ChapterPDF Available

Chapter 2 Ins and Outs of Network-Oriented Modeling

January 2020

DOI:10.1007/978-3-030-31445-3_2

In book: Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models
Chapter: 2
Publisher: Springer Publishers, 2020, to appear

Authors:

Vrije Universiteit Amsterdam

Network-Oriented Modeling has successfully been applied to obtain network models for a wide range of phenomena, including Biological Networks, Mental Networks, and Social Networks. In this chapter, it is discussed how the interpretation of a network as a causal network and taking into account dynamics in the form of temporal-causal networks, brings more depth. Thus main characteristics for a network structure are obtained: Connectivity in terms of the connections and their weights, Aggregation of multiple incoming connections in terms of combination functions, and Timing in terms of speed factors. The basics and the scope of applicability of such a Network-Oriented Modelling approach are discussed and illustrated. This covers, for example, Social Network models for social contagion or information diffusion, and Mental Network models for cognitive and affective processes. From the more fundamental side, it will be discussed how emerging network behavior can be related to network structure. This is Chapter 2 of this book: https://www.researchgate.net/publication/334576216

Content uploaded by Jan Treur

Content may be subject to copyright.

Content uploaded by Jan Treur

Content may be subject to copyright.

Content uploaded by Jan Treur

Content may be subject to copyright.

Content uploaded by Jan Treur

Content may be subject to copyright.

Content uploaded by Jan Treur

Content may be subject to copyright.

Content uploaded by Jan Treur

Content may be subject to copyright.

Content uploaded by Jan Treur

Content may be subject to copyright.

A preview of the PDF is not available

ResearchGate has not been able to resolve any citations for this publication.

The Ins and Outs of Network-Oriented Modeling: From Biological Networks and Mental Networks to Social Networks and Beyond

Article

Full-text available

Sep 2018

Jan Treur

Network-Oriented Modeling has successfully been applied to obtain network models for a wide range of phenomena, including Biological Networks, Mental Networks, and Social Networks. In this paper it is discussed how the interpretation of a network as a causal network and taking into account dynamics in the form of temporal-causal networks, brings more depth. The basics and the scope of applicability of such a Network-Oriented Modelling approach are discussed and illustrated. This covers, for example, Social Network models for social contagion or information diffusion, adaptive Mental Network models for Hebbian learning and adaptive Social Network models for evolving relationships. From the more fundamental side, it will be discussed how emerging network behavior can be related to network structure. This paper describes the content of my Keynote lecture at the 10th International Conference on Computational Collective Intelligence, ICCCI'18.

On the Applicability of Network-Oriented Modeling Based on Temporal-Causal Networks: Why Network Models Do Not Just Model Networks

Article

Full-text available

Mar 2017

Jan Treur

In this paper for a Network-Oriented Modelling perspective based on temporal-causal networks it is analysed how generic and applicable it is as a general modelling approach and as a computational paradigm. It is shown that network models do not just model networks. In : Journal of Information and Telecommunication 1(1), 2017, 23-40.

Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions

Book

Full-text available

Oct 2016

Jan Treur

This book has been written with a multidisciplinary audience in mind without assuming much prior knowledge. In principle, the detailed presentation in the book makes that it can be used as an introduction in Network-Oriented Modelling for multidisciplinary Master and Ph.D. students. In particular, this implies that, although also some more technical mathematical and formal logical aspects have been addressed within the book, they have been kept minimal, and are presented in a concentrated and easily avoidable manner in Part IV. Much of the material in this book has been and is being used in teaching multidisciplinary undergraduate and graduate students, and based on these experiences the presentation has been improved much. Sometimes some overlap between chapters can be found in order to make it easier to read each chapter separately. Lecturers can contact me for additional material such as slides, assignments, and software Springer full-text download: http://link.springer.com/book/10.1007/978-3-319-45213-5

Macromolecular networks and intelligence in microorganisms

Article

Full-text available

Jul 2014

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of Information and Communication Technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity – particularly activity of the human brain – with a phenomenon we call “intelligence”. Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as “human” and “brain” out of the defining features of “intelligence”, all forms of life – from microbes to humans – exhibit some or all characteristics consistent with “intelligence”. We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo.

What can Artificial Intelligence get from Neuroscience?

Data

Full-text available

Jul 2014

Steve M Potter

From Ordinary Differential Equations to Structural Causal Models: the deterministic case

Article

Full-text available

Apr 2013

We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM). Our exposition sheds more light on the concept of causality as expressed within the framework of Structural Causal Models, especially for cyclic models.

