Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Complex Systems".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 31949

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Guest Editor
Department of Computer Science, Lille University, Cité Scientifique, 59650 Villeneuve-d’Ascq, France
Interests: artificial intelligence; multi-agent systems; individual based simulation; agent based computational economics; game theory
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1. BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
2. Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
3. Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
Interests: artificial intelligence; smart cities; smart grids
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+I, 37007 Salamanca, Spain
Interests: artificial intelligence; multi-agent systems; cloud computing and distributed systems; technology-enhanced learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The relationship between individuality and aggregation is an important topic in complex systems science, as both aspects are facets of emergence. This problem has generally been addressed by adopting a classical individual- versus population-level approach, in which boundaries emerge in segregated communities: the existence of boundaries delimiting and interconnecting aggregates. It is therefore crucial to define the properties of complex systems correctly, such as generic agent-based models, with which to simulate communities situated in grid- and scale-free network environments. To do this, complexities may be resolved through simulation, modeling and analysis techniques, which help provide confidence regarding the behavior of such systems, especially of those operating in dynamic environments or under unexpected constraints. Moreover, modeling and simulation help reduce the risks and costs involved in the design and development of validation tests.

Understanding the emergent behaviors of complex systems, and ensuring their correct performance in different environments, will allow for their evolution, as well as that of the methodologies integrated in them.

Although various methodologies are being used for the development of Complex Systems Simulation, one of the most widely adopted approaches is based on the agent paradigm, which may be used to create simulations for dynamic continuous time systems and discrete event systems. Agent-based systems may be applied in all sorts of areas, including home automation, industry, smart cities and automotive sectors.

This Special Issue invites researchers to submit original quality studies regarding the domain of Complex Systems Simulation, and urges them to address its main sub-disciplines, which include, but are not limited to, the following:

  • Complex Systems
  • Simulating Complex Systems
  • Simulation, Modelling and Analysis Techniques
  • Reasoning in Complex Systems
  • Mathematical Modeling
  • Distributed Problem Solving
  • Agent-Based Simulation
  • Multi-Agent Systems (MAS)
  • Virtual Agent Organisations (VAO)
  • IoT and MAS
  • CPS and MAS
  • IoT, Smart Cities, Industry 4.0
  • Digital Twins
  • Ambient Intelligence

The deadline for papers from PAAMS is 28th February 2023.

Prof. Dr. Philippe Mathieu
Prof. Dr. Juan M. Corchado
Dr. Alfonso González-Briones
Dr. Fernando De la Prieta
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (16 papers)

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Editorial

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5 pages, 299 KiB  
Editorial
Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems
by Philippe Mathieu, Juan Manuel Corchado, Alfonso González-Briones and Fernando De la Prieta
Systems 2023, 11(10), 525; https://doi.org/10.3390/systems11100525 - 21 Oct 2023
Viewed by 1735
Abstract
Introduction  [...] Full article

Research

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31 pages, 1007 KiB  
Article
Conflicting Bundle Allocation with Preferences in Weighted Directed Acyclic Graphs: Application to Orbit Slot Allocation Problems
by Stéphanie Roussel, Gauthier Picard, Cédric Pralet and Sara Maqrot
Systems 2023, 11(6), 297; https://doi.org/10.3390/systems11060297 - 09 Jun 2023
Viewed by 874
Abstract
We introduce resource allocation techniques for problems where (i) the agents express requests for obtaining item bundles as compact edge-weighted directed acyclic graphs (each path in such a graph is a bundle whose valuation is the sum of the weights of the traversed [...] Read more.
