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In this work, we address the problem of airborne contaminant sensor placement in high-risk buildings where critical infrastructures are managed and operated, making them possible locations for terrorist attacks (such as governmental buildings and ministries, utilities, airports and hospitals). A new software is presented based on the "Matlab-CONTAM Toolbox" and the CONTAM multi-zone simulation software, to construct multiple scenarios of contamination events and to solve the multi-objective sensor placement problem for minimizing the average and maximum impact risk with respect to the contaminant mass inhaled impact metric. The use of the software is demonstrated in a case-study using the Holmes's House benchmark. The Toolbox is released under an open-source license at https://github.com/KIOS-Research/ matlab-contam-toolbox . This research work has been partially funded by the European Research Council under the ERC Advanced Grant ERC-2011-ADG-291508 "Fault-Adaptive Monitoring and Control of Complex Distributed Dynamical Systems" (FAULT-ADAPTIVE) and by the EC project HOME/2011/CIPS/AG "online identification of Failure and Attack on interdependent Critical InfrastructurES" (FACIES).

To understand mental self-government of the developing reading and writing brain, correlations of clustering coefficients on fMRI reading or writing tasks with BASC 2 Adaptivity ratings (time 1 only) or working memory components (time 1 before and time 2 after instruction previously shown to improve achievement and change magnitude of fMRI connectivity) were investigated in 39 students in grades 4 to 9 who varied along a continuum of reading and writing skills. A Philips 3T scanner measured connectivity during six leveled fMRI reading tasks (subword—letters and sounds, word—word-specific spellings or affixed words, syntax comprehension—with and without homonym foils or with and without affix foils, and text comprehension) and three fMRI writing tasks—writing next letter in alphabet, adding missing letter in word spelling, and planning for composing. The Brain Connectivity Toolbox generated clustering coefficients based on the cingulo-opercular (CO) network; after controlling for multiple comparisons and movement, significant fMRI connectivity clustering coefficients for CO were identified in 8 brain regions bilaterally (cingulate gyrus, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, superior temporal gyrus, insula, cingulum—cingulate gyrus, and cingulum—hippocampus). BASC2 Parent Ratings for Adaptivity were correlated with CO clustering coefficients on three reading tasks (letter-sound, word affix judgments and sentence comprehension) and one writing task (writing next letter in alphabet). Before instruction, each behavioral working memory measure (phonology, orthography, morphology, and syntax coding, phonological and orthographic loops for integrating internal language and output codes, and supervisory focused and switching attention) correlated significantly with at least one CO clustering coefficient. After instruction, the patterning of correlations changed with new correlations emerging. Results show that the reading and writing brain's mental government, supported by both CO ...

