Gotowa bibliografia na temat „Environmental uncertainty theory”

To examine the impact of environmental uncertainty on individuals’ intertemporal choices and the moderating effect of implicit personality theory, two studies were conducted. Study 1 investigated the moderating role of implicit personality theory in the influence of environmental uncertainty on intertemporal choice using questionnaires. Study 2 examined whether priming incremental personality theory could change entity theorists’ intertemporal preference in an uncertain environment. The results showed that implicit personality theory plays a moderating role in the influence of environmental uncertainty on intertemporal choice. For entity theorists, the delay discounting rate was positively correlated with environmental uncertainty. In contrast, for incremental theorists, the delay discounting rate was not significantly correlated with environmental uncertainty. After priming incremental personality theory, entity theorists’ delay discounting decreased significantly. Thus, we conclude that incremental personality theory buffers the effect of environmental uncertainty on intertemporal choice.

This study aims to examine the effect of environmental uncertainty and managers' personnel value on environmental disclosure. This study uses a saturated sampling of manufacturing companies listed on the Indonesia Stock Exchange, and the respondents are all managers’ levels. The total sample of this study is 161 manufacturing companies. The respondents got the questionnaires via email, and 64 respondents completed them. These can be processed using the Structural Equation Model - Partial Least Square. Findings. The result shows that environmental uncertainty and managers' personnel value have a positive effect on environmental disclosure. Furthermore, this result shows that the manager's personnel value is the most dominant influence of environmental disclosure. These research findings add to the literature, especially about environmental disclosure, and support contingency theory and structuration theory. The findings can give information especially environmental disclosure for the manufacturing companies in Indonesia Stock Exchange. The policymaker should concern about environmental disclosure for companies listed on Indonesia Stock Exchange. The limitation of this research is the low response from the respondents of the research. Another limitation is related to R- Square's results, which still need further research.

Abstract Uncertainty quantification is very important in environmental management to allow decision makers to consider the reliability of predictions of the consequences of decision alternatives and relate them to their risk attitudes and the uncertainty about their preferences. Nevertheless, uncertainty quantification in environmental decision support is often incomplete and the robustness of the results regarding assumptions made for uncertainty quantification is often not investigated. In this article, an attempt is made to demonstrate how uncertainty can be considered more comprehensively in environmental research and decision support by combining well-established with rarely applied statistical techniques. In particular, the following elements of uncertainty quantification are discussed: (i) using stochastic, mechanistic models that consider and propagate uncertainties from their origin to the output; (ii) profiting from the support of modern techniques of data science to increase the diversity of the exploration process, to benchmark mechanistic models, and to find new relationships; (iii) analysing structural alternatives by multi-model and non-parametric approaches; (iv) quantitatively formulating and using societal preferences in decision support; (v) explicitly considering the uncertainty of elicited preferences in addition to the uncertainty of predictions in decision support; and (vi) explicitly considering the ambiguity about prior distributions for predictions and preferences by using imprecise probabilities. In particular, (v) and (vi) have mostly been ignored in the past and a guideline is provided on how these uncertainties can be considered without significantly increasing the computational burden. The methodological approach to (v) and (vi) is based on expected expected utility theory, which extends expected utility theory to the consideration of uncertain preferences, and on imprecise, intersubjective Bayesian probabilities.

A central aspect of cultural evolutionary theory concerns how human groups respond to environmental change. Although we are painting with a broad brush, it is fair to say that prior to the twenty-first century, adaptation often happened gradually over multiple human generations, through a combination of individual and social learning, cumulative cultural evolution and demographic shifts. The result was a generally resilient and sustainable population. In the twenty-first century, however, considerable change happens within small portions of a human generation, on a vastly larger range of geographical and population scales and involving a greater degree of horizontal learning. As a way of gauging the complexity of societal response to environmental change in a globalized future, we discuss several theoretical tools for understanding how human groups adapt to uncertainty. We use our analysis to estimate the limits of predictability of future societal change, in the belief that knowing when to hedge bets is better than relying on a false sense of predictability.

Environmental impact assessment (EIA) is usually evaluated by many factors influenced by various kinds of uncertainty or fuzziness. As a result, the key issues of EIA problem are to rep­resent and deal with the uncertain or fuzzy information. D numbers theory, as the extension of Dempster-Shafer theory of evidence, is a desirable tool that can express uncertainty and fuzziness, both complete and incomplete, quantitative or qualitative. However, some shortcomings do exist in D numbers combination process, the commutative property is not well considered when multiple D numbers are combined. Though some attempts have made to solve this problem, the previous method is not appropriate and convenience as more information about the given evaluations rep­resented by D numbers are needed. In this paper, a data-driven D numbers combination rule is proposed, commutative property is well considered in the proposed method. In the combination process, there does not require any new information except the original D numbers. An illustrative example is provided to demonstrate the effectiveness of the method.

Sonar sensors are widely used in mobile robots applications such as navigation, map building, and localization. The performance of these sensors is affected by the environmental phenomena, sensor design, and target characteristics. Therefore, the readings obtained from these sensors are uncertain. This uncertainity is often modeled by using Probability Theory. However, the probablistic approach is valid when the available knowledge is precise which is not the case in sonar readings. In this paper, the behavior of sonar readings reflected from walls and corners are studied, then new models of angular uncertainty and radial imprecision for sonar readings obtained from corners and walls are proposed. These models are represented by using Possibility Theory, mainly possibility distributions.

