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Social scientists associate agent-based simulation (ABS) models with three ideas about explanation: they provide generative explanations, they are models of mechanisms, and they implement methodological individualism. In light of a philosophical account of explanation, we show that these ideas are not necessarily related and offer an account of the explanatory import of ABS models. We also argue that their bottom-up research strategy should be distinguished from methodological individualism.

Das vorliegende Papier dokumentiert die zugrundeliegenden Konzepte und Methoden des Räumlichen Agenten-basierten Wettbewerbsmodells (Spatial Agent-based Competition Model) SpAbCoM. Anwendungsbeispiele dieses Simulationsmodells untersuchen die Entscheidung bezüglich der räumlichen Preisstrategie von Unternehmen (GRAUBNER et al., 2011a) oder Preissetzung und Standortwahl im Rahmen eines räumlichen Wettbewerbsmodells, welches mehr als einen Wettbewerber und zweidimensionalen Marktgebiete berücksichtigt. Während das Simulationsmodell in den jeweiligen Arbeiten kurz anhand eines Beispiels eingeführt wird, dient das vorliegende Papier zwei Zielen. Zum Einen sollen die verwendeten computergestützten Konzepte, hier speziell Genetische Algorithmen (GA), detailliert vorgestellt werden. Zum Anderen besteht die Absicht dieser Dokumentation darin, einen Überblick über die Struktur von SpAbCoM und die während einer Simulation ablaufenden Prozesse zu gegeben. -- Agent-basierte Modellierung ; Genetische Algorithmen ; räumliche Preissetzung ; Standortmodell.

I develop an agent-based computational economics (ACE) model with which I evaluate the aggregate impact of labor market policies. The findings are that governmentfinanced training measures increase the outflow rate from unemployment to employment. Although the overall effect is positive this effect is achieved by reducing the outflow rate for those who do not receive subsidies. Furthermore, the outflow rate would have been downward-biased had one supposed a matching function that is exogenous to policies. ; Im Folgenden wird ein agenten-basiertes Modell entwickelt, mit dem die aggregierten Wirkungen von Arbeitsmarktpolitiken evaluiert werde können. Ein Resultat ist, dass die Subvention von Trainingsmaßnahmen die Übergangsrate von Arbeitslosigkeit in Beschäftigung erhöht. Obwohl der Gesamteffekt positiv ist, reduziert sich die Übergangsrate für all jene Arbeitslose, deren Ausgaben nicht subventioniert werden. Der Verdrängungseffekt ist bei einer plausiblen Parametrisierung des Modells in seiner Höhe ökonomisch relevant. Ferner wäre die Messung der Übergangsrate aus Arbeitslosigkeit in Beschäftigung nach unten verzerrt gewesen, hätte man in der Wirkungsanalyse angenommen, dass die Matching-Funktion exogen zu den Arbeitsmarktpolitiken ist.

The significant progress observed in the field of artificial economy opens up new possibilities for modeling economic growth. Agent-based models (ABM) allow leaving the concept of a representative agent in the past and linking investment decisions of economic agents at the micro level with long-term macroeconomic growth. Modern ABMs offer new algorithms for modeling expectations, agent interaction, technical progress, pricing, and production planning. Our article analyzes the current state of modeling investment in fixed assets in operating macroeconomic ABMs. The subject of the review is the families of models Eurace, CATS, KS, Jamel, Lagom. The authors also present the investment block of the agent-based multiregional input-output model (ABMIOM) being developed. Comparative analysis demonstrates that modern ABMs, as a rule, implement the principle of stock-flow consistency. Modeling the investment process requires detailing the commodity nomenclature, so that the initially adopted two-sector division into investment and consumer goods is replaced by more detailed structures, which gives rise to the problem of accounting for inter-sectoral relations in production and consumption. The Leontief production function copes with this problem, which is confirmed by its widespread use in ABM. The size of firms' investments is often derived from the need to expand capacity in accordance with the current production plan, so that planning turns out to be myopic, and long-term aspects in ABM are still largely unrealized. Nevertheless, already now ABMs reproduce many phenomena associated with the economic cycle. The developed ABMIOM provides horizontal consistency of cash flows between agents and analysis of results using input-output tables. ABMIOM represents a step forward in reflecting intersectoral and interregional flows. The model reproduces the growth and contraction of the economy as a result of independent investment decisions of individual firms and households, which is reflected in the sectoral and spatial structure of the economy. Further development of ABMIOM is associated with the modeling of savings, intrafirm finance, money market, innovation and technical progress.

This paper presents an agent-based model explaining voter knowledge in the context of electoral competition. It shows that a set of simple behavioral rules implemented by voters, parties and media outlets generates novel (and testable) predictions regarding the mass-mediated underpinnings of aggregated voter knowledge and party representativeness. More specifically, it finds that increasing competition among media outlets has a positive effect on the political knowledge of the electorate at large. It also finds that increasing media competition leads to parties that are more accountable to the median voter, but only when voters care about the quality of the news alone.

