Engineering Emergence

The evaluation of the emergent behaviour in complex systems requires an analytical framework which allows the observation of different phenomena that take place at different levels. In order to observe the dynamics of complex systems, it is necessary to perform simulations so that both local and the emergent behaviour can be observed. To this end, the way in which complex system simulators are built must be examined so that it will be feasible to model large scale scenarios. In this paper, the use of Model Driven Engineering methodology is proposed to deal with this issue. Among other benefits, it is shown that this methodology allows the representation and simulation of a complex system providing support for the analysis. This analysis is supported by a metamodel which describes the system components that are under study. The application of this methodology to the development of large scale simulators is explored through a case study. This case study analyses a complex socio-technical system: a power grid.

The Nuclear Factor-kappa B (NF-κB) signalling pathway is one of the key signalling pathways involved in the control and regulation of the immune system [3]. Activation of the NF-κB transcription factor is a tightly regulated event, with NF-κB normally sequestered in the cytosol of non-stimulated cells. Following activation of a cell membrane receptor and propagation of the signal via intracellular signalling to the IκB Kinase (IKK), phosphorylation-induced degradation of IκB inhibitors occurs to facilitate the release of NF-κB and its translocation to the nucleus. Dysregulation of the pathway is known to be involved in a large number of inflammatory diseases. Although considerable research has been performed since its discovery in 1986, we are still not in a position to control the signalling pathway, and thus limit the effects of NF-κB within promotion of inflammatory diseases. Through adherence to the CoSMoS framework, we are developing a computational model of the IL-1 stimulated...

This tutorial promotes good practice for exploring the rationale of systems pharmacology models. A safety systems engineering inspired notation approach provides much needed rigour and transparency in development and 1 application of models for therapeutic discovery and design of intervention strategies. Structured arguments over a models development, underpinning biological knowledge, and analyses of model behaviours, are constructed to determine the confidence that a model is fit for the purpose for which it will be applied.

The application of computational and mathematical modelling to explore the mechanics of biological systems is becoming prevalent. To significantly impact biological research, notably in developing novel therapeutics, it is critical that the model adequately represents the captured system. Confidence in adopting in silico approaches can be improved by applying a structured argumentation approach, alongside model development and results analysis. We propose an approach based on argumentation from safety-critical systems engineering, where a system is subjected to a stringent analysis of compliance against identified criteria. We show its use in examining the biological information upon which a model is based, identifying model strengths, highlighting areas requiring additional biological experimentation and providing documentation to support model publication. We demonstrate our use of structured argumentation in the development of a model of lymphoid tissue formation, specifically Pe...

Future systems will be too complex to design and implement explicitly. Instead, we will have to learn to engineer complex behaviours indirectly: through the discovery and application of local rules of behaviour, applied to simple process components, from which desired behaviours predictably emerge through dynamic interactions between massive numbers of instances. This talk considers such indirect engineering of emergence using a processoriented architecture. Different varieties of behaviour may emerge within a single application, with interactions between them provoking ever-richer patterns – almost social systems. We will illustrate with a study based on Reynolds' boids: emergent behaviours include flocking (of course), directional migration (with waves), fear and panic (of hawks), orbiting (points of interest), feeding frenzy (when in a large enough flock), turbulent flow and maze solving. With this kind of engineering, a new problem shows up: the suppression of the emergence ...

Complex systems are collections of independent agents interacting with each other and with their environment to produce emergent behaviour. Agent-based computer simulation is one of the main ways of studying complex systems. A naïve approach to such simulation can fare poorly, due to large communication overhead, and due to the scope for deadlock between the interacting agents sharing a computational platform. Agent interaction can instead be considered entirely from the point of view of the environment(s) within which the agents interact. Structuring a simulation using such Environment Orientation leads to a simulation that reduces communication overhead, that is effectively deadlock-free, and yet still behaves in the manner required. Additionally the Environment Orientation architecture eases the development of more sophisticated large-scale simulations, with multiple kinds of complex agents, situated in and interacting with multiple kinds of environments. We describe the Environment Orientation simulation architecture. We report on a number of experiments that demonstrate the effectiveness of the Environment Orientation approach: a simple flocking system, a flocking system with multiple sensory environments, and a flocking system in an external environment.