Described in this thesis is research that examined the influence of courtroom symbolism on jurors' ability to focus on the evidence presented in a criminal trial. This research is unique as participants were 'real' jurors who had, at the time of participation in the research, recently completed deliberations on a District Court trial. To date no other research has explored the interaction between symbolism in the courtroom and the juror experience. The broad research question examined in this research was: Do symbolic elements in the courtroom environment draw juror attention away from the evidence being presented?. Three theories drawn from environmental psychology were utilised in this research (i.e., environmental uncertainty theory, environmental arousal theory and environmental load theory). Additionally, Rapoport's (1983, 1990) theory, which was drawn from the architectural field of knowledge, was utilised. Rapoport's theory facilitated the measurement of symbolism in the form of environmental cues found in the courtroom. To address the broad research question, eight subordinate research questions were formulated those being: (1) Do trait anxiety and court related factors influence the amount of attention jurors pay to the elements of the courtroom environment?, (2) Do trait anxiety and court related factors influence the effect on jurors of the attention they paid to the elements of the courtroom environment?, (3) Do trait anxiety and court related factors influence the sense of stress or arousal in jurors?, (4) Is there an association between attention paid by jurors to the environmental cues found in the courtroom, their ability to perform their role as a juror and their sense of appreciation for the function of the law?, (5) Is there an association between attention paid by jurors to the environmental cues in the courtroom and a state of elevated stress?, (6) Does the amount of attention paid by jurors to environmental cues found in the courtroom diminish over time?, (7) Does the effect on jurors of the attention they paid to the environmental cues in the courtroom diminish over time? and, (8) Does the amount of stress jurors experience diminish over time?. The eight research questions were addressed in two studies. The first involved a survey of jurors who had completed deliberations in District Court trials in Brisbane and Cairns during the period 19th July, 2001 and 18th July, 2002 (N=192). This study examined the amount of attention jurors paid to four elements of the courtroom environment (i.e., the courtroom design, the appearance and behaviour of court officials, the appearance and behaviour of those associated with the offence and the task of being a juror). Also examined in this study was the influence on jurors of the attention they paid to the elements of the courtroom environment. Juror experience of state anxiety as measured by the State Trait Anxiety Inventory [STAI] (Spielberger, 1983) was also examined. Additionally, the influence of trait anxiety as measured by the STAI (Spielberger, 1983) and court related factors (i.e., location of trial, prior jury experience, nature of the offence and length of trial) on the juror experience was examined. Two time frames were examined (i.e., initial contact with the courtroom and midpoint of juror experience) which allowed the examination of the influence of time on the juror experience. Interviews with jurors who had completed the survey (N=19) comprised the second study. This study allowed jurors to describe their experience on a jury from a more personal perspective. The elements of the juror experience that distracted and reinforced their ability to focus on the evidence and facilitated a sense of appreciation for the function of the law were discussed. Also discussed were the elements of the juror experience that caused jurors to experience anxiety. Overall, the findings of this research indicated that although symbolism in the courtroom was linked to juror anxiety, this was positive and facilitated juror attention to the evidence and a sense of respect for the criminal justice system. That anxiety experienced by jurors facilitated their focus on the evidence is consistent with environmental arousal theory in that for optimum performance one must experience a certain level of arousal. Also confirmed by the findings of this research is environmental load theory, an element of which predicts that jurors will be task driven when experiencing environmental load. That these two theories are linked by an underlying construct is evidenced by the findings of this research. These findings open up possibilities for future theoretical research using environmental arousal theory and environmental load theory. The findings of this research also suggest that jurors found the symbolism in the courtroom environment distracting and that some elements of the juror experience were onerous and stressful. However, previous experience in the courtroom and lower trait anxiety moderated these factors. Consequently, consistent with the results of this research courts might benefit from implementing an orientation program for prospective jurors such that they are familiarised with the courtroom environment. Such a program would moderate any distress experienced by jurors. In the context of such adjustments by the courts, the influence of symbolism in the courtroom are considered beneficial to the juror experience and there is no need for the courts to alter the courtroom setting or robing practices of lawyers.

The purpose of this dissertation is to develop a theory that helps explain the conditions under which firms select certain project evaluation techniques. This study uses contingency theory to analyze the impact of environmental uncertainty on the choice of project evaluation techniques. In addition to a direct measure of uncertainty, several dimensions of uncertainty are included in this study. These dimensions of uncertainty include control structure, method of financing, foreign assets, method of growth, and product domination. This study also analyzes the use of project evaluation, management science and risk management techniques in US firms over time and in UK firms over time in order to compare to prior research. A comparison of firms in the two countries are also provided. The primary method of data collection was a survey instrument. Data were also collected from annual reports and various other public sources. The variables that appear significant in the choice of project evaluation technique in US firms are environmental uncertainty, control structure, method of financing, foreign assets, and product domination. The variable that appear significant in the choice of project evaluation technique in UK firms is method of financing. US firms favor discounted cash flow techniques although this study detected a slight decrease over time. UK firms continue to use non-discounted cash flow techniques, although the use of discounted cash flow techniques is widespread. There are significant differences between US and UK firms. US firms tend to use discounted cash flow techniques to a greater extent than UK firms. This research makes a significant contribution in attempting to develop a theory explaining the use of project evaluation techniques in firms in the US and UK. In addition, several other developments relating to project evaluation, management science and risk management are discussed. The results of this study can be used by managers in refining and improving their existing project evaluation processes.

Climate change and anticipated impacts of sea level rise such as increased coastal erosion, inundation, flooding due to storm surges and salt water intrusion to freshwater resources will affect all the countries but mostly small island countries of oceans and low-lying lands along coastlines. Turkey having 8333 km of coastline including physically, ecologically and socio-economically important low-lying deltas should also prepare for the impacts of sea level rise as well as other impacts of climate change while participating in adaptation and mitigation efforts. Thus, a coastal vulnerability assessment of Turkey to sea level rise is needed both as a part of coastal zone management policies for sustainable development and as a guideline for resource allocation for preparation of adaptation options for upcoming problems due to sea level rise. In this study, a fuzzy coastal vulnerability assessment model (FCVI) of a region to sea level rise using physical and human activity indicators of impacts of sea level rise which use commonly available data are developed. The results enable decision makers to compare and rank different regions according to their vulnerabilities to sea level rise, to prioritize impacts of sea level rise on the region according to the vulnerability of the region to each impact and to determine the most vulnerable parameters for planning of adaptation measures to sea level rise. The sensitivity and uncertainty analysis performed for the results of the model (FCVI) is the first time application of a fuzzy uncertainty analysis model to coastal vulnerability assessments. These analysis ensure that the decision makers could be able to interpret the results of such vulnerability assessments based primarily on expert perceptions accurately enough. This in turn, would increase the confidence levels of adaptation measures and as well as accelerate implementation of adaptation of coastal areas to climate change. The developed coastal vulnerability assessment model is applied successfully to determine the vulnerability of Gö
ksu, Gö
cek and Amasra regions of Turkey that have different geological, ecological and socio-economical properties. The results of the site studies show that Gö
ksu has high vulnerability, Gö
cek has moderate vulnerability and Amasra shows low vulnerability to sea level rise. These results are in accordance with the general literature on impacts of sea level rise at different geomorphological coastal areas thus the applicability of fuzzy vulnerability assessment model (FCVI) to coastal areas is validated.

