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Discretionary activities such as retail, food, and beverages generate a significant amount of non-aeronautical revenue within the aviation industry. However, they are rarely taken into account in computational airport terminal models. Since discretionary activities affect passenger flow and global airport terminal performance, discretionary activities need to be studied in detail. Additionally, discretionary activities are influenced by other airport terminal processes, such as check-in and security. Thus, discretionary activities need to be studied in relation to other airport terminal processes. The aim of this study is to analyze discretionary activities in a systemic way, taking into account interdependencies with other airport terminal processes and operational strategies used to manage these processes. An agent-based simulation model for airport terminal operations was developed, which covers the main handling processes and passenger decision-making with discretionary activities. The obtained simulation results show that operational strategies that reduce passenger queue time or increase passenger free time can significantly improve global airport terminal performance through efficiency, revenue, and cost.
Adin Mekić; Seyed Mohammadi Ziabari; Alexei Sharpanskykh. Systemic Agent-Based Modeling and Analysis of Passenger Discretionary Activities in Airport Terminals. Aerospace 2021, 8, 162 .
AMA StyleAdin Mekić, Seyed Mohammadi Ziabari, Alexei Sharpanskykh. Systemic Agent-Based Modeling and Analysis of Passenger Discretionary Activities in Airport Terminals. Aerospace. 2021; 8 (6):162.
Chicago/Turabian StyleAdin Mekić; Seyed Mohammadi Ziabari; Alexei Sharpanskykh. 2021. "Systemic Agent-Based Modeling and Analysis of Passenger Discretionary Activities in Airport Terminals." Aerospace 8, no. 6: 162.
Airport security checkpoints are the most important bottleneck in airport operations, but few studies aim to empirically understand them better. In this work we address this lack of data-driven quantitative analysis and insights about the security checkpoint process. To this end, we followed a total of 2277 passengers through the security checkpoint process at Rotterdam The Hague Airport (RTM), and published detailed timing data about their journey through the process. This dataset is unique in scientific literature, and can aid future researchers in the modelling and analysis of the security checkpoint. Our analysis showed important differences between six identified passenger types. Business passengers were found to be the fastest group, while passengers with reduced mobility (PRM) and families were the slowest two groups. We also identified events that hindered the performance of the security checkpoint, in which groups of passengers had to wait long for security employees or other passengers. A total of 335 such events occurred, with an average of 2.3 passengers affected per event. It was found that a passenger that had a high luggage drop time was followed by an event in 27% of the cases, which was the most frequent cause. To mitigate this waiting time of subsequent passengers in the security checkpoint process, we performed an experiment with a so-called service lane. This lane was used to process passengers that are expected to be slow, while the remaining lanes processed the other passengers. It was found that the mean throughput of the service lane setups was higher than the average throughput of the standard lanes, making it a promising setup to investigate further.
Stef Janssen; Régis Van Der Sommen; Alexander Dilweg; Alexei Sharpanskykh. Data-Driven Analysis of Airport Security Checkpoint Operations. Aerospace 2020, 7, 69 .
AMA StyleStef Janssen, Régis Van Der Sommen, Alexander Dilweg, Alexei Sharpanskykh. Data-Driven Analysis of Airport Security Checkpoint Operations. Aerospace. 2020; 7 (6):69.
Chicago/Turabian StyleStef Janssen; Régis Van Der Sommen; Alexander Dilweg; Alexei Sharpanskykh. 2020. "Data-Driven Analysis of Airport Security Checkpoint Operations." Aerospace 7, no. 6: 69.