We introduce resource allocation techniques for problems where (i) the agents express requests for obtaining item bundles as compact edge-weighted directed acyclic graphs (each path in such a graph is a bundle whose valuation is the sum of the weights of the traversed edges), and (ii) the agents do not bid on the exact same items but may bid on conflicting items that cannot be both assigned or that require accessing a specific resource with limited capacity. This setting is motivated by real applications such as Earth observation slot allocation, virtual network functions, or multi-agent path finding. We model several directed path allocation problems (vertex-constrained and resource-constrained), investigate several solution methods (qualified as exact or approximate, and utilitarian or fair), and analyze their performances on an orbit slot ownership problem, for realistic requests and constellation configurations. Full article
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22 pages, 1377 KiB  
Article
Agent-Based Collaborative Random Search for Hyperparameter Tuning and Global Function Optimization
by Ahmad Esmaeili, Zahra Ghorrati and Eric T. Matson
Systems 2023, 11(5), 228; https://doi.org/10.3390/systems11050228 - 05 May 2023
Cited by 5 | Viewed by 1444
Abstract
Hyperparameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracles to the utilization of general purpose black-box optimization [...] Read more.
Hyperparameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracles to the utilization of general purpose black-box optimization techniques. This paper proposes an agent-based collaborative technique for finding near-optimal values for any arbitrary set of hyperparameters (or decision variables) in a machine learning model (or a black-box function optimization problem). The developed method forms a hierarchical agent-based architecture for the distribution of the searching operations at different dimensions and employs a cooperative searching procedure based on an adaptive width-based random sampling technique to locate the optima. The behavior of the presented model, specifically against changes in its design parameters, is investigated in both machine learning and global function optimization applications, and its performance is compared with that of two randomized tuning strategies that are commonly used in practice. Moreover, we have compared the performance of the proposed approach against particle swarm optimization (PSO) and simulated annealing (SA) methods in function optimization to provide additional insights into its exploration in the search space. According to the empirical results, the proposed model outperformed the compared random-based methods in almost all tasks conducted, notably in a higher number of dimensions and in the presence of limited on-device computational resources. Full article
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14 pages, 435 KiB  
Article
An Infrastructure Cost and Benefits Evaluation Framework for Blockchain-Based Applications
by Miguel Pincheira, Elena Donini, Massimo Vecchio and Raffaele Giaffreda
Systems 2023, 11(4), 184; https://doi.org/10.3390/systems11040184 - 05 Apr 2023
Viewed by 1506
Abstract
Blockchain is currently a core technology for developing new types of decentralized applications. With the unique properties of blockchain, unique challenges and characteristics are introduced to the system. Among these characteristics, the infrastructure costs and benefits of the system are critical to evaluate [...] Read more.
Blockchain is currently a core technology for developing new types of decentralized applications. With the unique properties of blockchain, unique challenges and characteristics are introduced to the system. Among these characteristics, the infrastructure costs and benefits of the system are critical to evaluate the feasibility of any system and have yet to be addressed in the current literature. This work presents a framework for evaluating blockchain applications’ infrastructure costs and benefits. The framework includes a taxonomy to classify the related transactions, a model to evaluate the infrastructure costs and benefits in applications using public or private blockchains, and a methodology to guide the use of the model. The model is based on simple parameters that describe the systems, and the methodology helps to identify and estimate these parameters at any stage of the application life cycle. We quantitatively analyze three real use cases to demonstrate the framework’s merit. The analyses highlight the model’s accuracy by achieving the same results presented in the use cases. Furthermore, the use-case analyses emphasize the framework’s potential to evaluate different scenarios across the entire life cycle of blockchain-based applications. Full article
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16 pages, 405 KiB  
Article
It’s All about Reward: Contrasting Joint Rewards and Individual Reward in Centralized Learning Decentralized Execution Algorithms
by Peter Atrazhev and Petr Musilek
Systems 2023, 11(4), 180; https://doi.org/10.3390/systems11040180 - 30 Mar 2023
Viewed by 1396
Abstract
This paper addresses the issue of choosing an appropriate reward function in multi-agent reinforcement learning. The traditional approach of using joint rewards for team performance is questioned due to a lack of theoretical backing. The authors explore the impact of changing the reward [...] Read more.