This dissertation addresses path planning for an autonomous vehicle navigating in a two dimensional environment for which an a priori map is inaccurate and for which the environment is sensed in real-time. For this class of application, planning decisions must be made in real-time. This work is motivated by the need for fast autonomous vehicles that require planning algorithms to operate as quickly as possible. In this dissertation, we first study the case in which there are only static obstacles in the environment. We propose a hybrid receding horizon control path planning algorithm that is based on level-set methods. The hybrid method uses global or local level sets in the formulation of the receding horizon control problem. The decision to select a new level set is made based on certain matching conditions that guarantee the optimality of the path. We rigorously prove sufficient conditions that guarantee that the vehicle will converge to the goal as long as a path to the goal exists. We then extend the proposed receding horizon formulation to the case when the environment possesses moving obstacles. Since all of the results in this dissertation are based on level-set methods, we rigorously investigate how level sets change in response to new information locally sensed by a vehicle. The result is a dynamic fast marching algorithm that usually requires significantly less computation that would otherwise be the case. We demonstrate the proposed dynamic fast marching method in a successful field trial for which an autonomous surface vehicle navigated four kilometers through a riverine environment.
Ph. D.

Thesis (Ph. D.)--Mechanical Engineering, Georgia Institute of Technology, 2008.
Committee Chair: Bras, Bert; Committee Member: Allen, Janet; Committee Member: Chameau, Jean-Lou; Committee Member: McGinnis, Leon; Committee Member: Paredis, Chris.

This qualitative, phenomenological research study addressed the research question: What is the experience of leaders when they think strategically in a VUCA environment? The study explored what happens when leaders think strategically in a VUCA environment and how such thinking occurs. Of specific interest were the triggers of strategic thinking, the strategic questions being asked, and the methods used to develop insight. The term VUCA stands for volatility, uncertainty, complexity, and ambiguity and is used interchangeably in this study with the term "complex" to represent the Department of Defense (DoD) acquisition program management environment (Army, 1998).

Ten acquisition program managers and deputy program managers for major DoD acquisition programs were selected as referred by naval aviation acquisition program executive officers. Data were collected through in-depth interviews and transcribed to capture the program managers' lived experience and the meaning they made (Seidman, 2006). Data were analyzed and themes developed using Moustakas's (1994) modification of the Stevick-Colaizzi-Keen method as a guide.

The study had four findings: (1) strategic thinking utilizes an extensive range of knowledge, abilities, and conditions that enable clarity of thought; (2) strategic thinking occurs deliberately as both a high-level creative and a tactically grounded process; (3) strategic thinking is fueled by iterative individual and group analytical and dialogical activities to address the knowledge needed to create strategic-to-tactical linkages and frameworks; and (4) strategic thinking is a deeply personal experience that evokes a wide range of positive and negative emotions. The study concluded that strategic thinking is a cognitive, emotional, and behavioral phenomenon that is both high-level and tactically grounded and is fueled by individual and group analytical and dialogical activities to address needed knowledge, enable clarity of thought, and create strategic-to-tactical linkages and mental models to develop enabling strategies. Further, the characterization of the VUCA environment needs to include the structural elements that may impede the ability to adapt and respond, and the triggers for strategic thinking need to include having the explicit responsibility to think strategically. Implications for theory, practice, and future research are offered.

The decentralized Markov decision process (Dec-POMDP) is a powerful formal model for studying multiagent problems where cooperative, coordinated action is optimal, but each agent acts based on local data alone. Unfortunately, it is known that Dec-POMDPs are fundamentally intractable: they are NEXP-complete in the worst case, and have been empirically observed to be beyond feasible optimal solution.To get around these obstacles, researchers have focused on special classes of the general Dec-POMDP problem, restricting the degree to which agent actions can interact with one another. In some cases, it has been proven that these sorts of structured forms of interaction can in fact reduce worst-case complexity. Where formal proofs have been lacking, empirical observations suggest that this may also be true for other cases, although less is known precisely.This thesis unifies a range of this existing work, extending analysis to establish novel complexity results for some popular restricted-interaction models. We also establish some new results concerning cases for which reduced complexity has been proven, showing correspondences between basic structural features and the potential for dimensionality reduction when employing mathematical programming techniques.As our new complexity results establish that worst-case intractability is more widespread than previously known, we look to new ways of analyzing the potential average-case difficulty of Dec-POMDP instances. As this would be extremely difficult using the tools of traditional complexity theory, we take a more empirical approach. In so doing, we identify new analytical measures that apply to all Dec-POMDPs, whatever their structure. These measures allow us to identify problems that are potentially easier to solve on average, and validate this claim empirically. As we show, the performance of well-known optimal dynamic programming methods correlates with our new measure of difficulty. Finally, we explore the approximate case, showing that our measure works well as a predictor of difficulty there, too, and provides a means of setting algorithm parameters to achieve far more efficient performance.

To infer the hidden states from the noisy observations and make predictions based on a set of input states and output observations are two challenging problems in many research areas. Examples of applications many include position estimation from various measurable radio signals in indoor environments, self-navigation for autonomous cars, modeling and predicting of the traffic flows, and flow pattern analysis for crowds of people. In this thesis, we mainly use the Bayesian inference framework for position estimation in an indoor environment, where the radio propagation is uncertain. In Bayesian inference framework, it is usually hard to get analytical solutions. In such cases, we resort to Monte Carlo methods to solve the problem numerically. In addition, we apply Bayesian nonparametric modeling for trajectory learning in sport analytics. The main contribution of this thesis is to propose sequential Monte Carlo methods, namely particle filtering and smoothing, for a novel indoor positioning framework based on proximity reports. The experiment results have been further compared with theoretical bounds derived for this proximity based positioning system. To improve the performance, Bayesian non-parametric modeling, namely Gaussian process, has been applied to better indicate the radio propagation conditions. Then, the position estimates obtained sequentially using filtering and smoothing are further compared with a static solution, which is known as fingerprinting. Moreover, we propose a trajectory learning framework for flow estimation in sport analytics based on Gaussian processes. To mitigate the computation deficiency of Gaussian process, a grid-based on-line algorithm has been adopted for real-time applications. The resulting trajectory modeling for individual athlete can be used for many purposes, such as performance prediction and analysis, health condition monitoring, etc. Furthermore, we aim at modeling the flow of groups of athletes, which could be potentially used for flow pattern recognition, strategy planning, etc.

AbstractThis chapter asks what insights long-term historical information from before the Great Acceleration and Anthropocene might offer to policy and practice in the twenty-first century. Conventional sustainability research usually focuses on shallower time horizons that could miss insightful environmental and social processes evolving over centuries to millennia. Although we push for increased engagement with historical researchers, parallels between pre-modern and contemporary environmental and societal challenges need to be treated with caution. So-called cases of societal collapse—often associated with environmental calamities—provide limited or at best flawed parallels with challenges faced today. The pitfalls of reductionism and determinism that often attend collapse discourse account for social agency and complexity in incomplete and unconvincing ways. Instead, we argue that historical evidence should serve as context to environmental problems faced today, as antecedents of the accelerated environmental change of later modernity rather than as direct analogies. Historical antecedents can be understood, to an extent, as previous experiments against which to test and improve theory or to structure possibilistic scenarios that help anticipate unexpected social and environmental challenges. In concluding, we suggest that researcher in historical sciences and the humanities require resources, space and incentives to explore sticky questions of uncertainty, risk, and vulnerability to environmental change together with global change researchers, policymakers, and environmental practitioners.

