What software engineering has to offer to agent-based social simulation

In simulation projects it is generally beneficial to have a toolset that allows following a more formal approach to system analysis, model design, and model implementation. Such formal approaches are developed for supporting a systematic proceeding by making different steps explicit as well as by providing a precise language to express the results of those steps, documenting not just the final model but also intermediate steps. This chapter consists of two parts: The first gives an overview of which tools developed in Software Engineering can and have been adapted to agent-based social simulation; the second part demonstrates with the help of an informative example how some of these tools can be combined into an overall structured approach to model development

Agent-Based Simulation Engineering

The history of agent-based models started in the 1970ies with singular yet path-breaking examples such as the Segregation model by T. Schelling [Schelling, 1971]. From end of the 80ies on more and more agent-based models were developed and implemented. However, almost no simulation engineering happened. Due to the relation to social sciences, mostly sociologists and psychologists used the paradigm of simulated humans based on rather complex models of human decision making to model hypotheses and theories about societal dynamics. The resulting models were complex but abstract. The role of empirical embeddedness is still discussed in the area of social simulation. Practioneers from more engineering-oriented domains like traffic simulation or researchers from domains with long simulation background like theoretical biology or engineering found the techniques associated with agent-based simulation interesting, yet not mature enoughto actually apply them. Agent-based simulation definitely is a highly valuable tool, especially when studying complex self-organizing systems in many domains. Thus, the question arises, what shows the maturity of a simulation paradigm and how the achievement of a high level of applicability can be brought forward? The answer is basically that engineering-like development and some form of good practice have to be established. In particular, this leads to the following issues that have to be addressed for fostering the development of agent-based models. Deep understanding of the “object”, that means understanding of agent-based models themselvesand what particular feature is useful in what particular context. Development of best practice: Establishing knowledge about how to build an agent-basedmodel efficiently and in a way that costs can be a priori estimated. Until now, none of these items is solved in a satisfying way. However, they are necessarily achieved at least partially for improving the broad applicability of agent-based modeling and simulation. Steps leading to the general aim of this book – fostering the applicability of agent-based simulation – can be derived from these considerations. A basic prerequisite and therefore first step is collecting specific knowledge about agent-based simulation and the context of its appropriate application. This refers to properties of simulation questions and modeling targets as well as to theoretical and empirical requirements for model design, implementation and usage. The second step concerns the development of an agent-based simulation. Although the general process model for developing simulation models, presented in every simulation textbook, can also be applied for agent-based simulation, the problem goes deeper than just using an appropriate specification or implementation language. Agent-based simulations are generative. It is not jus tdescribing what was observed, but finding agent behavior and interactions that produce a particular phenomenon. This idea has several consequences ranging from missing micro-macro links over non-linear models and tendencies to full detail to several levels of validation. Thus, developing methods for bridging the gap between macro-level objectives and appropriate micro-level programs in a systematic and reproducible way is the challenge for agent-based simulation engineering. A third step must consider practical application of the theoretical foundations. Basically,learning how to model for simulation possesses the same characteristics as learning how to program software. One might read about language constructs, but how its actually working is only experience-able by doing it. Therefore, a detailed presentation of simulation models and theirconstruction has to be part of a book about simulation engineering. Thus, this book sums up experiences in methodological research and application of agent-based simulation, especially in modeling complex and self-organizing systems. This book is a further step towards systematic engineering of agent-based models involving appropriate meta-models, procedures for development, conceptual and technical design and validation of models. It bridges the gap between established techniques related to modeling and simulation and the approaches and requirements for complex agent-based simulation modeling.This is the Habilitation Thesis handed in at the University of Würzburg, Germany in 2009. It was accepted. The Venia Legendi in Computer Science was awarded to Franziska Klügl in January 2010.</p

"Activity"-based Modelling of Behaviour and its Support for Multi-Agent Simulation

