Search

This paper presents a bibliometric analysis within the research domain dedicated to the utilization of agent-based modeling (ABM) in the field of transportation. By employing specific keywords related to both agent-based modeling and transportation, we have identified and extracted 1016 scholarly papers from the ISI Web of Science database, spanning the period from 2002 to 2023. Through the application of bibliometric methods, we have systematically examined key contributors, affiliations of academic institutions, influential publications, and renowned journals within this domain. Our analysis reveals a consistent and robust growth in scholarly interest pertaining to agent-based modeling in the field of transportation throughout the considered period. Notably, within approximately four decades of ABM's application in transportation, a distinct upward trajectory began in 2008, culminating in the year 2021. The entire considered period witnessed a remarkable surge in paper production, characterized by an annual growth rate of 21.67%. Furthermore, employing an n-gram analysis, we have delineated and discussed the principal areas within transportation that have progressively benefited from the advancements in agent-based modeling. Prominently, the domains of air transport and road transport have exhibited substantial development over time, while the implications of climate change have emerged as a persistent concern throughout the entire study period. [ABSTRACT FROM AUTHOR]

This paper describes the work done by sixth grade students to achieve and sustain productive and personally meaningful lines of inquiry with computational models. The capacity to frame interactions with tools as dialogic exchanges with co-participants is a productive practice for disciplinary engagement in science and for computational thinking (Chandrasekharan and Nersessian 2015; Dennet 1989; Latour 1993; Pickering 1995). We propose that computational models have unique affordances for dialogic interaction because they are probabilistic and iteratively executable, features that provide an entry point for students to adopt stances that treat computational models as participants. Our analysis reveals that existing patterns in students' social interactions are resources for interacting flexibly with computational tools as participants. In particular, we found that students treated computational models as participants in three ways: (1) as conversational peers, (2) as co-constructors of lines of inquiry, and (3) as projections of students' agency and identity. Our data also demonstrate that students take on flexible, rather than fixed, stances toward computational participants. These stances parallel scientists' interactions with non-human entities, which often involve treating tools as agentive participants in inquiry (Latour 1999; Pickering 1995), affording students a pathway to practices at the intersection of disciplinary engagement and computational thinking. [ABSTRACT FROM AUTHOR]

Copyright of Idealkent: Journal of Urban Studies is the property of Idealkent and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Incentive contracts often do not govern all task elements for which an employee is responsible. Prior research, particularly in the tradition of principal-agent theory, has studied incomplete incentive contracts as multi-task problems focusing on how to motivate the employee to incur effort for a not-contracted task element. Thus, emphasis is on the "vertical" relation between superior and subordinate, where both are modeled as gifted economic actors. This paper takes another perspective focusing on the "horizontal" interferences of—contracted and not-contracted—task elements across various employees in an organization and, hence, on the complexity of an organization's task environment. In order to disentangle the interactions among tasks from agents' behavior, the paper pursues a minimal intelligence approach. An agent-based simulation model based on the framework of NK fitness landscapes is employed. In the simulation experiments, artificial organizations search for superior performance, and the experiments control for the complexity of the task environment and the level of contractual incompleteness. The results suggest that the complexity of the task environment in terms of interactions among task elements may considerably shape the effects of incomplete incentive contracts. In particular, the results indicate that moderate incompleteness of incentive contracts may be beneficial with respect to organizational performance when intra-organizational complexity is high. This is caused by stabilization of search resulting from incomplete contracts. Moreover, interactions may induce that the not-contracted task elements could serve as means objectives, i.e., contributing to achieving contracted task elements. [ABSTRACT FROM AUTHOR]

Optimizing Resource Allocation in Fire Departments (RAFD) is crucial for enhancing Fire Protection Services (FPS) and ultimately saving lives. Efficient RAFD ensures that fire departments have the necessary resources to respond effectively to emergencies. This paper presents a method for optimizing RAFD based on performance assessment results, examining its impact on Fire Department (FD) efficiency in Portugal. Evaluating data from 353 FDs, two RAFD optimization methods were assessed: one adhering to Portuguese regulations and constraints, such as budget allocation limitations, and another without such constraints. Integrating a slack-based data envelopment analysis model and mixed-integer linear programming, the study found that incorporating FD efficiency scores in RAFD improved overall efficiency at national, district, and FD levels. While adherence to Portuguese regulations led to balanced resource allocation and a 4% performance improvement at the national level, relaxing constraints yielded an 8% improvement, albeit with potential performance deterioration in some FDs. The detailed budget and efficiency metric analysis provided in this paper offers actionable insights for fire protection services enhancement. This underscores the importance of diverse optimization strategies to enhance FD efficiency, with implications for decision-makers at the Portuguese National Authority for Emergency and Civil Protection and similar organizations globally. [ABSTRACT FROM AUTHOR]

