PyCX: a Python-based simulation code repository for complex systems education

The context of our work is the complex systems simulation. Complex systems modelling and simulation require to take into account the interactions between the different components. For instance, an airport ground traffic simulation requires to model the planes with the pilots, the control tower, the

Data mining with a multitude of methodologies is a good basis for the integration of intelligent systems. Small, specialised systems have a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require domain expertise and more compact approaches at the basic level. This paper focuses on the integration of methodologies in the smart adaptive applications. Statistical methods and arti cial neural networks form a good basis for the data-driven analysis of interactions and fuzzy logic introduces solutions for knowledge-based understanding the system behaviour and the meaning of variable levels. Efficient normalisation, scaling and decomposition approaches are the key methodologies in developing large-scale applications. Linguistic equation (LE) approach originating from fuzzy logic is an efficient technique for these problems. The nonlinear scaling methodology based on advanced statistical analysis is the corner stone in representing the variable meanings in a compact way to introduce intelligent indices for control and diagnostics. The new constraint handling together with generalised norms and moments facilitates recursive parameter estimation approaches for the adaptive scaling. Well-known linear methodologies are used for the steady state, dynamic and case-based modelling in connection with the cascade and interactive structures in building complex large scale applications. To achieve insight and robustness the parameters are de ned separately for the scaling and the interactions. Introduction EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 2 SNE 24(1) – 4/2014 ON 1 Steady-State Modelling multiple input, multiple output MIMO response surface methodology RSM multiple input, single output MISO Fuzzy set theory Extension principle Type-2 fuzzy Figure 1: Methodologies for modelling of complex system. EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 3 O N Linguistic fuzzy models Takagi-Sugeno (TS) fuzzy models Singleton models Fuzzy relational models Figure 2: Combined fuzzy modelling. Artificial neural networks ANN Linear networks multilayer perceptron MLP backpropagation learning Neurofuzzy systems Figure 3: A fuzzy neuron. function expansion Approximate reasoning 2 Decomposition Methodologies EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 4 SNE 24(1) – 4/2014 ON Decomposition Figure 4: Decomposition for modelling. Hierarchical clustering Partitioning-based clustering algorithms fuzzy clustering Fuzzy c-means (FCM) Subtractive clustering Neural clustering Robust clustering number of clusters Composite local model Linear parameter varying (LPV) models Piecewise affine (PWA) systems Fuzzy models Multiple neural network systems mixed approach EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 5 O N 3 Adaptive Nonlinear Scaling Membership 3.1 Working point and feasible ranges Figure 5: Nonlinear scaling [28] EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 6 SNE 24(1) – 4/2014 ON 3.2 Membership definitions linguistic range linguistic values • the corner points (Figure 5) are good for visualisation; • the parameters suit for tuning; • the coefficients are used in the calculations. 3.3 Adaptation of nonlinear scaling EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 7 O N 4 Intelligent Systems 4.1 LE models 4.2 Hybrid LE systems EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 8 SNE 24(1) – 4/2014 ON extension principle linguistification

Complexity science offers many theories such as chaos theory and coevolutionary theory. These theories illustrate a large set of real life systems and help decipher their nonlinear and unpredictable behaviours. Categorizing an observed Complex System among these theories depends on the aspect that we intend to study, and it can help better understand the phenomena that occur within the system. This article aims to give an overview on Complex Systems and their modelling. Therefore, we compare these theories based on their main behavioural characteristics, e.g. emergence, adaptability, and dynamism. Then we compare the methods used in the literature to model and simulate Complex Systems, and we propose and discuss simple guidelines to help understand one's Complex System and choose the most adequate model to simulate it.

Cellular Automata (CA), characterised by its massive parallelism, constitutes a powerful tool for modelling and simulating complex systems. And CA has been applied successfully to very large scale integration (VLSI) fabrication processes. In this work, a solution has been developed, which handles the complicated boundary and initial conditions imposed by the advancement of the Si-SiO 2 and SiO 2 -air fronts during oxidation in unbounded as well as in bounded domains. The solution uses a two-dimensional technology computeraided design system based on CA, which was named CA_OX_TCAD. This system is fully automated and is also able to support the hardware implementation of the corresponding CA algorithm. It leads directly to the FPGA implementation of the CA algorithm, which produces as output the corresponding VHDL code. The simulation results are in very good agreement with experimental data found in the literature.

Occupant behavior in buildings can contribute significantly to building energy demand and consumption. As a result, occupant behavior interventions to promote sustainability are becoming more widespread. Due to the expense in applying interventions, researchers have begun using computer simulations to analyze potential outcomes and better understand how complex systems can affect intervention success, in particular the effect of social network structure. In previous literature, studies have only evaluated social network effects using static social networks which are far from reality. Therefore, in this study we evaluate how a behavior intervention, here a comparative feedback system, is affected as social networks evolve over time using agent-based modeling. Results indicate that static social networks are much less volatile in their behavior and tend to have more convergent behavior relative to dynamic social networks. This implies that for normative interventions, dynamic networks have increased uncertainty in intervention outcome compared to static networks.

Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods

There has been a long histroy of the System Dynamics developed by J.Forrester and its analysis of complex system behaviors. It is necessary to know the detailed system structure and a large amount statistical data to build the flow diagram when using this method. However, our quantitative analysis show that the integrated model can be seen as a combination of many abstracting grey system modeles which may be decomposed from complex system. In this way, the integrated model can be simulated on a computer. The approach of combining quantitative analysis with simulation has the following features, (1) A continuous differential model is established using grey generating numbers. (2) The integrated model can be run on a computer to simulate the system behavior using DYNAMO language. In this paper, the approach of modelling the available abstracting grey dynamic systems and of simulating the integrated systems is described. The system analysis of experimental example show that grey system modelling is very useful in System Dynamics model.

