Reduce Decision Time Research Articles

Transforming Agriculture with Advanced Robotic Decision Systems via Deep Recurrent Learning

Global farming methods have improved owing to supply demand, human resource shortages, and seasonal fluctuations. Integrating automated mechanics, smart controllers, and artificial intelligence has become imperative to tackle these problems. Through self-learning and wise decision-making, this integration speeds up processing and encourages sustainability. To help modern farming techniques become more efficient and environmentally friendly, this paper presents a Pliant Decision System (PDS) specifically made for Farming Robots (FRs). The FRs are designed to be time-synchronized and multi-operational, which allows for guided agricultural practices that provide sustainable results. The robotic operations are planned to finish the agricultural processes with individual sustainability verification once they are first classified according to traditional techniques. Two layers of deep recurrent learning are used to evaluate sustainability. Verifying individual completion and sustainability via timely execution and cumulative effect is the primary emphasis of the first layer. In the second layer, the seasonal consistency of production improvements is compared using dual analyses. By training the learning model to synchronize and complete robotic activities promptly, people can execute judgments and synchronize successive agricultural operations throughout the year. The proposed method outwits the existing methods by leveraging sustainable factors by 8.24%, completion by 11.1%, promptness by 7.79%, synchronization by 6.75%, and reducing decision time by 12.07% for the varying process times.

Vision transformer-convolution for breast cancer classification using mammography images: A comparative study

Breast cancer is a significant global health concern, highlighting the critical importance of early detection for effective treatment of women’s health. While convolutional networks (CNNs) have been the best for analysing medical images, recent interest has emerged in leveraging vision transformers (ViTs) for medical data analysis. This study aimed to conduct a comprehensive comparison of three systems a self-attention transformer (VIT), a compact convolution transformer (CCT), and a tokenlearner (TVIT) for binary classification of mammography images into benign and cancerous tissue. Thorough experiments were performed using the DDSM dataset, which consists of 5970 benign and 7158 malignant images. The performance accuracy of the proposed models was evaluated, yielding results of 99.81% for VIT, 99.92% for CCT, and 99.05% for TVIT. Additionally, the study compared these results with the current state-of-the-art performance metrics. The findings demonstrate how convolution-attention mechanisms can effectively contribute to the development of robust computer-aided systems for diagnosing breast cancer. Notably, the proposed approach achieves high-performance results while also minimizing the computational resources required and reducing decision time.

Feasibility of Gastrografin Use for Adhesive Small Bowel Obstruction in Low-Income Countries

IntroductionSmall bowel obstruction (SBO) is one of the most common causes for hospital admission in Ethiopia. The use of water-soluble contrast agents (WSCAs) such as Gastrografin to manage adhesive SBO can predict nonoperative resolution of SBO and reduce decision time to surgery and length of hospital stay. However, nothing is known about practice patterns and Gastrografin use in low-income settings. We sought to characterize current management practices, including use of WSCAs, as well as outcomes for patients with SBO in Addis Ababa, Ethiopia. MethodsWe conducted a mixed-methods study consisting of a survey of surgeons throughout Ethiopia and a retrospective record review at five public, tertiary care–level teaching hospitals in Addis Ababa. ResultsOf the 76 surgeons who completed the survey, 63% had heard of the use of WSCAs for SBO and only 11% used oral agents for its management. Chart review of 149 patients admitted with SBO showed the most common etiology was adhesion (39.6% of admissions), followed by small bowel volvulus (20.8%). Most patients (83.2%) underwent surgery during their admission. The most common diagnosis in patients who did not require surgery was also adhesion (68.0%), as well as for those who had surgery (33.9%), followed by small bowel volvulus (24.2%). ConclusionsThe etiology of SBO in Ethiopia may be changing, with postoperative adhesions becoming more common than other historically more prevalent causes. Although a Gastrografin protocol as a diagnostic and potentially therapeutic aid for SBO is feasible in this population and setting, challenges can be anticipated, and future studies of protocol implementation and effectiveness are needed to further inform its utility in Ethiopia and other low-income and middle-income countries.