Airport surface movement operations are complex processes with many types of adverse events which require resilient, safe, and efficient responses. One regularly occurring adverse event is that of runway reconfiguration. Agent-based distributed planning and coordination has shown promising results in controlling operations in complex systems, especially during disturbances. In contrast to the centralised approaches, distributed planning is performed by several agents, which coordinate plans with each other. This research evaluates the contribution of agent-based distributed planning and coordination to the resilience of airport surface movement operations when runway reconfigurations occur. An autonomous Multi-Agent System (MAS) model was created based on the layout and airport surface movement operations of Schiphol Airport in the Netherlands. Within the MAS model, three distributed planning and coordination mechanisms were incorporated, based on the Conflict-Based Search (CBS) Multi-Agent Path Finding (MAPF) algorithm and adaptive highways. MAS simulations were run based on eight days of real-world operational data from Schiphol Airport and the results of the autonomous MAS simulations were compared to the performance of the real-world human operated system. The MAS results show that the distributed planning and coordination mechanisms were effective in contributing to the resilient behaviour of the airport surface movement operations, closely following the real-world behaviour, and sometimes even surpassing it. In particular, the mechanisms were found to contribute to more resilient behaviour than the real-world when considering the taxi time after runway reconfiguration events. Finally, the highway included distributed planning and coordination mechanisms contributed to the most resilient behaviour of the airport surface movement operations.
Konstantine Fines; Alexei Sharpanskykh; Matthieu Vert. Agent-Based Distributed Planning and Coordination for Resilient Airport Surface Movement Operations. Aerospace 2020, 7, 48 .
AMA StyleKonstantine Fines, Alexei Sharpanskykh, Matthieu Vert. Agent-Based Distributed Planning and Coordination for Resilient Airport Surface Movement Operations. Aerospace. 2020; 7 (4):48.
Chicago/Turabian StyleKonstantine Fines; Alexei Sharpanskykh; Matthieu Vert. 2020. "Agent-Based Distributed Planning and Coordination for Resilient Airport Surface Movement Operations." Aerospace 7, no. 4: 48.
Airports are attractive targets for terrorists, as they are designed to accommodate and process large amounts of people, resulting in a high concentration of potential victims. A popular method to mitigate the risk of terrorist attacks is through security patrols, but resources are often limited. Game theory is commonly used as a methodology to find optimal patrol routes for security agents such that security risks are minimized. However, game-theoretic models suffer from payoff uncertainty and often rely solely on expert assessment to estimate game payoffs. Experts cannot incorporate all aspects of a terrorist attack in their assessment. For instance, attacker behavior, which contributes to the game payoff rewards, is hard to estimate precisely. To address this shortcoming, we proposed a novel empirical game theory approach in which payoffs are estimated using agent-based modeling. Using this approach, we simulated different attacker and defender strategies in an agent-based model to estimate game-theoretic payoffs, while a security game was used to find optimal security patrols. We performed a case study at a regional airport, and show that the optimal security patrol is non-deterministic and gives special emphasis to high-impact areas, such as the security checkpoint. The found security patrol routes are an improvement over previously found security strategies of the same case study.
Stef Janssen; Diogo Matias; Alexei Sharpanskykh. An Agent-Based Empirical Game Theory Approach for Airport Security Patrols. Aerospace 2020, 7, 8 .
AMA StyleStef Janssen, Diogo Matias, Alexei Sharpanskykh. An Agent-Based Empirical Game Theory Approach for Airport Security Patrols. Aerospace. 2020; 7 (1):8.
Chicago/Turabian StyleStef Janssen; Diogo Matias; Alexei Sharpanskykh. 2020. "An Agent-Based Empirical Game Theory Approach for Airport Security Patrols." Aerospace 7, no. 1: 8.
Both security and efficiency are important performance areas of air transport systems. Several methods have been proposed to assess security risks and estimate efficiency independently, but only few of these methods identify relationships between security risks and efficiency performance indicators. To analyze security, efficiency, and the relationships relations between them, an agent-based methodology was proposed in this work. This methodology combines an agent-based security risk assessment approach with agent-based efficiency estimation. The methodology was applied to a case study that analyzes security regarding an Improvised Explosive Device (IED) attack, different commonly used efficiency performance indicators in the aviation domain, such as queuing time for passengers, and the relationships between them. Results showed that reducing security risks and improving efficiency were not always conflicting objectives. Reducing the number of passengers before the security checkpoint was found to be an effective measure to reduce security risks and improve efficiency aspects. Furthermore, results showed that airports should attempt to spread passengers across the available space as much as possible to reduce the impact of an IED attack.