This paper addresses the issue of choosing an appropriate reward function in multi-agent reinforcement learning. The traditional approach of using joint rewards for team performance is questioned due to a lack of theoretical backing. The authors explore the impact of changing the reward function from joint to individual on learning centralized decentralized execution algorithms in a Level-Based Foraging environment. Empirical results reveal that individual rewards contain more variance, but may have less bias compared to joint rewards. The findings show that different algorithms are affected differently, with value factorization methods and PPO-based methods taking advantage of the increased variance to achieve better performance. This study sheds light on the importance of considering the choice of a reward function and its impact on multi-agent reinforcement learning systems. Full article
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21 pages, 1717 KiB  
Article
Enhancing Local Decisions in Agent-Based Cartesian Genetic Programming by CMA-ES
by Jörg Bremer and Sebastian Lehnhoff
Systems 2023, 11(4), 177; https://doi.org/10.3390/systems11040177 - 28 Mar 2023
Viewed by 973
Abstract
Cartesian genetic programming is a popular version of classical genetic programming, and it has now demonstrated a very good performance in solving various use cases. Originally, programs evolved by using a centralized optimization approach. Recently, an algorithmic level decomposition of program evolution has [...] Read more.
Cartesian genetic programming is a popular version of classical genetic programming, and it has now demonstrated a very good performance in solving various use cases. Originally, programs evolved by using a centralized optimization approach. Recently, an algorithmic level decomposition of program evolution has been introduced that can be solved by a multi-agent system in a fully distributed manner. A heuristic for distributed combinatorial problem-solving was adapted to evolve these programs. The applicability of the approach and the effectiveness of the used multi-agent protocol as well as of the evolved genetic programs for the case of full enumeration in local agent decisions has already been successfully demonstrated. Symbolic regression, n-parity, and classification problems were used for this purpose. As is typical of decentralized systems, agents have to solve local sub-problems for decision-making and for determining the best local contribution to solving program evolution. So far, only a full enumeration of the solution candidates has been used, which is not sufficient for larger problem sizes. We extend this approach by using CMA-ES as an algorithm for local decisions. The superior performance of CMA-ES is demonstrated using Koza’s computational effort statistic when compared with the original approach. In addition, the distributed modality of the local optimization is scrutinized by a fitness landscape analysis. Full article
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28 pages, 1125 KiB  
Article
Engineering IoT-Based Open MAS for Large-Scale V2G/G2V
by Nikolaos I. Spanoudakis, Charilaos Akasiadis, Georgios Iatrakis and Georgios Chalkiadakis
Systems 2023, 11(3), 157; https://doi.org/10.3390/systems11030157 - 19 Mar 2023
Viewed by 1931
Abstract
In this paper, we aimed to demonstrate how to engineer Internet of Things (IoT)-based open multiagent systems (MASs). Specifically, we put forward an IoT/MAS architectural framework, along with a case study within the important and challenging-to-engineer vehicle-to-grid (V2G) and grid-to-vehicle (G2V) energy transfer [...] Read more.