Durch Zusammenführung traditioneller Methoden zur individuenbasierten Simulation und dem Konzept der Multiagentensysteme steht mit der Multiagentensimulation eine Methodik zur Verfügung, die es ermöglicht, sowohl technisch als auch konzeptionell eine neue Ebene an Detaillierung bei Modellbildung und Simulation zu erreichen. Ein Modell beruht dabei auf dem Konzept einer Gesellschaft: Es besteht aus einer Menge interagierender, aber in ihren Entscheidungen autonomen Einheiten, den Agenten. Diese ändern durch ihre Aktionen ihre Umwelt und reagieren ebenso auf die für sie wahrnehmbaren Änderungen in der Umwelt. Durch die Simulation jedes Agenten zusammen mit der Umwelt, in der er "lebt", wird die Dynamik im Gesamtsystem beobachtbar. In der vorliegenden Dissertation wurde ein Repräsentationsschema für Multiagentensimulationen entwickelt werden, das es Fachexperten, wie zum Beispiel Biologen, ermöglicht, selbständig ohne traditionelles Programmieren Multiagentenmodelle zu implementieren und mit diesen Experimente durchzuführen. Dieses deklarative Schema beruht auf zwei Basiskonzepten: Der Körper eines Agenten besteht aus Zustandsvariablen. Das Verhalten des Agenten kann mit Regeln beschrieben werden. Ausgehend davon werden verschiedene Strukturierungsansätze behandelt. Das wichtigste Konzept ist das der "Aktivität", einer Art "Verhaltenszustand": Während der Agent in einer Aktivität A verweilt, führt er die zugehörigen Aktionen aus und dies solange, bis eine Regel feuert, die diese Aktivität beendet und eine neue Aktivität auswählt. Durch Indizierung dieser Regeln bei den zugehörigen Aktivitäten und Einführung von abstrakten Aktivitäten entsteht ein Schema für eine vielfältig strukturierbare Verhaltensbeschreibung. Zu diesem Schema wurde ein Interpreter entwickelt, der ein derartig repräsentiertes Modell ausführt und so Simulationsexperimente mit dem Multiagentenmodell erlaubt. Auf dieser Basis wurde die Modellierungs- und Experimentierumgebung SeSAm ("Shell für Simulierte Agentensysteme") entwickelt. Sie verwendet vorhandene Konzepte aus dem visuellen Programmieren. Mit dieser Umgebung wurden Anwendungsmodelle aus verschiedenen Domänen realisiert: Neben abstrakten Spielbeispielen waren dies vor allem Fragestellungen zu sozialen Insekten, z.B. zum Verhalten von Ameisen, Bienen oder der Interaktion zwischen Bienenvölkern und Milbenpopulationen.In this thesis a representational scheme for multi-agent simulations was developed. This framework enables domain experts - e.g. biologists - to build models and carry out experiments without having to understand and use traditional programming languages. The resulting declarative framework is based on two concepts: the body of an agent can be modelled by a set of state variables. The behaviour of the agents can be described best by using rules. With this as a starting point various approaches for structuring the description are examined. The most important concept is the concept of "activity" - a kind of "behavioural state": While the agent is in a certain activity A, it carries out the sequence of actions that is associated with A - and continues with it until a rule fires thus terminating the activity A and selecting a new one. By indexing these rules at the activity they are terminating and by introducing abstract activities, a framework for behaviour modelling emerges that can be structured in multifarious ways. An interpreter executing this representation scheme was developed in order to allow simulation experiments with such a multi-agent model. This simulator was integrated into a modelling and simulation environment, named SeSAm ("Shell for Simulated Agent-Systems"). Using this framework several models in different application domains are implemented: They are ranging from simple games to complex models, especially of social insects - e.g. the behaviour of ants or bees or the interactions between bee hives and mite populations