This paper explores the impact of an increasing number of prosumers in electricity supply systems and investigates how market mechanisms can mitigate the negative effects. The Regional Energy Market Model simulates a supply system based on cellular structures, employing agent-based modeling to capture individual behaviors and simulate real market dynamics. This study includes various supply scenarios, such as a solely photovoltaic scenario and a technically diversified scenario with biogas-fueled combined heat and power units. For each scenario, fixed and flexible pricing scenarios are simulated to analyze their effects. The findings reveal that systems heavily reliant on photovoltaics experience negative effects at certain points due to seasonal limitations, while technically diversified supply scenarios demonstrate fewer drawbacks. Flexible pricing systems stimulate demand in a manner beneficial to the system, creating regional added value, and contributing to the balance between generation and consumption, depending on the supply scenario. However, the study underscores that economic incentives alone are insufficient for balancing generation and consumption. The results highlight the importance of exploring opportunities through the interplay of economic incentive mechanisms and technical possibilities. [ABSTRACT FROM AUTHOR]

Despite the popularity of the "behavioral approach," there is as yet a lack of guidance on the selection and use of appropriate behavioral theories for specific planning purposes. Based on a literature review of 318 articles in spatial planning-related journals, this paper presents a portfolio of behavioral theories by types of behavior, key variables, rules, and research methods. In addition, based on the survey of twenty-two international experts, it cross-validates the findings and highlights particularly appropriate theories for certain types of behavior dealt in related disciplines. Finally, the paper derives discussion points including the applicability of various behavioral theories in urban models such as space and time-sensitive dynamic simulations. [ABSTRACT FROM AUTHOR]

Although interdisciplinarity is often touted as a necessity for modern research, the evidence on the relative impact of sectorial versus to interdisciplinary science is qualitative at best. In this paper we leverage the bibliographic data set of the American Physical Society to quantify the role of interdisciplinarity in physics, and that of talent and luck in achieving success in scientific careers. We analyze a period of 30 years (1980-2009) tagging papers and their authors by means of the Physics and Astronomy Classification Scheme (PACS), to show that some degree of interdisciplinarity is quite helpful to reach success, measured as a proxy of either the number of articles or the citations score. We also propose an agent-based model of the publication-reputation-citation dynamics which reproduces the trends observed in the APS data set. On the one hand, the results highlight the crucial role of randomness and serendipity in real scientific research; on the other, they shed light on a counterintuitive effect indicating that the most talented authors are not necessarily the most successful ones. [ABSTRACT FROM AUTHOR]

This paper presents a systemic view of human cognition that suggests complexityis an essential feature of such a system. It draws on the embodied, distributed, and extended cognition paradigms to outline the elements and the mechanisms that define cognition. In doing so, it uses an agent-based computational model (the TS 1.0.5Model) with a focus on learning mechanisms as they reflect on individual competence to gain insights on how cognition works. Results indicate that cognitive dynamics do not depend solely on macro structural elements, nor do they depend uniquely on individual characteristics. Instead, more insights and understanding are available through the consideration of all elements together as they co-evolve and interact over time. This perspective illustrates the essential role of how we define the meso domain and constitutes a clear indication that cognitive systems are indeed complex. [ABSTRACT FROM AUTHOR]

Background: Simulations, ludic or otherwise, have so far struggled to gain a foothold in mainstream historiography. Some authors suggest there may be fundamental incompatibilities between history and the language of simulations and scholarly games. Others believe that designing, employing, and validating historical simulations may be simply too costly and/or labor-intensive to justify their widespread adoption. Intervention: This paper intends to identify points of friction between historiography and simulation-based research and suggest practical solutions to these issues. Methods: My discussion is based on the description and analysis of a case study, the ThomondSim/ The Triumphs of Turlough research project. The initiative consisted of the development and application of an agent-based computational model (ABM) and a scholarly board game to investigate the possible associations between economic, environmental, and military hazards in 13th and 14th centuries Ireland. Results and Discussion: Ensuring the simulations matched historical evidence to a standard deemed acceptable by the historiographical community limited their phase space, compromising their capacity to explore emergent phenomena. The intricacy of the underlying conceptual model suited the ABM better than the board game, which struggled to reconcile complexity with good game design practices. The board game, however, proved to be an effective validating tool for the ABM. Limitations and Suggestions for Further Research: The project espoused an overt simulation- and game-centric approach, paying little attention to unguided play. Recent literature suggests that fostering, rather than hindering, playful exploration could address some of the pitfalls identified by this project. Conclusion: Play could be a means of reconciliation between simulational, ludic, and historiographical practices. However, to ensure that projects adhere to epistemic standards, it is recommended that a methodology is developed to integrate it into research in ways that can be tested and evaluated. [ABSTRACT FROM AUTHOR]

