Model Checking Games for the Quantitative mu-Calculus

We investigate quantitative extensions of modal logic and the modal mu-calculus, and study the question whether the tight connection between logic and games can be lifted from the qualitative logics to their quantitative counterparts. It turns out that, if the quantitative mu-calculus is defined in an appropriate way respecting the duality properties between the logical operators, then its model checking problem can indeed be characterised by a quantitative variant of parity games. However, these quantitative games have quite different properties than their classical counterparts, in particular they are, in general, not positionally determined. The correspondence between the logic and the games goes both ways: the value of a formula on a quantitative transition system coincides with the value of the associated quantitative game, and conversely, the values of quantitative parity games are definable in the quantitative mu-calculus

Model Checking the Quantitative mu-Calculus on Linear Hybrid Systems

We study the model-checking problem for a quantitative extension of the modal mu-calculus on a class of hybrid systems. Qualitative model checking has been proved decidable and implemented for several classes of systems, but this is not the case for quantitative questions that arise naturally in this context. Recently, quantitative formalisms that subsume classical temporal logics and allow the measurement of interesting quantitative phenomena were introduced. We show how a powerful quantitative logic, the quantitative mu-calculus, can be model checked with arbitrary precision on initialised linear hybrid systems. To this end, we develop new techniques for the discretisation of continuous state spaces based on a special class of strategies in model-checking games and present a reduction to a class of counter parity games.Comment: LMCS submissio

Social Networking Sites in the Aftermath of a Crisis - the Enabling Role for Self-organization

During crisis circumstances, people increasingly rely on social networking sites (SNS). SNS offer new ways for people to participate and communicate, including seeking local and timely information and activating their social networks quickly, which in turn supports self-organization during crisis events. However, little is known about the motives that influence people’s different SNS usage behavior for the goal of self-organization and the underlying mechanisms of this behavior. Based on uses and gratifications theory and the literature on crisis communication and crisis informatics, this conceptual paper argues that during crisis circumstances, particular needs influence people’s SNS usage for the goal of self-organization. In addition, the paper investigates the conceptual underpinnings of self-organization via SNS. This paper, therefore, contributes to theory by developing a conceptual model for the analysis of self-organization via SNS during crisis circumstances

A systematic literature review on information systems for disaster management and proposals for its future research agenda

Emergency management information systems (EMIS) are fundamental for responding to disasters effectively since they provide and process emergency-related information. A literature stream has emerged that corresponds with the increased relevance of the wide array of different information systems that have been used in response to disasters. In addition, the discussion around systems used primarily within responder organizations broadened to systems such as social media that are open to the general public. However, a systematic review of the EMIS literature stream is still missing. This literature review presents a timeline of EMIS research from 1990 up to 2021. It shows the types of information system scholars focused on, and what disaster response functions they supported. It furthermore identifies challenges in EMIS research and proposes future research directions

THE LIFE CYCLE OF DATA LABELS IN ORGANIZATIONAL LEARNING: A CASE STUDY OF THE AUTOMOTIVE INDUSTRY

Data labels are an integral input to develop machine learning (ML) models. In complex domains, labels represent the externalized product of complex knowledge. While prior research discussed labels typically as input of ML models, we explore their role in organizational learning (OL). Based on a case study of a German car manufacturer, we contextualize a framework of OL to the use of labels in organizations informing about organizational members who work with labels, requirements of label-based tools, label-related tasks, and impediments of label-related task performance. From our findings, we derive propositions about the role of labels in OL and outline future research opportunities. Our results inform theory about the role of labels in OL and can guide practitioners leveraging labels to create and transfer knowledge within organizations

Higher Independence

We study higher analogues of the classical independence number on $\omega$. For $\kappa$ regular uncountable, we denote by $i(\kappa)$ the minimal size of a maximal $\kappa$-independent family. We establish ZFC relations between $i(\kappa)$ and the standard higher analogues of some of the classical cardinal characteristics, e.g. $\mathfrak{r}(\kappa)\leq\mathfrak{i}(\kappa)$ and $\mathfrak{d}(\kappa)\leq\mathfrak{i}(\kappa)$. For $\kappa$ measurable, assuming that $2^\kappa=\kappa^+$ we construct a maximal $\kappa$-independent family which remains maximal after the $\kappa$-support product of $\lambda$ many copies of $\kappa$-Sacks forcing. Thus, we show the consistency of $\kappa^+=\mathfrak{d}(\kappa)=\mathfrak{i}(\kappa)<2^\kappa$. We conclude the paper with interesting open questions and discuss difficulties regarding other natural approaches to higher independence

A Protection-Motivation Perspective to Explain Intention to Use and Continue to Use Mobile Warning Systems

Mobile emergency warning apps are essential for effective emergency communication – of course, provided the population intends to use them. Drawing on protection motivation theory, the study validated a psychometric model to explain what motivates individuals to install a warning app for the first time and to keep using it over time. Multi-group covariance-based structural equation modeling was used to model the answers to a survey that measured the drivers of intention to begin using or intention to continue using a warning app. The model shows that, for both non-users and users, trust, social influence, and response efficacy positively and maladaptive rewards negatively affect intention to use and intention to continue use warning apps. However, perceived vulnerability influences only intention to use, whereas response cost and self-efficacy affect continued use intention. Hence, this study enhances the theoretical understanding of technology-enabled protection behaviors and provides practitioners with a list of factors to consider for pushing the adoption and continued use of emergency warning applications