This thesis explores how multi-agent-based simulation can be used for transport policy analysis. Transport policies are often used as a means to reach governmental goals, such as environmental targets to reduce the impact of transportation. To predict how transportation is influenced by policies, public authorities often make use of simulation models. A structured review of such models is made focussing on important transport chain characteristics. We argue that to properly predict the actual environmental, economic, and logistical effects of transport policies, the logistical decisions made in transport chains must be modelled appropriately. Such decisions, e.g., concern the choice of producer and traffic mode, planning of transportation, production, and terminal handling. The review concludes that models currently used for transport policy analysis fail to capture many of these characteristics. We argue that agent-based models have the potential to include these aspects since they are able to explicitly model the actual decision making in transport chains. We have identified a set of generic roles in transport chains where each role is responsible for certain decisions. A multi-agent-based simulator, TAPAS, has been developed in which these roles are modelled as agents. Thus, the decision making in transport chains and its influence by the application of transport policies are captured. The decisions lead to the execution of the logistical operations which in turn have consequences on the logistics, economic, and environmental performance. The usage of TAPAS is illustrated by presenting two scenarios based on realworld transport chains. Simulation experiments of the scenarios have been performed where different types of transport policies are introduced. The simulation results are analysed, e.g., by comparing the results to similar studies and by sensitivity analysis of input parameters. To facilitate the validation and generalisation of simulation results we suggest making use of typical transport chains and roles characterised by, e.g., product type and geographical locations. The type of studies that TAPAS can support are described and compared to studies typically made with traditional models. Transport policies which are relevant to examine are described and their potential influence on transport chains are analysed. The possible usage of TAPAS is discussed and related to different types of users. Public authorities can, e.g., use TAPAS to complement studies using traditional models. This can improve the accuracy of the simulation results by the inclusion of more logistical aspects. Large companies are another type of user which, e.g., can use TAPAS to analyse new market segments, such as new product types or new consumers, where historical data is not available.

Current approaches to build social networking systems are based on a centralized architecture because it allows a simple browser-based user experience and makes easier and more efficient to implement many algorithms used in a social networking site (e.g., friend suggestion), However this kind of architecture has many drawbacks for its users, e.g., lack of privacy, lack of anonymity, risks of censorship and operating costs. This paper presents a system, called Blogracy, which uses widespread and stable peer-to-peer technologies, such as distributed hash tables and BitTorrent, for coping with intrinsic defects of centralized architectures and for being the basis of solid distributed social networking platforms. Moreover, Blogracy takes advantages of multi-agent systems for simplifying the implementation of social network services in a decentralized setting.

from Scythe : Proceedings and Bulletin of the International Data Farming Community, Issue 1 Workshop 13 ; Agent-based models have recently gained in popularity for modelling military operations. They purposefully leave out detailed physical attributes of the military entities concerned if this is not expected to have any bearing on the study at hand. This allows scenarios to be run relatively fast, over many excursions in order to discover unique situations or tactics where friendly forces can achieve dominance over an enemy. Another key feature of agent-based models is that, although the one-to-one interaction between various agents and their environment may be quite simple, the combined effect of many agents interacting can lead to complicated group dynamics and emergent behaviour. In this regard, agent-based models have the potential to represent the more chaotic and intangible aspects of military conflicts.

Tesis Doctoral le?da en la Universidad Rey Juan Carlos de Madrid en 2013. Director de la Tesis: Sascha Ossowski ; Governments around the globe are heavily investing in upgrading the ageing infrastructure of the electricity grid. The imperative for this is driven primarily by regulatory requirements and the high cost of inefficiently delivering energy. The infrastructure is continuously improving, as more and more smart meters are installed, coupled with the proliferation of controllable loads and distributed generation. However, network operators, utilities, as well as end-consumers and small-scale producers are struggling to extract value from such systems and are exploring new ways for optimizing the performance of their deployed assets. This thesis introduces a multiagent approach for modelling the emerging complexity of the energy industry. The multiagent system paradigm is an ideal candidate for delivering a framework that captures the inherent distributed and dynamic nature of smart grids. While the traditionally centralized management of the system becomes less viable in the context of distributed generation and controllable loads, the underlying thread of this thesis advocates the design and implementation of coordination mechanisms capable to integrate and manage a large-scale integration of such devices via agent-based control. We begin by proposing dynamic micro-grids, a new conceptual organization of the network, adequate to integrate today?s traditional users into an interactive, internetlike system, in the sense that power flow will become bidirectional and energy management will become distributed in the grid due to the many actors involved in the operation of the system. The mechanisms proposed for micro-grid formation are oriented towards producing sub-systems of the grid that are exhibiting reduced transmission losses and an efficient utilization of renewables, as well as endowing the system with self-adaptation techniques for coping with dynamic environments. We further aim to enhance the ...

Land use activity is a major issue and challenge for town and country planners. Modelling and managing urban growth is a complex problem. Cities are now recognised as complex, non-linear and dynamic process systems. The design of a system that can handle these complexities is a challenging prospect. Local governments that implement urban growth models need to estimate the amount of urban land required in the future given anticipated growth of housing, business, recreation and other urban uses within the boundary. There are so many negative implications related to the type of inappropriate urban development, such as increased traffic and demand for mobility, reduced landscape attractiveness, land use fragmentation, loss of biodiversity and alterations of the hydrological cycle. The aim of this study was to use an agent-based model as a powerful tool for simulating urban growth patterns. Our study area was Sanandaj city located in the west of Iran. Landsat imageries acquired in 2000 and 2006 were used. The dataset used included distance to principle roads, distance to residential areas, elevation, slope, distance to green spaces and distance to region centres, land price and distance to fault. In this study, an appropriate methodology for urban growth modelling using satellite remotely sensed data was presented and evaluated. Percent correct match (PCM), figure of merit and kappa statistics were used to evaluate the simulation results. DOI: http://dx.doi.org/10.5755/j01.erem.71.2.11504