Discrete Event Modeling and Simulation Technologies: A Tapestry of Systems and AI-Based Theories and Methodologies

Book

Jan 2001

The initial ideas behind this edited volume started in spring of 1998 - some two years before the sixtieth birthday of Bernard P. Zeigler. The idea was to bring together distinguished researchers, colleagues, and former students of Professor Zeigler to present their latest findings at the AIS' 2000 conference. During the spring of 1999, the initial ideas evolved into creating a volume of articles surrounding seminal concepts pertaining to modeling and simulation as proposed, developed, and advocated by Professor Zeigler throughout his scientific career. Also included would be articles describing progress covering related aspects of software engineering and artificial intelligence. As this volume is emphasizing concepts and ideas spawned by the work of Bernard P. Zeigler, it is most appropriate to offer a biographical sketch of his scientific life, thus putting into a historical perspective the contributions presented in this volume as well as new research directions that may lie ahead! Bernard P. Zeigler was born March 5, 1940, in Montreal, Quebec, Canada, where he obtained his bachelor's degree in engineering physics in 1962 from McGill University. Two years later, having completed his MS degree in electrical engineering at the Massachusetts Institute of Technology, he spent a year at the National Research Council in Ottawa. Returning to academia, he became a Ph. D. student in computer and communication sciences at the University of Michigan, Ann Arbor.

Structural Models: An Introduction to the Theory of Directed Graphs.

Article

Sep 1966
J AM STAT ASSOC

Chapter 3. Understanding Principles of the Dynamic Biochemical Networks of Life Through Systems Biology

Chapter

Jan 2014

Systems Biology brings the potential to discover fundamental principles of Life that cannot be discovered by considering individual molecules. This chapter discusses a number of early, more recent and upcoming discoveries of such network principles. These range from the balancing of fluxes through metabolic networks, the potential of those networks for truly individualized medicine, the time dependent control of fluxes and concentrations in metabolism and signal transduction, the ways in which organisms appear to regulate metabolic processes vis-à-vis limitations therein, trade-offs in robustness and fragility, and a relation between robustness and time dependences in the cell cycle. The robustness considerations will lead to the issue whether and how evolution has been able to put in place design principles of control engineering such as infinite robustness and perfect adaptation in the hierarchical biochemical networks of cell biology.

Mind as Motion: Explo-rations in the Dynamics of Cognition

Article

Mar 1998

Project

Design of Adaptive Temporal-Causal Network Models for Handling Extreme Emotions

In this project, it is addressed by computational modeling how extreme, stressful emotions affect mental processes, and how therapies such as various mindfulness therapies can be used to handle suc h extreme emotions. ... [more]

Project

Modeling of Human Trust

Technological advancements in the last century have opened numerous venues and great challenges. The use of machines and devices in everyday human life has assigned them a new societal role. Envisi oning the machine to act as an educator, helper, supporter, mediator, negotiator, moderator, doctor, and a daily companion is the debate of the day. Emergence of such a techno-human society has brought many challenges to technologists and social scientists. A main question that still stands is whether such a societal setup will be stable. One of the crucial factors for such a setup to be successful is the humans’ levels of trust in machines. To make such artefacts more human-aware with respect to their trust, dynamical models of trust should be designed, verified, validated and embedded into them. These models will enable the machine to estimate human trust and adapt accordingly. In existing literature there are several computational models of trust which silently assume a rational basis of trust. This performance oriented, system-theoretic view of trust is not the true representation of human behaviour. As reported in many recent studies humans usually do not behave rationally under the influence of personal perception, feeling and biases. This notion of trust is called human-based trust in this project. Both system-theoretic and human-based trust models have their own domain of applications in the current socio-technological world. As interactions in the socio-technological world can be classified into three types, namely, human-human, human-machine and machine-machine. System-theoretic trust has a wide application for cases when two autonomous machines communicate or interact with each other. These machines do not have human aspects. Hence, the performance-oriented view of utility, past experiences and institutions enforcements can provide them enough to trust on each other or not. Different from a machine-machine view of interaction, human-based trust has an application in both human-machine and human-human interactions. Hence, understanding of human trust dynamics in these perspectives is necessary for progress and effective utilization of current socio-technological advancements. This project deals with modelling of human-based trust and validation of these models, which is an essential component for human-human and human-machine interaction posed under the challenges of the postmodern human society. ... [more]

Project

Negative aspects of social behavior

Negative aspects of social behavior

Project

A Computational Analysis of Psychopathy based on a Network-Oriented Modeling Approach

I studied Lifestyle Informatics, a variant of Artificial Intelligence, with a focus on psychology and neuroscience at the Vrije Universiteit of Amsterdam. For my bachelor's thesis is researched how the brains of psychopaths differ from healthy individuals using neuroscientific literature and created a temporal-causal network to represent the decision-making process in a situation of moral judgment for psychopaths. ... [more]

Article

Full-text available

The Ins and Outs of Network-Oriented Modeling: From Biological Networks and Mental Networks to Socia...

