From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Air Traffic Management Safety Challenges

The primary goal of the Air Traffic Management (ATM) system is to control accident risk. ATM safety has improved over the decades for many reasons, from better equipment to additional safety defences. But ATM safety targets, improving on current performance, are now extremely demanding. Safety analysts and aviation decision-makers have to make safety assessments based on statistically incomplete evidence. If future risks cannot be estimated with precision, then how is safety to be assured with traffic growth and operational/technical changes? What are the design implications for the USA’s ‘Next Generation Air Transportation System’ (NextGen) and Europe’s Single European Sky ATM Research Programme (SESAR)? ATM accident precursors arise from (eg) pilot/controller workload, miscommunication, and lack of upto- date information. Can these accident precursors confidently be ‘designed out’ by (eg) better system knowledge across ATM participants, automatic safety checks, and machine rather than voice communication? Future potentially hazardous situations could be as ‘messy’ in system terms as the Überlingen mid-air collision. Are ATM safety regulation policies fit for purpose: is it more and more difficult to innovate, to introduce new technologies and novel operational concepts? Must regulators be more active, eg more inspections and monitoring of real operational and organisational practices

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Improved root cause analysis supporting resilient production systems

Manufacturing companies struggle to be efficient and effective when conducting root cause analyses of production disturbances; a fact which hinders them from creating and developing resilient production systems. This article aims to describe the challenges and enablers identified in current research relating to the different phases of root cause analysis. A systematic literature review was conducted, in which a total of 14 challenges and 17 enablers are identified and described. These correlate to the different phases of root cause analysis. Examples of challenges are “need for expertise”, “employee bias”, “poor data quality” and “lack of data integration”, among others. Examples of enablers are “visualisation tools”, “collaborative platforms”, “thesaurus” and “machine learning techniques”. Based on these findings, the authors also propose potential areas for further research and then design inputs for new solutions to improve root cause analysis. This article provides a theoretical contribution in that it describes the challenges and enablers of root cause analysis and their correlation to the creation of resilient production systems. The article also provides practical contributions, with an overview of current research to support practitioners in gaining insights into potential solutions to be implemented and further developed, with the aim of improving root cause analysis in production systems

Root cause analysis for resilient production systems through Industry 4.0 technologies

Creating and developing resilient production systems is critical if manufacturing companies are to thrive in a globally competitive market. Being flexible and agile, resilient systems can avoid, withstand, adapt to and recover from disturbances. A crucial ability is learning from experienced disturbances so they can be avoided in future. This is commonly done in manufacturing companies by performing a root cause analysis. However, the current practiceof root cause analysis lacks efficiency and effectiveness, which contributes to the high reoccurrence of disturbances encountered daily by manufacturing companies. Fortunately, with the introduction of Industry 4.0 technologies, the process of root cause analysis is expected to change greatly. With the aim of supporting practitioners in improving their root cause analysis processes, this research focuses on: (1) describing the current challenges; (2) describing the requirements for new technological solutions; and (3) identifying and designing new technological solutions, given the context of Industry 4.0. To do so, a qualitative approach was adopted, inspired by design science research (DSR) and based on six studies involving manufacturing companies and technology providers.Regarding the main challenges, the results of this research indicate that manufacturing companies are still performing unstructured root cause analysis, relying on experts to identify root causes and struggling to know how to analyse and integrate relevant data effectively. Furthermore, regarding requirements, the results of this research indicate that technological solutions for root cause analysis should be data-driven and easy to use. They should integrate different data sources, allow secure collaboration and support employee learning. Based on the requirements, the results of this research indicate that the leading technological solutions involve such things as data analytics, the development of thesauruses of disturbances and their causes, the design of specific data architectures and systems for root cause analysis and the design of platforms for stronger collaboration. Finally, in this research, specific high-level designs are proposed for an application to support root cause analysis of machine stops; and a collaborative platform for root cause analysis at the value-chain level. This research has practical and theoretical implications. Its results may be used directly by practitioners to gaininsight into potential improvements to their practices and as input for developing specific root cause analysis applications. The results of this research also advance knowledge in the field of root cause analysis by providing empirical evidence of challenges, requirements and solutions