In this paper, we aimed to demonstrate how to engineer Internet of Things (IoT)-based open multiagent systems (MASs). Specifically, we put forward an IoT/MAS architectural framework, along with a case study within the important and challenging-to-engineer vehicle-to-grid (V2G) and grid-to-vehicle (G2V) energy transfer problem domain. The proposed solution addresses the important non-functional requirement of scalability. To this end, we employed an open multiagent systems architecture, arranging agents as modular microservices that were interconnected via a multi-protocol Internet of Things platform. Our approach allows agents to view, offer, interconnect, and re-use their various strategies, mechanisms, or other algorithms as modular smart grid services, thus enabling their seamless integration into our MAS architecture, and enabling the solution of the challenging V2G/G2V problem. At the same time, our IoT-based implementation offers both direct applicability in real-world settings and advanced analytics capabilities via enabling digital twin models for smart grid ecosystems. We have described our MAS/IoT-based architecture in detail; validated its applicability via simulation experiments involving large numbers of heterogeneous agents, operating and interacting towards effective V2G/G2V; and studied the performance of various electric vehicle charging scheduling and V2G/G2V-incentivising electricity pricing algorithms. To engineer our solution, we used ASEME, a state-of-the-art methodology for multiagent systems using the Internet of Things. Our solution can be employed for the implementation of real-world prototypes to deliver large-scale V2G/G2V services, as well as for the testing of various schemes in simulation mode. Full article
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28 pages, 1198 KiB  
Article
Deep Reinforcement Learning Reward Function Design for Autonomous Driving in Lane-Free Traffic
by Athanasia Karalakou, Dimitrios Troullinos, Georgios Chalkiadakis and Markos Papageorgiou
Systems 2023, 11(3), 134; https://doi.org/10.3390/systems11030134 - 02 Mar 2023
Cited by 3 | Viewed by 2806
Abstract
Lane-free traffic is a novel research domain, in which vehicles no longer adhere to the notion of lanes, and consider the whole lateral space within the road boundaries. This constitutes an entirely different problem domain for autonomous driving compared to lane-based traffic, as [...] Read more.
Lane-free traffic is a novel research domain, in which vehicles no longer adhere to the notion of lanes, and consider the whole lateral space within the road boundaries. This constitutes an entirely different problem domain for autonomous driving compared to lane-based traffic, as there is no leader vehicle or lane-changing operation. Therefore, the observations of the vehicles need to properly accommodate the lane-free environment without carrying over bias from lane-based approaches. The recent successes of deep reinforcement learning (DRL) for lane-based approaches, along with emerging work for lane-free traffic environments, render DRL for lane-free traffic an interesting endeavor to investigate. In this paper, we provide an extensive look at the DRL formulation, focusing on the reward function of a lane-free autonomous driving agent. Our main interest is designing an effective reward function, as the reward model is crucial in determining the overall efficiency of the resulting policy. Specifically, we construct different components of reward functions tied to the environment at various levels of information. Then, we combine and collate the aforementioned components, and focus on attaining a reward function that results in a policy that manages to both reduce the collisions among vehicles and address their requirement of maintaining a desired speed. Additionally, we employ two popular DRL algorithms—namely, deep Q-networks (enhanced with some commonly used extensions), and deep deterministic policy gradient (DDPG), which results in better policies. Our experiments provide a thorough investigative study on the effectiveness of different combinations among the various reward components we propose, and confirm that our DRL-employing autonomous vehicle is able to gradually learn effective policies in environments with varying levels of difficulty, especially when all of the proposed rewards components are properly combined. Full article
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17 pages, 1898 KiB  
Article
The Hidden Side of Electro-Mobility: Modelling Agents’ Financial Statements and Their Interactions with a European Focus
by Jonatan J. Gómez Vilchez and Roberto Pasqualino
Systems 2023, 11(3), 132; https://doi.org/10.3390/systems11030132 - 01 Mar 2023
Viewed by 1217
Abstract
While much attention has been given, to date, to subsidies and taxes, the literature on the topic is yet to address less visible aspects of electro-mobility. These include the interactions among players, including money exchanges, and balance sheet issues. Analysing these is needed, [...] Read more.
