C++ Language: Fit For Purpose in Embedded Systems

International audienceSo you’re an embedded developer. You know that C is the right language for the job, although sometimes thosemaintenance cycles can be, well, repetitive. You sometimes get that nagging feeling that you are coding like anautomaton, repeatedly creating basic iterations over structures that are remarkably similar to ones from last weekor last month

An experimental study of the nature of consumer expectations

Although important both theoretically and practically, the nature of consumer economicexpectation formation has been little studied, particularly by psychologists. The mostrelevant previous research suggests that expectations are based on a heuristic that resultsin them being significantly biased. Further, relevant indicator series are poorly utilized.However, this earlier research used a task lacking in potentially important features of thereal world, and this may have impaired performance. In the current experiment,participants received a more ecologically-valid task. Although there was still evidence ofheuristic use, leading to suboptimal performance and bias, this performance wassignificantly better than anticipated from previous research, particularly regarding use ofindicator series. However, when a strong trend in the criterion series allowed accurateforecasting without consideration of indicators, they were little used. I conclude thatexpectations are formed by first extrapolating the criterion series and only if that workspoorly is other relevant information considered. Thus consumers appear to trade-offaccuracy against effort, such that more effort is expended only when some threshold ofacceptable performance fails to be reached

Training Course on Steering an Expert Knowledge Elicitation : Final Report

EFSA’s scientific expertise and capacity consists of the members of the Scientific Panels, the Scientific Committee, their Working Groups, and the Authority’s own scientific staff as well as the scientists in Member State institutions working with EFSA. The overall objective of this project was to organize and deliver high quality training courses to meet the needs identified by EFSA to implement Expert Knowledge Elicitation (EKE) approach for quantifying uncertainty in food safety risk assessment. As outcome of the project a training course was developed on ‘Steering an Expert KnowledgeElicitation’. The course covered two working days and was conducted three times during the year 2015. The three courses had 73 participants in total, whereof 17 EFSA experts, 50 EFSA Staff and 6 Network members. This report contains a summary of the project, a technical description of the training, the final curriculum, the training materials, results from evaluation of the course by the participants, and recommendations for future training on this subject

Use of expert knowledge to anticipate the future : issues, analysis and directions

Unless the anticipation problem is routine and short-term, and objective data are plentiful, expert judgment will be needed. Risk assessment is analogous to anticipation of the future in that models need to be developed and applied to data. Since objective data are often scanty, expert knowledge elicitation (EKE) techniques have been developed for risk assessment that allow model development and parametrization using expert judgments with minimal cognitive and social biases. Here, we conceptualize how EKE can be developed and applied to support anticipation of the future. Accordingly, we first define EKE as an entire process, that involves considering experts as a source of data, and that comprises various methods for ensuring the quality of this data, including – selecting the best experts, training experts in normative aspects of anticipation, and combining judgments of several experts – as well as eliciting unbiased estimates and constructs from experts. We detail aspects of the papers that constitute the Special Issue and analyse these in terms of the stages within the EKE future-anticipation process that they address. We identify the remaining gaps in our knowledge. Our conceptualization of EKE to support anticipation of the future is compared and contrasted with the extant research effort into judgmental forecasting

Virtuous opinion change in structured groups

Although the individual has been the focus of most research into judgment and decision-making (JDM), important decisions in the real world are often made collectively rather than individually, a tendency that has increased in recent times with the opportunities for easy information exchange through the Internet. From this perspective, JDM research that factors in this social context has increased generalizability and mundane realism relative to that which ignores it. We delineate a problem-space for research within which we locate protocols that are used to study or support collective JDM, identify a common research question posed by all of these protocols—‘What are the factors leading to opinion change for the better (‘virtuous opinion change’) in individual JDM agents?’—and propose a modeling approach and research paradigm using structured groups (i.e., groups with some constraints on their interaction), for answering this question. This paradigm, based on that used in studies of judge-adviser systems, avoids the need for real interacting groups and their attendant logistical problems, lack of power, and poor experimental control. We report an experiment using our paradigm on the effects of group size and opinion diversity on judgmental forecasting performance to illustrate our approach. The study found a U-shaped effect of group size on the probability of opinion change, but no effect on the amount of virtuous opinion change. Implications of our approach for development of more externally valid empirical studies and theories of JDM, and for the design of structured-group techniques to support collective JDM, are discussed