We have been working for several years on the representation, study and simulation of complex systems by means of formal methods. APL2 and other programming languages have been used to develop our tools and experiments. This paper summarizes our APL2 works on fractal sets, automatic programming of cellular automata and simulation of multi agent systems.

Agent-based modeling is a promising tool for familiarizing students with complex systems as well as programming skills. Human–environment systems, for instance, entail complex interdependencies that need to be considered when modeling these systems. This complexity is often neglected in teaching modeling approaches. For a heterogeneous group of master’s students at a German university, we pre-built an agent-based model. In class, this was used to teach modeling impacts of land use policies and markets on ecosystem services. As part of the course, the students had to perform small research projects with the model in groups of two. This study aims to evaluate how well students could deal with the complexity involved in the model based on their group work outcomes. Chosen indicators were, e.g., the appropriateness of their research goals, the suitability of the methods applied, and how well they acknowledged the limitations. Our study results revealed that teaching complex systems does not need to be done with too simplistic models. Most students, even with little background in modeling and programming, were able to deal with the complex model setup, conduct small research projects, and have a thoughtful discussion on the limitations involved. With adequate theoretical input during lectures, we recommend using models that do not hide the complexity of the systems but foster a realistic simplification of the interactions.

Matlab has become a popular choice for researchers across various fields due to its versatility, ease of use, and powerful analytical capabilities. In this paper, we explore the role of Matlab as the ultimate solution for research challenges. We first discuss the benefits of using Matlab in research, including its ability to handle complex mathematical computations, data visualization, and simulation of complex systems.

Over the course of three to four decades, most well-established companies lose their dominating position in the market or fail entirely. Their failure occurs even though they have resources for sensing shifting market trends, skills and assets to develop next-generation technologies, and the financial means to fill skill gaps and afford risky investments. Nevertheless, incumbents obviously find it very difficult to invest in innovation that takes attention and resources away from a highly successful core business. A solution to this "innovator's dilemma" is the concept of "organizational ambidexterity," which has garnered considerable attention among researchers in organization and innovation. According to empirical findings and emergent theory, companies can improve their financial performance and ensure their long-term survival by balancing their innovation activities, so that they are equally focused on exploratory (discontinuous) and exploitative (incremental, continuous) innovations. But how can such a balance be achieved? The literature on the organizational theory and related fields (product innovation, knowledge management, creativity, etc.) identifies more than 300 contributing factors to innovation and ambidexterity: many are interdependent so that their impacts compound or cancel each other. Moreover, for many factors, there is limited empirical data and the size of impacts is unknown. To understand which managerial actions lead to ambidexterity, this dissertation develops a novel approach to the study and analysis of complex casual systems with high uncertainty: exploratory fuzzy cognitive mapping. Fuzzy Cognitive Mapping (FCM) is a semi-quantitative system modeling and simulation technique. It is used to represent qualitative information about complex systems as networks of casual relationships that can be studied computationally. Exploratory modeling and analysis (EMA) is a new approach to modeling and simulation of complex systems when there is high uncertainty about the structural properties of the system. This work is the first to combine both approaches. The work makes several contributions: First, it shows that only a small fraction of management interventions will actually lead to ambidexterity while most will, at best, improve one type of innovation at the expense of the other. Second, it provides a simulation tool to management researchers and practitioners that allows them to test ideas for improving ambidexterity against a model that reflects our current collective knowledge about innovation. And third, it develops a range of techniques (and software code) for exploratory FCM modeling, such as methods for transforming qualitative data to FCM, for exploratory simulation of large and complex FCM models, and for data

Can complex systems really be simulated?

Multi-Agent Systems (MAS) stand out as a particularly suitable paradigm for designing complex systems. Its elementary constituents are termed “agents”, that is software entities which exhibit autonomous and flexible behaviours. However, most widely used models still consider agents as atomic entities. This may be a problem for MAS dedicated to the study and simulation of complex systems that frequently exhibit hierarchical structures. Indeed, almost all proposals for agent architectures have not addressed the general problem of treating collections of “agents” as higher-order entities, for example by treating organisations as agents. This chapter focuses on the definition of Holonic Multi-Agent Systems (HMAS) which are a special type of MAS based on the concept of “holon”. Holons are self-similar entities that represent whole-part constructs and can be viewed either as higher-level system components or as wholes composed of other holons as substructures. Furthermore, a framework for engineering HMAS based on organisational concepts is introduced.

Epidemic episodes such as the COVID-19 has shown the need for simulation tools to support decision making, predict the results of control actions, and mitigating the effects of the virus. Simulation methods have been widely used by healthcare researchers and practitioners to improve the planning and management of hospitals and predict the spread of disease. Simulating all involved aspects of an epidemic episode requires the modelling and simulation of large and complex Discrete Event Systems (DESs), supported by modular and hierarchical models easy to use for experts, and that can be translated to efficient code for distributed simulation. This paper presents a model driven engineering (MDE) approach to support the modelling of healthcare systems (HS) in epidemic episodes combining different perspectives, and the translation to efficient code for scalable distributed simulations.

An Approach of the Grey System Modelling and Simulation for Complex Systems was published in Systems Analysis and Simulation 1988, I: Theory and Foundations. Proceedings of the International Symposium held in Berlin (GDR), September 12–16, 1988 on page 117.