While much attention has been given, to date, to subsidies and taxes, the literature on the topic is yet to address less visible aspects of electro-mobility. These include the interactions among players, including money exchanges, and balance sheet issues. Analysing these is needed, as it helps identify additional mechanisms that may affect electro-mobility. This paper reports a modelling exercise that applies the system dynamics method, with its focus on stock and flow variables. The resulting simulation model captures the financial statements of several macro agents. The results show that the objective of the study is met: the model remains ‘stock-flow consistent’, meaning that assets and equity and liabilities balance out. By attaining this, the model serves as a coherent framework that makes the “hidden” side of electro-mobility visible, for the first time, based on current state-of-the-art, with the implication that it facilitates the analysis of potential financial factors that may either jeopardise or be conducive to faster road electrification. We conclude that the incorporation of the financial statements of key electro-mobility agents and their interlinkages in a simulation model is both a feasible and desired property for policy-relevant models. Full article
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24 pages, 7670 KiB  
Article
Multi-Category Innovation and Encroachment Strategy Evolution of Composite E-Commerce Platform Based on Multi-Agent Simulation
by Ziyan Wang and Tianjian Yang
Systems 2022, 10(6), 215; https://doi.org/10.3390/systems10060215 - 11 Nov 2022
Cited by 1 | Viewed by 1456
Abstract
An in-depth study of the product encroachment behavior on the composite e-commerce platform is of great significance to standardize the platform economy. This paper studies product encroachment behavior of composite e-commerce platforms with double-differentiated multi-product competition and constructs a game model of product [...] Read more.
An in-depth study of the product encroachment behavior on the composite e-commerce platform is of great significance to standardize the platform economy. This paper studies product encroachment behavior of composite e-commerce platforms with double-differentiated multi-product competition and constructs a game model of product innovation by an independent seller and product encroachment by the platform owner. Using multi-agent simulation, we simulate the bounded rational decision-making and interaction process of multiple agents in multiple periods and analyze the main parameters’ influence. Results indicate the following: (1) In dual-differentiated multi-product competition, the third-party seller is more willing to invest in innovating high-quality category P, and the profit-driven platform owner only encroaches on the new variants of category P. (2) The larger consumers’ platform owner preference can encourage the third-party seller to innovate high-quality new products. The increase in vertical differentiation of categories can enhance the third-party seller’s innovation motivation for the traffic-attracting category. (3) A reasonable commission rate set by the platform owner can ensure the variety of variants of various categories, thereby expanding the sales scope of the composite e-commerce platform. Diseconomies of scale of category diversity management costs hinder the growth of product variety in the online marketplace. Full article
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18 pages, 1498 KiB  
Article
Agent-Based Modeling of Consensus Group Formation with Complex Webs of Beliefs
by Ismo T. Koponen
Systems 2022, 10(6), 212; https://doi.org/10.3390/systems10060212 - 09 Nov 2022
Cited by 2 | Viewed by 2233
Abstract
Formation of consensus groups with shared opinions or views is a common feature of human social life and also a well-known phenomenon in cases when views are complex, as in the case of the formation of scholarly disciplines. In such cases, shared views [...] Read more.
Formation of consensus groups with shared opinions or views is a common feature of human social life and also a well-known phenomenon in cases when views are complex, as in the case of the formation of scholarly disciplines. In such cases, shared views are not simple sets of opinions but rather complex webs of beliefs (WoBs). Here, we approach such consensus group formation through the agent-based model (ABM). Agents’ views are described as complex, extensive web-like structures resembling semantic networks, i.e., webs of beliefs. In the ABM introduced here, the agents’ interactions and participation in sharing their views are dependent on the similarity of the agents’ webs of beliefs; the greater the similarity, the more likely the interaction and sharing of elements of WoBs. In interactions, the WoBs are altered when agents seek consensus and consensus groups are formed. The consensus group formation depends on the agents’ sensitivity to the similarity of their WoBs. If their sensitivity is low, only one large and diffuse group is formed, while with high sensitivity, many separated and segregated consensus groups emerge. To conclude, we discuss how such results resemble the formation of disciplinary, scholarly consensus groups. Full article
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15 pages, 842 KiB  
Article
Practical Formalism-Based Approaches for Multi-Resolution Modeling and Simulation
by Jang Won Bae and Il-Chul Moon
Systems 2022, 10(5), 174; https://doi.org/10.3390/systems10050174 - 29 Sep 2022
Viewed by 1364
Abstract
Multi-resolution modeling (MRM) has been considered as an ideal form of simulation to acquire low-resolution scalability as well as high-resolution modeled details. Although both practical and theoretical interests exist in MRM, actual implementations were quite different in terms of cases and methods. Specifically, [...] Read more.