Stef Janssen; Alexei Sharpanskykh; Richard Curran. Agent-based modelling and analysis of security and efficiency in airport terminals. Transportation Research Part C: Emerging Technologies 2019, 100, 142 -160.
AMA StyleStef Janssen, Alexei Sharpanskykh, Richard Curran. Agent-based modelling and analysis of security and efficiency in airport terminals. Transportation Research Part C: Emerging Technologies. 2019; 100 ():142-160.
Chicago/Turabian StyleStef Janssen; Alexei Sharpanskykh; Richard Curran. 2019. "Agent-based modelling and analysis of security and efficiency in airport terminals." Transportation Research Part C: Emerging Technologies 100, no. : 142-160.
Modern airports operate under high demands and pressures, and strive to satisfy many diverse, interrelated, sometimes conflicting performance goals. Airport performance areas, such as security, safety, and efficiency are usually studied separately from each other. However, operational decisions made by airport managers often impact several areas simultaneously. Current knowledge on how different performance areas are related to each other is limited. This paper contributes to filling this gap by identifying and quantifying relations and trade-offs between the detection performance of illegal items and the average queuing time at airport security checkpoints. These relations and trade-offs were analyzed by simulations with a cognitive agent model of airport security checkpoint operations. By simulation analysis a security checkpoint performance curve with three different regions was identified. Furthermore, the importance of focus on accuracy for a security operator is shown. The results of the simulation studies were related to empirical research at an existing regional airport.
Arthur Knol; Alexei Sharpanskykh; Stef Janssen. Analyzing airport security checkpoint performance using cognitive agent models. Journal of Air Transport Management 2018, 75, 39 -50.
AMA StyleArthur Knol, Alexei Sharpanskykh, Stef Janssen. Analyzing airport security checkpoint performance using cognitive agent models. Journal of Air Transport Management. 2018; 75 ():39-50.
Chicago/Turabian StyleArthur Knol; Alexei Sharpanskykh; Stef Janssen. 2018. "Analyzing airport security checkpoint performance using cognitive agent models." Journal of Air Transport Management 75, no. : 39-50.
With ever-growing numbers of passengers and complexity of the air transport system, it becomes more and more of a challenge to manage the system in an effective, safe, and resilient manner. This is especially evident when disruptions occur. Understanding and improving resilience of the air transport system and its adaptive capacity to disruptions is essential for the system’s uninterrupted successful performance. Using theoretical findings from behavioral sciences, this paper makes the first steps towards formalization of the adaptive capacity of resilience of the air transport system with a particular focus on its ability to anticipate. To this end, an expressive logic-based language called Temporal Trace Language is used. The proposed approach is illustrated by a case study, in which anticipatory mechanisms are implemented in an agent-based airport terminal operations model, to deal with a disruptive scenario of unplanned and challenging passenger demand at the security checkpoint. Results showed that the timing of an adaptive action could have a significant influence on reducing the risk of saturation of the system, where saturation implies performance loss. Additionally, trade-off relations were obtained between cost, corresponding to the extra resources mobilized, and the benefits, such as a decrease in risk of saturation of the passenger queue.
Anne-Nynke Blok; Alexei Sharpanskykh; Matthieu Vert. Formal and computational modeling of anticipation mechanisms of resilience in the complex sociotechnical air transport system. Complex Adaptive Systems Modeling 2018, 6, 7 .
AMA StyleAnne-Nynke Blok, Alexei Sharpanskykh, Matthieu Vert. Formal and computational modeling of anticipation mechanisms of resilience in the complex sociotechnical air transport system. Complex Adaptive Systems Modeling. 2018; 6 (1):7.
Chicago/Turabian StyleAnne-Nynke Blok; Alexei Sharpanskykh; Matthieu Vert. 2018. "Formal and computational modeling of anticipation mechanisms of resilience in the complex sociotechnical air transport system." Complex Adaptive Systems Modeling 6, no. 1: 7.
Borrdephong Rattanagraikanakorn; Alexei Sharpanskykh; Michiel J. Schuurman; Derek Gransden; Henk Blom; Christophe De Wagter. Characterizing UAS Collision Consequences in Future UTM. 2018 Aviation Technology, Integration, and Operations Conference 2018, 1 .