Chapter 1 Introduction: The Goal and Contents of This Dissertation 1 ECOLOGICALLY RATIONAL HEURISTICS 2 ACT-R: A UNIFIED THEORY OF COGNITION 5 ORGANIZATION OF THE DISSERTATION 8 Chapter 2 Methodological Preliminaries 10 INTRODUCTION 10 OVERVIEW OF THE CHAPTER AND INTRODUCTORY DEFINITIONS 11 HOW TO SELECT BETWEEN FORMAL MODELS: A SHORT OVERVIEW 12 CONCLUSION 18 Chapter 3 How Memory Aids Strategy Selection 19 ABSTRACT 19 INTRODUCTION 19 HOW MEMORY AIDS STRATEGY SELECTION: THE HYPOTHESIS OF NON-OVERLAPPING COGNITIVE NICHES 21 JUDGMENTS FROM THE ACCESSIBILITY OF MEMORIES 22 THE ADAPTIVE TOOLBOX 23 OVERVIEW OF THE CHAPTER 25 AN ACT-R MODEL OF NON-OVERLAPPING COGNITIVE NICHES 26 OVERVIEW OF SIMULATION STUDIES 1 AND 2 33 HOW I CALIBRATED THE MODEL TO PREDICT MEMORY RETRIEVAL (SIMULATION STUDY 1) 33 TESTS OF THE TARTLE NICHE AND TARTLE–KNOWLEDGE NICHE HYPOTHESES (SIMULATION STUDY 2) 42 WHEN WOULD A PERSON USING THE FLUENCY HEURISTIC MAKE ACCURATE INFERENCES? (EXPERIMENTS 1–6; SIMULATION STUDIES 3, 4) 44 ACCURACY AS A DETERMINANT OF STRATEGY SELECTION (EXPERIMENT 7) 53 EFFORT AS A DETERMINANT OF STRATEGY SELECTION (EXPERIMENT 8) 58 TIME AS A DETERMINANT OF STRATEGY SELECTION (EXPERIMENT 9) 62 GENERAL DISCUSSION 67 CONCLUDING REMARKS: EMERGING COGNITIVE NICHES 72 Chapter 4 Models of Recognition-based Multi-alternative Inference 74 ABSTRACT 74 INTRODUCTION 74 GENERALIZING THE RECOGNITION HEURISTIC: ELIMINATION BY RECOGNITION 75 ALTERNATIVE MODELS OF INFERENCE: A COMPETITION 76 TOWARD A THEORY OF STRATEGY SELECTION BY DEFAULT 79 OVERVIEW OF THE EXPERIMENTS: RECOGNITION IN POLITICAL ELECTIONS 82 DO CONFLICTING CUES OVERRULE THE RELIANCE ON RECOGNITION? (EXPERIMENT 11; REANALYSIS OF EARLIER ELECTION STUDY) 83 GENERALIZING THE RECOGNITION HEURISTIC: HOW EPISODIC KNOWLEDGE AIDS USING RECOGNITION (EXPERIMENT 12) 89 IS A LACK OF KNOWLEDGE INFORMATIVE ABOUT THE PREDICTIVE ACCURACY OF RECOGNITION? (EXPERIMENT 13) 98 HOW WELL DO COMPENSATORY MODELS PREDICT INDIVIDUAL INFERENCES? (EXPERIMENT 14; REANALYSIS OF EXPERIMENT 7) 102 DO ...

This thesis explores the role of adaptive learning in explaining empirical phenomena such as the Great Inflation. We show that the historic monetary policy deviated from the optimally recommended policy under learning. These deviations are caused by Brainard-type uncertainty which induces a conservative policy stance. Accounting for both imperfect knowledge and estimation uncertainty, the optimal policy under adaptive learning is consistent with historic policy behavior. However, we show that an optimizing but informationally constrained policy maker would most likely have experienced a Great Inflation like episode. Furthermore, we develop an estimation method to implement the adaptive learning hypothesis into DSGE modelling, thus extending the standard estimation toolbox.

The forward search (FS) is a general method of robust data fitting that moves smoothly from very robust to maximum likelihood estimation. The regression procedures are included in the MATLAB toolbox FSDA. The work on a SAS version of the FS originates from the need for the analysis of large datasets expressed by law enforcement services operating in the European Union that use our SAS software for detecting data anomalies that may point to fraudulent customs returns. Specific to our SAS implementation, the fsdaSAS package, we describe the approximation used to provide fast analyses of large datasets using an FS which progresses through the inclusion of batches of observations, rather than progressing one observation at a time. We do, however, test for outliers one observation at a time. We demonstrate that our SAS implementation becomes appreciably faster than the MATLAB version as the sample size increases and is also able to analyse larger datasets. The series of fits provided by the FS leads to the adaptive data-dependent choice of maximally efficient robust estimates. This also allows the monitoring of residuals and parameter estimates for fits of differing robustness levels. We mention that our fsdaSAS also applies the idea of monitoring to several robust estimators for regression for a range of values of breakdown point or nominal efficiency, leading to adaptive values for these parameters. We have also provided a variety of plots linked through brushing. Further programmed analyses include the robust transformations of the response in regression. Our package also provides the SAS community with methods of monitoring robust estimators for multivariate data, including multivariate data transformations.