It is clearly in the tradition of biologists to conceptualize the dynamical evolution of biological systems in terms of statetransitions of biological objects. This paper is mainly concerned with (but obviously not limited too) the immunological branch of biology and shows how the adoption of UML (Unified Modeling Language) state-transition diagrams can ease the modeling, the understanding, the coding, the manipulation or the documentation of population-based immune software model generally defined as a set of ordinary differential equations (ODE), describing the evolution in time of populations of various biological objects. Moreover, that same UML adoption naturally entails a far from negligible representational economy since one graphical item of the diagram might have to be repeated in various places of the mathematical model. First, the main graphical elements of the UML state-transition diagram and how they can be mapped onto a corresponding ODE mathematical model are presented. Then, two already published immune models of thymocyte behavior and time evolution in the thymus, the first one originally conceived as an ODE population-based model whereas the second one as an agent-based one, are refactored and expressed in a state-transition form so as to make them much easier to understand and their respective code easier to access, to modify and run. As an illustrative proof, for any immunologist, it should be possible to understand faithfully enough what the two software models are supposed to reproduce and how they execute with no need to plunge into the Java or Fortran lines.

Abstract. We summarise the existing CoSMoS approach to modelling and simulating complex systems, then introduce how the various CoSMoS models are related via their metamodel, and demonstrate the generality of the process by discussing its application to engineering bio-inspired systems.

As part of research towards the CoSMoS unified infrastructure for modelling and simulating complex systems, we review uses of definitional and descriptive models in natural science and computing, and existing integrated platforms. From these, we identify requirements for engineering models of complex systems, and consider how some of the requirements could be met, using state-of-the-art model management and a mobile, process-oriented computing paradigm.

Complex systems are collections of independent agents interacting with each other and with their environment to produce emergent behaviour. Agent-based computer simulation is one of the main ways of studying complex systems. A naïve approach to such simulation can fare poorly, due to large communication overhead, and due to the scope for deadlock between the interacting agents sharing a computational platform. Agent interaction can instead be considered entirely from the point of view of the environment(s) within which the agents interact. Structuring a simulation using such Environment Orientation leads to a simulation that reduces communication overhead, that is effectively deadlock-free, and yet still behaves in the manner required. Additionally the Environment Orientation architecture eases the development of more sophisticated large-scale simulations, with multiple kinds of complex agents, situated in and interacting with multiple kinds of environments. We describe the Environment Orientation simulation architecture. We report on a number of experiments that demonstrate the effectiveness of the Environment Orientation approach: a simple flocking system, a flocking system with multiple sensory environments, and a flocking system in an external environment.

Abstract. We summarise the existing CoSMoS approach to modelling and simulating complex systems, then introduce how the various CoSMoS models are related via their metamodel, and demonstrate the generality of the process by discussing its application to engineering bio-inspired systems.

Abstract. Complex systems are often simulated to provide a basis for research or analysis. However, complex systems simulation often fails to properly demonstrate that the constructed simulation is an adequate tool to support investigation of the system under study. To address this issue we adopt and adapt argumentation techniques traditionally used for safety critical systems (SCS). Here we present part of an on-going case-study in which these techniques are used to demonstrate that two different implementations of a ...

We describe an approach to building a hybrid multi-scale model, using a Petri net model for the top layer, and object-oriented models at the lower layers, with a rigorous definition of how the layers compose. We apply this approach to building a model of prostate cell division and differentiation, with each model layer describing the processes at a suitable level of abstraction. This provides a systematic modular approach to modelling and to model validation, allowing use of diverse modelling techniques in developing multi-scale models with no loss of rigour or validity. We use this prostate model to develop a prototype simulation, and demonstrate that it is capable of simulating biologically-relevant numbers of cells.

