Agent Based Modeling and Simulation: An Informatics Perspective

The term computer simulation is related to the usage of a computational model in order to improve the understanding of a system's behavior and/or to evaluate strategies for its operation, in explanatory or predictive schemes. There are cases in which practical or ethical reasons make it impossible to realize direct observations: in these cases, the possibility of realizing 'in-machina' experiments may represent the only way to study, analyze and evaluate models of those realities. Different situations and systems are characterized by the presence of autonomous entities whose local behaviors (actions and interactions) determine the evolution of the overall system; agent-based models are particularly suited to support the definition of models of such systems, but also to support the design and implementation of simulators. Agent-Based models and Multi-Agent Systems (MAS) have been adopted to simulate very different kinds of complex systems, from the simulation of socio-economic systems to the elaboration of scenarios for logistics optimization, from biological systems to urban planning. This paper discusses the specific aspects of this approach to modeling and simulation from the perspective of Informatics, describing the typical elements of an agent-based simulation model and the relevant research.Multi-Agent Systems, Agent-Based Modeling and Simulation

The GI-Learner Approach: Learning Lines for Geospatial Thinking in Secondary Schools

This paper introduces basic considerations that inform education for geospatial thinking, as proposed in the KA2 Erasmus Plus GI-Learner project. It reports on some initial state-of-the-art activities of the project, presents a list of GI-Learner competences based on a broad literature review and establishes a roadmap for future support activities for geospatial learning

A critical cluster analysis of 44 indicators of author-level performance

This paper explores the relationship between author-level bibliometric indicators and the researchers the "measure", exemplified across five academic seniorities and four disciplines. Using cluster methodology, the disciplinary and seniority appropriateness of author-level indicators is examined. Publication and citation data for 741 researchers across Astronomy, Environmental Science, Philosophy and Public Health was collected in Web of Science (WoS). Forty-four indicators of individual performance were computed using the data. A two-step cluster analysis using IBM SPSS version 22 was performed, followed by a risk analysis and ordinal logistic regression to explore cluster membership. Indicator scores were contextualized using the individual researcher's curriculum vitae. Four different clusters based on indicator scores ranked researchers as low, middle, high and extremely high performers. The results show that different indicators were appropriate in demarcating ranked performance in different disciplines. In Astronomy the h2 indicator, sum pp top prop in Environmental Science, Q2 in Philosophy and e-index in Public Health. The regression and odds analysis showed individual level indicator scores were primarily dependent on the number of years since the researcher's first publication registered in WoS, number of publications and number of citations. Seniority classification was secondary therefore no seniority appropriate indicators were confidently identified. Cluster methodology proved useful in identifying disciplinary appropriate indicators providing the preliminary data preparation was thorough but needed to be supplemented by other analyses to validate the results. A general disconnection between the performance of the researcher on their curriculum vitae and the performance of the researcher based on bibliometric indicators was observed.Comment: 28 pages, 7 tables, 2 figures, 2 appendice

Theory borrowing in IT-rich contexts : lessons from IS strategy research

While indigenous theorizing in information systems has clear merits, theory borrowing will not, and should not, be eschewed given its appeal and usefulness. In this article, we aim at increasing our understanding of modifying of borrowed theories in IT-rich contexts. We present a framework in which we discuss how two recontextualization approaches of specification and distinction help with increasing the IT-richness of borrowed constructs and relationships. In doing so, we use several illustrative examples from information systems strategy. The framework can be used by researchers as a tool to explore the multitude of ways in which a theory from another discipline can yield the understanding of IT phenomena

Closing the Loop of Big Data Analytics: the Case of Learning Analytics

Much of the literature on business analytics assumes a straightforward relationship from human behaviour to data and from data to analytical insights that can be used to steer operations. At the same time, more critical scholars have suggested that the implications of big data analytics can go beyond improved decision making, sometimes twisting or even undermining managerial efforts. We adapt a theory of reactivity, originally developed to study university rankings, to identify various unintended effects of the application of big data analytics in an organizational setting. More specifically, we study the perceptions of a sophisticated learning analytics system among staff mem-bers of an internationally recognized business school. We find evidence for four reactive effects: re-allocation of resources, change in values, redefinition of work and practices, and gaming, and map these to four underlying reactive mechanisms: commensuration, self-fulfilling prophecies, reverse engineering and narratives. The study contributes toward theoretically broader, but also more practical understanding of big data analytics: reactivity may dilute the methodological validity of analytics to describe organisational and business environment for managerial purposes, yet the understanding of reactive effects makes a more potent use of analytics possible in organisational settings