A 33-Gb/s/Pin 1.09-pJ/Bit Single-Ended PAM-3 Transceiver With Ground-Referenced Signaling and Time-Domain Decision Technique for Multi-Chip Module Memory Interfaces

A three-level pulse amplitude modulation (PAM-3) transceiver (TRX) with improved simultaneous switching output (SSO) noise and reference voltage margin was studied. PAM-3 signaling with an insertion of 3 bits in 2-unit intervals (UI) was implemented using the upper, under, and ground voltages, achieving 150% pin efficiency. The ground-referenced signaling (GRS) charge pump driver reduced SSO noise and maintained ground as the solitary return current path. Capacitive peaking and middle edge-rate boosting were implemented for transmitter (TX) equalization. With the receiver (RX), the ground level was used as a reference voltage for a single-to-differential (S2D) amplifier. PAM-3 signaling was converted to binary data using the time-domain decision technique in comparators. The decision time of comparators for data and reference voltage was compared. This technique shows the increased voltage margin, the reduced decision time, and the decreased error rate. The reduced decision time improved the performance of the PAM-3 decision feedback equalizer (DFE). A clock disabled the circuit, and a low-power return-to-zero (RZ) to non-RZ (NRZ) converter was configured for the proposed comparator output cases. The prototype TRX was fabricated in a 28-nm complementary metal–oxide silicon (CMOS) process with a surface area of 0.006 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{2}$</tex-math> </inline-formula> . The total power efficiency was recorded as 1.09 pJ/b at 33 Gb/s/pin. Through the flame retardant-4 (FR-4) 20-mm printed circuit board (PCB) channel and coaxial cable, a bit error rate (BER) of 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-12}$</tex-math> </inline-formula> was measured.

Automatic breast cancer diagnosis based on hybrid dimensionality reduction technique and ensemble classification.

Feature selection in the face of high-dimensional data can reduce overfitting and learning time, and at the same time improve the accuracy and efficiency of the system. Since there are many irrelevant and redundant features in breast cancer diagnosis, removing such features leads to more accurate prediction and reduced decision time when dealing with large-scale data. Meanwhile, ensemble classifiers are powerful techniques to improve the prediction performance of classification models, where several individual classifier models are combined to achieve higher accuracy. In this paper, an ensemble classifier algorithm based on multilayer perceptron neural network is proposed for the classification task, in which the parameters (e.g., number of hidden layers, number of neurons in each hidden layer, and weights of links) are adjusted based on an evolutionary approach. Meanwhile, this paper uses a hybrid dimensionality reduction technique based on principal component analysis and information gain to address this problem. The effectiveness of the proposed algorithm was evaluated based on the Wisconsin breast cancer database. In particular, the proposed algorithm provides an average of 17% better accuracy compared to the best results obtained from the existing state-of-the-art methods. Experimental results show that the proposed algorithm can be used as an intelligent medical assistant system for breast cancer diagnosis.

A cognitive and participative decision-making model for transportation planning under different uncertainty levels

Decision-making processes related to transportation systems are often very complex, belonging to the class of “wicked problems” in social studies. The literature and the media abound in examples of transportation planning “failures” including decisions made, delayed or withdrawn without any traceable motivations, public opposition, public opposition, extra costs/implementation times and outright cancellation, wrong traffic/revenue forecasts. Planning failures stem from several causes, including technical errors in forecasting predictable effects, lack of consensus, new governance cycles and inability to recognize the intrinsic uncertainty affecting some key variables. The underlying assumption of this paper is that the quality of the decision-making process critically depends on how the process is structured and managed. The paper proposes a conceptual model to represent and organize transport planning as a cognitive decision-making process with strong involvement of different stakeholders in various stages, accounting for different levels of uncertainty and including short- and long-term decisions options. The result is a planning process that allows for changes in objectives, strategies and decisions during different stages of the process itself, as is usually the case when different governance and socio-economic cycles succeed over time. However, the process maintains a form of procedural consistency and is intended to counteract the “Penelope syndrome”, whereby decisions made under previous governance cycles are undone regardless of their merits and costs. The real-life implementation of the proposed model requires strong “political commitment” to an open and accountable decision-making process, a technical structure able to act as “process owner” coordinating technical and engagement activities, and a general societal attitude towards organized participation to public decision making. The proposed model was applied to the Regional Transportation Plan of Veneto in Italy where the above conditions applied. A number of lessons were learnt from that exercise: i) stakeholders’ engagement revealed an important potential to manage uncertainty and increase consensus; ii) flexibility and robustness with respect to medium–to–high uncertainty levels already recognized at the time of the plan (immediately before the COVID-19 crisis) were an appreciated feature of the plan; iii) separation between mature decisions and those needing further analysis was a success strategy, reducing political “decision costs” (from naysaying) by delaying decisions still recognizing underlying needs; iv) reduced decision time (a largely bi-partisan vote in less than one year).

