About
1,302
Publications
182,095
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Introduction
YouTube channel on Self-Modeling Networks: https://www.youtube.com/channel/UCCO3i4_Fwi22cEqL8M_PgeA.
YouTube channel on Behaviour Dynamics in Social Networks course: https://www.youtube.com/channel/UCaeA1Wcvv7jpDGgDda17L3g/featured.
VU homepage at
https://research.vu.nl/en/persons/j-treur/publications/.
Additional affiliations
January 1986 - January 1990
Position
- Professor (Assistant)
Description
- Research and Teaching in AI and its logical foundations.
August 1978 - December 1985
Amsterdam University of Applied Sciences
Position
- Lecturer
Description
- Teaching Mathematics, Didactics of Mathematics and Computer Science, Psychology of Learning, and Educational Science.
Education
September 1973 - February 1976
September 1969 - September 1973
Publications
Publications (1,302)
Using self-modeling networks to model dynamics, adaptation, and control of internal mental models. For video presentations for many of the chapters, see https://www.youtube.com/playlist?list=PLtJH8O7BvdycENlCsAEraVybqz1I6T9sd.
Although there is much literature on organisational learning, mathematical formalisation and computational simulation of it is considered a very challenging topic with almost no literature addressing it in a principled manner. This book provides an overview of recent work on mathematical formalisation and computational simulation of organisational...
For a video presentation, see https://www.youtube.com/watch?v=PRUzrkf1mW4. When people interact, their behaviour tends to become synchronised, a mutual coordination process that fosters short-term adaptations, like increased affiliation, and long-term adaptations, like increased bonding. This paper addresses for the first time how such short-term a...
Although there is much literature on multimodal interaction, multimodal synchrony analysis, and related behavioural adaptivity, mathematical formalisation and computational simulation of it is a nontrivial topic. Moreover, the subjective, agent-oriented perspective on synchrony analysis has not yet received much attention in the literature. This bo...
When people interact, their behaviour tends to become synchronized, a mutual coordination process that fosters short-term adaptations, like increased affiliation, and long-term adaptations, like increased bonding. This paper addresses for the first time how such short-term and long-term adaptivity induced by synchronization can be modeled computati...
Ensuring patient safety and security through cyberspace requires that all care professionals operate as a team and community. In order to be successful, it is of paramount importance that all members of the team have a shared understanding of the diagnosis, the condition of the patient, the secure use of medical devices, and the plan of action. At...
Biological or mental processes are usually considered to form complex dynamical systems. Within the literature, such dynamical systems are often described by indicating what are called pathways. On the one hand, pathways are based on the structure of the contextual world configuration that enables them, for example, parts of the structure of an org...
For a presentation video, see https://www.youtube.com/watch?v=0ax_u9v9klw. In this paper, a fifth-order adaptive self-modelling network model is introduced to describe epigenetic involvement in the development of anxiety disorders and its regulation by a possible epigenetics-based therapeutic method. Multiple orders of adaptivity are used in the mo...
Awarded the BICA*AI 2022 Best Innovative Research Award.
For a video presentation, see: https://www.youtube.com/watch?v=Zi-ptLRGC3Q. During interaction, humans often adapt their behaviour toward each other. This behavioural adaptivity may concern short-term effects such as affiliation but also long-term effects such as bonding. Interaction can inv...
This chapter comprises selected short and extended abstracts of invited talks and discussion panels that took place at BICA*AI'23
For a presentation video, see https://www.youtube.com/watch?v=QAhh4GL_lcs. This article presents a multi-adaptive network model integrating multiple adaptation mechanisms, specifically focusing on five types of adaptation mechanisms. Two of them address first-order adaptation by learning to respond to others and first-order adaptation by bonding wi...
There is much literature on multimodal interaction, multimodal synchrony analysis, and related behavioural adaptivity, but mathematical formalisation and computational simulation of it is a nontrivial topic. Moreover, the subjective, agent-oriented perspective on synchrony analysis has not yet received much attention in the literature. This present...