The U.S. Committee for Refugees and Immigrants estimated that there were over 33 million refugees and internally displaced persons (IDPs) in the world at the beginning of 2005. IDP/Refugee communities behave in complex ways making it difficult to make policy decisions regarding the provision of humanitarian aid and health and safety. This paper reports the construction of an agent-based model that has been used to study humanitarian assistance policies executed by governments and NGOs that provide for the health and safety of refugee communities. Agent-based modeling (ABM) was chosen because the more widely used alternatives impose unrealistic restrictions and assumptions on the system being modeled and primarily apply to aggregate data. We created intelligent agents representing institutions, organizations, individuals, infrastructure, and governments and analyzed the resulting interactions and emergent behavior using a Central Composite Design of Experiments with five factors. The resulting model allows policy makers and analysts to create scenarios, to make rapid changes in parameters, and provides a test bed for concepts and strategies. Policies can be examined to see how refugee communities might respond to alternative courses of action and how these actions are likely to affect the health and well-being of the community. [ABSTRACT FROM AUTHOR]

The central subject of this paper is the relation between the macro-level of the Society and the micro-level of its individuals. In complex real-life systems, this relationship is not trivial and even the result at the macro-level can be counter-intuitive. One of the first models that highlights this is the Sakoda and Schelling's model of spatial segregation where a person uses a selfish rule to decide whether or not to leave his current place; on the basis of the ratio of "compatible" agents in the neighborhood of each individual, simulations have shown that tolerant agents group together beyond what their own tolerance imposes. This paper compares the macro-behavior arising from either selfish or altruist micro-motives; for this purpose, we propose agent-based computational models based, respectively, on selfishness, altruism and a mixture of both. Computational simulations show that (i) in a totally altruistic population, a vast majority becomes satisfied as if the agents were themselves selfish and end up spatially segregated; (ii) altruist micro-motives allow to reduce both the micro-macro gap and the segregation phenomenon; (iii) in a mixed population, the macro-effect depends both on the respective weight of each micro-motives and the allocation strategy. [ABSTRACT FROM AUTHOR]

Construction organizations are project-based organizations in which both competencies and performance develop through project execution. A competency measure is a combination of knowledge, skill, processes, practices, and/or technology possessed by a construction organization. Performance measures are used to assess an organization's competitiveness within its industry. An organization can analyze competencies to predict and improve performance. Previous studies investigated project- and organization-level competencies with respect to performance measures. However, multilevel assessment is needed, although it is challenging because the nature of construction competency and performance measures is complex, dynamic, and subjectively uncertain. To address these challenges, this paper presents a methodology for developing a fuzzy agent–based model (FABM) of competency and performance that models a set of organizational- and project-level competency measures as decision-making entities (i.e., agents) and predicts multiple performance measures as their emergent behavior. A case study was implemented to demonstrate, verify, and validate the proposed FABM model, with encouraging results in performance prediction. The academic contribution of this paper is providing a novel systematic, bottom-up modeling approach for measuring and assessing competencies at the project and organization levels and mapping these multilevel competencies to construction performance measures. Furthermore, the outcomes of this study are expected to support construction practitioners by providing a set of comprehensive hierarchical competency and performance metrics at the project and organization levels for managerial actions taken to identify, construct, and develop competency models to assess performance at both levels. [ABSTRACT FROM AUTHOR]

Understanding the cognitive evolution of researchers as they progress in academia is an important but complex problem; one that belongs to a class of problems, which often require the development of models to gain further understanding of the intricacies of the domain. The research question that we address in this paper is: how to effectively model this temporal cognitive mental development of prolific researchers? Our proposed solution is based on noting that the academic progression and notability of a researcher are linked with a progressive increase in the citation count for the scholar's refereed publications, quantified using indices such as the Hirsch index. We propose the use of an yearly increment of a scholar's cognition quantifiable by means of a function of the scholar's citation index, thereby considering the index as an indicator of the discrete approximation of the scholar's cognitive development. Using validated agent-based modeling, a paradigm presented as part of our previous work aimed at the development of a cognitive agent-based computing framework, we present both formal as well as visual agent-based complex network representations for this cognitive evolution in the form of a temporal cognitive level network model. As proof of the effectiveness of this approach, we demonstrate the validation of the model using historic data of citations. [ABSTRACT FROM AUTHOR]