September 2018 · LNCS Transactions on Computational Collective Intelligence

Jan Treur

Network-Oriented Modeling has successfully been applied to obtain network models for a wide range of phenomena, including Biological Networks, Mental Networks, and Social Networks. In this paper it is discussed how the interpretation of a network as a causal network and taking into account dynamics in the form of temporal-causal networks, brings more depth. The basics and the scope of ... [Show full abstract] applicability of such a Network-Oriented Modelling approach are discussed and illustrated. This covers, for example, Social Network models for social contagion or information diffusion, adaptive Mental Network models for Hebbian learning and adaptive Social Network models for evolving relationships. From the more fundamental side, it will be discussed how emerging network behavior can be related to network structure. This paper describes the content of my Keynote lecture at the 10th International Conference on Computational Collective Intelligence, ICCCI'18.

Chapter

Full-text available

A Temporal-Causal Network Modeling Approach: With Biological, Neurological and Social Processes as I...

October 2016

Jan Treur

This chapter introduces the Network-Oriented Modeling approach presented in this book. It enables to design complex models in the form of temporal-causal networks, that on the one hand can be described by high level conceptual representations and on the other hand by formal numerical representations of the model. Both representations can easily be translated into each other in a systematic ... [Show full abstract] manner. Dedicated software has been developed to support the design of models in a conceptual manner, and to automatically transform them into a numerical and executable format and performing simulation experiments. The modeling format used makes it easy to take into account complex dynamics based on interrelating causal cycles as described by theories and findings about brain processes known from Cognitive, Affective and Social Neuroscience, and complex dynamics based on social interactions. In this chapter also the scope of applicability is discussed in general terms; it is shown that any smooth dynamical system can be modeled by a temporal-causal network. A variety of example models in subsequent chapters illustrates the applicability of the approach in detail.

Chapter

Full-text available

Chapter 3 A Unified Approach to Represent Network Adaptation Principles by Network Reification

January 2020

Jan Treur

In this chapter, the notion of network reification is introduced: a construction by which a given (base) network is extended by adding explicit states representing the characteristics defining the base network’s structure. This is explained for temporal-causal networks where connection weights, combination functions, and speed factors are the characteristics for Connectivity, Aggregation, and ... [Show full abstract] Timing describing the network structure. Having the network structure represented in an explicit manner within the extended network enables to model the adaptation of the base network by dynamics within the reified network: an adaptive network is represented by a non-adaptive network. It is shown how the approach provides a unified modeling perspective on representing network adaptation principles across different domains. This is illustrated for a number of well-known network adaptation principles such as for Hebbian learning in Mental Networks and for network evolution based on homophily in Social Networks. This is Chapter 3 of the book https://www.researchgate.net/publication/334576216

Chapter

Full-text available

Adaptive Networks at the Crossroad of AI and Formal, Biological, Medical and Social Sciences

September 2020

Jan Treur

Supporting videos, for example, Using Self-Modeling Networks to Model Adaptive Causality, can be viewed at the YouTube channel on Self-Modeling Networks: https://www.youtube.com/channel/UCCO3i4_Fwi22cEqL8M_PgeA. In this paper, it has been illustrated how a network-oriented modeling approach based on temporal-causal networks can be used to model adaptive processes in different domains and thus ... [Show full abstract] serve as a unifying factor for multiple sciences. The approach makes use of mathematical relations and functions as declarative building blocks to be used in a standard temporal-causal network format. A dedicated software environment is available which includes a combination function library for aggregation of multiple impacts within a temporal-causal network, with more than 35 already predefined basic combination functions. This software environment makes design and simulation for such network models relatively easy. The approach is illustrated by three examples of adaptive network models: a first-order adaptive network model for bonding by homophily, a second-order adaptive network model for plasticity and metaplasticity for emotion regulation dysfunction in disorders, and a fourth-order adaptive network model for evolutionary processes related to pathogens and pregnancy.