AbstractUncertainty evaluation and sensitivity testing of the functions and parameters used in ERA Acute serve two functions. ERA Acute is a deterministic model which is sensitive to the range of values used for the parameters. Parameters have inherent uncertainties as to what their true values are, and functions may have varying strength of knowledge. The individual functions were tested with respect to their sensitivity towards variation of the parameter values using both deterministic and stochastic testing. Based on the testing, an uncertainty scoring system was used to identify and prioritize the most important parameters for reducing uncertainty. Recommendations for handling the uncertainty and securing comparability in spite of uncertainty were set up as a conclusion of the studies.

AbstractThe adaptation to the multiple facets of climate/global change challenges the conventional means of water system planning. Numerous demand and supply management options are often available, from which a portfolio of adaptation measures needs to be selected in a context of high uncertainty about future conditions. A framework is developed to integrate inputs from the two main approaches commonly used to plan for adaptation. The proposed “top–down meets bottom–up” approach provides a systematic and practical method for supporting the selection of adaptation measures at river basin level by comprehensively integrating the goals of economic efficiency, social acceptability, environmental sustainability, and adaptation robustness. The top-down approach relies on the use of a chain of models to assess the impact of global change on water resources and its adaptive management over a range of climate projections. Future demand scenarios and locally prioritized adaptation measures are identified following a bottom-up approach through a participatory process with the relevant stakeholders and experts. Cost-effective combinations of adaptation measures are then selected using a hydro-economic model at basin scale. The resulting adaptation portfolios are climate checked to define a robust program of measures based on trade-offs between adaptation costs and reliability. Valuable insights are obtained on the use of uncertain climate information for selecting robust, reliable, and resilient water management portfolios. Finally, cost allocation and equity implications are analyzed through the comparison of economically rational results (cooperative game theory) and the application of social justice principles.

AbstractThe effectiveness of governance depends on the knowledge upon which decisions are based. Knowledge veracity is particularly significant when future conditions are uncertain. In the context of global climate change, communities around the world, including the residents of Rapa Nui (Easter Island, Chile), face tremendous uncertainty in resource availability. In the context of these looming challenges, prehistoric Rapa Nui is often treated as a warning about human-induced ecological catastrophe. With contemporary populations of the island wrestling about issues of governance, it is vital that researchers carefully validate their knowledge about the island’s past. Despite the claims of traditional narratives, new empirical research on Rapa Nui indicates that the traditional “collapse” narrative has no basis. Instead, the island is now known to have been sustainable from its prehistory until European contact. These findings point to the potential of alternative action models and new governance structures.

AbstractIn this paper we present a review of the most important notions and characterizations of differentiability and necessary optimality conditions for a fuzzy multiobjective problem. As basis of this review, we first study the fundamental aspects of the notions of differentiability for interval valued functions, since the fuzzy environment and the interval environment are closely related. Those aspects are related to the different definitions of difference for intervals and their drawbacks, the different definitions and characterizations of the differentiability for interval-valued functions and their drawbacks and how they have been solved in the literature. Based on the most important and meaning results on interval valued functions you can find in the literature, a review on notions of differentiability in fuzzy context is given, both in the case of functions of one variable, and several variables. And finally we present the review results of the necessary optimality conditions for fuzzy multiobjective problems and the main conclusions.

Safety assessment requires the interaction of a large number of disciplines to model the environmental phenomena necessary to evaluate the safety of the disposal system. In this complex process, the identification and quantification of both types of uncertainties, random and epistemic, plays a very important role for confidence building. In this work an application of the concept of total uncertainty to radioactive waste disposal facilities safety assessment is proposed. By combining both types of uncertainty, aleatoric and epistemic, in the same framework, this approach ultimately aims to assess the confidence one can pose in the safety-assessment decisions.

Green security concerns the protection of the world's wildlife, forests, and fisheries from poaching, illegal logging, and illegal fishing. Unfortunately, conservation efforts in green security domains are constrained by the limited availability of defenders, who must patrol vast areas to protect from attackers. Artificial intelligence (AI) techniques have been developed for green security and other security settings, such as US Coast Guard patrols and airport screenings, but effective deployment of AI in these settings requires learning adversarial behavior and planning in complex environments where the true dynamics may be unknown. My research develops novel techniques in machine learning and game theory to enable the effective development and deployment of AI in these resource-constrained settings. Notably, my work has spanned the pipeline from learning in a supervised setting, planning in stochastic environments, sequential planning in uncertain environments, and deployment in the real world. The overarching goal is to optimally allocate scarce resources under uncertainty for environmental conservation.

This paper is based on the JIP study for the oceanographic hindcast and environmental load statistics of fixed steel structures at South China Sea under tropical cyclone conditions. It focuses on the sensitivity and comparison studies by exploring the degrees and reasons of variability that may occur in determination of design environmental conditions resulting from the selection of the design standards and approaches. The bias and efficiency in extreme values prediction are examined with respect to modeling uncertainty and statistical uncertainty. The long term distributions of maximum wave height as well as the associated wave period conditional on the design wave height are derived following the storm event based method. The approaches for combing wave, current and wind to define the design conditions and the associated biases on design load are investigated. Second order random and spreading wave theory is adopted to estimate the extreme wave crest height distributions. The extreme water level issue is addressed and recommendations are given for setting the deck elevation to achieve the explicit wave-in-deck probabilities. The studies are carried out by applying a dataset of a grid point containing 182 typhoons spanning 40 continuous years to demonstrate the analytical procedures in an understandable fashion. The results of this paper should lead to improvements in prediction of the environmental conditions for design of new-built structures to attain their target safety levels, as well as for assessment of existing structures to demonstrate their fitness-for-purpose.