Process cooling for food production is an energy-intensive industry with complex interactions and restrictions that complicate the ability to utilize energy-flexibility due to unforeseen consequences in production. Therefore, methods for assessing the potential flexibility in individual facilities to enable the active participation of process-cooling facilities in the electricity system are essential, but not yet well discussed in the literature. Therefore, this paper introduces an assessment method based on multi-method simulation and multi-objective optimization for investigating energy flexibility in process cooling, with a case study of a Danish process-cooling facility for canned-meat food production. Multi-method simulation is used in this paper: multi-agent-based simulation to investigate individual entities within the process-cooling system and the system's behavior; discrete-event simulation to explore the entire process-cooling flow; and system dynamics to capture the thermophysical properties of the refrigeration unit and states of the refrigerated environment. A simulation library is developed, and is able to represent a generic production-flow of the canned-food process cooling. A data-driven symbolic-regression approach determines the complex logic of individual agents. Using a binary tuple-matrix for refrigeration-schedule optimization, the refrigeration-cycle operation is determined, based on weather forecasts, electricity price, and electricity CO2 emissions without violating individual room-temperature limits. The simulation results of one-week's production in October 2020 show that 32% of energy costs can be saved and 822 kg of CO2 emissions can be reduced. The results thereby show the energy-flexibility potential in the process-cooling facilities, with the benefit of overall production cost and CO2 emissions reduction; at the same time, the production quality and throughput are not influenced. [ABSTRACT FROM AUTHOR]

Evaluating the resilience of the innovation ecosystem to maintain its performance, in the sense of resistance to disruption and recovery after it, has recently received more attention. Several studies have been conducted to model different ecosystems and evaluate their resilience. However, modeling the innovation ecosystem from a holistic perspective and performing a quantitative assessment of its resilience have received less attention. This paper models the innovation ecosystem holistically and evaluates its resilience index using a quantitative approach through five main steps. In the first step, a case study related to the innovation ecosystem of Iran’s Ministry of Energy, called the Power Innovation Ecosystem, is modeled by combining system dynamics and agent-based modeling. Upon validating the model in the second step, the disruption of the loss of experts is investigated in the third step, and all possible actions to recover each actor are analyzed. In the fourth step, the performance of the ecosystem is simulated before and after the disruption using the data gathered in the previous steps. Finally, resilience is calculated in two different ways in the fifth step. Several improvement solutions are also suggested when considering that the resilience index of the innovation ecosystem is at a medium level. This research may assist policymakers in observing the resilience level of the innovation ecosystem based on the proposed model. By applying strategic changes to this model, they can determine the effects of their policies and make the most appropriate decisions to increase the resilience of the innovation ecosystem. [ABSTRACT FROM AUTHOR]

This paper introduces an Agent-Based Model (ABM) designed to investigate the dynamics of the Internet of Things (IoT) ecosystem, focusing on dynamic coalition formation among IoT Service Providers (SPs). Drawing on insights from our previous research in 5G network modeling, the ABM captures intricate interactions among devices, Mobile Network Operators (MNOs), SPs, and customers, offering a comprehensive framework for analyzing the IoT ecosystem's complexities. In particular, to address the emerging challenge of dynamic coalition formation among SPs, we propose a distributed Multi-Agent Dynamic Coalition Formation (MA-DCF) algorithm aimed at enhancing service provision and fostering collaboration. This algorithm optimizes SP coalitions, dynamically adjusting to changing demands over time. Through extensive experimentation, we evaluate the algorithm's performance, demonstrating its superiority in terms of both payoff and stability compared to three classical coalition formation algorithms: static coalition, non-overlapping coalition, and random coalition. This study significantly contributes to a deeper understanding of the IoT ecosystem's dynamics and highlights the potential benefits of dynamic coalition formation among SPs, providing valuable insights and opening future avenues for exploration. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive agent-based model for the spread of an infection in a network of cities. Directional mobility is defined between each two cities and can take different values. The work examines the role that such mobility levels play in containing the infection with various vaccination coverage and age distributions. The results indicate that mobility reduction is sufficient to control the disease under all circumstances and full lockdowns are not a necessity. It has to be reduced to different ratios depending on the vaccination level and age distribution. A key finding is that increasing vaccination coverage above a certain level does not affect the mobility suppression level required to control the infection anymore for the cases of young population and heterogeneous age distributions. By investigating several migration and commuting patterns, it is found that shutting mobility in a few local places is favored against reducing mobility over the entire country network. In addition, commuting -and not migration-influences the spread level of the infection. The work offers an exclusive combined network-based and agent-based model that makes use of randomly generated mobility matrices. [ABSTRACT FROM AUTHOR]