Structured groups make more accurate veracity judgements than individuals

Groups often make better judgements than individuals, and recent research suggests that this phenomenon extends to the deception detection domain. The present research investigated whether the influence of groups enhances the accuracy of judgements, and whether group size influences deception detection accuracy. Two-hundred fifty participants evaluated written statements with a pre-established detection accuracy rate of 60% in terms of veracity before viewing either the judgements and rationales of several other group members or a short summary of the written statement and revising or restating their own judgements accordingly. Participants' second responses were significantly more accurate than their first, suggesting a small positive effect of structured groups on deception detection accuracy. Group size did not have a significant effect on detection accuracy. The present work extends our understanding of the utility of group deception detection, suggesting that asynchronous, structured groups outperform individuals at detecting deception

Virtuous opinion change in structured groups

Although the individual has been the focus of most research into judgment and decision-making (JDM), important decisions in the real world are often made collectively rather than individually, a tendency that has increased in recent times with the opportunities for easy information exchange through the Internet. From this perspective, JDM research that factors in this social context has increased generalizability and mundane realism relative to that which ignores it. We delineate a problem-space for research within which we locate protocols that are used to study or support collective JDM, identify a common research question posed by all of these protocols—‘What are the factors leading to opinion change for the better (‘virtuous opinion change’) in individual JDM agents?’—and propose a modeling approach and research paradigm using structured groups (i.e., groups with some constraints on their interaction), for answering this question. This paradigm, based on that used in studies of judge-adviser systems, avoids the need for real interacting groups and their attendant logistical problems, lack of power, and poor experimental control. We report an experiment using our paradigm on the effects of group size and opinion diversity on judgmental forecasting performance to illustrate our approach. The study found a U-shaped effect of group size on the probability of opinion change, but no effect on the amount of virtuous opinion change. Implications of our approach for development of more externally valid empirical studies and theories of JDM, and for the design of structured-group techniques to support collective JDM, are discussed

Online training courses on Expert Knowledge Elicitation (EKE)

This report summarises the training courses delivered under the contract OC/EFSA/AMU/2021/02 EKE: “Develop and conduct online training courses on Expert Knowledge Elicitation (EKE)”. The objective of the courses was to develop and conduct online training courses on applying the methodology described in the EFSA Guidance on Expert Knowledge Elicitation in Food and Feed Safety Risk Assessment” for EFSA staff and experts, as well as corresponding experts from EU member states. In addition to the three standard EKE methods (Sheffield, Delphi and Cooke), the training included a semi-formal method of EKE. All these methods may be used when EKE is performed within an existing EFSA working group to support uncertainty analysis as outlined in “The principles and methods behind EFSA's Guidance on Uncertainty Analysis in Scientific Assessment”. In total, 12 courses were organised: two on “Steering an Expert Knowledge Elicitation”, two on “Conduct of the Sheffield protocol for an EKE”, one on “Conduct of the Cooke protocol for an EKE”, one on “Conduct of the Delphi protocol for an EKE”, two on “Conduct of a Semi-formal EKE”, two on “Reporting an Expert Knowledge Elicitation” and two on “Writing an Evidence Dossier for an Expert Knowledge Elicitation”. The courses had in total 149 participants and received very good feedback from the participants with a mean value of 4.2 of 5 possible, considering all numerical questions in the feedback questionnaire. Recommendations for future activities on training EKE methodologies are provided

BARD : a structured technique for group elicitation of Bayesian networks to support analytic reasoning

In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting better probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require (but do not include) substantial upfront training, do not provide much guidance on either the model building process or on using the model for reasoning and reporting, and provide no support for building BNs collaboratively. Here, we contribute a detailed description and motivation for our new methodology and application, Bayesian ARgumentation via Delphi (BARD). BARD utilizes BNs and addresses these shortcomings by integrating (1) short, high-quality e-courses, tips, and help on demand; (2) a stepwise, iterative, and incremental BN construction process; (3) report templates and an automated explanation tool; and (4) a multiuser web-based software platform and Delphi-style social processes. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyze a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and (optionally) use it to produce a written analytic report. Initial experiments demonstrate that, for suitable problems, BARD aids in reasoning and reporting. Comparing their effect sizes also suggests BARD's BN-building and collaboration combine beneficially and cumulatively