Dampening affect via expectations: The case of ambivalence.

People often form attitudes based on a mixture of positive and negative information. This can result in mixed evaluative reactions that are associated with feeling conflicted and undecided (i.e., felt ambivalence). In the present research, we examined whether expectations of receiving mixed information could dampen felt ambivalence compared to situations where the mixed information was instead unexpected. In six experiments, expectancies of receiving mixed information-either explicitly provided or implied via patterns of behavior-led people to feel less ambivalent about a target person who had engaged in mixed social behaviors. Expectations of mixed information reduced felt ambivalence to receipt of such information in comparison to having no explicit expectancies (Studies 1-3, 5-6), negative expectancies (Study 2), positive expectancies (Study 3), and compared with expectancies formed merely on the basis of behavioral patterns (Study 4). The extent to which people felt that their expectancies of mixed information regarding the target were confirmed (vs. disconfirmed) significantly accounted for the reductions in felt ambivalence. Finally, lower felt ambivalence via manipulated expectations accounted for reduced decision time in a workplace simulation about recommending promotion or termination of an employee (Study 6). Overall, these findings bridge the extensive literatures in attitudes, social judgment, and expectations, showing that expecting mixed information can lead to reductions in felt ambivalence that have consequences for behavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Automation in nursing decision support systems: A systematic review of effects on decision making, care delivery, and patient outcomes.

The study sought to summarize research literature on nursing decision support systems (DSSs ); understand which steps of the nursing care process (NCP) are supported by DSSs, and analyze effects of automated information processing on decision making, care delivery, and patient outcomes. We conducted a systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, CINAHL, Cochrane, Embase, Scopus, and Web of Science were searched from January 2014 to April 2020 for studies focusing on DSSs used exclusively by nurses and their effects. Information about the stages of automation (information acquisition, information analysis, decision and action selection, and action implementation), NCP, and effects was assessed. Of 1019 articles retrieved, 28 met the inclusion criteria, each studying a unique DSS. Most DSSs were concerned with two NCP steps: assessment (82%) and intervention (86%). In terms of automation, all included DSSs automated information analysis and decision selection. Five DSSs automated information acquisition and only one automated action implementation. Effects on decision making, care delivery, and patient outcome were mixed. DSSs improved compliance with recommendations and reduced decision time, but impacts were not always sustainable. There were improvements in evidence-based practice, but impact on patient outcomes was mixed. Current nursing DSSs do not adequately support the NCP and have limited automation. There remain many opportunities to enhance automation, especially at the stage of information acquisition. Further research is needed to understand how automation within the NCP can improve nurses' decision making, care delivery, and patient outcomes.

Reliability refinement in VANET with hybrid jamming attacks using novel index based voting algorithm

Vehicular Adhoc Network (VANET) constitutes a basis for the Intelligent Transportation Systems. VANET can be extensively used in safety and non-safety applications. DSRC Safety purposes are essential usages in VANET, have the basic objective of improving and enhancing safety through notifying drivers in hazards or impending accidents. The dynamic changes in topology are frequent in VANET, resulting in several jamming attacks. Hybrid jamming attacks involve the combination of the features of constant, deceptive and random jamming attacks. In this research work a novel Index Based Voting Approach (IBVA) has been proposed to avoid hybrid jamming as an integral component of devoted short range communication safety applications in VANET. The aim of this work focuses on hybrid jamming detection strategy in terms of node centric misbehaviour detection. The authenticities of vehicular nodes were determined by a new Recursive Candidate Elimination Algorithm (RCEA). Experimental results show that the time required to make decisions 0.3 s, which is reduced decision time compared to previous algorithm reported in the literature. The proposed IBVA algorithm in VANET improves the reliability in terms of malicious nodes detection and reducing the decision which in turn improves the network performance.

