable names that signal who is acting. By drawing on social‐science theories as we offer our grammar of accumulations, activities, and relationships in the language of actions accessible to stakeholders, we help connect experiential understandings to richer, dynamic explanations people can internalize and so discover situated steps to implement policies informed by modeling. © 2024 The Author(s). System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Sea Purslane's (Sesuvium portulacastrum) role in nutrient removal and microbial community dynamics of the mariculture wastewater bioremediation systems

The development of intensive mariculture requires effective wastewater treatment solutions, and mariculture wastewater bioremediation systems (MWBSs) are increasingly used for this purpose. One important part of these systems is the macrophyte unit. In this study, we established a MWBS consisting of four sections: sedimentation, biofilm, shellfish, and macrophytes, to investigate the role of sea purslane (Sesuvium portulacastrum) in nutrient removal and microbial community dynamics through controlled experiments (CE) and in situ observations (ISO). Our findings revealed the removal rates of CODMn in the low-density and high-density groups were 45.18 ± 5.42% and 48.25 ± 5.70% in CE, respectively. Furthermore, the removal rate of CODMn in the ISO was 85.28 ± 4.09%. Sea purslane notably enhanced microbial diversity and overall species count in the water. The bacterial community experienced three successional stages in the water and demonstrated more stability in the root system in CE. The stochastic processes were critical in shaping the community assembly. The bacterial dispersal in sea purslane treatment groups was higher than in the control group. Importantly, stochastic processes contributed more to the microbial community in the root system. Furthermore, the influence of stochastic processes was stronger in ISO than in CE. Therefore, it is speculated that the sea purslane demonstrated a substantial impact on microbial community structure and exhibited positive effects on wastewater nutrient removal. This study provided a reference for optimizing treatment areas within comprehensive wastewater treatment systems.

Pattern formation in odd viscoelastic fluids

Nonreciprocal interactions fueled by local energy consumption can be found in biological and synthetic active matter at scales where viscoelastic forces are important. Such systems can be described by “odd” viscoelasticity, which assumes fewer material symmetries than traditional theories. Here we study odd viscoelasticity analytically and using lattice Boltzmann simulations. We identify a pattern-forming instability which produces an oscillating array of fluid vortices, and we elucidate which features govern the growth rate, wavelength, and saturation of the vortices. Our observation of pattern formation through odd mechanical response can inform models of biological patterning and guide engineering of odd dynamics in soft active matter systems. Published by the American Physical Society 2024

System dynamics simulation follow-up fuzzy cognitive map for investigating the effect of risks on relief in crisis management

Given the significant threat posed by natural disasters like earthquakes to communities, it is important to assess the associated risks beforehand in order to minimize human and financial losses. This study focuses on identifying existing risks caused by an earthquake crisis by designing a network that evaluates its impact on urban infrastructure. The resulting system dynamics simulation model was used to analyze the frequency of different hazards and estimate the amount of relief goods needed in eight earthquake scenarios. The Fuzzy Cognitive Mapping (FCM) technique was also employed to determine the weight, impact, and effectiveness of each hazard, thus allowing for a ranking of the earthquake crisis scenarios based on their severity. FCM evaluation results showed that the most important and influential risks in an earthquake crisis were fire (with a weight value of 0.124), damage to first-class buildings (0.099), lack of hospital capacity (0.096), and damage to the power grid (0.095). Furthermore, it was observed that mitigating or eliminating the most significant risks would lead to a significant decrease in the demand for relief goods in each scenario. In fact, by addressing the top three earthquake risks, the demand for medicine reduced by 147%. Finally, the ranked results of the earthquake scenarios indicate that fire (with an impact of 12%), damage to the water network, and lack of capacity in relief centers (both with an impact of 6%) are the most and least effective risks, respectively.

Analysis of the Aquaponic System Sustainability via System Dynamics Modelling – FEW Nexus Approach

AbstractAquaponic systems integrate aquaculture and hydroponics, recycling nutrient-enriched water from fish tanks to grow plants, significantly reducing carbon emissions, water use and production costs compared to other methods. It is considered a sustainable solution for food production, addressing issues such as climate change and eutrophication. Particularly valuable in family farming, it increases the diversity and quality of food, while reducing its environmental impact. However, despite its potential, aquaponics lacks recognition in public policies, making its widespread adoption difficult. Quantifying its benefits is crucial for strategic planning and the formulation of policies to support family farming and the transition to sustainability, in line with global objectives. Therefore, there is a need to comprehensively quantify the benefits of aquaponics, particularly in terms of the Food-Energy-Water (FEW) nexus, to support decision-making and policy formulation for sustainable agriculture. The nexus concept encompasses highly complex systems requiring robust tools capable of analysing the interrelationships between multiple components. Aiming to analyse the degree of sustainability of aquaponics systems on family property in the FEW nexus context, a System Dynamics Modelling (SDM) coupling Socio-economic and environmental indicators was developed. The results obtained demonstrated the efficiency of using SDM as an analysis and support tool for decision-making. Additionally, they prove the environmental viability of food production via aquaponic systems.