This speech addresses the use of self-modeling temporal-causal networks to model adaptive biological, mental and social processes of any order of adaptation. This modeling approach integrates network-oriented and causal modeling perspectives by interpreting network connections as causal connections and adding dynamics of causal effects and adaptivi...
This paper presents an adaptive network model in the context of joint action and social bonding. Exploration of mechanisms for mental and social network models are presented, specifically focusing on adaptation by bonding based on homophily and Hebbian learning during joint rhythmic action. The paper provides a comprehensive explanation of these co...
This study analyses the effects of emotional coregulation in the interaction between a distressed human and a bot. Through the coregulation premises, an emotion contagion process impacts the emotional system, which initially increases the distress level of the bot due to the influence of the human’s emotions. Configuring the bot with optimal emotio...
This paper presents a potential model for understanding the development and maintenance of Anorexia Nervosa, a serious and potentially life-threatening eating disorder characterized by severe food intake restrictions, leading to extreme weight loss and a distorted body image. The proposed model is an adaptive temporal-causal network model that cons...
For a presentation video, see https://www.youtube.com/watch?v=fY4L5qrhIDI. This paper presents an approach to enhancing neonatal care through the application of artificial intelligence (AI). Utilizing network-oriented modeling methodologies, the study aims to develop a network model to improve outcomes in neonatal respiratory support. The introduct...
For a presentation video, see https://www.youtube.com/watch?v=O828ATmVXIw. In this paper, an integrative fifth-order biological and mental network model is introduced, to demonstrate epigenetics effects in Post-Traumatic Stress Disorder (PTSD), using an adaptive network modeling approach based on temporal-casual networks. The network model assesses...
For a presentation video, see https://www.youtube.com/watch?v=YU37XS4oAak. Although making mistakes is a crucial part of learning, it is still often being avoided in companies as it is considered as a shameful incident. This goes hand in hand with a mindset of a boss who dominantly believes that mistakes usually have negative consequences and there...
For a presentation video, see https://youtu.be/-Ie06bSApzY?list=PLtJH8O7BvdyexHLMTIcGj0kBkSvO1LsYz. In this paper, a first-order adaptive self-modeling network model is introduced to model information overload in the context of cyclical usage of smartphone apps. The model consists of interacting attention resources and emotional responses to both a...
The research reported here analyses the relationship between group synchrony and group bonding through a novel adaptive computational dynamical system model. By simulating multimodal interactions within a group of four agents, the study uncovers patterns in group cohesion in the sense of emerging multimodal group synchrony and related group bonding...
This paper contributes a computational analysis of how informal learning within organisations often takes place. The approach covers asking questions, the influence of approachability and presence, as well as direct and indirect answering of questions asked. This is done by modeling different persons with their mental states, internal mental models...
In this paper, an adaptive temporal-causal network model is presented for a normal night’s sleep and for how disturbances and their timing interfere with such a normal night of sleep. The goal of this computational model is to explore the area of how sleep disturbances influence a person’s health. This was achieved by simulating single and multiple...
For a presentation video, see https://youtu.be/QAhh4GL_lcs. Religion can be a controversial topic to discuss. Depending on the person asked, religion can seem to have many wonderful or awful aspects and impacts. Regardless the view, emotion often takes the upper hand over objectivity. This paper aims to contribute to a more objective look on the po...
For a presentation video, see https://www.youtube.com/watch?v=JeeSS5QoUUc. This paper presents a computational agent model that analyses the regulation of sensory processing and behavioural responses to stimuli in autism spectrum disorder (ASD). The model incorporates feedback loops and accounts for the heterogeneity, variability, and adaptivity of...
For a presentation video, see https://www.youtube.com/watch?v=InTPvsQv0us. The spread of rumors, otherwise known as gossiping, is an inevitable part of life for most people. Therefore, it is important to understand the way that information is actually spread through social environments of smaller proportions. This spread is modeled using a higher-o...