Susan Stepney has created novel research in areas as diverse as formal software modelling and evolutionary computing. One theme that spans almost her whole career is the use of patterns to capture and express solutions to software engineering problems. This paper considers two extremes, both in time and topic: patterns for formal modelling languages, and patterns related to the principled modelling and simulation of complex systems.

In May 2019, a workshop on principled development of future agent-based simulations was held at Keele University. Participants spanned companies and academia, and a range of domains of interest, as well as participant career stages. This report summarizes the discussions and main outcomes from this workshop.

Research based on computer simulations, especially that conducted through agent-based experimentation, is often criticised for not being a reliable source of informationthe simulation software can hide errors or flawed designs that inherently bias results. Consequently, the academic community shows both enthusiasm and lack of trust for such approaches. In order to gain confidence is using engineered systems, domains such as Safety Critical Systems employ structured argumentation techniques as means of explicitly relating claims to evidence-in other words, requirements to deliverables. We argue here that structured argumentation should be used in the development and validation process of simulation-driven research. Making use of the Goal Structuring Notation, we provide insights into how more trustworthy outcomes can be obtained through argumentation-driven validation.

As part of research towards the CoSMoS unified infrastructure for modelling and simulating complex systems, we review uses of definitional and descriptive models in natural science and computing, and existing integrated platforms. From these, we identify requirements for engineering models of complex systems, and consider how some of the requirements could be met, using state-of-the-art model management and a mobile, process-oriented computing paradigm.

Individual or agent-based simulation is an important tool for research involving understanding of complex systems. For a research tool to be useful, its use must be understood, and it must be possible to interpret the results of using the tool in the context of the research. This paper presents the partial validity argument for ongoing work on prostate cell simulation (a companion paper describes the models and implementation of the simulation). This is the basis for a discussion of issues in the validation of complex systems simulations used as scientific research tools.

Complex systems are often simulated to provide a basis for research or analysis. However, complex systems simulation often fails to properly demonstrate that the constructed simulation is an adequate tool to support investigation of the system under study. To address this issue we adopt and adapt argumentation techniques traditionally used for safety critical systems (SCS). Here we present part of an on-going case-study in which these techniques are used to demonstrate that two different implementations of a complex system simulation are adequately equivalent. This is a first step in producing further simulations of the system under study, which will be shown to be valid models on which to explore particular ecological phenomena.

Software systems are today one of the most complex artifacts, they are simultaneously used by hundred-thousand of people sometimes in risk real time operations, such as auctions or electronic commerce. Nevertheless, it is a common practice to deploy them without the expected performance. Software Performance Engineering has emerged as a discipline to complement Software Engineering research in order to address this kind of problems. In this work, we survey some recent contributions in the field of Software Performance ...

We introduce and explore a new statechart (sc) abstraction method. We define simplified statecharts (ssc) and discuss the use of action abstraction in ssc models. We isolate sc DNA from UML sc models, and show how this sc DNA can be used to define behavior model metrics and more generally, to manage object behavior.

Petri nets and statecharts can model concurrent systems in a succinct way. While translations from statecharts to Petri nets exist, a well-defined translation from Petri nets to statecharts is lacking. Such a translation should map an input net to a corresponding statechart, having a structure and behaviour similar to that of the input net. Since statecharts can only model a restricted form of concurrency, not every Petri net has a corresponding statechart. We identify a class of Petri nets, called statechartable nets, that can be translated to corresponding statecharts. Statechartable Petri nets are structurally defined using the novel notion of an area. We also define a structural translation that maps each statechartable Petri net to a corresponding statechart. The translation is proven sound and complete for statechartable Petri nets.

Individual or agent-based simulation is a potentially important tool for research involving understanding of complex systems. For a research tool to be useful, its use must be understood, and it must be possible to interpret the results of using the tool in the context of the research. This paper discusses issues in the validation of complex systems simulations used as scientific research tools. Specifically, it presents parts of a validation argument developed during construction of a simulation of cells in the prostate-a companion paper describes the models and implementation of the simulation.