Late time dynamics in SUSY saddle-dominated scrambling through higher-point OTOC

In this article, we propose higher-point out-of-time-order correlators (OTOCs) as a tool to differentiate chaotic from saddle-dominated dynamics in late times. As a model, we study the scrambling dynamics in supersymmetric quantum mechanical systems. Using the eigenstate representation, we define the 2N-point OTOC using two formalisms, namely the ’Tensor Product formalism’ and the ’Partner Hamiltonian formalism’. We analytically find that the 2N-point OTOC for the supersymmetric 1D harmonic oscillator is in exact agreement with that of the 1D bosonic harmonic oscillator system. We show that the higher-point OTOC is a more sensitive measure of scrambling than the usual 4-point OTOC. To demonstrate this, we analyze a supersymmetric sextic 1D oscillator, for which the bosonic partner system has an unstable saddle in the phase space, while the saddle is absent in the fermionic counterpart. For such a system, we show that the saddle-dominated scrambling, higher anharmonic potential effects, and the supersymmetric OTOC exhibit similar dynamics due to supersymmetry constraints. Finally, we illustrate that the late-time dynamics of the higher-point OTOC become oscillatory after the peak for saddle-dominated scrambling and anharmonic oscillator systems. We propose the higher-point OTOC as a probe of late-time dynamics in non-chaotic systems that exhibit fast early-time scrambling.

Linear operator theory of phase mixing

Abstract We study solutions of the collisionless Boltzmann equation (CBE) in a functional Koopman representation. This facilitates the use of linear spectral techniques characteristic of the analysis of Schrödinger-type equations. For illustrative purposes, we consider the classical phase mixing of a non-interacting distribution function in a quartic potential. Solutions are determined perturbatively relative to a harmonic oscillator. We impose a form of coarse-graining by choosing a finite dimensional basis to represent the distribution function and time evolution operators, which sets a minimum length scale on phase space structure. We observe a relationship between the dimension of the representation and the multiplicity of the harmonic oscillator eigenvalues. System dynamics are understood in terms of degenerate subspaces of the linear operator spectra. Each subspace is associated with a mode of the harmonic oscillator, the first two being bending and breathing structures. The quartic potential splits the degenerate eigenvalues within each subspace. This facilitates the formation of spiral structure as deformations from the harmonic oscillator modes. We ultimately argue that this construction provides a promising avenue for study of self-interacting systems experiencing phase mixing, which is an outstanding problem in the context of the Gaia DR2 vertical phase space spirals.

Second order perturbation theory of two-scale systems in fluid dynamics

Second order perturbation theory of two-scale systems in fluid dynamics

Effect of colored noise on precursors of thermoacoustic instability in model gas turbine combustors

In this work, we numerically investigate the dynamics of a prototypical thermoacoustic system, the generalized Van der Pol oscillator, in the presence of additive noise of varying color (correlation time) and intensity while the system undergoes supercritical and subcritical Hopf bifurcation. We specifically investigate the influence of noise color on trends in the coherence factor and the Hurst exponent in the subthreshold region to assess their reliability as instability precursors. The Hurst exponent is found reliable only for correlation times much larger than the time scale of the instability while the coherence factor is found to be reliable for the entire range of noise color investigated. These inferences are found to hold for both supercritical and subcritical bifurcation cases.

Safe tracking control for unmanned autonomous helicopter under actuator faults and outside disturbances

This paper considers the problem on tracking control for the medium-scale unmanned autonomous helicopter (UAH) system under external disturbances, actuator faults, and full state constraints. In comparison with existing results, this paper does not only consider asymmetric state constraints but also jointly take the faults and disturbances into account on the UAH system, estimating and compensating for them separately, which can effectively enhance the tracking performance. First, the dynamics of nonlinear UAH system is divided into the position subsystem and the attitude one. Second, an improved barrier Lyapunov function (BLF) is constructed to handle the problem of asymmetric state constraints. Third, in order to estimate the external disturbances and actuator faults, two coupled generalized proportional integral observers (GPIOs) and two fault estimators are designed, respectively. Fourth, based on above estimations, improved BLFs, and backstepping method, two tracking control laws for the position and attitude loops are presented and an overall closed-loop system is established. Then, a sufficient condition ensuring uniform boundedness of tracking and estimation errors is derived. Finally, some simulating results demonstrate the effectiveness and advantage of the proposed control scheme.

