Search Results (164)

Background: Counterfeit governance in e-commerce platforms is a real-world problem characterized by strategic interdependence, information asymmetry and dynamic feedback among platforms, merchants and consumers. Linear or single-actor regulatory approaches are often insufficient to address such complexity, as platform regulation, merchant behaviour, consumer trust and online reputation mechanisms jointly shape the evolution of market trust. Methods: Guided by systems thinking, this study conceptualizes counterfeit governance as a socio-technical system and develops a tripartite evolutionary game model involving e-commerce platforms, merchants and consumers. The model delineates the system boundary around transaction-stage platform governance and incorporates key variables, including regulatory costs, fraudulent gains, honest transaction costs, subsidies, penalties, consumer transaction costs, online word-of-mouth influence and reputation losses. System dynamics simulation using Vensim PLE 10.5.2 is then employed to examine evolutionary trajectories, local stability, parameter sensitivity and threshold regions. Results: The results show that weak or static regulation may allow fraudulent strategies to persist or fluctuate, whereas stronger penalties and subsidies for honest transactions can reshape the payoff structure and promote merchant integrity. Transaction-related consumer incentives and enhanced online word-of-mouth mechanisms support consumer trust and stabilize the system toward trustworthy transactions. Conclusions: This study operationalizes systems thinking through system dynamics, while evolutionary game theory provides behavioural micro-foundations for stakeholder strategy adjustment in digital platform governance. Full article

Green smart city development increasingly depends on cooperation between public authorities and enterprises, yet existing research has not adequately explained how government-side enabling conditions and enterprise-side capabilities are structurally connected. This study identifies the key factors affecting green smart city development from the perspective of government–enterprise cooperation and examines their causal relationships, hierarchical structure, driving power, and dependence. A systematic literature review and Delphi consultation with ten experts from government agencies, enterprises, and universities in China were used to establish a framework of ten factors. The enterprise factors include technologies, innovation, responsibilities, management, and talents, while the government factors include databases, platforms, infrastructure, funds, and policy system. An integrated DEMATEL-ISM-MICMAC approach was then applied to analyze the relationships among these factors. The results show that policy system and funds are the fundamental driving factors, infrastructure and talents serve as key transmission factors, and technologies and innovation are highly connected but mainly dependent on upstream conditions. Responsibilities show relatively weak structural connectivity and become more influential when embedded in formal institutional and managerial mechanisms. This study develops a unified analytical framework that links government enabling conditions with enterprise implementation capabilities, clarifies the internal structure of green smart city development, and provides practical guidance for improving policy coordination, financial support, digital foundations, and enterprise capacity in China and other comparable contexts. Full article

The efficiency of epidemiological models is based on two characteristics: speed and accuracy. While Equation-Based Modeling (EBM) enables agile calculations, its approximations lack precision because they exclude population heterogeneity. On the other hand, Agent-Based Modeling (ABM) integrates individuality into the model, but it requires considerable processing time to obtain results. In this work, a sequential strategy is developed to generate ABM that preserves population heterogeneity while reducing processing time. This model is obtained through an optimization process that identifies the set of parameters whose approximation to the pandemic’s real data minimizes error. The process begins with Particle Swarm Optimization (PSO), whose cost function is implemented via EBM, enabling comparison of the population projections against the real data recorded by health institutions in three major Mexican cities. The resulting parameters are transferred to an ABM modeled in NetLogo and validated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). The results show that calibrated parameters during the optimization stage, when applied in ABM simulations, generate epidemiological scenarios that reflect the pandemic’s actual behavior. These findings indicate that EBM-based calibration supports ABM experimentation while reducing the computational cost of agent-level optimization. Full article