System dynamics modelling has been used for around 40 years to address complex, systemic, dynamic problems, those often described as wicked. But, system dynamics modelling is not an exact science and arguments about the most suitable techniques to use in which circumstances, continues. The nature of these wicked problems is investigated through a series of case studies where poor situational awareness among stakeholders was identified. This was found to be an underlying cause for management failure, suggesting need for better ways of recognising and managing wicked problem situations. Human cognition is considered both as a limitation and enabler to decision-making in wicked problem environments. Naturalistic and deliberate decision-making are reviewed. The thesis identifies the need for integration of qualitative and quantitative techniques. Case study results and a review of the literature led to identification of a set of principles of method to be applied in an integrated framework, the aim being to develop an improved way of addressing wicked problems. These principles were applied to a series of cases in an action research setting. However, organisational and political barriers were encountered. This limited the exploitation and investigation of cases to varying degrees. In response to a need identified in the literature review and the case studies, a tool is designed to facilitate analysis of multi-factorial, non-linear causality. This unique tool and its use to assist in problem conceptualisation, and as an aid to testing alternate strategies, are demonstrated. Further investigation is needed in relation to the veracity of combining causal influences using this tool and system dynamics, broadly. System dynamics modelling was found to have utility needed to support analysis of wicked problems. However, failure in a particular modelling project occurred when it was found necessary to rely on human judgement in estimating values to be input into the models. This was found to be problematic and unacceptably ...

Global efforts to further improve exclusive breastfeeding rates have not been successful, in part because effective scaling‐up frameworks and roadmaps have not been developed. The Becoming Breastfeeding Friendly (BBF) toolbox includes an evidence‐based index, the BBF Index (BBFI), to guide the development and tracking of large scale, well‐coordinated, multisector national breastfeeding promotion programmes. This paper describes the development of the BBFI, which is grounded in the Breastfeeding Gear Model complex adaptive systems framework. The BBFI was developed by the BBF Steering Committee in collaboration with a high‐level Technical Advisory Group following the Delphi consensus methodology. Key benchmarks and definitions were informed by evidence‐based health, nutrition, and newborn survival initiatives identified from the academic and grey literature. The BBFI consists of 8 gears (54 benchmarks): Advocacy (4); Political Will (3); Legislation and Policies (10); Funding and Resources (4); Training and Program Delivery (17); Promotion (3); Research and Evaluation (10); and Coordination, Goals, and Monitoring (3). Scores are generated for 8 gear scores plus a total country score to gauge the scaling‐up enabling environment. The BBFI provides an evidence‐based index to assist countries in (a) assessing their readiness to scale up breastfeeding programmes and (b) tracking scaling‐up progress.

This article synthesizes and compares environmental governance theories. For each theory we outline its main tenets, claims, origin, and supporting literature. We then group the theories into focused versus combinatory frameworks for comparison. The analysis resonates with many types of ecosystems; however, to make it more tangible, we focus on coastal systems. First, we characterize coastal governance challenges and then later link salient research questions arising from these challenges to the theories that may be useful in answering them. Our discussion emphasizes the usefulness of having a diverse theoretical toolbox, and we argue that if governance analysts are more broadly informed about the theories available, they may more easily engage in open-minded interdisciplinary collaboration. The eight theories examined are the following: polycentricity, network governance, multilevel governance, collective action, governmentality (power / knowledge), adaptive governance, interactive governance theory (IGT), and evolutionary governance theory (EGT). Polycentricity and network governance both help examine the links or connections in governance processes. Polycentricity emphasizes structural configurations at a broader level, and network governance highlights agency and information flow within and between individuals or organizations. Collective action theory is helpful for examining community level governance, and helps analyze variables hindering or enabling self-organization and shared resource outcomes. In contrast, multilevel governance helps understand governance integration processes between localities, regions, and states across administrative, policy, or legal dimensions. Governmentality is helpful for understanding the role of discourse, power, knowledge, and narratives in governance, such as who creates them and who becomes governed by them with what effect. Adaptive governance helps analyze the links between context, change, and resilience. IGT helps examine the interdependencies between the systems being governed and the governing systems. EGT is helpful for unpacking how coevolutionary processes shape governance and the options for change.

