Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

Lessons learned: structuring knowledge codification and abstraction to provide meaningful information for learning

Purpose – To increase the spread and reuse of lessons learned (LLs), the purpose of this paper is to develop a standardised information structure to facilitate concise capture of the critical elements needed to engage secondary learners and help them apply lessons to their contexts. Design/methodology/approach – Three workshops with industry practitioners, an analysis of over 60 actual lessons from private and public sector organisations and seven practitioner interviews provided evidence of actual practice. Design science was used to develop a repeatable/consistent information model of LL content/structure. Workshop analysis and theory provided the coding template. Situation theory and normative analysis were used to define the knowledge and rule logic to standardise fields. Findings – Comparing evidence from practice against theoretical prescriptions in the literature highlighted important enhancements to the standard LL model. These were a consistent/concise rule and context structure, appropriate emotional language, reuse and control criteria to ensure lessons were transferrable and reusable in new situations. Research limitations/implications – Findings are based on a limited sample. Long-term benefits of standardisation and use need further research. A larger sample/longitudinal usage study is planned. Practical implications – The implementation of the LL structure was well-received in one government user site and other industry user sites are pending. Practitioners validated the design logic for improving capture and reuse of lessons to render themeasily translatable to a new learner’s context. Originality/value – The new LL structure is uniquely grounded in user needs, developed from existing best practice and is an original application of normative and situation theory to provide consistent rule logic for context/content structure

Theory of deferred action: Agent-based simulation model for designing complex adaptive systems

Deferred action is the axiom that agents act in emergent organisation to achieve predetermined goals. Enabling deferred action in designed artificial complex adaptive systems like business organisations and IS is problematical. Emergence is an intractable problem for designers because it cannot be predicted. We develop proof-of-concept, conceptual proto-agent model, of emergent organisation and emergent IS to understand better design principles to enable deferred action as a mechanism for coping with emergence in artefacts. We focus on understanding the effect of emergence when designing artificial complex adaptive systems by developing an exploratory proto-agent model and evaluate its suitability for implementation as agent-based simulation

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

Identifying the science and technology dimensions of emerging public policy issues through horizon scanning

Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique [1]. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security.Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique [1]. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security