Knowledge-intensive projects in research and development (R&D), software, and high-technology sectors are delivered by knowledge workers who each command several skills. When many such projects compete for a shared pool of multi-skilled workers, and neither project arrival times nor durations are known in advance, deciding who works on what at each moment becomes a sequential decision problem. Therefore, we propose the multi-skilled, multi-project dynamic scheduling problem with random project arrivals and uncertain durations and formulate it as a Markov decision process (MDP) that minimizes the total human resource cost. Since the cost-to-go function is computationally intractable, we develop a rollout-based approximate dynamic programming (ADP) algorithm that approximates it via Monte Carlo simulation embedded with a randomized base policy and restricts the action space to ten representative allocation policies. On benchmarks extended from the Project Scheduling Problem Library (PSPLIB), the proposed policy lowers the average total cost by 3.1% to 16.7% relative to Q-learning on medium- and large-scale instances while completing more projects with shorter delays; on small-scale instances Q-learning attains a lower nominal cost, revealing a trade-off among cost, completion rate, and delay. Full article

This study considers a 1-m-1 hybrid flow shop scheduling problem that simultaneously incorporates four practical constraints: lot streaming, no-wait, blocking, and sequence-dependent setup times. Although each of these characteristics has been studied individually in the literature, their joint consideration in a single HFS model has received little attention. The problem is motivated by a real-world order sequencing problem in insulation board manufacturing, where all four constraints arise simultaneously from the production process. To formally characterize the problem, we develop a mixed-integer programming formulation that captures all operational constraints. For practical-scale problems, we propose several dispatching heuristics that can obtain sufficiently good solutions within a short computation time. We further develop a genetic algorithm as an independent solution approach to obtain high-quality solutions close to the optimum within a reasonable computation time. Computational experiments on instances generated based on real insulation board production characteristics demonstrate that the proposed algorithms outperform a benchmark greedy rule, and sensitivity analyses reveal the effects of setup time magnitude and the number of parallel machines on scheduling performance. Full article

Geographical indication (GI) products are often understood through terroir, certification, and branding, yet less attention has been paid to the feedback dynamics through which their market value is amplified and destabilized. This paper applies systems thinking to the post-harvest value–trust subsystem of Xinhui Chenpi, a traditional Chinese aged citrus pericarp product with GI protection, to examine how value amplification and systemic vulnerability emerge through interacting feedback mechanisms. Drawing on 28 semi-structured interviews conducted between September 2023 and December 2024, supplemented by participant observation, policy and standard documents, field-based market observations, and contextual media materials, the study develops a preliminary causal loop diagram (CLD) with 15 endogenous feedback variables, 3 boundary value-input variables, 4 exogenous contextual inputs, and 21 causal links (18 endogenous and 3 value-input). The model identifies two reinforcing loops and two balancing loops through which price expectations, holding incentives, credible circulation supply, perceived scarcity, misrepresentation, and trust erosion interact. The trust-erosion loop shows how premium-driven misrepresentation increases claim uncertainty, weakens open-market consumer trust, reduces credible open-market liquidity, and further contracts credible circulation supply. Buyer exit and delayed supply response operate as limited balancing mechanisms because their effects are segment-dependent and constrained by aging and verification delays. The proposed CLD suggests that high-value mechanisms in aging-dependent GI products may also generate structural vulnerabilities, with implications for managing consumer trust, claim verification, and credible circulation in premium markets. Full article

Modeling complex systems typically involves multiscale analysis, multiphysics coupling, and cross-domain integration. However, existing methodologies predominantly focus on constructing and solving specific equations, lacking a unified, explicit expression for underlying logical structures, which limits model comparison and combination. To address this issue, this paper proposes Axiomatic System Dynamics (ASD), a formal modeling language that decouples a model’s logical structure from its mathematical form by formalizing a mathematical model as a combination of a conceptual model and its mathematical realization. Grounded in primitive concepts—state, action, and parameter—ASD introduces generalized constitutive relationships to formulate a unified representation of system evolution. On this basis, a modular “conceptual model first, mathematical model second” paradigm is established to explicitly characterize causal relationships and identify logical isomorphisms across varied mathematical models. Through case studies spanning Newtonian mechanics, ideal gases, material constitutive relationships, and soil consolidation theories, we demonstrate that classical cross-domain theories can be formalized as specific implementations of their respective underlying system dynamics “mother structure.” Ultimately, ASD provides a meta-modeling framework independent of concrete mathematical forms, establishing a methodological foundation for logical expression, model comparison, and the meso-level deductive synthesis of cross-domain theories. Full article