For a presentation video, see https://www.youtube.com/watch?v=a7unxRXTZss. This article presents the use of second-order adaptive network models of hospital teams consisting of doctors and nurses, interacting together. A variety of scenarios are modelled and simulated, in relation with respiratory distress of a neonate, along with the integration o...
Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tas...
Anorexia nervosa is a serious and potentially life-threatening eating disorder characterized by severe restrictions on food intake, leading to extreme weight loss and a distorted body image. Despite its prevalence and the significant impact it has on individuals and their families, the underlying causes of anorexia nervosa are not fully understood....
For a video presentation, see https://youtu.be/ad5e6ep_FhA. This paper discusses how knowledge from psychology and neuroscience is a useful source for developing computational causal models of mental processes that can be virtualized and then used as artificial humans in interaction sessions with natural humans. It is explained how such sessions ca...
This volume discusses the definitional problems and conceptual strategies involved in defining the human. By crossing the boundaries of disciplines and themes, it offers a transdisciplinary platform for exploring the new ideas of the human and adjusting to the dynamic in which we are plunged. The emerging cyborgs and transhumans call for an urgent...
This study analyses the effects of emotional coregulation in the interaction between a distressed human and a bot. Through the coregulation premises, it is expected that an initial emotion contagion process will be applied to the emotional system, increasing the bot's distress level due to the influence of the human's distress. With the bot is set...
In this chapter, key findings presented in this volume are summarized and evaluated to demonstrate the usefulness and great potential of the adaptive dynamical system approach based on self-modeling networks in providing a useful structure to formalise, analyse and simulate multilevel organisational learning processes. Moreover, future perspectives...
In this chapter, equilibrium analysis for network models is addressed and applied to a network model of multilevel organisational learning. Equilibrium analysis can consider both properties of aggregation characteristics and properties of connectivity characteristics of a network. For connectivity characteristics, it is shown by introducing a form...
In this chapter, first it is shown how any (smooth) adaptive dynamical system can be modeled in a unique, canonical way as a self-modeling network model. In this way, any adaptive dynamical system has its canonical representation as a self-modeling network model and can be analysed based on this canonical representation. This is applied in particul...
This chapter investigates computationally the following research hypotheses: (1) Higher flexibility and discretion in organisational culture results in better mistake management and thus better organisational learning, (2) Effective organisational learning requires a transformational leader to have both high social and formal status and consistency...
Transformative Organisational Change becomes more and more significant both practically and academically, especially in the context of organisational culture and learning. However computational modeling and formalization of organisational change and learning processes are still largely unexplored. This chapter aims to provide an adaptive network mo...
This chapter addresses formalisation and computational modeling of context-sensitive control over multilevel organisational learning and in particular the role of the leadership style in influencing feed forward learning flows. It addresses a realistic case study with focus on the role of managers for control of multilevel organisational learning....
For a video presentation, see https://www.youtube.com/watch?v=ETTyiIemtjw. This chapter describes how the recently developed self-modeling network modeling approach for multilevel organisational learning has been tested on applicability for a real-world case of a project-based organisation. The modeling approach was able to successfully address thi...
For a video on YouTube, see https://www.youtube.com/watch?v=mFMmHQgo8Rs. This research addresses the influence of leadership and communication on learning within an organisation by direct mutual interactions in dyads. This is done in combination with multilevel organisational learning as an alternative route, which includes feed forward and feedbac...
For a video presentation at BICA'21, see https://www.youtube.com/watch?v=Tdz_LxzmDWc. Aggregation of developed individual mental models to obtain shared mental models for the organisation is a crucial process for organisational learning. This aggregation process usually does not only depend on the mental models used as input for it, but also on sev...
Within organisational learning, aggregation of developed individual mental models to obtain shared mental models for the organisation is a crucial process. This aggregation process usually does not only depend on the mental models used as input for it, but also on several context factors that may vary over circumstances and time. This means that fo...