We describe our use of the CoSMoS process to structure an incremental change of a biological simulation. The domain is auxin transport canalisation. An existing simulator is refactored to handle aspects of 2D and 3D space more efficiently, and enhanced to include more realistically-shaped plant cells. The CoSMoS process supports clear separation of concerns, allowing us to concentrate on the biological model and the implementation decisions separately. This gives a clear and welljustified simulator design that can be validated by biologists, yet still allows efficient implementation.

We propose a multi-layer architecture for simulating emergent properties. This is implemented as a form of cellular automata at the lowest layer, with mobile processes to represent objects at multiple up- per layers. This architecture supports multiple levels of emergence.

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions.

The basic CoSMoS process concerns the design, implementation, and use of a simulation built from scratch. However, the CoSMoS approach may be tailored and adapted for other styles of use. Here we describe how it has been applied to analyse and re-engineer an existing simulation, that of Schelling's Bounded Neighbourhood Model.

In this paper, we discuss the implementation of a simple pedestrian simulation that uses a multi agent based design pattern developed by the CoSMoS research group. Given the nature of Multi Agent Systems (MAS), parallel processing techniques are inevitably used in their implementation. Most of these approaches rely on conventional parallel programming techniques, such as threads, Message Passing Interface (MPI) and Remote Method Invocation (RMI). The CoSMoS design patterns are founded on the use of Communicating Sequential Processes (CSP), a parallel computing paradigm that emphasises a process oriented rather than object oriented programming perspective.

In this paper, we discuss the implementation of a simple pedestrian simulation that uses a multi agent based design pattern developed by the CoSMoS research group. Given the nature of Multi Agent Systems (MAS), parallel processing techniques are inevitably used in their ...

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a theoretical framework that supports developing simulations as scientific instruments, and we select ...

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a theoretical framework that supports developing simulations as scientific instruments, and we select ...

Meta-modelling is a key technique in Model Driven Engineering, where it is used for language engineering and domain modelling. However, mainstream approaches like the OMG's Meta-Object Facility provide little support for abstraction, modularity, reusability and extendibility of (meta-)models, behaviours and transformations.

As part of research towards the CoSMoS unified infrastructure for modelling and simulating complex systems, we review uses of definitional and descriptive models in natural science and computing, and existing integrated platforms. From these, we identify requirements for engineering models of complex systems, and consider how some of the requirements could be met, using state-of-the-art model management and a mobile, process-oriented computing paradigm.

A naïve implementation of a complex system simulation with its plethora of interacting agents would be to represent those interactions as direct communications between the agents themselves. Considerations of the real world that a complex system inhabits shows that agent interactions are actually mediated by the environment within which they are embedded and which embodies facilities used by the agents. This suggests an "environment oriented" simulation architecture. Here we motivate and describe an abstract software architecture for an environment oriented approach to complex systems simulation, and sketch the implementation of this architecture in a number of different ways.

Abstract. We summarise the existing CoSMoS approach to modelling and simulating complex systems, then introduce how the various CoSMoS models are related via their metamodel, and demonstrate the generality of the process by discussing its application to engineering bio-inspired systems.

As part of research towards the CoSMoS unified infrastructure for modelling and simulating complex systems, we review uses of definitional and descriptive models in natural science and computing, and existing integrated platforms. From these, we identify requirements for engineering models of complex systems, and consider how some of the requirements could be met, using state-of-the-art model management and a mobile, process-oriented computing paradigm.