We update our theory of interdependence for autonomous human-machine teams operating in open systems (A-HMT-S). In closed systems, desired outcomes can be easily obtained with rational models (e.g., game theory); there, uncertainty can only be studied as part of a system’s internal com-plexity. In hindsight, the problems with closed system models are obvious: they are fragile, hard to replicate, and not generalizable, the latter being the fatal flaw for autonomous human-machine teams and systems. Surprisingly, no amount or aggregation of data from individuals can be recombined to replicate social data. In contrast, with open systems, interdependence theo-ry is state dependent, reactive to every situation and change, especially the environmental and social uncertainty caused by competition or conflict. More important, in contrast to social science’s reliance on the independent and identically distributed (i.i.d.) data derived from individuals, interde-pendence theory is generalizable. But before we start, we acknowledge that machine learning is a closed system model, context dependent, and that ex-isting artificial intelligence (AI) models are insufficient to produce auton-omy today. Thus, we built a mathematical model based on first principles around interdependence and applicable to intelligent autonomous teams of any sort. With our model of interdependence, among the results we have found: reactiveness to bistable information requires intelligence, and boundaries as a barrier to impede its unwanted flow; independent infor-mation cannot replicate teammate dependence, effects, nor performance; in-terdependence creates tradeoffs between the structure and performance of autonomous systems, that, as byproducts, affords metrics, deception, sup-pression, and vulnerability, the latter being a new field of research that we have discovered and that is the motivation for innovation, mergers and ac-quisitions. We close with a brief review of future research opportunities.

Log-facies classification aims to predict a vertical profile of facies at well location with log readings or rock properties calculated in the formation evaluation and/or rock-physics modeling analysis as input. Various classification approaches are described in the literature and new ones continue to appear based on emerging Machine Learning techniques. However, most of the available classification methods assume that the inputs are accurate and their inherent uncertainty, related to measurement errors and interpretation steps, is usually neglected. Accounting for facies uncertainty is not a mere exercise in style, rather it is fundamental for the purpose of understanding the reliability of the classification results, and it also represents a critical information for 3D reservoir modeling and/or seismic characterization processes. This is particularly true in wells characterized by high vertical heterogeneity of rock properties or thinly bedded stratigraphy. Among classification methods, probabilistic classifiers, which relies on the principle of Bayes decision theory, offer an intuitive way to model and propagate measurements/rock properties uncertainty into the classification process. In this work, the Bayesian classifier is enhanced such that the most likely classification of facies is expressed by maximizing the integral product between three probability functions. The latters describe: (1) the a-priori information on facies proportion (2) the likelihood of a set of measurements/rock properties to belong to a certain facies-class and (3) the uncertainty of the inputs to the classifier (log data or rock properties derived from them). Reliability of the classification outcome is therefore improved by accounting for both the global uncertainty, related to facies classes overlap in the classification model, and the depth-dependent uncertainty related to log data. As derived in this work, the most interesting feature of the proposed formulation, although generally valid for any type of probability functions, is that it can be analytically solved by representing the input distributions as a Gaussian mixture model and their related uncertainty as an additive white Gaussian noise. This gives a robust, straightforward and fast approach that can be effortlessly integrated in existing classification workflows. The proposed classifier is tested in various well-log characterization studies on clastic depositional environments where Monte-Carlo realizations of rock properties curves, output of a statistical formation evaluation analysis, are used to infer rock properties distributions. Uncertainty on rock properties, modeled as an additive white Gaussian noise, are then statistically estimated (independently at each depth along the well profile) from the ensemble of Monte-Carlo realizations. At the same time, a classifier, based on a Gaussian mixture model, is parametrically inferred from the pointwise mean of the Monte Carlo realizations given an a-priori reference profile of facies. Classification results, given by the a-posteriori facies proportion and the maximum a-posteriori prediction profiles, are finally computed. The classification outcomes clearly highlight that neglecting uncertainty leads to an erroneous final interpretation, especially at the transition zone between different facies. As mentioned, this become particularly remarkable in complex environments and highly heterogeneous scenarios.

Addressing the public health threat posed by AMR is a national strategic priority for the UK, which has led to both a 20-year vision of AMR and a 5-year (2019 to 2024) AMR National Action Plan (NAP). The latter sets out actions to slow the development and spread of AMR with a focus on antimicrobials. The NAP used an integrated ‘One-Health’ approach which spanned people, animals, agriculture and the environment, and calls for activities to “identify and assess the sources, pathways, and exposure risks” of AMR. The FSA continues to contribute to delivery of the NAP in a number of ways, including through furthering our understanding of the role of the food chain and AMR.Thorough cooking of food kills vegetative bacterial cells including pathogens and is therefore a crucial step in reducing the risk of most forms of food poisoning. Currently, there is uncertainty around whether cooking food is sufficient to denature AMR genes and mobile genetic elements from these ‘dead’ bacteria to prevent uptake by ‘live’ bacteria in the human gut and other food environments - therefore potentially contributing to the overall transmission of AMR to humans. This work was carried out to assess these evidence gaps.

Complex systems modelling is already implemented in critical policy areas such as climate change and health. It could also play an important role in the nuclear weapons sphere – by opening alternative pathways that may help mitigate risks of confrontation and escalation – but such modelling has yet to be fully embraced by policymakers in this community. By applying a complexity lens, policy- and decision-makers at all stages along the nuclear chain of command might better understand how their actions could have significant consequences for international security and peace. Nuclear decision-making is shaped by, and interacts with, the ever-changing international security environment and nuclear weapons policy. Tackling problems in the nuclear weapons policy field requires the implementation of ‘system of systems’ design principles, mathematical modelling approaches and multidisciplinary analysis. This research paper presents nuclear weapons decision-making as a complex endeavour, with individual decisions being influenced by multiple factors such as reasoning, intuition (gut feeling), biases and system-level noise. At a time of crisis, these factors may combine to cause risks of escalation. The authors draw on past examples of near nuclear use to examine decision-making in the nuclear context as a ‘wicked problem’, with multi-layered, interacting and constantly fluctuating elements.

Topicality of modeling information transparency is determined by the influence it has on the effectiveness of management decisions made by an economic entity in the context of uncertainty and information asymmetry. It has been found that information transparency is a poorly structured category which acts as a qualitative characteristic of information and at certain levels forms an additional spectrum of properties of the information that has been adequately perceived or processed. As a result of structuring knowledge about the factor environment, a fuzzy cognitive model of information transparency was constructed in the form of a weighted digraph. Structural analysis and scenario forecasting of optimal alternatives of the fuzzy cognitive model made it possible to evaluate the classes of factors, identify their limited relations, establish the centrality of the roles of information transparency and information and communication security in the system built and evaluate their importance when modeling the situation self-development. Information visibility, reliability and availability have been found to have the strongest impact on the system. Taking into account different initial weights of the key factors — information transparency and information and communication security — the study substantiates the strategic ways for economic entities to achieve their goals in the context of uncertainty and information asymmetry, which allows us to use this approach as a tool for strategic management in the information environment.

