On the Formal Verification of Diffusion Phenomena in Open Dynamic Agent Networks

International audienceThe paper is a contribution at the interface of social network theory and multi-agent systems. As realistic models of multi-agent systems, we assume agent networks to be open, that is, agents may join or leave the network at run-time, and dynamic, that is, the network structure may change as a result of agents actions. We provide a formal model of open dynamic agent networks (ODAN) in terms of interpreted systems, and define the problem of model checking properties of diffusion phenomena, such as the spread of information or diseases, expressed in a first-order version of computation-tree logic. We establish the decidability of the model checking problem by showing that, under specific conditions, the verification of infinite-state ODAN can be reduced to model checking finite bisimulations

Overview on agent-based social modelling and the use of formal languages

Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft

The diffusion of knowledge in industrial districts and clusters

ABSTRACT The dissemination of knowledge in industrial districts (ID) and clusters has often been linked to the existence of a specific tacit knowledge. Thus, the companies belonging to ID specialization sector might sustain a distinctive competitive advantage against isolated firms. However, the observation of technological changes in recent decades and the presence of ID whose technological intensity has dramatically increased in the same period suggest the existence and need for codified knowledge in these agglomerations. As result of tacit knowledge decline, the economic performance of ID could move backwards, given the greater ease to imitate and reproduce their contextual knowledge by competitor firms located in not district areas. The paper discusses the above assumptions, suggesting the existence of combinations/hybridizations of both types of knowledge in ID, which we have named locational-translational knowledge. This third type of knowledge could explain the maintenance of ID contextual advantages even in presence of higher doses of codified knowledge. This would require the presence of agents acting as interfaces able to absorb new pieces of codified knowledge in order to combine them with local knowledge for adjusting the specific needs of ID. However, we argue the existence of several constraints, such as the size of 'creative market district’, in ID which may require the opening of ID to knowledge imported from academic institutions and other formal research organizations, in contrast with autarky or isolation suggested by tacit knowledge. Finally, an analysis of the ID evolution enables us to appreciate that the process of absorption, combination and dissemination of external knowledge may have existed throughout the life cycle of ID but supported, at each stage, for different institutional agents: the 'impannatore', the 'cappofiliera' firm and, lastly, for formal knowledge-oriented institutions such as the above referred.

Computational modelling and simulations in tourism: A primer

Abstract The aim of this contribution is to briefly sketch and discuss the main issues that concern the activities of modelling and simulating complex phenomena and systems. The focus is on numerical and computational techniques. We discuss the validity of these methods and examine the different steps to be taken for ensuring a correct, accurate and reliable implementation. The approach is essentially of general methodological nature, regardless of specific techniques or tools

Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations

The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded. The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded

To boldly go:an occam-π mission to engineer emergence

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 paper describes a process-oriented architecture for fine-grained concurrent systems that enables experiments with such indirect engineering. Examples are presented showing the differing complex behaviours that can arise from minor (non-linear) adjustments to low-level parameters, the difficulties in suppressing the emergence of unwanted (bad) behaviour, the unexpected relationships between apparently unrelated physical phenomena (shown up by their separate emergence from the same primordial process swamp) and the ability to explore and engineer completely new physics (such as force fields) by their emergence from low-level process interactions whose mechanisms can only be imagined, but not built, at the current time

Errors and Artefacts in Agent-Based Modelling

The objectives of this paper are to define and classify different types of errors and artefacts that can appear in the process of developing an agent-based model, and to propose activities aimed at avoiding them during the model construction and testing phases. To do this in a structured way, we review the main concepts of the process of developing such a model – establishing a general framework that summarises the process of designing, implementing, and using agent-based models. Within this framework we identify the various stages where different types of errors and artefacts may appear. Finally we propose activities that could be used to detect (and hence eliminate) each type of error or artefact.Verification, Replication, Artefact, Error, Agent-Based Modelling, Modelling Roles