AMA StyleBorrdephong Rattanagraikanakorn, Alexei Sharpanskykh, Michiel J. Schuurman, Derek Gransden, Henk Blom, Christophe De Wagter. Characterizing UAS Collision Consequences in Future UTM. 2018 Aviation Technology, Integration, and Operations Conference. 2018; ():1.
Chicago/Turabian StyleBorrdephong Rattanagraikanakorn; Alexei Sharpanskykh; Michiel J. Schuurman; Derek Gransden; Henk Blom; Christophe De Wagter. 2018. "Characterizing UAS Collision Consequences in Future UTM." 2018 Aviation Technology, Integration, and Operations Conference , no. : 1.
Failures to comply with safety regulations are currently a major issue at many airline ground service organizations across the world. To address this issue, approaches based on an external regulation of the employees’ behavior have been proposed. Unfortunately, an externally imposed control is often not internalized by employees and has a short term effect on their performance. To achieve a long-term effect, employees need to be internally motivated to adhere to regulations. To understand the role of motivation for compliance in ground service organizations, a formal agent-based model is proposed in this chapter based on theories from Social Science. The model incorporates cognitive, social, and organizational aspects. The model was simulated and partially validated by a case study performed at a real airline ground service organization. The model was able to reproduce behavioral patterns related to compliance of the platform employees in this study.
Alexei Sharpanskykh; Rob Haest. Understanding and Predicting Compliance with Safety Regulations at an Airline Ground Service Organization. Advances in Intelligent Systems and Computing 2017, 528, 379 -392.
AMA StyleAlexei Sharpanskykh, Rob Haest. Understanding and Predicting Compliance with Safety Regulations at an Airline Ground Service Organization. Advances in Intelligent Systems and Computing. 2017; 528 ():379-392.
Chicago/Turabian StyleAlexei Sharpanskykh; Rob Haest. 2017. "Understanding and Predicting Compliance with Safety Regulations at an Airline Ground Service Organization." Advances in Intelligent Systems and Computing 528, no. : 379-392.
According to aviation statistics, most of the safety occurrences happen not in the air, but on the ground. Management of airlines and airports often consider failures to comply with safety-related regulations as important contributors to safety occurrences. To address the issue of compliance, approaches based on external regulation of the employees’ behavior were proposed. Unfortunately, an externally imposed control is often not internalized by employees and has a short-term effect on their performance. To achieve a long-term effect, employees need to be internally motivated to adhere to regulations. To understand the role of motivation for compliance in ground service organizations, in this paper a formal agent-based model is proposed based on theories from social science with a wide empirical support. The model incorporates cognitive, social, and organizational aspects. The model was simulated and partially validated by a case study performed at a real airline ground service organization. The model was able to reproduce behavioral patterns related to compliance of the platform employees in this study. Based on the model, global sensitivity analysis was performed. The results of this analysis together with the simulation results were used to generate recommendations to improve compliance.
Alexei Sharpanskykh; Rob Haest. An agent-based model to study compliance with safety regulations at an airline ground service organization. Applied Intelligence 2016, 45, 881 -903.
AMA StyleAlexei Sharpanskykh, Rob Haest. An agent-based model to study compliance with safety regulations at an airline ground service organization. Applied Intelligence. 2016; 45 (3):881-903.
Chicago/Turabian StyleAlexei Sharpanskykh; Rob Haest. 2016. "An agent-based model to study compliance with safety regulations at an airline ground service organization." Applied Intelligence 45, no. 3: 881-903.
Safety culture is often understood as encompassing organizational members’ shared attitudes, beliefs, perceptions and values associated with safety. Safety culture theory development is fraught with inconsistencies and superficiality of measurement methods, because the dynamic and political nature of culture is often ignored. Traditionally, safety culture is analyzed by survey-based approaches. In this paper we propose a novel, systemic, interdisciplinary approach for investigating safety culture that combines multi-agent system modeling with organizational ethnography. By using this approach, mechanisms of emergence of safety culture from daily practices, operations and interactions of organizational actors can be modeled and analyzed. The approach is illustrated by a case study from the aircraft maintenance domain, based on existing ethnographic data. Using the proposed approach we were able to reproduce and explain emergent characteristic patterns of commitment to safety in the maintenance organization from this study. The model can be used for theory development and as a management tool to evaluate non-linear impacts of organizational arrangements on workers’ commitment to safety.