Customer satisfaction offers economic benefits and competitive advantage to manufacturing enterprises. However, a gap exists in understanding how to achieve customer satisfaction due to the complexities involved in coordinating various subsystems of an enterprise in an efficient manner. In this paper, we identified four internal and external-to-the-firm customer satisfaction drivers which include: government policies, supply chain reliability, knowledge management, and manufacturing performance criteria such as on-time delivery, quality and cost. We then developed a conceptual framework to define the non-linear mathematical relationships between drivers using system dynamics modeling. We conducted “what-if “scenario analyses for a base case, optimistic case and pessimistic case using three intervention policies: “investing resources in knowledge management”, “inclusion of a safety stock”, and “decreasing cost of inventory” to study the impact of these policies on customer satisfaction. This work contributes to customer satisfaction research by presenting a decision-support tool that will enable the leadership of manufacturing enterprises to create a simplistic mental model of the complex interactions of an enterprise using easy-to-use mathematical relationships to capture system dynamics over time. Full article

Multilevel public finance is a social-administrative system in which authority, fiscal resources, information, and implementation responsibilities circulate across government tiers. China’s Province-Managing-County (PMC) reform provides a case for evaluating how governance redesign affects county-recorded fiscal expenditure. We define the system boundary as the province–prefecture–county fiscal governance chain and decompose the reform into administrative power delegation (D1), which changes decision rights, and fiscal direct reporting (D2), which changes fiscal-flow paths. Using a county-level panel of 2219 counties in 31 provinces from 2000 to 2019, we combine generalized synthetic control, Matrix Completion, panel unconditional quantile regression, and spatial diagnostics. The average effect is positive in the preferred gsynth specification and the Matrix Completion benchmark, but the magnitude is model-dependent: 16.7% under gsynth and 8.1% under Matrix Completion, with further sensitivity to latent-factor choices. Reform-type estimates and a common-model CATE equality test suggest stronger estimated effects for D1 than D2, interpreted as institutional heterogeneity rather than causal dominance. Distributional and spatial diagnostics indicate weaker lower-tail effects and geographically uneven absorption. The findings suggest that changing decision rights and fiscal-flow paths can reshape county fiscal system outputs. Full article

This paper aims to assess the relationship between foreign direct investment (FDI) and economic development/employment rate over the period 2013–2023 for Romania, as well as for other European Union country groups (Central and Eastern Europe, Northern and Western Europe and Peripheral Europe). In this respect, we used the Panel FEGLS method adjusted with cross-section SUR and found a positive relationship between FDI and Gross Domestic Product (GDP) per capita for all panels, the strongest estimated relationship being identified for Romania (followed by the one specific to Central and Eastern European states), considering the important role of the level of economic development in shaping these differences. Regarding the relationship between FDI and employment rate, we also found positive coefficients, the highest ones being identified for Central and Eastern Europe and Romania. However, the weakest estimated relationship between FDI and GDP per capita/employment was identified for the Peripheral Europe countries. Full article

We re-conceptualize corporate debt default risk (EDF) as an emergent state variable of a coupled financial system and ask how capital-market opening reshapes its equilibrium. Extending the structural credit-risk framework with three interacting subsystem channels—external financing, investment efficiency, and information disclosure—we derive a closed-form result showing that an exogenous increase in liberalization strictly reduces the system-level corporate debt default probability through three complementary channels. We then exploit the staggered roll-out of China’s Shanghai–Hong Kong and Shenzhen–Hong Kong Stock Connect (HSGT) programs as a quasi-natural experiment on a panel of 21,351 firm-year observations over 2011–2023. A difference-in-differences (DID) estimator confirms a significant stabilizing effect on the firm’s market-implied default probability that is robust to an extensive battery of identification and specification checks; mechanism regressions confirm all three model-implied channels. The stabilizing effect is further amplified in firms facing greater environmental uncertainty and greater customer concentration—precisely the regimes in which our model predicts the underlying subsystem coupling to be most fragile. Our findings recast capital-market opening as a system-level intervention that simultaneously re-balances financing, investment, and information subsystems of the financial system, with implications for financial-stability policy in emerging economies. Full article