We study the use of multi-level Monte Carlo methods for wind engineering. This report brings together methodological research on uncertainty quantification and work on target applications of the ExaQUte project in wind and civil engineering. First, a multi-level Monte Carlo for the estimation of the conditional value at risk and an adaptive algorithm are presented. Their reliability and performance are shown on the time-average of a non-linear oscillator and on the lift coefficient of an airfoil, with both preset and adaptively refined meshes. Then, we propose an adaptive multi-fidelity Monte Carlo algorithm for turbulent fluid flows where multilevel Monte Carlo methods were found to be inefficient. Its efficiency is studied and demonstrated on the benchmark problem of quantifying the uncertainty on the drag force of a tall building under random turbulent wind conditions. All numerical experiments showcase the open-source software stack of the ExaQUte project for large-scale computing in a distributed environment.

Climate resilience is the ability to anticipate, prepare for, and respond to hazardous events, trends, or disturbances related to climate (C2ES, 2022). Mitigation focuses on reducing the human impacts contributing to climate change (Burton, 2007, cited in Rousell & Cutter-Mackenzie-Knowles, 2020). Adaptation is about increasing people’s adaptive capacity, reducing the vulnerability of communities and managing risks (Anderson, 2012). Anderson further defines adaptation as not just being able to adapt from one stable climate to another but having the skills to adapt to uncertainty and make informed decisions in a changing environment. While ‘climate change’ is the term used throughout these briefs, it should be read as a shorthand for a more inclusive approach, which also captures associated environmental degradation. The Intergovernmental Panel on Climate Change (IPCC) warned, in their latest report, that global surface temperatures will continue to increase until 2050 (IPCC, 2021, p. 17). This will take place regardless of human intervention to reduce greenhouse gas emissions. The report also warns that the traditional technocratic approaches are insufficient to tackle the challenge of climate change, and that greater focus on the structural causes is needed. High- and upper-middle-income countries have been persistently shown to be the biggest contributors to the global carbon dioxide emissions, with lower income countries facing the most disruptive climate hazards, with Africa countries particularly vulnerable (CDP, 2020; IPCC, 2021). The vulnerability of low-income contexts exacerbates this risk, as there is often insufficient infrastructure and resources to ensure resilience to climate hazards (IPCC, 2021). For decades, advocates of climate change education have been highlighting the potential of education to help mitigate against climate change, and support adaptation efforts. However, implementation has been patchy, with inconsistent approaches and a lack of evidence to help determine the most effective way forward.This paper is divided into three sections, drawing together evidence on the key aspects of system reform,green and resilient infrastructure and Curriculum, pedagogy, assessment and teacher development.

The vadose (unsaturated) zone forms a major hydrologic link between the ground surface and underlying aquifers. To understand properly its role in protecting groundwater from near surface sources of contamination, one must be able to analyze quantitatively water flow and contaminant transport in variably saturated subsurface environments that are highly heterogeneous, often consisting of multiple geologic units and/or high and/or low permeability inclusions. The specific objectives of this research were: (i) to develop efficient and accurate tools for probabilistic delineation of dominant geologic features comprising the vadose zone; (ii) to develop a complementary set of data analysis tools for discerning the fractal properties of hydraulic and transport parameters of highly heterogeneous vadose zone; (iii) to develop and test the associated computational methods for probabilistic analysis of flow and transport in highly heterogeneous subsurface environments; and (iv) to apply the computational framework to design an “optimal” observation network for monitoring and forecasting the fate and migration of contaminant plumes originating from agricultural activities. During the course of the project, we modified the third objective to include additional computational method, based on the notion that the heterogeneous formation can be considered as a mixture of populations of differing spatial structures. Regarding uncertainly analysis, going beyond approaches based on mean and variance of system states, we succeeded to develop probability density function (PDF) solutions enabling one to evaluate probabilities of rare events, required for probabilistic risk assessment. In addition, we developed reduced complexity models for the probabilistic forecasting of infiltration rates in heterogeneous soils during surface runoff and/or flooding events Regarding flow and transport in variably saturated, spatially heterogeneous formations associated with fine- and coarse-textured embedded soils (FTES- and CTES-formations, respectively).We succeeded to develop first-order and numerical frameworks for flow and transport in three-dimensional (3-D), variably saturated, bimodal, heterogeneous formations, with single and dual porosity, respectively. Regarding the sampling problem defined as, how many sampling points are needed, and where to locate them spatially in the horizontal x₂x₃ plane of the field. Based on our computational framework, we succeeded to develop and demonstrate a methdology that might improve considerably our ability to describe quntitaively the response of complicated 3-D flow systems. The results of the project are of theoretical and practical importance; they provided a rigorous framework to modeling water flow and solute transport in a realistic, highly heterogeneous, composite flow system with uncertain properties under-specified by data. Specifically, they: (i) enhanced fundamental understanding of the basic mechanisms of field-scale flow and transport in near-surface geological formations under realistic flow scenarios, (ii) provided a means to assess the ability of existing flow and transport models to handle realistic flow conditions, and (iii) provided a means to assess quantitatively the threats posed to groundwater by contamination from agricultural sources.

Highlights • Individual investors prefer to have an investment with high ROI rather than a low-profit investment with environmental and social benefits. • Males invest and donate more money than females in terms of quantity and frequency. • People with a level of education above an associate degree (D3) have a significantly higher level of willingness to invest and donate to green project, compared to people with a lower level of education. • In general, people with a higher income level have a higher willingness to invest. However, there is no proof on the relationship between level of income and willingness to donate. • The age increases have a positive correlation with the willingness to invest in green project. Nevertheless, people >44 years old are more interested in donating than investing. • The younger generation (<44 years) tends to pick higher returns and short payback periods compared to the older generations (>44 years). • The respondents tend to invest and donate to the project located in the frontier, outermost, and least developed region (3T) even though the majority of the respondents are from Java, Madura, and Bali. • A social project such as health and education are preferable projects chosen by the respondents to invest and donate to, followed by the conservation, climate crisis, region’s welfare, and clean energy access. • Clean energy has not been seen as one of the preferred targets for green project investors and donors due to the poor knowledge of its direct impact on the environment and people’s welfare. • The average willingness to invest and donate is IDR 10,527,004 and IDR 2,893,079/person/annum with desired return on investment (ROI) and payback period (PP) of 5–8% 24 months, respectively. • Respondents prefer to donate more money to reward donations than donations without reward. • There is an enormous potential of crowdfunding as green project alternative financing, including renewable energy. The total investment could reach up to IDR 192 trillion (USD 13.4 billion)/annum and up to IDR 46 trillion (USD 3.2 billion)/annum for donation. • The main bottlenecks are poor financial literacy and the lack of platforms to facilitate public participation. • COVID-19 has decreased willingness to pay and invest due to income reduction and the uncertain economic recovery situation. However, it makes people pay more attention to the sustainability factor (shifting paradigm in investment).