Oxytocin modulation of self-referential processing is partly replicable and sensitive to oxytocin receptor genotype

Intranasal oxytocin (OXT) has been associated with effects on diverse social-emotional domains in humans, however progress towards a therapeutic application of OXT in disorders with social-emotion impairments is currently hampered by poor replicability. Limited statistical power and individual differences in biological factors, such as oxytocin receptor (OXTR) genetics, may have contributed to these variable findings. To this end, employing a validated oxytocin-sensitive trait judgment paradigm, we present a pharmaco-genetic study aiming at (1) replicating previous findings suggesting that intranasal oxytocin (24 IU) reduces the self-referential bias in a large sample of n = 170 male subjects, (2) determining whether variations in common receptor polymorphisms (rs237887, rs2268491, rs2254298, rs53576, rs2268498) influence sensitivity to oxytocin's behavioral effects. We confirmed that in the whole sample oxytocin influenced self-other distinction in terms of reduced decision time. However, oxytocin only influenced decision time in rs53576 G carriers, whereas effects on subsequent memory performance were only found in rs2268498 TT homozygotes. In summary, the current study partially replicates our previous findings showing that oxytocin reduces the self-referential bias and suggests that sensitivity to its effects in this domain are receptor genotype dependent.

Hispania Begins its Second Hundred Years

Hispania Begins its Second Hundred Years Sheri Spaine Long Hispania's one-hundredth anniversary issue (100.5) is now in print and available for download gratis (see www.hispaniajournal.com). The centenary issue offers visionary articles and response essays on the future of teaching Spanish and Portuguese. As the journal begins a new century of its publication, we look forward to the work ahead and acknowledge our past achievements and contributors. The milestone of the centenary issue gives us the opportunity to reflect on our past and contemplate our future. During 2017, sadly, we lost the tenth Editor of Hispania Dr. Estelle Irizarry, Professor of Spanish Emerita of Georgetown University. Irizarry was Hispania's first female editor. She provided the journal with stellar leadership from 1993–2000. She was an accomplished editor, professor, and scholar. Perhaps best known for her scholarship on the DNA of Christopher Columbus, her publications and projects in languages and literature were many and varied. During her tenure at the journal, Hispania garnered second place for the "Phoenix Award for Significant Editorial Achievement" from the Council of Editors of Learned Journals (CELJ) in 1995. Her editorship was also known for implementing new technology. She produced a CD of all articles published in Hispania dating from its inception in 1917 through 1990. This project paved the way for further digitization of the journal in more recent years. Irizarry also improved efficiency and reduced decision time by introducing submissions via email. Close colleague and luminary in her own right Dr. Barbara Mujica, Professor of Spanish Emerita, also of Georgetown University, wrote a tribute to Irizarry that aptly captures her intellectual work, generous spirit, and humanity that begins on the next page. Looking forward, our profession has much work ahead to serve heritage learners (HL) more comprehensively. I invited Hispania Associate Editor Dr. Maria Carreira, Professor of Spanish at California State University, Long Beach and the Co-director of the National Heritage Language Resource Center at UCLA, to pen a response to the recent publication by the Commission on Language Learning of the American Academy of Arts and Sciences (AAAS) titled America's Languages: Investing in Language Education for the 21st Century (2017). Carreira makes the case for the oft-underutilized assets of HLs in building language proficiency. There are implications for HL students of Spanish and Portuguese. Carreira offers a concise summary of the development of research and the current state of HL education and demonstrates the lack of systematic implementation of targeted HL education in the United States at all levels. This reality contradicts one of the five priorities put forth in the report by the AAAS: to advance language proficiency through the education of heritage learners. Over the last few decades, as Carreira points out, the burden has fallen to instructors to innovate HL instruction. There has been scant institutional support or acknowledgement of such efforts. She calls for the hiring of administrators who are committed to this cause to be able to institutionalize and regularize HL instruction within the curriculum. She offers data and arguments for Spanish HLs. In particular, students of Spanish who are heritage speakers are likely to be useful in the context of your institution as you advocate to serve HL learners of Spanish and Portuguese. As Hispania moves forward, watch for more on this important topic. I encourage you to read Carreira's essay in the upcoming pages. [End Page 1] Sheri Spaine Long Editor of Hispania Copyright © 2018 AATSP