Non-attributed graph sequence offers a powerful formalism for modeling the structural dynamics of complex systems—such as social networks, urban infrastructures, and document transmission pathways—where vertex interactions evolve over time without explicit attribute information. Mining association rules from such sequences to uncover recurring topological patterns have attracted growing interest. Yet two fundamental challenges remain: (1) how to effectively encode edge-level temporal dynamics in non-attributed settings, and (2) how to perform efficient and semantically meaningful temporal association rule mining under structural uncertainty. To address these within a systems-oriented framework, we propose two novel algorithms: the weighted temporal association rule mining algorithm and the fuzzy weighted temporal association rule mining algorithm. The first algorithm introduces time-dependent numerical weights to quantify the strength and persistence of vertex connectivity, integrating them into support and confidence measures to capture both the intensity and evolution of interactions. The second algorithm extends this by incorporating fuzzy set theory, modeling ambiguous or context-sensitive relationships (e.g., indistinct links or weakly correlated vertices) and generating fuzzy-weighted rules that enhance interpretability for real-world system analysis. Evaluated through five comprehensive experiments across diverse datasets and scales using standard metrics (support, confidence, rule count, running time), our methods produce more selective rule sets and achieve lower computational times compared to the classical Apriori algorithm. The proposed approaches thus establish a robust, data-driven foundation for analyzing temporal evolution and structural uncertainty in dynamic complex systems—providing a generalizable methodology applicable beyond domain-specific constraints. Full article

This study investigated undergraduates’ conceptualization of systems thinking (ST). An open-ended question was administered pre- and post-course. Pre-test findings revealed limited conceptualization, with most students unable to articulate core ST attributes. Post-course responses showed reasonable improvement, with seven key attributes—interconnectedness, feedback, causality, systems boundary, mapping, emergent behaviour, and synthesis—emerging to varying extents in their responses. While nearly all students indicated interconnectedness and mapping, fewer mentioned feedback and systems boundary, indicating these as higher-order cognitive skills. A continuum was also developed to categorize students’ conceptualization from inadequate to canonical; this also indicated that only a few students demonstrated engagement with the key attributes of ST. Novel analytical approaches such as attributes prevalence tables, attributes continuum, and evolution of threshold concepts have contributed to different modes for exploring ST in the responses. Findings underscore the complexity of ST and the challenges in fostering holistic conceptualization. Overall, the study highlights a nuanced engagement with the attributes of ST from the intervention and suggests that further work is necessary to better foster these among the students. Full article

Information infrastructure is essential for digital transformation and AI-enabled services, but its operation also involves high electricity consumption and carbon emissions. This study develops a tripartite evolutionary game model involving the government, information-infrastructure operators and the public, and integrates it with system dynamics to examine how regulatory mechanisms influence operators’ net-zero behaviours. The model focuses on operational-stage information infrastructure. Initial parameters are calibrated using the 2023 China Statistical Yearbook on Resources and Environment and expert consultation, with key variables measured by operational revenue, net-zero costs, regulatory costs, incentives, penalties, public scrutiny costs and environmental losses. The results show that operators’ net-zero behaviours may fluctuate under weak or static regulation. Government incentives, penalties and public scrutiny can promote net-zero operations, while dynamic reward–penalty mechanisms are more effective in stabilising behavioural evolution. This study extends evolutionary game theory and system dynamics to the net-zero governance of information infrastructure and provides an adaptive regulatory framework for coordinating government regulation, operator behaviour and public participation. Full article

Job shop scheduling holds significant importance due to its relevance and impact on various industrial and manufacturing systems. Aiming at the job shop scheduling problem with random machine breakdowns, a multi-objective optimization model is established, which considers both the makespan and expected makespan delay simultaneously. Considering that the expected makespan delay cannot be calculated analytically, this paper proposes a symbolic regression-based construction method, which can automatically learn a surrogate evaluation measure. Then, a multi-objective evolutionary algorithm is proposed for solving this model, where the constructed surrogate evaluation measure is used to replace the expected makespan delay for fitness evaluation, to achieve rapid evaluation and efficient optimization. Finally, extensive simulation experiments are conducted on 40 benchmark problems of job shop scheduling, which verify the effectiveness of the proposed method and its advantages in computational efficiency. Full article