Effective action on resilience and climate change adaptation can be a complex task—requiring coordinated efforts from the highest levels of government to individual households and firms. The Adaptation Principles offer a guide to effective climate change adaptation, containing hands-on guidance to the design, implementation and monitoring of national adaptation strategies. It specifies six guiding principles, which correspond to common policy domains: 1) Ensuring resilient foundations through rapid and inclusive development; 2) Facilitating the adaptation of firms and people; 3) Adapting land use and protecting critical public assets and services; 4) Increasing people's capacity to cope with and recover from shocks; 5) Anticipating and managing macroeconomic and fiscal risks; 6) Ensuring effective implementation through prioritization and continuous monitoring. While outlining these universal Adaptation Principles, this guide shows that each country needs to tailor these actions to its specific needs and priorities. To guide this process, Adaptation Principles offers concrete and practical tools: Screening questions to identify the most urgent and effective actions, toolboxes illustrating common datasets and methodologies to support decisions, indicators to monitor and evaluate progress, and case studies on how the COVID-19 pandemic influences priorities in taking effective adaptation action.

Um Daten am effektivsten zu nutzen, muss die moderne Ökonometrie ihren Werkzeugkasten an Modellen erweitern und neu denken. Das Feld, in dem diese Transformation am besten beobachtet werden kann, ist die kausale Inferenz. Diese Dissertation verfolgt die Absicht Probleme zu untersuchen, Lösungen zu präsentieren und neue Methoden zu entwickeln Machine Learning zu benutzen, um kausale Parameter zu schätzen. Dafür werden in der Dissertation zuerst verschiedene neuartige Methoden, welche als Ziel haben heterogene Treatment Effekte zu messen, eingeordnet. Im zweiten Schritt werden, basierend auf diesen Methoden, Richtlinien für ihre Anwendung in der Praxis aufgestellt. Der Parameter von Interesse ist der "conditional average treatment effect" (CATE). Es kann gezeigt werden, dass ein Vergleich mehrerer Methoden gegenüber der Verwendung einer einzelnen Methode vorzuziehen ist. Ein spezieller Fokus liegt dabei auf dem Aufteilen und Gewichten der Stichprobe, um den Verlust in Effizienz wettzumachen. Ein unzulängliches Kontrollieren für die Variation durch verschiedene Teilstichproben führt zu großen Unterschieden in der Präzision der geschätzten Parameter. Wird der CATE durch Bilden von Quantilen in Gruppen unterteilt, führt dies zu robusteren Ergebnissen in Bezug auf die Varianz. Diese Dissertation entwickelt und untersucht nicht nur Methoden für die Schätzung der Heterogenität in Treatment Effekten, sondern auch für das Identifizieren von richtigen Störvariablen. Hierzu schlägt diese Dissertation sowohl die "outcome-adaptive random forest" Methode vor, welche automatisiert Variablen klassifiziert, als auch "supervised randomization" für eine kosteneffiziente Selektion der Zielgruppe. Einblicke in wichtige Variablen und solche, welche keine Störung verursachen, ist besonders in der Evaluierung von Politikmaßnahmen aber auch im medizinischen Sektor wichtig, insbesondere dann, wenn kein randomisiertes Experiment möglich ist. ; To use data effectively, modern econometricians need to expand and rethink their toolbox. One field where such a transformation has already started is causal inference. This thesis aims to explore further issues, provide solutions, and develop new methods on how machine learning can be used to estimate causal parameters. I categorize novel methods to estimate heterogeneous treatment effects and provide a practitioner's guide for implementation. The parameter of interest is the conditional average treatment effect (CATE). It can be shown that an ensemble of methods is preferable to relying on one method. A special focus, with respect to the CATE, is set on the comparison of such methods and the role of sample splitting and cross-fitting to restore efficiency. Huge differences in the estimated parameter accuracy can occur if the sampling uncertainty is not correctly accounted for. One feature of the CATE is a coarser representation through quantiles. Estimating groups of the CATE leads to more robust estimates with respect to the sampling uncertainty and the resulting high variance. This thesis not only develops and explores methods to estimate treatment effect heterogeneity but also to identify confounding variables as well as observations that should receive treatment. For these two tasks, this thesis proposes the outcome-adaptive random forest for automatic variable selection, as well as supervised randomization for a cost-efficient selection of the target group. Insights into important variables and those that are not true confounders are very helpful for policy evaluation and in the medical sector when randomized control trials are not possible.