Collective predictive coding hypothesis: symbol emergence as decentralized Bayesian inference.

Understanding the emergence of symbol systems, especially language, requires the construction of a computational model that reproduces both the developmental learning process in everyday life and the evolutionary dynamics of symbol emergence throughout history. This study introduces the collective predictive coding (CPC) hypothesis, which emphasizes and models the interdependence between forming internal representations through physical interactions with the environment and sharing and utilizing meanings through social semiotic interactions within a symbol emergence system. The total system dynamics is theorized from the perspective of predictive coding. The hypothesis draws inspiration from computational studies grounded in probabilistic generative models and language games, including the Metropolis-Hastings naming game. Thus, playing such games among agents in a distributed manner can be interpreted as a decentralized Bayesian inference of representations shared by a multi-agent system. Moreover, this study explores the potential link between the CPC hypothesis and the free-energy principle, positing that symbol emergence adheres to the society-wide free-energy principle. Furthermore, this paper provides a new explanation for why large language models appear to possess knowledge about the world based on experience, even though they have neither sensory organs nor bodies. This paper reviews past approaches to symbol emergence systems, offers a comprehensive survey of related prior studies, and presents a discussion on CPC-based generalizations. Future challenges and potential cross-disciplinary research avenues are highlighted.

Modelling seasonal variation of sediment connectivity and its interplay with river forms

Functional connectivity refers to the system dynamics and sediment transfer in a geomorphological system. It is usually quantified through digital elevation models of difference at two time-steps but measuring and quantifying sediment connectivity variation between the time-steps remains a challenge. In this study we utilised a combined approach of morphodynamic model and field observation for assessing seasonal spatio-temporal variation of longitudinal functional connectivity over a 6-kilometre sub-arctic river reach. Comprehensive field datasets including sediment sampling, monitoring of morphological change and continuous discharge and water level measurements, were collected multiple times during the year to calibrate and validate the model for one year. The results revealed that connectivity inside the reach is episodical and not all the morphological adjustment was tied to discharge. Connectivity varied spatio-temporally inside the reach and sediment source and sink areas could be detected. Outside of the hydrological controlled episodes, meander evolutional stage and grain size had notable role in sediment connectivity in the short-term, and therefore underlaying morphological adjustment in the long-term. This approach can contribute to enhancing already existing connectivity indices towards a functional model considering the seasonality and periodicity of connectivity in more detail and would therefore enhance river management and prediction of geomorphic systems functioning.

Research on Dynamic Subsidy Based on Deep Reinforcement Learning for Non-Stationary Stochastic Demand in Ride-Hailing

The ride-hailing market often experiences significant fluctuations in traffic demand, resulting in supply-demand imbalances. In this regard, the dynamic subsidy strategy is frequently employed by ride-hailing platforms to incentivize drivers to relocate to zones with high demand. However, determining the appropriate amount of subsidy at the appropriate time remains challenging. First, traffic demand exhibits high non-stationarity, characterized by multi-context patterns with time-varying statistical features. Second, high-dimensional state/action spaces contain multiple spatiotemporal dimensions and context patterns. Third, decision-making should satisfy real-time requirements. To address the above challenges, we first construct a Non-Stationary Markov Decision Process (NSMDP) based on the assumption of ride-hailing service systems dynamics. Then, we develop a solution framework for the NSMDP. A change point detection method based on feature-enhanced LSTM within the framework can identify the changepoints and time-varying context patterns of stochastic demand. Moreover, the framework also includes a deterministic policy deep reinforcement learning algorithm to optimize. Finally, through simulated experiments with real-world historical data, we demonstrate the effectiveness of the proposed approach. It performs well in improving the platform’s profits and alleviating supply-demand imbalances under the dynamic subsidy strategy. The results also prove that a scientific dynamic subsidy strategy is particularly effective in the high-demand context pattern with more drastic fluctuations. Additionally, the profitability of dynamic subsidy strategy will increase with the increase of the non-stationary level.