A video presentation of this paper at BICA'21 can be found at the YouTube Self-Modeling Networks channel at https://www.youtube.com/watch?v=StRGmqY0QD0. Within organisational learning literature, mental models are considered a vehicle for both individual learning and organisational learning. By learning individual mental models (and making them exp...
Multilevel organisational learning concerns an interplay of different types of learning at individual, team, and organisational levels. These processes use complex dynamic and adaptive mechanisms. A second-order adaptive network model for this is introduced here and illustrated.
For a video presentation, see the Self-Modeling Networks channel: https://www.youtube.com/watch?v=ah6SsLOoOV8. This chapter describes a multi-level adaptive network model for mental processes making use of shared mental models in the context of organisational learning in team-related performances. The chapter describes the value of using shared men...
Using a proper mental model during mental processes is often crucial. Such a mental model has to be learned and maintained; this involves mental model adaptation. Metacognition is applied to control use and adaptation in a context-sensitive manner. In this chapter, a second-order adaptive network model for handling mental models, covering their use...
For a video presentation, see https://www.youtube.com/watch?v=x0hYoTIEo9E. This chapter addresses formalisation and computational modeling of multilevel organisational learning, which is one of the major challenges for the area of organisational learning. It is discussed how various conceptual mechanisms in multilevel organisational learning as ide...
Networks provide an intuitive, declarative way of modeling which has turned out to be suitable for many types of applications that involve complex dynamics. In many cases also adaptivity plays a role. By using algorithmic or procedural descriptions for the adaptation processes as is often the approach followed , easily leads to less declarative and...
As organisational learning concerns highly dynamic, multilevel and non-linear processes involving several contextual factors, it is far from trivial how computational analysis and simulation can be developed in a systematic and transparent manner for it. This chapter serves as a brief introduction of the many challenges to be addressed for such an...
Previous reports show that a substantial proportion of (near) medical errors in the operating theatre is attributable to ineffective communication between healthcare professionals. Speaking up about observed medical errors is a safety behaviour which promotes effective communication between health care professionals, consequently optimising patient...
Interpersonal synchrony is associated with better interpersonal affiliation. No matter how well-affiliated people are, interruptions or transitions in synchrony rebound to occur. One might intuitively expect that transitions in synchrony negatively affect affiliation or liking. Empirical evidence, however, suggests that time periods with interrupti...
In this paper, an adaptive temporal-causal network model is presented for a normal night's sleep and for how disturbances and their timing interfere with such a normal night of sleep. The goal of this computational model is to explore the area of how sleep disturbances influence a person's health.
This paper investigates computationally the following research hypotheses: (1) Higher flexibility and discretion in organizational culture results in better mistake management and thus better organizational learning, (2) Effective organizational learning requires a transformational leader to have both high social and formal status and consistency,...
Job burnout has been on the rise in the past decade, especially amongst the younger working generation. While work environmental aspects play an important role in predicting burnout, variations in personality traits are integral for understanding the syndrome's risk factors, processes, and outcomes. This paper studies the complex interaction of per...
This paper addresses formalisation and computational modeling of context-sensitive control over multilevel organisational learning and in particular the role of the leadership style in influencing feed forward learning flows. It addresses a realistic case study with focus on the role of managers for control of multilevel organisational learning. To...
Multilevel organizational learning concerns an interplay of different types of learning at individual, team, and organizational levels. These processes use complex dynamic and adaptive mechanisms. A second-order adaptive network model for this is introduced here and illustrated.
This paper addresses formalisation and computational modelling of context-sensitive control over multilevel organisational learning and in particular the role of the leadership style in influencing feed forward learning flows. It addresses a realistic case study with focus on the role of managers for control of multilevel organisational learning. T...
Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). Deep Learning (DL) algorithms are grounded in non-linear and complex artificial neural systems that progressively extract higher-level features from data. DL is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. It is al...
Social interaction often occurs through emerging patterns. Such patterns develop over time due to mental processes that in turn are adaptive. In this speech, an agent-based adaptive network modeling perspective is used for computational analysis and simulation of the different forms of adaptivity that play a role. It is shown how mechanisms based o...