Agent-Based Simulation Engineering

The history of agent-based models started in the 1970ies with singular yet path-breaking examples such as the Segregation model by T. Schelling [Schelling, 1971]. From end of the 80ies on more and more agent-based models were developed and implemented. However, almost no simulation engineering happened. Due to the relation to social sciences, mostly sociologists and psychologists used the paradigm of simulated humans based on rather complex models of human decision making to model hypotheses and theories about societal dynamics. The resulting models were complex but abstract. The role of empirical embeddedness is still discussed in the area of social simulation. Practioneers from more engineering-oriented domains like traffic simulation or researchers from domains with long simulation background like theoretical biology or engineering found the techniques associated with agent-based simulation interesting, yet not mature enoughto actually apply them. Agent-based simulation definitely is a highly valuable tool, especially when studying complex self-organizing systems in many domains. Thus, the question arises, what shows the maturity of a simulation paradigm and how the achievement of a high level of applicability can be brought forward? The answer is basically that engineering-like development and some form of good practice have to be established. In particular, this leads to the following issues that have to be addressed for fostering the development of agent-based models. Deep understanding of the “object”, that means understanding of agent-based models themselvesand what particular feature is useful in what particular context. Development of best practice: Establishing knowledge about how to build an agent-basedmodel efficiently and in a way that costs can be a priori estimated. Until now, none of these items is solved in a satisfying way. However, they are necessarily achieved at least partially for improving the broad applicability of agent-based modeling and simulation. Steps leading to the general aim of this book – fostering the applicability of agent-based simulation – can be derived from these considerations. A basic prerequisite and therefore first step is collecting specific knowledge about agent-based simulation and the context of its appropriate application. This refers to properties of simulation questions and modeling targets as well as to theoretical and empirical requirements for model design, implementation and usage. The second step concerns the development of an agent-based simulation. Although the general process model for developing simulation models, presented in every simulation textbook, can also be applied for agent-based simulation, the problem goes deeper than just using an appropriate specification or implementation language. Agent-based simulations are generative. It is not jus tdescribing what was observed, but finding agent behavior and interactions that produce a particular phenomenon. This idea has several consequences ranging from missing micro-macro links over non-linear models and tendencies to full detail to several levels of validation. Thus, developing methods for bridging the gap between macro-level objectives and appropriate micro-level programs in a systematic and reproducible way is the challenge for agent-based simulation engineering. A third step must consider practical application of the theoretical foundations. Basically,learning how to model for simulation possesses the same characteristics as learning how to program software. One might read about language constructs, but how its actually working is only experience-able by doing it. Therefore, a detailed presentation of simulation models and theirconstruction has to be part of a book about simulation engineering. Thus, this book sums up experiences in methodological research and application of agent-based simulation, especially in modeling complex and self-organizing systems. This book is a further step towards systematic engineering of agent-based models involving appropriate meta-models, procedures for development, conceptual and technical design and validation of models. It bridges the gap between established techniques related to modeling and simulation and the approaches and requirements for complex agent-based simulation modeling.This is the Habilitation Thesis handed in at the University of Würzburg, Germany in 2009. It was accepted. The Venia Legendi in Computer Science was awarded to Franziska Klügl in January 2010.</p

– Coherence and Coordination, Multiagent Systems General Terms Economics

An important prerequisite for traffic management is to find efficient ways to model and predict traffic flow. Here we are presenting a naïve model for the route choice adaptation of learning commuters with heuristics based behaviour. Our simulation results show that the heuristics learnt lead to a situation similar to that obtained in real experiments

Approaches for resolving the dilemma between model structure refinement and parameter calibration in agentbased simulations

Agent-based simulations form a valuable tool for learning about real world societies and global behaviors of systems emerging from microscopic relationships. Calibration of model parameters for detailed agent-based models is a big problem for standard calibration techniques, due to the large parameter search spaces, long simulation run times, uncertainties in the structural model design and different observation levels upon which the model needs to be calibrated. In this paper we propose several methods to improve the calibration process of agent-based simulations