1.1 Macroeconomic summary Economic recovery has consistently outperformed the technical staff’s expectations following a steep decline in activity in the second quarter of 2020. At the same time, total and core inflation rates have fallen and remain at low levels, suggesting that a significant element of the reactivation of Colombia’s economy has been related to recovery in potential GDP. This would support the technical staff’s diagnosis of weak aggregate demand and ample excess capacity. The most recently available data on 2020 growth suggests a contraction in economic activity of 6.8%, lower than estimates from January’s Monetary Policy Report (-7.2%). High-frequency indicators suggest that economic performance was significantly more dynamic than expected in January, despite mobility restrictions and quarantine measures. This has also come amid declines in total and core inflation, the latter of which was below January projections if controlling for certain relative price changes. This suggests that the unexpected strength of recent growth contains elements of demand, and that excess capacity, while significant, could be lower than previously estimated. Nevertheless, uncertainty over the measurement of excess capacity continues to be unusually high and marked both by variations in the way different economic sectors and spending components have been affected by the pandemic, and by uneven price behavior. The size of excess capacity, and in particular the evolution of the pandemic in forthcoming quarters, constitute substantial risks to the macroeconomic forecast presented in this report. Despite the unexpected strength of the recovery, the technical staff continues to project ample excess capacity that is expected to remain on the forecast horizon, alongside core inflation that will likely remain below the target. Domestic demand remains below 2019 levels amid unusually significant uncertainty over the size of excess capacity in the economy. High national unemployment (14.6% for February 2021) reflects a loose labor market, while observed total and core inflation continue to be below 2%. Inflationary pressures from the exchange rate are expected to continue to be low, with relatively little pass-through on inflation. This would be compatible with a negative output gap. Excess productive capacity and the expectation of core inflation below the 3% target on the forecast horizon provide a basis for an expansive monetary policy posture. The technical staff’s assessment of certain shocks and their expected effects on the economy, as well as the presence of several sources of uncertainty and related assumptions about their potential macroeconomic impacts, remain a feature of this report. The coronavirus pandemic, in particular, continues to affect the public health environment, and the reopening of Colombia’s economy remains incomplete. The technical staff’s assessment is that the COVID-19 shock has affected both aggregate demand and supply, but that the impact on demand has been deeper and more persistent. Given this persistence, the central forecast accounts for a gradual tightening of the output gap in the absence of new waves of contagion, and as vaccination campaigns progress. The central forecast continues to include an expected increase of total and core inflation rates in the second quarter of 2021, alongside the lapse of the temporary price relief measures put in place in 2020. Additional COVID-19 outbreaks (of uncertain duration and intensity) represent a significant risk factor that could affect these projections. Additionally, the forecast continues to include an upward trend in sovereign risk premiums, reflected by higher levels of public debt that in the wake of the pandemic are likely to persist on the forecast horizon, even in the context of a fiscal adjustment. At the same time, the projection accounts for the shortterm effects on private domestic demand from a fiscal adjustment along the lines of the one currently being proposed by the national government. This would be compatible with a gradual recovery of private domestic demand in 2022. The size and characteristics of the fiscal adjustment that is ultimately implemented, as well as the corresponding market response, represent another source of forecast uncertainty. Newly available information offers evidence of the potential for significant changes to the macroeconomic scenario, though without altering the general diagnosis described above. The most recent data on inflation, growth, fiscal policy, and international financial conditions suggests a more dynamic economy than previously expected. However, a third wave of the pandemic has delayed the re-opening of Colombia’s economy and brought with it a deceleration in economic activity. Detailed descriptions of these considerations and subsequent changes to the macroeconomic forecast are presented below. The expected annual decline in GDP (-0.3%) in the first quarter of 2021 appears to have been less pronounced than projected in January (-4.8%). Partial closures in January to address a second wave of COVID-19 appear to have had a less significant negative impact on the economy than previously estimated. This is reflected in figures related to mobility, energy demand, industry and retail sales, foreign trade, commercial transactions from selected banks, and the national statistics agency’s (DANE) economic tracking indicator (ISE). Output is now expected to have declined annually in the first quarter by 0.3%. Private consumption likely continued to recover, registering levels somewhat above those from the previous year, while public consumption likely increased significantly. While a recovery in investment in both housing and in other buildings and structures is expected, overall investment levels in this case likely continued to be low, and gross fixed capital formation is expected to continue to show significant annual declines. Imports likely recovered to again outpace exports, though both are expected to register significant annual declines. Economic activity that outpaced projections, an increase in oil prices and other export products, and an expected increase in public spending this year account for the upward revision to the 2021 growth forecast (from 4.6% with a range between 2% and 6% in January, to 6.0% with a range between 3% and 7% in April). As a result, the output gap is expected to be smaller and to tighten more rapidly than projected in the previous report, though it is still expected to remain in negative territory on the forecast horizon. Wide forecast intervals reflect the fact that the future evolution of the COVID-19 pandemic remains a significant source of uncertainty on these projections. The delay in the recovery of economic activity as a result of the resurgence of COVID-19 in the first quarter appears to have been less significant than projected in the January report. The central forecast scenario expects this improved performance to continue in 2021 alongside increased consumer and business confidence. Low real interest rates and an active credit supply would also support this dynamic, and the overall conditions would be expected to spur a recovery in consumption and investment. Increased growth in public spending and public works based on the national government’s spending plan (Plan Financiero del Gobierno) are other factors to consider. Additionally, an expected recovery in global demand and higher projected prices for oil and coffee would further contribute to improved external revenues and would favor investment, in particular in the oil sector. Given the above, the technical staff’s 2021 growth forecast has been revised upward from 4.6% in January (range from 2% to 6%) to 6.0% in April (range from 3% to 7%). These projections account for the potential for the third wave of COVID-19 to have a larger and more persistent effect on the economy than the previous wave, while also supposing that there will not be any additional significant waves of the pandemic and that mobility restrictions will be relaxed as a result. Economic growth in 2022 is expected to be 3%, with a range between 1% and 5%. This figure would be lower than projected in the January report (3.6% with a range between 2% and 6%), due to a higher base of comparison given the upward revision to expected GDP in 2021. This forecast also takes into account the likely effects on private demand of a fiscal adjustment of the size currently being proposed by the national government, and which would come into effect in 2022. Excess in productive capacity is now expected to be lower than estimated in January but continues to be significant and affected by high levels of uncertainty, as reflected in the wide forecast intervals. The possibility of new waves of the virus (of uncertain intensity and duration) represents a significant downward risk to projected GDP growth, and is signaled by the lower limits of the ranges provided in this report. Inflation (1.51%) and inflation excluding food and regulated items (0.94%) declined in March