Multi-resolution modeling (MRM) has been considered as an ideal form of simulation to acquire low-resolution scalability as well as high-resolution modeled details. Although both practical and theoretical interests exist in MRM, actual implementations were quite different in terms of cases and methods. Specifically, MRM implementations range from parameter-based interoperation to model exchanges with different resolutions, yet it is difficult to observe a method that focuses on both of these aspects. To this end, this paper introduces a formalism or multi-resolution translational Discrete Event System Specification (MRT-DEVS). Focusing on the practical perspective, MRT-DEVS intends to ease the implementation’s difficulty and reduce the simulation’s execution costs. Specifically, MRT-DEVS embeds state and event translation functions into the model’s specifications so that it enables MRM with less complex mechanisms in terms of operations. Using the provided case study and a reduction to other MRM methods, the theoretical soundness of the proposed method is supported. Moreover, we discussed the pros and the cons of the proposed method from various MRM perspectives. We expect that with all the provided information, MRMS users would consider the proposed method as a practical option to implement their models. Full article
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23 pages, 3738 KiB  
Article
Research on the Evolutionary Path of Eco-Conservation and High-Quality Development in the Yellow River Basin Based on an Agent-Based Model
by Aiwu Zhao, Jingyi Wang, Zhenzhen Sun and Hongjun Guan
Systems 2022, 10(4), 105; https://doi.org/10.3390/systems10040105 - 27 Jul 2022
Cited by 4 | Viewed by 1675
Abstract
The high-quality economic and social development of the Yellow River Basin is a combined system comprising the coordinated development of “economy–resources–environment–society”, with resources and the ecological environment bearing capacity as the constraints, and green innovative development as the driving force. Based on the [...] Read more.
The high-quality economic and social development of the Yellow River Basin is a combined system comprising the coordinated development of “economy–resources–environment–society”, with resources and the ecological environment bearing capacity as the constraints, and green innovative development as the driving force. Based on the systematic analysis of the structural dimensions of the composite system, this paper uses the balanced indicators and their coordinated development effectiveness to describe the development quality of the macro-composite system. In order to reveal the mechanism of the evolutionary path of the macro system, the resource- and environment-bearing capacity, regional high-quality development potential, regional innovation capacity, and high-quality development guarantee capacity are adopted as the main attributes and decision-making basis of the autonomous agents. The simulation results show that, under the existing development model, the economic development of all of the provinces in the Yellow River Basin will be constrained by resources and the environment. However, different policy scenarios significantly affect the evolutionary trends of economic development, resource consumption, and the environmental pollution situation. The mechanisms to overcome the bottleneck of the resource and ecological constraints are different for these policies, and the effects of the same policy in different provinces are also not the same. Full article
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17 pages, 1924 KiB  
Article
A Novel Public Opinion Polarization Model Based on BA Network
by Yuanjian Ye, Renjie Zhang, Yiqing Zhao, Yuanyuan Yu, Wenxin Du and Tinggui Chen
Systems 2022, 10(2), 46; https://doi.org/10.3390/systems10020046 - 09 Apr 2022
Cited by 2 | Viewed by 2556
Abstract
At present, the polarization of online public opinion is becoming more frequent, and individuals actively participate in attitude interactions more and more frequently. Thus, online views have become the dominant force in current public opinion. However, the rapid fermentation of polarized public opinion [...] Read more.