Computer simulations constitute a significant scientific tool for promoting scientific understanding of natural phenomena and dynamic processes. Substantial leaps in computational force and software engineering methodologies now allow the design and development of large-scale biological models, which – when combined with advanced graphics tools – may produce realistic biological scenarios, that reveal new scientific explanations and knowledge about real life phenomena. A state-of-the-art simulation system termed Reactive Animation (RA) will serve as a study case to examine the contemporary philosophical debate on the scientific value of simulations, as we demonstrate its ability to form a scientific explanation of natural phenomena and to generate new emergent behaviors, making possible a prediction or hypothesis about the equivalent real-life phenomena. 9.1 Introduction Computer simulations constitute a significant scientific tool for promoting scientific understanding of natural phenomena and dynamic processes in diverse disciplines, including biology. The need of culling significant knowledge and insights from vast amounts of empirical data, generated in recent decades about biological molecules and the millions of interactions among them, has promoted the development of innovative sophisticated computational methods and helped form new interdisciplinary research fields. A group of researchers have developed over the last decade a computational approach termed Reactive Animation (RA) for simulating complex biological systems (Vainas et al. 2011). The dynamic characteristics of the biological objects are described based on cellular and molecular data collected from lab experiments. These data are integrated bottom-up by computational tools and methods to create a comprehensive, dynamic, interactive simulation (with front-end animated

In this paper, we discuss the implementation of a simple pedestrian simulation that uses a multi agent based design pattern developed by the CoSMoS research group. Given the nature of Multi Agent Systems (MAS), parallel processing techniques are inevitably used in their ...

Abstract In studying complex systems, agent-based simulations offer the possibility of directly modelling components in an environment. However, the scientific value of agent-based simulations has been limited by inadequate scientific rigour. The paper focuses on agent-based simulations that are used in biological and bio-medical research. Starting from a review of best practice in simulation engineering, the paper identifies some of the key activities in developing complex systems simulations that support scientific research, and ...

Abstract In studying complex systems, agent-based simulations offer the possibility of directly modelling components in an environment. However, the scientific value of agent-based simulations has been limited by inadequate scientific rigour. The paper focuses on agent-based simulations that are used in biological and bio-medical research. Starting from a review of best practice in simulation engineering, the paper identifies some of the key activities in developing complex systems simulations that support scientific research, and ...

A na¨õve implementation of a complex system simu- lation with its plethora of interacting agents would be to rep- resent those interactions as direct communications between the agents themselves. Considerations of the real world that a com- plex system inhabits shows that agent interactions are actually mediated by the environment within which they are embedded and which embodies facilities used

Classical evolutionary algorithms have been extremely successful at solving certain problems. But they implement a very simple model of evolutionary biology that misses out several aspects that might be exploited by more sophisticated algorithms. We have previously critiqued the traditional naïve approach to bio-inspired algorithm design, that moves straight from a simplistic description of the biology into some algorithm. Here we present a process for developing richer evolutionary algorithms abstracted from various processes of biological evolution, with a corresponding richer analogical computational structure, and indicate how that might be further abstracted.

The basic CoSMoS process concerns the design, implementation, and use of a simulation built from scratch. However, the CoSMoS approach may be tailored and adapted for other styles of use. Here we describe how it has been applied to analyse and re-engineer an existing simulation, that of Schelling's Bounded Neighbourhood Model.

Complex Systems Modelling and Simulation (CoSMoS) was a 4 year EPSRC funded research project at the Universities of York and Kent in the UK. As part of that project, the research team developed the CoSMoS approach to assist the building and use of fit-for-purpose computational simulations of complex systems, and initiated a series of international workshops to disseminate best practice in CoSMoS. This special issue brings together several authors from the first six workshops.

Abstract In studying complex systems, agent-based simulations offer the possibility of directly modelling components in an environment. However, the scientific value of agent-based simulations has been limited by inadequate scientific rigour. The paper focuses on agent-based simulations that are used in biological and bio-medical research. Starting from a review of best practice in simulation engineering, the paper identifies some of the key activities in developing complex systems simulations that support scientific research, and ...

We describe our use of the CoSMoS process to structure an incremental change of a biological simulation. The domain is auxin transport canalisation. An existing simulator is refactored to handle aspects of 2D and 3D space more efficiently, and enhanced to include more realistically-shaped plant cells. The CoSMoS process supports clear separation of concerns, allowing us to concentrate on the biological model and the implementation decisions separately. This gives a clear and welljustified simulator design that can be validated by biologists, yet still allows efficient implementation. Domain Model Platform Model Simulation Platform Results Model Domain Research Context