This paper presents the new Clustering-Based Hybrid Particle Swarm Optimization (CBHPSO) algorithm. This algorithm was designed to solve bi-objective optimization problems and it was used for finding trade-offs in a multisectoral agent-based model of trade interactions. This model includes multiple interacting agent enterprises belonging to different economic sectors. At the same time, the values of the control parameters of this stochastic multiagent system (MAS) need to be optimized. Therefore, CBHPSO has been developed and aggregated with this MAS by means of the objective functions. The main feature of the CBHPSO algorithm consists in the use of clustering techniques, such as the k-means algorithm, to form subsets of non-dominated solutions shared among the swarm particles in each cluster. The values of the performance metrics employed for CBHPSO and other well-known multi-objective evolutionary algorithms (SPEA2, NSGA-II, FCGA and BORCGA-BOPSO and MOPSO) were compared. As a conclusion, it was found that the velocities of decision variables in the particle swarm depend on specific non-dominant solutions of the clusters involved. It can be said that the main advantage of CBHPSO lies in the quality of the Pareto front approximation. Thus, it was demonstrated that the CBHPSO algorithm can be applied to search for improved characteristics of the employed MAS. [ABSTRACT FROM AUTHOR]

Disasters associated with natural hazards as well as climate change are happening within complex socio-economic systems and desired system states, including sustainable development and resource management, are formulated on the global as well as regional and national levels. However, complex system approaches are yet only rudimentarily incorporated in related applications, and we discuss modeling as well as policy challenges focusing on fiscal risk. As an intermediate step we suggest a gap approach which we relate to fiscal stress levels a complex system may experience due to natural hazard events. We argue that in case of no gaps one can assume a no stress situation and therefore modeling of disruptions including cascading effects is less necessary. However, at the same time we also acknowledge that there is an urgent need to address corresponding challenges with complex systemmethods. Policy-wise our paper responds to concerns for real-world applications and can provide insights to support current discussions within the UNFCCC and Paris Agreement around both adaptation finance and the new funding arrangements for loss and damage from climate impacts established at COP27. [ABSTRACT FROM AUTHOR]

The rise of social media has led to a new degree of spontaneous volunteering in the aftermath of disasters, even in border areas. However, spontaneous volunteering across national borders creates further obstacles that need to be addressed and prepared for. This paper introduces an agent-based simulation to examine coordination procedures of spontaneous volunteers in crisis scenarios, particularly the specifics of travel restrictions imposed after the COVID-19 pandemic. Moreover, research is conducted on the effects of insufficient or delayed coordination of the population's offer of voluntary help, which reduces the motivation of volunteers and leads to missed request for help.8. The results show that coordination of spontaneous volunteers is a key factor for helping the affected population, especially in a pandemic, where the possibilities to help are limited. These results are discussed with regard to costs that need to be considered in order to establish coordination, as well as health issues and motivation of spontaneous volunteers in different coordination scenarios. [ABSTRACT FROM AUTHOR]

The paper discusses the implications of disaggregation within the post-Keynesian debate on the long-run convergence of the degree of capacity utilization toward the normal one. The debate is related to the emergence of Harrodian instability inside multiplier–accelerator models and has been characterized by two "opposite" positions: the supermultiplier (SM) and the neo-Kaleckian approaches. These approaches solve the instability issue differently, but both share the postulate that an equilibrium position is such only if the desired state of firms is realized. In the long run, the economy converges toward a fully adjusted position where the realized degree of capacity utilization is equal to the normal/desired one. In this paper, we develop an Agent Based—Stock Flow Consistent version of the SM model showing that once multiplier–accelerator mechanisms are explicitly reproduced in a multi-firm economy, the accumulation process can be stable without requiring any convergence between the actual and normal rate. Conversely, the modeled economy displays two emergent properties: the fluctuations of the business cycle arise endogenously, and the long-run aggregate degree of capacity utilization persistently fluctuates around a level lower than the normal one. To this extent, the quasi-steady state corresponds to a situation where the desired state of agents is not realized and single elements are not in equilibrium, but the aggregate is. Finally, we compare outcomes produced from the model according to different scenarios on firms' heterogeneity and network symmetries. [ABSTRACT FROM AUTHOR]