compared to December, continuing below the 3% target. The decline in inflation in this period was below projections, explained in large part by unanticipated increases in the costs of certain foods (3.92%) and regulated items (1.52%). An increase in international food and shipping prices, increased foreign demand for beef, and specific upward pressures on perishable food supplies appear to explain a lower-than-expected deceleration in the consumer price index (CPI) for foods. An unexpected increase in regulated items prices came amid unanticipated increases in international fuel prices, on some utilities rates, and for regulated education prices. The decline in annual inflation excluding food and regulated items between December and March was in line with projections from January, though this included downward pressure from a significant reduction in telecommunications rates due to the imminent entry of a new operator. When controlling for the effects of this relative price change, inflation excluding food and regulated items exceeds levels forecast in the previous report. Within this indicator of core inflation, the CPI for goods (1.05%) accelerated due to a reversion of the effects of the VAT-free day in November, which was largely accounted for in February, and possibly by the transmission of a recent depreciation of the peso on domestic prices for certain items (electric and household appliances). For their part, services prices decelerated and showed the lowest rate of annual growth (0.89%) among the large consumer baskets in the CPI. Within the services basket, the annual change in rental prices continued to decline, while those services that continue to experience the most significant restrictions on returning to normal operations (tourism, cinemas, nightlife, etc.) continued to register significant price declines. As previously mentioned, telephone rates also fell significantly due to increased competition in the market. Total inflation is expected to continue to be affected by ample excesses in productive capacity for the remainder of 2021 and 2022, though less so than projected in January. As a result, convergence to the inflation target is now expected to be somewhat faster than estimated in the previous report, assuming the absence of significant additional outbreaks of COVID-19. The technical staff’s year-end inflation projections for 2021 and 2022 have increased, suggesting figures around 3% due largely to variation in food and regulated items prices. The projection for inflation excluding food and regulated items also increased, but remains below 3%. Price relief measures on indirect taxes implemented in 2020 are expected to lapse in the second quarter of 2021, generating a one-off effect on prices and temporarily affecting inflation excluding food and regulated items. However, indexation to low levels of past inflation, weak demand, and ample excess productive capacity are expected to keep core inflation below the target, near 2.3% at the end of 2021 (previously 2.1%). The reversion in 2021 of the effects of some price relief measures on utility rates from 2020 should lead to an increase in the CPI for regulated items in the second half of this year. Annual price changes are now expected to be higher than estimated in the January report due to an increased expected path for fuel prices and unanticipated increases in regulated education prices. The projection for the CPI for foods has increased compared to the previous report, taking into account certain factors that were not anticipated in January (a less favorable agricultural cycle, increased pressure from international prices, and transport costs). Given the above, year-end annual inflation for 2021 and 2022 is now expected to be 3% and 2.8%, respectively, which would be above projections from January (2.3% and 2,7%). For its part, expected inflation based on analyst surveys suggests year-end inflation in 2021 and 2022 of 2.8% and 3.1%, respectively. There remains significant uncertainty surrounding the inflation forecasts included in this report due to several factors: 1) the evolution of the pandemic; 2) the difficulty in evaluating the size and persistence of excess productive capacity; 3) the timing and manner in which price relief measures will lapse; and 4) the future behavior of food prices. Projected 2021 growth in foreign demand (4.4% to 5.2%) and the supposed average oil price (USD 53 to USD 61 per Brent benchmark barrel) were both revised upward. An increase in long-term international interest rates has been reflected in a depreciation of the peso and could result in relatively tighter external financial conditions for emerging market economies, including Colombia. Average growth among Colombia’s trade partners was greater than expected in the fourth quarter of 2020. This, together with a sizable fiscal stimulus approved in the United States and the onset of a massive global vaccination campaign, largely explains the projected increase in foreign demand growth in 2021. The resilience of the goods market in the face of global crisis and an expected normalization in international trade are additional factors. These considerations and the expected continuation of a gradual reduction of mobility restrictions abroad suggest that Colombia’s trade partners could grow on average by 5.2% in 2021 and around 3.4% in 2022. The improved prospects for global economic growth have led to an increase in current and expected oil prices. Production interruptions due to a heavy winter, reduced inventories, and increased supply restrictions instituted by producing countries have also contributed to the increase. Meanwhile, market forecasts and recent Federal Reserve pronouncements suggest that the benchmark interest rate in the U.S. will remain stable for the next two years. Nevertheless, a significant increase in public spending in the country has fostered expectations for greater growth and inflation, as well as increased uncertainty over the moment in which a normalization of monetary policy might begin. This has been reflected in an increase in long-term interest rates. In this context, emerging market economies in the region, including Colombia, have registered increases in sovereign risk premiums and long-term domestic interest rates, and a depreciation of local currencies against the dollar. Recent outbreaks of COVID-19 in several of these economies; limits on vaccine supply and the slow pace of immunization campaigns in some countries; a significant increase in public debt; and tensions between the United States and China, among other factors, all add to a high level of uncertainty surrounding interest rate spreads, external financing conditions, and the future performance of risk premiums. The impact that this environment could have on the exchange rate and on domestic financing conditions represent risks to the macroeconomic and monetary policy forecasts. Domestic financial conditions continue to favor recovery in economic activity. The transmission of reductions to the policy interest rate on credit rates has been significant. The banking portfolio continues to recover amid circumstances that have affected both the supply and demand for loans, and in which some credit risks have materialized. Preferential and ordinary commercial interest rates have fallen to a similar degree as the benchmark interest rate. As is generally the case, this transmission has come at a slower pace for consumer credit rates, and has been further delayed in the case of mortgage rates. Commercial credit levels stabilized above pre-pandemic levels in March, following an increase resulting from significant liquidity requirements for businesses in the second quarter of 2020. The consumer credit portfolio continued to recover and has now surpassed February 2020 levels, though overall growth in the portfolio remains low. At the same time, portfolio projections and default indicators have increased, and credit establishment earnings have come down. Despite this, credit disbursements continue to recover and solvency indicators remain well above regulatory minimums. 1.2 Monetary policy decision In its meetings in March and April the BDBR left the benchmark interest rate unchanged at 1.75%.

The sixth Rejuvenate dialogue was held on 15th September 2022. Six panellists working across contexts and themes joined the Rejuvenate team on a discussion on uncertainty and it’s intersection with child and youth rights and participation. From past research, we know that young people do not necessarily view uncertainty in their lives as negative. This is especially true for some of the most-marginalised youth who see their ‘certainty’ as poverty, unemployment and environmental fragility. This dialogue focuses on how adults, including those working on policy and practice in rights and philanthropy, can learn from how children and youth positively navigate uncertainty. Further, this dialogue makes the case for working with children and youth if we are to find new, more hopeful futures in the face of uncertainty.