Programmable graphics processing units (GPUs) have emerged as excellent computational platforms for certain general-purpose applications. The data parallel execution capabilities of GPUs specifically point to the potential for effective use in simulations of agent-based models (ABM). In this paper, the computational efficiency of ABM simulation on GPUs is evaluated on representative ABM benchmarks. The runtime speed of GPU-based models is compared to that of traditional CPU-based implementation, and also to that of equivalent models in traditional ABM toolkits (Repast and NetLogo). As expected, it is observed that, GPUbased ABM execution affords excellent speedup on simple models, with better speedup on models exhibiting good locality and fair amount of computation per memory element. Execution is two to three orders of magnitude faster with a GPU than with leading ABM toolkits, but at the cost of decrease in modularity, ease of programmability and reusability. At a more fundamental level, however, the data parallel paradigm is found to be somewhat at odds with traditional modelspecification approaches for ABM. Effective use of data parallel execution, in general, seems to require resolution of modeling and execution challenges. Some of the challenges are identified and related solution approaches are described.

Abstract not found

An effective Decision Support System (DSS) should help its users improve decision-making in complex, information-rich, dynamic environments. We present a feature gap analysis of current decision support technologies, and we identify a set of DSS Desiderata, properties that can contribute both effectiveness and flexibility to users in such environments. We show that there is a gap between the features provided by current DSS technologies and the DSS Desiderata we aim for. We present a design-science approach that extends the boundaries of human decisionmakers by creating a new and innovative artifact called “evaluator service networks ” at the confluence of people, organizations, and technology. Our artifact enables users to compose decision behaviors from separate, configurable components, and allows dynamic construction of analysis and modeling tools from small, single-purpose evaluator services. The result is a network that can easily be configured to test hypotheses and analyze the impact of various choices for elements of decision processes. We have implemented and tested this design in an interactive version of the MinneTAC trading agent, an agent designed for the Trading Agent Competition for Supply Chain Management. We present an example of an evaluator service network that determines sales prices in a rich, dynamic trading environment. Additionally we describe visual interface elements that allow users to see and manipulate the configuration of the network, and to construct economic dashboards that can display the current and historical state of any node in the network.

Indoor position estimation constitutes a central task in home-based assisted living environments. Such environments often rely on a heterogeneous collection of low-cost sensors whose diversity and lack of precision has to be compensated by advanced techniques for localization and tracking. Although there are well established quantitative methods in robotics and neighboring fields for addressing these problems, they lack advanced knowledge representation and reasoning capacities. Such capabilities are not only useful in dealing with heterogeneous and lacking information but moreover they allow for a better inclusion of semantic information and more general homecare and patient-related knowledge. We address this problem and investigate how state-of-theart localization and tracking methods can be combined with answer set programming, as a popular knowledge representation and reasoning formalism. We report upon a case-study and provide a first experimental evaluation of knowledge-based position estimation. 1

Objective: This research is designed to present a methodological approach for problems encountered with cockpits perspective to collaborate decision making in order to support the Air Transport Management (ATM) with simulation software development and system behaviour simulation. Background: Collaborative decision making (CDM) is a means to challenge punctuality and reliability issues at congested airports in order to raise operational capacity at the airport and air space structure, not by sophisticated optimization algorithms rather than just information sharing and common situational awareness. Initial concepts of CDM emphasize the importance of global collaboration among interactions between airport operators, air traffic control (ATC), airlines, ground handling service providers and the central flow management unit (CFMU). From cockpits perspective, CDM should be considered as a means to allow participation of flight crews in air-traffic decision making that affects them. This applies to all decisions ranging from operational to safety issues during all phases of flight / ground operation. From cockpits perspective, two main issues should be addressed: • How can information sharing / common situational awareness between flight crews and

Agents offer a new and exciting way of understanding the world of work. In this paper we describe the development of agent-based simulation models, designed to help to understand the relationship between people management practices and retail performance. We report on the current development of our simulation models which includes new features concerning the evolution of customers over time. To test the features we have conducted a series of experiments dealing with customer pool sizes, standard and noise reduction modes, and the spread of customers ’ word of mouth. To validate and evaluate our model, we introduce new performance measure specific to retail operations. We show that by varying different parameters in our model we can simulate a range of customer experiences leading to significant differences in performance measures. Ultimately, we are interested in better understanding the impact of changes in staff behavior due to changes in store management practices. Our multi-disciplinary research team draws upon expertise from work psychologists and computer scientists. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents offer potential for fostering sustainable organizational capabilities in the future.

the easyABMS methodology in the logistics domain

Abstract. In this paper, we introduce an integration study which combines Data Mining (DM) and Agent Based Modeling and Simulation (ABMS). This study, as a new paradigm for DM/ABMS, is concerned with two approaches: (i) applying DM techniques in ABMS investigation, and inversely (ii) utilizing ABMS results in DM research. Detailed description of each approach is presented in this paper. A conclusion and the future work of this (integration) study are given at the end.

� Agent-‐based modeling and simula4on (ABMS) is a new approach to modeling systems composed of autonomous, interac4ng agents. Computa4onal advances, new modeling know-‐how, and specialized agent-‐based modeling toolkits have enabled the development of agent-‐based models spanning the full range of applica4on domains. Applica4ons range from modeling trader behavior in the stock market to modeling consumer purchasing decisions, from predic4ng the possible spread of an H1N1 epidemic to modeling the immune system at the cellular level, from modeling the behavior and gene4c evolu4on of microbes to iden4fying plausible causes of the fall of ancient civiliza4ons, from modeling the predator-‐ prey rela4onship between killer whales and other marine mammals to assessing the viability of new markets for space tourism, from building whole economies of agents “from the bo]om-‐up ” to modeling military units opera4ng in urban environments, and many more. � Such progress suggests that ABMS could have the poten4al to have far-‐reaching impact on the use of models, whether the impact is on business and government use of computers to support decision-‐ making and policy analysis or whether it is on scien4sts ’ use of agent-‐based models as electronic laboratories for extended experimenta4on beyond what is possible in the tradi4onal laboratory

In recent years the interdisciplinary field of computational social science (CSS) has developed theory and research for developing spatial “agent-based ” social simulation (SABSS) models of human societies situated in ecosystems that often comprise land cover and climate. This paper explains the needs and demand for GIS in these types of agent-based models, with an emphasis on models applied to Eastern Africa and Inner Asia and relevance for understanding and analyzing development issues. The models are based on MASON (Multi-Agent Simulator Of Networks and Neighborhoods), an open-source simulation environment in the Java language and suitable for developing ABSS models with GIS for representing spatial features.