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Humans in the Loop

From lethal drones to cancer diagnostics, humans are increasingly working with complex and artificially intelligent algorithms to make decisions which affect human lives, raising questions about how best to regulate these human-in-the-loop systems. We make four contributions to the discourse. First, contrary to the popular narrative, law is already profoundly and often problematically involved in governing human-in-the-loop systems: it regularly affects whether humans are retained in or removed from the loop. Second, we identify the MABA-MABA trap, which occurs when policymakers attempt to address concerns about algorithmic incapacities by inserting a human into a decision-making process. Regardless of whether the law governing these systems is old or new, inadvertent or intentional, it rarely accounts for the fact that human-machine systems are more than the sum of their parts: they raise their own problems and require their own distinct regulatory interventions. But how to regulate for success? Our third contribution is to highlight the panoply of roles humans might be expected to play, to assist regulators in understanding and choosing among the options. For our fourth contribution, we draw on legal case studies and synthesize lessons from human factors engineering to suggest regulatory alternatives to the MABA-MABA approach. Namely, rather than carelessly placing a human in the loop, policymakers should regulate the human-in-the-loop system

Humans in the Loop

From lethal drones to cancer diagnostics, humans are increasingly working with complex and artificially intelligent algorithms to make decisions which affect human lives, raising questions about how best to regulate these “human-in-the-loop” systems. We make four contributions to the discourse. First, contrary to the popular narrative, law is already profoundly and often problematically involved in governing human-in-the-loop systems: it regularly affects whether humans are retained in or removed from the loop. Second, we identify “the MABA-MABA trap,” which occurs when policymakers attempt to address concerns about algorithmic incapacities by inserting a human into a decisionmaking process. Regardless of whether the law governing these systems is old or new, inadvertent or intentional, it rarely accounts for the fact that human-machine systems are more than the sum of their parts: they raise their own problems and require their own distinct regulatory interventions. But how to regulate for success? Our third contribution is to highlight the panoply of roles humans might be expected to play, to assist regulators in understanding and choosing among the options. For our fourth contribution, we draw on legal case studies and synthesize lessons from human factors engineering to suggest regulatory alternatives to the MABA-MABA approach. Namely, rather than carelessly placing a human in the loop, policymakers should regulate the human-in-the-loop system

Humans in the Loop

From lethal drones to cancer diagnostics, humans are increasingly working with complex and artificially intelligent algorithms to make decisions which affect human lives, raising questions about how best to regulate these “human-in-the-loop” systems. We make four contributions to the discourse. First, contrary to the popular narrative, law is already profoundly and often problematically involved in governing human-in-the-loop systems: it regularly affects whether humans are retained in or removed from the loop. Second, we identify “the MABA-MABA trap,” which occurs when policymakers attempt to address concerns about algorithmic incapacities by inserting a human into a decisionmaking process. Regardless of whether the law governing these systems is old or new, inadvertent or intentional, it rarely accounts for the fact that human-machine systems are more than the sum of their parts: they raise their own problems and require their own distinct regulatory interventions. But how to regulate for success? Our third contribution is to highlight the panoply of roles humans might be expected to play, to assist regulators in understanding and choosing among the options. For our fourth contribution, we draw on legal case studies and synthesize lessons from human factors engineering to suggest regulatory alternatives to the MABA-MABA approach. Namely, rather than carelessly placing a human in the loop, policymakers should regulate the human-in-the-loop system

Humans in the Loop

From lethal drones to cancer diagnostics, humans are increasingly working with complex and artificially intelligent algorithms to make decisions which affect human lives, raising questions about how best to regulate these “human in the loop” systems. We make four contributions to the discourse. First, contrary to the popular narrative, law is already profoundly and often problematically involved in governing human-in-the-loop systems: it regularly affects whether humans are retained in or removed from the loop. Second, we identify “the MABA-MABA trap,” which occurs when policymakers attempt to address concerns about algorithmic incapacities by inserting a human into decision making process. Regardless of whether the law governing these systems is old or new, inadvertent or intentional, it rarely accounts for the fact that human-machine systems are more than the sum of their parts: They raise their own problems and require their own distinct regulatory interventions. But how to regulate for success? Our third contribution is to highlight the panoply of roles humans might be expected to play, to assist regulators in understanding and choosing among the options. For our fourth contribution, we draw on legal case studies and synthesize lessons from human factors engineering to suggest regulatory alternatives to the MABA-MABA approach. Namely, rather than carelessly placing a human in the loop, policymakers should regulate the human-in-the-loop system