Threatening communication is a widely applied method in behavior change interventions, which at the same time has been heavily criticized in the psychological literature. The current paper describes a study of the reasons for this persistent wide application of threatening communication. We conducted qualitative interviews with 33 key actors in behavior change intervention development in The Netherlands. Specifically, we interviewed intervention developers, policymakers, politicians, scientists, and advertising professionals. The interviews were transcribed and subsequently coded using NVivo. We found that participants most closely involved with the actual intervention development were generally convinced that threatening information was to be prevented, but often did not understand the exact processes involved. They were often under the impression that rather than a potent efficacy enhancing element, a behavioral suggestion would suffice to prevent threatening communication from backfiring. As participants were further removed from the actual intervention development, they generally tended to be more in favor of threatening communication. The main reasons for use of threatening information were to attract attention or prompt self-reflection through confrontation, because target population members were assumed to like threatening information and respond rationally to increased risk perceptions by changing their behavior, or simply because no alternatives were available. In addition, intervention developers frequently had to deal with supervisors or funders who preferred threatening communication. Thus, when communicating with practitioners, it seems fruitful to provide them with a toolbox of evidence-based behavior change methods that promote adaptive, rather than maladaptive, behavior; to promote basing interventions on the most relevant behavioral determinants as identified by determinant analyses; and to equip intervention developers with the tools to persuade other key stakeholders that fear is a bad ...

Threatening communication is a widely applied method in behavior change interventions, which at the same time has been heavily criticized in the psychological literature. The current paper describes a study of the reasons for this persistent wide application of threatening communication. We conducted qualitative interviews with 33 key actors in behavior change intervention development in The Netherlands. Specifically, we interviewed intervention developers, policymakers, politicians, scientists, and advertising professionals. The interviews were transcribed and subsequently coded using NVivo. We found that participants most closely involved with the actual intervention development were generally convinced that threatening information was to be prevented, but often did not understand the exact processes involved. They were often under the impression that rather than a potent efficacy enhancing element, a behavioral suggestion would suffice to prevent threatening communication from backfiring. As participants were further removed from the actual intervention development, they generally tended to be more in favor of threatening communication. The main reasons for use of threatening information were to attract attention or prompt self-reflection through confrontation, because target population members were assumed to like threatening information and respond rationally to increased risk perceptions by changing their behavior, or simply because no alternatives were available. In addition, intervention developers frequently had to deal with supervisors or funders who preferred threatening communication. Thus, when communicating with practitioners, it seems fruitful to provide them with a toolbox of evidence-based behavior change methods that promote adaptive, rather than maladaptive, behavior; to promote basing interventions on the most relevant behavioral determinants as identified by determinant analyses; and to equip intervention developers with the tools to persuade other key stakeholders that fear is a bad ...

Natural resources are essential for maintaining or improving people's livelihood, especially in Africa. Integrated management of natural resources (INRM) is a way to maintain ecosystems capacity to produce a broad range of goods and services considering African socio-economic conditions and institutional frames. Despite the availability of many tools, expertise, local practices and indigenous knowledge, the concept of INRM has hardly been brought into practice and the building blocks in many cases still need to be integrated. Effective INRM in many cases is not achieved due to a lack of exchange of information and a lack of coordination between many actors involved at different scales. Furthermore, external pressures are affecting the availability of natural resources. Many of the poorest people in the world typically are highly vulnerable to external shocks (e.g. drought, floods, famine, disease outbreaks). AfroMaison is making use of what is available to contribute to a better integration of the main components of natural resource management into a coherent integrated and adaptive management process at meso-scale. We define the meso-scale as that level (sub-national) to which power has shifted after decentralization in many countries in Africa. Due to the relative youth of mesoscale authorities and institutions, their capacity for integrated natural resources management (INRM) needs to be strengthened. From a natural resources point of view, the meso-scale corresponds to a landscape, ecosystems or a river (sub)basin. The challenge of AFROMAISON is to provide a holistic toolbox and operational framework for INRM that can be applied in a variety of environmental and socioeconomic conditions in Africa. At the same time, following a participatory analysis of opportunities and challenges, it provides participatory management options for operational INRM, which are both embedded in local traditions and culture, and are scientifically sound.