Probabilistic dynamic resilience quantification for infrastructure systems in multi-hazard environments

Resilience has been evolving as a key criterion for infrastructure systems as it ensures the system's dynamic performance pre-, during, and post-hazard disruptions. However, estimating these performances is challenging due to system and operation complexities, and the probabilistic dynamic nature of infrastructure system. Moreover, infrastructure systems are usually exposed to multi-hazard environments, with their own probabilistic behavior, leading to additional complexity in terms of estimating the system response and, subsequently, the overall system resilience. As such, this study develops a probabilistic resilience-centric system dynamics modeling approach to quantify infrastructure dynamic resilience based on a holistic representation of infrastructure systems under multi-hazard scenarios, whereby the probabilistic natures of both the hazards and system are incorporated. Unlike the traditional resilience quantification approaches that represent system resilience by a single value calculated after the system's full recovery, the developed model focuses on tracking the temporal evolution of system resilience along the entire period of system performance deterioration and recovery. A real-world hydropower dam, as an example for infrastructure systems, in British Columbia, Canada is used as a demonstration application to show model utility in developing resilience-guided assessment plans for infrastructure systems. Overall, the developed approach empowers the decision-makers with insights into critical operational periods, the required time to reach specified resilience targets, and the efficiency of risk mitigation measures in real-time.

Population Oscillations and Ubiquitous Coherences in Multilevel Quantum Systems Driven by Incoherent Radiation.

We consider incoherent excitation of multilevel quantum systems, e.g., molecules with multiple vibronic states. We show that (1) the geometric constraints of the matter-field coupling operator guarantee that noise-induced coherences will be generated in all systems with four or more incoherent transitions between energy eigenstates and (2) noise-induced coherences can lead to population oscillations due to quantum interference via coherence transfer between pairs of states in the ground and excited manifolds. Our findings facilitate the experimental detection of noise-induced coherent dynamics in complex quantum systems.

Joint Impact of Maturation Delay and Fear Effect on the Population Dynamics of a Predator-Prey System

Joint Impact of Maturation Delay and Fear Effect on the Population Dynamics of a Predator-Prey System

Groundwater Dynamics in African Endorheic Basins in Arid to Semi-Arid Transition Zones: The Batha Aquifer System, NE Chad

This study investigates the Batha endorheic basin in Chad, situated east of the Lake Chad basin in the arid to semi-arid Sahelian zone. This region has not yet undergone comprehensive geological and hydrogeological studies. More broadly, the transition zone between semi-arid and arid climates has been minimally explored. This research aims to evaluate the resources and dynamics of this multi-layered system using a combined geology-hydrogeology-hydrochemistry-isotopes approach. The multilayer system includes sedimentary layers (Quaternary, Pliocene, and Eocene) over a crystalline basement. A piezometric investigation of the system shows a general SE–NW groundwater, indicating an interconnection between all layers. Hydrochemical analyses identifies four main facies (calcium-bicarbonate, sodium-bicarbonate, sulphate-sodium, and mixed), primarily controlled by water–rock interaction with secondary influences from base-exchange and evaporation. Saturation indices indicate that these waters are close to equilibrium with the calcite-Mg phases, gaylussite and gypsum. Stable isotopes (oxygen-18 and deuterium) categorize groundwater into three groups: ancient water, recent and older meteoric water mixtures affected by evaporation, and mixtures more heavily impacted by evaporation. Tritium contents reveal three groups: current rainwater, modern water, and sub-modern water. These results indicate that ionic and isotopic differentiations cannot be strictly linked to specific layers, confirming the interconnected nature of the Batha system. The observed heterogeneity is mainly influenced by lithological and climatic variations. This study, though still limited, enhances significantly the understanding of the basin’s functioning and supports the rational exploitation of its vital resources for the Batha area’s development. Future investigations to complete the present study are highlighted.

Context-Specific Dynamics of Centrally Planned Macro Program for School Infrastructure Reform

The educational program evolved at the national level manifests in a general macro program. The school infrastructure development program through the President’s Education Reform Program (PERP) is one of the centrally developed educational programs in Nepal, as the case of this research article. The objective of this article is to analyze the context dynamics of centrally evolved school infrastructure (ICT and classroom building) program installation. The primary data employed in this article were drawn from the stakeholders of three program school cases situated in Sarlahi and Rautahat districts belonging to an embedded multiple-case study design of qualitative research methodology. FGD, key informant (KI) interviews, and general observation methods were used for information elicitation. The research findings reveal that centrally developed macro program policy interacts with school system-specific micro-contexts or central policy contexts which make program policy be implemented irrespective of the prioritized need, adapted with a modified technical design responding to the school-specific resources, and practiced with diverse school leadership styles. In the lapses of coordination and commitment of the tiered governance systems, the PERP or any other macro-program policy becomes strange for the implementers. The complex interaction of school-specific systems dynamics consisting of school infrastructural needs, stakeholder involvement, established school leadership styles, and initiation of local government authority make central macro policy be enacted and adapted rather than be linearly implemented. This implies incorporating the school-specific dynamics as a core component of the education plan for the formation of pragmatic program policy to enact effectively.