Elmar Kiesling

Agent-based simulation of innovation diffusion: a review

Mathematical modeling of innovation diffusion has attracted strong academic interest since the early 1960s. Traditional diffusion models have aimed at empirical generalizations and hence describe the spread of new products parsimoniously at the market level. More recently, agent-based modeling and simulation has increasingly been adopted since it operates on the individual level and, thus, can capture complex emergent phenomena highly relevant in diffusion research. Agent-based methods have been applied in this context both as intuition aids that facilitate theory-building and as tools to analyze real-world scenarios, support management decisions and obtain policy recommendations. This review addresses both streams of research. We critically examine the strengths and limitations of agent-based modeling in the context of innovation diffusion, discuss new insights agent-based models have provided, and outline promising opportunities for future research. The target audience of the paper includes both researchers in marketing interested in new findings from the agent-based modeling literature and researchers who intend to implement agent-based models for their own research endeavors. Accordingly, we also cover pivotal modeling aspects in depth (concerning, e.g., consumer adoption behavior and social influence) and outline existing models in sufficient detail to provide a proper entry point for researchers new to the field.

A multicriteria decision support system for competence-driven project portfolio selection

The systematic and proactive development of human resources is of major importance in organizations that rely heavily on the competencies of their employees when engaging in innovative endeavors. Human capital, however, is not only a resource required for conducting research, but also the eventual result of that research. When selecting a research portfolio, the decision-maker (DM) thus needs to take into consideration both current and future competence requirements, as well as other financial and nonfinancial objectives and constraints. We introduce a proper multicriteria decision support system (MCDSS) that first determines the set of Pareto-efficient solutions and then allows the DM to interactively filter and/or explore this set in various ways. Its practical application is demonstrated by means of a showcase at the Electronic Commerce Competence Center (EC3) in Vienna, Austria.

An agent-based simulation model for the market diffusion of a second generation biofuel

Second generation biofuels are widely considered a promising energy alternative to conventional (fossil) fuels. Although they will not completely replace fossil fuels (e.g., due to the limited availability of biomass), these high-quality biofuels can contribute to reducing emissions and strengthening a countrys energy autonomy. In Austria, a team at the Vienna University of Technology is developing a biomass-to-liquid technology based on the Fischer-Tropsch synthesis. While the remaining technical obstacles are expected to be overcome in due time, the market introduction of the novel biofuel also requires substantial investments. In this context, we developed an agent-based simulation model that can provide potential investors with forecasts for the biofuels market diffusion. This paper describes the model and presents simulation results.

An Experimental Comparison of Two Interactive Visualization Methods for Multicriteria Portfolio Selection

We compare two visualization methods for interactive portfolio selection: heatmaps and parallel coordinates. To this end, we conducted an experiment to analyze differences in terms of subjective user evaluations and in terms of objective measures referring to effort, convergence, and the structure of the search process. Results indicate that subjects who used the parallel coordinates visualization found the method easier to use, perceived the selection process as being less effortful, and experienced less decisional conflict than subjects who used the heatmap visualization. Concerning objective measures, we did not find significant differences in the time taken to complete the selection task. However, we found that subjects who used parallel coordinates engaged in a more exploratory approach when investigating the space of efficient portfolios. Finally, the experiments clearly showed that decision-making styles play an important role in users’ attitude toward the visualization method. Our findings suggest that the choice of visualization method has a considerable impact on both the users’ subjective experiences when using a decision support system for portfolio selection, and on their objective performance.

Game-based learning in technology management education

Management games allow students to obtain valuable first-hand experience that is of particular value in a field such as technology management, which usually involves substantial risk as well as significant time lags between a managerial decision and its effects. In drawing from more than fifteen years of experience with management games in higher education, we have developed a management simulation for teaching technology management, a field that is increasingly regarded an important area of study for engineering students. This paper outlines a blended-learning design for a course on technology management, describes the various didactical elements as well as their effects in class, and addresses the technical implementation of the management game as a service-oriented multi-tier application in Java.

A Spatial Simulation Model For The Diffusion Of A Novel Biofuel On The Austrian Market.

To ensure long-term security of energy supply and to mitigate climate change, sustainable low-carbon alternatives to fossil fuels are needed. Second generation biomassto-liquid biofuels provide such an alternative and are widely considered a promising technology that may overcome the environmental and economic problems associated with first generation biofuels. In Austria, a research group at the Vienna University of Technology is developing such a biofuel based on Fischer-Tropsch synthesis. While the remaining technical challenges are expected to be overcome in due time, the market introduction also requires substantial investments. In this work, we introduce an agent-based simulation model that can provide potential investors with forecasts for the biofuel’s market diffusion. The model considers initial and repeat purchases, multiple competing products, and the spatial dispersion of both consumers and potential points of sale. Given limited production capacity, the latter feature supports decision makers in choosing (initial) points of sale with respect to rich sources of biomass as well as the geographic concentration of consumers.

Taming the logs - Vocabularies for semantic security analysis

Abstract Due to the growing complexity of information systems and the increasing prevalence and sophistication of threats, security management has become an enormously challenging task. To identify suspicious activities, security analysts need to monitor their systems constantly, which involves coping with high volumes of heterogeneous log data from various sources. Processes to aggregate these disparate logs and trigger alerts when particular events occur are often automated today. However, these methods are typically based on regular expressions and statistical correlations and do not involve any interpretation of the context in which an event occurred and do not allow for inference or sophisticated rules. Inspection and in-depth analysis of log information to link events from various sources (e.g., firewall, syslog, web server log, database log) and establish causal chains has therefore largely remained a tedious manual search process that scales poorly with a growing number of heterogeneous log sources, log volumes, and the increasing complexity of attacks. In this paper, we make the case for a semantic approach to tackle these challenges. By lifting raw log data and modeling their context, events can be linked to rich background knowledge, integrated based on causal relations, and interpreted in a context-specific manner. This builds a foundation for more comprehensive extraction of the meaning of events from unstructured log messages. Based on the results, we envision a platform to partly automate security monitoring and support analysts in coping with fast evolving threat landscapes, alleviate alert fatigue, improve situational awareness, and expedite incidence response.

Linked data processing provenance: towards transparent and reusable linked data integration

The growth of Linked Data has created a promising environment for data exploration and a growing number of tools allow users to interactively integrate data from various sources. Eliciting the reliability of the results of such ad-hoc integration processes, consistently recreating those results, and identifying changes upon re-execution, however, can be difficult. Automated process provenance trail creation can provide major benefits in this context, because (i) it enables users to trace the contribution of individual sources and processing steps to the final outcome and judge whether the result can be trusted; (ii) it ensures repeatability and raises the trustworthiness of results; (iii) it ideally enables reconstruction of Linked Data integration processes from the provenance information embedded in the final result. In this paper, we present a provenance model that facilitates automatic generation of semantic provenance information for generic Linked Data integration processes. We implement the generic model in a collaborative mashup environment and evaluate it by means of an example application. We find that the model provides a solid foundation for verifiability and contributes towards making Linked Data integration processes more open, transparent, and reusable, which is crucial in domains where the origin of data is essential, such as, for instance, statistical analyses, scientific research, and data journalism.