David Passenier; Colin Mols; Jan Bím; Alexei Sharpanskykh. Modeling safety culture as a socially emergent phenomenon: a case study in aircraft maintenance. Computational and Mathematical Organization Theory 2016, 22, 487 -520.
AMA StyleDavid Passenier, Colin Mols, Jan Bím, Alexei Sharpanskykh. Modeling safety culture as a socially emergent phenomenon: a case study in aircraft maintenance. Computational and Mathematical Organization Theory. 2016; 22 (4):487-520.
Chicago/Turabian StyleDavid Passenier; Colin Mols; Jan Bím; Alexei Sharpanskykh. 2016. "Modeling safety culture as a socially emergent phenomenon: a case study in aircraft maintenance." Computational and Mathematical Organization Theory 22, no. 4: 487-520.
Maintaining high levels of safety under conditions of ever increasing air traffic is a challenging task. Failures to comply with safety-related regulations are often considered to be important contributors to safety occurrences. To address the issue of compliance, approaches based on external regulation of the employees’ behavior were proposed. Unfortunately, an externally imposed control is often not internalized by employees and has a short-term effect on their performance. To achieve a long-term effect, employees need to be internally motivated to adhere to regulations. Theories from social sciences propose that team processes play an important role in the dynamics of individual motivation. In this paper an agent-based model is proposed, by which the impact of social interaction and coordination in teams of platform employees on their individual motivation and compliance with safety regulations at an airline ground service organization are explored. The model was simulated and partially validated by a case study performed at a real airline ground service organization. The model was able to reproduce behavioral patterns of compliance of the platform employees in this study.
Alexei Sharpanskykh; Rob Haest. An Agent-Based Model to Study Effects of Team Processes on Compliance with Safety Regulations at an Airline Ground Service Organization. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 492 -500.
AMA StyleAlexei Sharpanskykh, Rob Haest. An Agent-Based Model to Study Effects of Team Processes on Compliance with Safety Regulations at an Airline Ground Service Organization. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():492-500.
Chicago/Turabian StyleAlexei Sharpanskykh; Rob Haest. 2015. "An Agent-Based Model to Study Effects of Team Processes on Compliance with Safety Regulations at an Airline Ground Service Organization." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 492-500.
Trust is a social phenomenon that impacts the situation awareness of individuals and indirectly their decision-making. However, most of the existing computational models of situation awareness do not take interpersonal trust into account. Contrary to those models, this study introduces a computational, agent-based situation awareness model incorporating trust to enable building more human-like decision making tools. To illustrate the proposed model, a simulation case study has been conducted in the airline operation control domain. According to the results of this study, the trustworthiness of information sources had a significant effect on airline operation controller’s situation awareness.
Reyhan Aydoğan; Alexei Sharpanskykh; Julia Lo. A Trust-Based Situation Awareness Model. Computer Vision 2015, 8953, 19 -34.
AMA StyleReyhan Aydoğan, Alexei Sharpanskykh, Julia Lo. A Trust-Based Situation Awareness Model. Computer Vision. 2015; 8953 ():19-34.
Chicago/Turabian StyleReyhan Aydoğan; Alexei Sharpanskykh; Julia Lo. 2015. "A Trust-Based Situation Awareness Model." Computer Vision 8953, no. : 19-34.
There is broad consensus that situation awareness (SA) plays a key role in agent-based modelling of complex sociotechnical systems. However in the social sciences and human factors literature there are different views on what SA is and how it could be modelled. More specifically, one school of research considers SA as the process of gaining awareness, another school refers to it as to the product of gaining awareness, whereas the third school sees SA as a combination of the process and product. Typically, agent-based modelling of SA is done from the second view for each individual agent, possibly with additional social components to enable interaction. Current developments in multiagent systems indicate that social abilities and relations between agents should be not an addition, but at the core of any model of a sociotechnical system. To address this issue, we develop a mathematical modelling framework of SA relations between agents which supports all three views. The use of the framework is demonstrated by an example of retrospective accident modelling from the aviation domain.