Background: Contemporary public health problems connect to the social determinants of health, with a growing recognition of social inclusion as imperative to sustainable development. In this quest for social inclusion, early childhood and families are of particular interest. Although co-creation is suggested as viable path to support well-being, less is known how social inclusion might be co-created in practice. The aim of this study was to explore how Participatory Action Research (PAR) can be a tool for transformative practices in a local community, pointing to kindergartens as meeting places for recognizing social inclusion as a common value in early childhood. Methods: A qualitative PAR study was embedded in a Norwegian municipality as an integrated part of their local public health work. The study involved a wide range of participants and stakeholders in three kindergartens and the wider community. Together, we explored potentials for co-creating social inclusion to achieve well-being through cycles of transformative actions and reflections. Reflexive thematic analysis was applied to generate patterns and themes in the data. Results: The participants formulated and took on ownership to an inclusive agenda through the PAR-process. Acts of inclusion was framed by an intersection between political aims of achieving health and well-being for all and public value co-creation unfolding at the level of the place, in the context of the Norwegian welfare regime. To feel valued and adding value was seen as important aspects for social inclusion. Four themes were generated from analysis; (1) Co-creating a shared vision of inclusive communities, (2) Becoming aware and empowered through caring, sharing and collaboration, (3) Places and spaces of inclusiveness in kindergartens and beyond, and (4) Valuing and practicing inclusion, and signs of transformative change. Conclusions: Through the PAR process, parents, kindergartens employees, community members and policy makers appear to have opened a creative toolbox for inclusive and transformational change through formulating and co-creating inclusion and well-being as public values. The results suggest that local actors might support adaptive social systems to taking on relational responsibility for inclusive processes and outcomes in the pursuit of well-being for all. ; publishedVersion

Background: Contemporary public health problems connect to the social determinants of health, with a growing recognition of social inclusion as imperative to sustainable development. In this quest for social inclusion, early childhood and families are of particular interest. Although co-creation is suggested as viable path to support well-being, less is known how social inclusion might be co-created in practice. The aim of this study was to explore how Participatory Action Research (PAR) can be a tool for transformative practices in a local community, pointing to kindergartens as meeting places for recognizing social inclusion as a common value in early childhood. Methods: A qualitative PAR study was embedded in a Norwegian municipality as an integrated part of their local public health work. The study involved a wide range of participants and stakeholders in three kindergartens and the wider community. Together, we explored potentials for co-creating social inclusion to achieve well-being through cycles of transformative actions and reflections. Reflexive thematic analysis was applied to generate patterns and themes in the data. Results: The participants formulated and took on ownership to an inclusive agenda through the PAR-process. Acts of inclusion was framed by an intersection between political aims of achieving health and well-being for all and public value co-creation unfolding at the level of the place, in the context of the Norwegian welfare regime. To feel valued and adding value was seen as important aspects for social inclusion. Four themes were generated from analysis; (1) Co-creating a shared vision of inclusive communities, (2) Becoming aware and empowered through caring, sharing and collaboration, (3) Places and spaces of inclusiveness in kindergartens and beyond, and (4) Valuing and practicing inclusion, and signs of transformative change. Conclusions: Through the PAR process, parents, kindergartens employees, community members and policy makers appear to have opened a creative toolbox for inclusive and transformational change through formulating and co-creating inclusion and well-being as public values. The results suggest that local actors might support adaptive social systems to taking on relational responsibility for inclusive processes and outcomes in the pursuit of well-being for all. ; publishedVersion