At present, the polarization of online public opinion is becoming more frequent, and individuals actively participate in attitude interactions more and more frequently. Thus, online views have become the dominant force in current public opinion. However, the rapid fermentation of polarized public opinion makes it very easy for actual topic views to go to extremes. Significantly, negative information seriously affects the healthy development of the social opinion ecology. Therefore, it is beneficial to maintain national credibility, social peace, and stability by exploring the communication structure of online public opinions, analyzing the logical model of extreme public attitudes, and guiding the communication of public opinions in a timely and reasonable manner. Starting from the J–A model and BA network, this paper explores the specific attributes of individuals and opinion network nodes. By incorporating parameters such as individual conformity and the strength of individual online relationships, we established a model of online group attitude polarization, then conducted simulation experiments on the phenomenon of online opinion polarization. Through simulations, we found that individual conformity and the difference in environmental attitude greatly influence the direction of opinion polarization events. In addition, crowd mentality makes individuals spontaneously choose the side of a particular, extreme view, which makes it easier for polarization to form and reach its peak. Full article
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11 pages, 5998 KiB  
Article
Disinformation in Social Networks and Bots: Simulated Scenarios of Its Spread from System Dynamics
by Alfredo Guzmán Rincón, Ruby Lorena Carrillo Barbosa, Nuria Segovia-García and David Ricardo Africano Franco
Systems 2022, 10(2), 34; https://doi.org/10.3390/systems10020034 - 10 Mar 2022
Cited by 2 | Viewed by 3495
Abstract
Social networks have become the scenario with the greatest potential for the circulation of disinformation, hence there is a growing interest in understanding how this type of information is spread, especially in relation to the mechanisms used by disinformation agents such as bots [...] Read more.
Social networks have become the scenario with the greatest potential for the circulation of disinformation, hence there is a growing interest in understanding how this type of information is spread, especially in relation to the mechanisms used by disinformation agents such as bots and trolls, among others. In this scenario, the potential of bots to facilitate the spread of disinformation is recognised, however, the analysis of how they do this is still in its initial stages. Taking into consideration what was previously stated, this paper aimed to model and simulate scenarios of disinformation propagation in social networks caused by bots based on the dynamics of this mechanism documented in the literature. For achieving the purpose, System dynamics was used as the main modelling technique. The results present a mathematical model, as far as disinformation by this mechanism is concerned, and the simulations carried out against the increase in the rate of activation and deactivation of bots. Thus, the preponderant role of social networks in controlling disinformation through this mechanism, and the potential of bots to affect citizens, is recognised. Full article
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Other

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20 pages, 2169 KiB  
Technical Note
Earned Value Management Agent-Based Simulation Model
by Manuel Castañón-Puga, Ricardo Fernando Rosales-Cisneros, Julio César Acosta-Prado, Alfredo Tirado-Ramos, Camilo Khatchikian and Elías Aburto-Camacllanqui
Systems 2023, 11(2), 86; https://doi.org/10.3390/systems11020086 - 07 Feb 2023
Viewed by 1811
Abstract
Agile project management (APM) can be defined as an iterative approach that promotes satisfying customer requirements, adjusts to change, and develops a working product in rapidly changing environments. Managers usually apply agile management as the project management approach in projects requiring extraordinary speed [...] Read more.
Agile project management (APM) can be defined as an iterative approach that promotes satisfying customer requirements, adjusts to change, and develops a working product in rapidly changing environments. Managers usually apply agile management as the project management approach in projects requiring extraordinary speed and flexibility in their processes. Earned value management (EVM) is a fundamental part of project management to establish practical measures. Often, managers use a task board to visually represent the work on a project and the path to completion. Still, managing an agile project can be a challenging endeavor. In this paper, we propose an agent-based model describing the management of tasks within a project using earned value assessment and a task board. Our model illustrates how EVM yields an efficient method to measure a project’s performance by comparing actual progress against planned activities, thus facilitating the formulation of more accurate predicted estimations. As proof of concept, we leverage our implementation to calculate EVM performance indexes according to a performance measurement baseline (PMB) in a task board fashion. Full article
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