Henk Blom; Alexei Sharpanskykh. Modelling situation awareness relations in a multiagent system. Applied Intelligence 2015, 43, 412 -423.
AMA StyleHenk Blom, Alexei Sharpanskykh. Modelling situation awareness relations in a multiagent system. Applied Intelligence. 2015; 43 (2):412-423.
Chicago/Turabian StyleHenk Blom; Alexei Sharpanskykh. 2015. "Modelling situation awareness relations in a multiagent system." Applied Intelligence 43, no. 2: 412-423.
Decision making under stressful circumstances, e.g., during evacuation, often involves strong emotions and emotional contagion from others. In this paper the role of emotions in social decision making in large technically assisted crowds is investigated. For this a formal, computational model is proposed, which integrates existing neurological and cognitive theories of affective decision making. Based on this model several variants of a large scale crowd evacuation scenario were simulated. By analysis of the simulation results it was established that (1) human agents supported by personal assistant devices are recognised as leaders in groups emerging in evacuation; (2) spread of emotions in a crowd increases the resistance of agent groups to opinion changes; (3) spread of emotions in a group increases its cohesiveness; (4) emotional influences in and between groups are, however, attenuated by personal assistant devices, when their number is large.
Alexei Sharpanskykh; Kashif Zia. Understanding the Role of Emotions in Group Dynamics in Emergency Situations. Transactions on Petri Nets and Other Models of Concurrency XV 2014, 28 -48.
AMA StyleAlexei Sharpanskykh, Kashif Zia. Understanding the Role of Emotions in Group Dynamics in Emergency Situations. Transactions on Petri Nets and Other Models of Concurrency XV. 2014; ():28-48.
Chicago/Turabian StyleAlexei Sharpanskykh; Kashif Zia. 2014. "Understanding the Role of Emotions in Group Dynamics in Emergency Situations." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 28-48.
In this paper an agent-based social contagion model with an underlying dynamic network is proposed and analyzed. In contrast to the existing social contagion models, the strength of links between agents changes gradually rather than abruptly based on a threshold mechanism. An essential feature of the model – the ability to form clusters – is extensively investigated in the paper analytically and by simulation. Specifically, the distribution of clusters in random and scale-free networks is investigated, the dynamics of links within and between clusters are determined, the minimal distance between two clusters is identified. Moreover, model abstraction methods are proposed by using which aggregated opinion states of clusters of agents can be approximated with a high accuracy. These techniques also improve the computational efficiency of social contagion models (up to 6 times).
Alexei Sharpanskykh; Jan Treur. Modelling and analysis of social contagion in dynamic networks. Neurocomputing 2014, 146, 140 -150.
AMA StyleAlexei Sharpanskykh, Jan Treur. Modelling and analysis of social contagion in dynamic networks. Neurocomputing. 2014; 146 ():140-150.
Chicago/Turabian StyleAlexei Sharpanskykh; Jan Treur. 2014. "Modelling and analysis of social contagion in dynamic networks." Neurocomputing 146, no. : 140-150.
Decision making under stressful circumstances may involve strong emotions and requires adequate prediction and valuation capabilities. In a social context contagion from others plays an important role as well. Moreover, agents adapt their decision making based on their experiences over time. Knowledge of principles from neuroscience provides an important source of inspiration to model such processes. In this paper an adaptive agent-based computational model is proposed to address the above-mentioned aspects in an integrative manner. As an application adaptive decision making of an agent in an emergency evacuation scenario is explored. By means of formal analysis and simulation, the model has been explored and evaluated.
Alexei Sharpanskykh; Jan Treur. An adaptive agent model for affective social decision making. Biologically Inspired Cognitive Architectures 2013, 5, 72 -81.