Organisational learning emerges as a cyclic interplay of various mechanisms at different levels. To analyse and simulate organisational learning computationally, the self-modeling network modelling approach from AI provides a powerful means to address the complexity of the interaction of different mechanisms and the control over them. In this joint...
Keynote Speech at the Fifth International Conference on Social Science, Public Health and Education, SSPHE'22, Sanya, China
For a video presentation, see https://www.youtube.com/watch?v=GeW4ZbQtdGE. This paper in the Cognitive Systems Research journal introduces a novel controlled adaptive mental causal network model addressing how dreams overnight can influence creativity in waking life. The network model depicts in a causal, dynamic, and generic manner which adaptive...
In this paper, a second-order adaptive model of a highly sensitive person's behaviors and biological foundation is presented. The model illustrates how genetic variance affects the serotonin level, leading to a certain level of neuronal hyperexcitability, which in turn influences behaviors, shaping different levels of sensory processing sensitivity...
In this paper, an adaptive network model is presented of neurodegeneration processes over time. Mechanisms involved in these processes were used in the model which was used for computational analysis. This analysis provided insights into the interaction between the role of risk and protective factors with respect to lipid and alpha-synuclein homeos...
Fear involves activating the fight-or-flight response in order to prevent harm from dangerous threats. However, dysregulation of chronic fear is harmful and can lead to disorders such as post-traumatic stress disorder. Propranolol has been shown to reduce the fear response by reducing norepinephrine. This paper proposes a second-order adaptive netw...
This paper describes how the recently developed self-modeling network modeling approach for multilevel organisational learning has been tested on applicability for a real-world case of a project-based organisation. The modeling approach was able to successfully address this complex case by designing a third-order adaptive network model. Doing this,...
In this paper a second-order adaptive network model is introduced for presenting a user with content they like on a platform. The platform's method is using its so-called fake state. Simulation results have been performed with different scenarios for different starting values of the user and the platform. In all scenarios the platform's method can...
When humans interact, multiple types of adaptivity occur, concerning their behaviour toward each other. For example, these types of behavioural adaptivity include short-term effects such as affiliation but also long-term effects such as bonding. Moreover, some forms of behavioural adaptivity apply to specific persons, whereas other forms apply in a...
For a video presentation, see https://www.youtube.com/watch?v=khUzq4GGSAc
This joint Keynote Speech focuses on agent modeling for multimodal interactions of both humans and artificial agents. For a video, see https://www.youtube.com/watch?v=aVhm7a1PXJg. Human interaction often happens through different modalities such as movements, facial expressions, and verbal utterances. These multimodal human interactions often becom...
Processes of multilevel organisational learning emerge as a cyclic interplay of various mechanisms at different levels. To analyse and simulate them computationally, the self-modeling network modelling approach from AI provides a powerful means to address the complexity of the interaction of different adaptation mechanisms and the control over them...
For a video presentation, see https://www.youtube.com/watch?v=SR98-3DWRsM. In this paper, it is shown how second-order adaptive agent-based network models can be used to model a medical team supported by a virtual AI Coach. It is illustrated for the case of a newborn baby in danger. The design of these computational agent models is based on an adap...
Questions
Question (1)
Recently I found an interesting overview paper of Thomas Valente on network interventions in general: Valente, T.W. (2012) Network Interventions. Science 337, 49-53, 2012. DOI: 10.1126/science.1217330 (see http://www.sciencemag.org/content/337/6090/49.abstract?sid=0f32a56b-ae0d-4270-af95-0314b4299ed2 At the end he writes: “To date, however, few of the many network intervention alternatives have been tested in laboratory or real-world settings, and it is unclear which network interventions work best under what conditions.” What I am looking for more specifically is work that addresses network interventions, for example in health or lifestyle context, in order to provide supporting effects from the network on specific individuals who need some support.