Weaving Lines of Inquiry: Promoting Transdisciplinarity as a Distinctive Way of Undertaking Sport Science Research

Abstract: The promotion of inter- and multidisciplinarity — broadly drawing on other disciplines to help collaboratively answer important questions to the field — has been an important goal for many professional development organisations, universities, and research institutes in sport science. While welcoming collaboration, this opinion piece discusses the value of transdisciplinary research for sports science. The reason for this is that inter- and multidisciplinary research are still bound by disciplinary convention — often leading sport science researchers to study about a phenomenon based on pre-determined disciplinary ways of conceptualising, measuring, and doing. In contrast, transdisciplinary research promotes contextualised study with a phenomenon, like sport, unbound by disciplinary confines. It includes a more narrative and abductive way of performing research, with this abduction likely opening new lines of inquiry for attentive researchers to follow. It is in the weaving of these lines where researchers can encounter new information, growing knowledge in-between, through, and beyond the disciplines to progressively entangle novel and innovative insights related to a phenomenon or topic of interest. To guide innovation and the development of such research programmes in sport science, we lean on the four cornerstones of transdisciplinarity proposed by Alfonso Montuori, exemplifying what they could mean for such research programmes in sport science

Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts

Presenting a predictive model's performance is a communication bottleneck that threatens collaborations between data scientists and subject matter experts. Accuracy and error metrics alone fail to tell the whole story of a model - its risks, strengths, and limitations - making it difficult for subject matter experts to feel confident in their decision to use a model. As a result, models may fail in unexpected ways or go entirely unused, as subject matter experts disregard poorly presented models in favor of familiar, yet arguably substandard methods. In this paper, we describe an iterative study conducted with both subject matter experts and data scientists to understand the gaps in communication between these two groups. We find that, while the two groups share common goals of understanding the data and predictions of the model, friction can stem from unfamiliar terms, metrics, and visualizations - limiting the transfer of knowledge to SMEs and discouraging clarifying questions being asked during presentations. Based on our findings, we derive a set of communication guidelines that use visualization as a common medium for communicating the strengths and weaknesses of a model. We provide a demonstration of our guidelines in a regression modeling scenario and elicit feedback on their use from subject matter experts. From our demonstration, subject matter experts were more comfortable discussing a model's performance, more aware of the trade-offs for the presented model, and better equipped to assess the model's risks - ultimately informing and contextualizing the model's use beyond text and numbers

Picturing Currere : envisioning-experiences within learning

Currere reconceptualises curriculum as understanding learning experiences. This paper outlines potentials for pictures in re-envisioning currere for a world growing in complexity.Vision, as the privileged sense for acquiring knowledge, is often regarded deterministically - seeing is believing. But, as sophisticated technology becomes more significant in the control of knowledge systems - as \u27reality\u27 becomes more virtual - personal visual experiences are becoming harder to generate, interpret and authenticate. Knowledge technology is increasing exposure to \u27mediated messages\u27 and diminishing the ability to validate them. I see this as problematic for the authenticity of learning within a world that is intensifying in its complexity.My work reaches beyond determinist/technological views of learning to explore envisioning-experiences within learning. I am particularly interested in ways that enacting with pictures embodies individuals, communities, and the world within understandings of complexity and authenticity. In practical terms, this involves interactively and reflexively \u27doing pictures\u27 as a personal process in learning for deeper understanding.The paper explores three issues: * Text and pictures: learning, thinking, and knowing, as textual dominions that marginalise pictures. * Enactivism and learning: an approach to learning for complex communities that embodies mindful thinking within haptic experiences. * Enactive picturing: laying a personal processual path with learning that complexifies understandings for authenticating experiences - doing-walking-talking - with pictures.<br /