AMA StyleAlexei Sharpanskykh, Jan Treur. An adaptive agent model for affective social decision making. Biologically Inspired Cognitive Architectures. 2013; 5 ():72-81.
Chicago/Turabian StyleAlexei Sharpanskykh; Jan Treur. 2013. "An adaptive agent model for affective social decision making." Biologically Inspired Cognitive Architectures 5, no. : 72-81.
Social decision making under stressful circumstances may involve strong emotions and contagion from others. Recent developments in Social Neuroscience have revealed neural mechanisms by which social contagion of cognitive and emotional states can be realised. In this paper, based on these mechanisms, an agent-based computational model is proposed. Furthermore, it is demonstrated how the proposed cognitive model can be transformed into an equivalent behavioural model without any cognitive states. As an application of the model, a computational analysis was performed of patterns in crowd behaviour, in particular by agent-based simulation of a real-life incident that took place on May 4, 2010 in Amsterdam. The results of the model analysis show the inclusion of contagion of belief, emotion, and intention states of agents results in better reproduction of the incident than non-inclusion.
Tibor Bosse; Mark Hoogendoorn; Michel C A Klein; Alexei Sharpanskykh; Jan Treur; C. Natalie Van Der Wal; Arlette Van Wissen. Agent-Based Modelling of Social Emotional Decision Making in Emergency Situations. Understanding Complex Systems 2013, 79 -117.
AMA StyleTibor Bosse, Mark Hoogendoorn, Michel C A Klein, Alexei Sharpanskykh, Jan Treur, C. Natalie Van Der Wal, Arlette Van Wissen. Agent-Based Modelling of Social Emotional Decision Making in Emergency Situations. Understanding Complex Systems. 2013; ():79-117.
Chicago/Turabian StyleTibor Bosse; Mark Hoogendoorn; Michel C A Klein; Alexei Sharpanskykh; Jan Treur; C. Natalie Van Der Wal; Arlette Van Wissen. 2013. "Agent-Based Modelling of Social Emotional Decision Making in Emergency Situations." Understanding Complex Systems , no. : 79-117.
Social contagion models describe an evolution of states of individual agents under influence of their neighbouring agents by mutual contagion of these states. Although the behaviour of individual agents in such models is often simple, global dynamics that emerge from interaction of a large number of agents is non-trivial. In this paper abstraction techniques are proposed that allow approximating with a high accuracy global patterns of behaviour emerging in social contagion models with underlying dynamic networks. Furthermore, these techniques improve substantially the computational efficiency of social contagion models. In particular, they allow a 6 times speed up of the simulation of the model described in the paper.
Alexei Sharpanskykh. Abstraction of Social Contagion Models with Dynamic Networks. Computer Vision 2013, 8083, 51 -61.
AMA StyleAlexei Sharpanskykh. Abstraction of Social Contagion Models with Dynamic Networks. Computer Vision. 2013; 8083 ():51-61.
Chicago/Turabian StyleAlexei Sharpanskykh. 2013. "Abstraction of Social Contagion Models with Dynamic Networks." Computer Vision 8083, no. : 51-61.
In this paper an agent-based social contagion model with an underlying dynamic network is proposed and analysed. In contrast to the existing social contagion models, the strength of links between agents changes gradually rather than abruptly based on a threshold mechanism. An essential feature of the model – the ability to form clusters – is extensively investigated in the paper analytically and by simulation. Specifically, the distribution of clusters in random and scale-free networks is investigated, the dynamics of links within and between clusters are determined, the minimal distance between two clusters is identified.
Alexei Sharpanskykh; Jan Treur. Modelling and Analysis of Social Contagion Processes with Dynamic Networks. Computer Vision 2013, 8083, 40 -50.
AMA StyleAlexei Sharpanskykh, Jan Treur. Modelling and Analysis of Social Contagion Processes with Dynamic Networks. Computer Vision. 2013; 8083 ():40-50.
Chicago/Turabian StyleAlexei Sharpanskykh; Jan Treur. 2013. "Modelling and Analysis of Social Contagion Processes with Dynamic Networks." Computer Vision 8083, no. : 40-50.