Combining Climate Scenarios and Risk Management Approach—A Finnish Case Study

Climate change impacts on nature and the environment have been widely discussed and studied. Traditionally, a company’s continuity management is based on risk analysis. There are also attempts to implement scenario-based methods in the risk management procedures of companies. For industrial decision makers, it is vital to acknowledge the impacts of climate change with regards to their adaptation strategies. However, a scenario-based approach is not always the most effective way to analyze these risks. This paper investigates the integration of scenario and risk-based methods for a company’s adaptation planning. It considers the uncertainties of the climate change scenarios and the recognized risks as well as suitable adaptation strategies. The paper presents the results of climate risk analysis prepared for two Finnish hydropower plants. The introduced method was first piloted in 2008 and then again in 2015. The update of the analysis pointed out that at the company level, the climate risks and other risks originating from governmental or political decisions form an intertwined wholeness where the origin of the risk is difficult to outline. It seems that, from the business point of view, the main adaptation strategies suggested by the integrated risk and scenarios approach are those that support buying “safety margins” in new investments and reducing decision time horizons. Both of these adaptation strategies provide an advantage in the circumstances where also political decisions and societal changes have a great effect on decision making.

Sustainable competitiveness based on resilience and innovation – an alternative approach

The pressure to reduce decision time cycles and to be able to react and anticipate competitors is a requisite for survival and the key to success. Therefore, companies should apply new models to increase their competitiveness using new trends and concepts. Based on this need, this paper aims to contribute to the improvement of companies' strategic planning process. With this purpose. it is necessary to review existing models of competitiveness to be able to create an alternative approach. The model proposed combines two new concepts – resilience and innovation – and integrates the principles of sustainability (triple bottom line) as a way to create competitive advantage. This approach also considers business evaluation models, such as the European Foundation for Quality Management (EFQM) Excellence model and the Shingo operational excellence model, and tools that reduce the strategic execution gap (for example the balanced scorecard). Establishing a relation between resilience, innovation and sustainable competitiveness, this approach is (1) differentiating, (2) able to add value (more focused and more efficient), (3) applicable to several business sectors and to different levels of technological sophistication and monitoring maturity, and (4) adaptable to distinctive cultural environments. Through differentiated criteria of evaluation, it allows the identification of the current competitive positioning of organizations, and the setting of parameters for implementing sustainable competitiveness.

The Relationship of Uncertainty and Complexity with Observed Bias and Interindividual Differences When Timing Audiovisual Signals

Although in the last few decades there was an enormous progress in understanding the timing of auditory and visual signals, there are still three puzzling things yet to be unexplained: 1) often inconclusive results, 2) results depending on the task employed (such as synchrony judgment, SJ, or temporal order judgment, TOJ, tasks), and 3) high interindividual differences. Examples for the first point are studies investigating if the brain is capable of actively compensating for the different propagation speed of sound and light. Some studies finding support for such a mechanism and others do not. Given that our brain is, in opposite to a typical lab-situation, continuously bombarded with different audiovisual signals at a time it follows that the brain needs also to “decide” which auditory and visual neural activation belongs to the external event in question. Therefore, we will argue that an active compensation mechanism is insufficient and that, above all, the brain deals with an inverse problem when having to link neural signals in the presence of plenty of other competing ones. This inverse problem implies for the brain uncertainty about which of the neural signals belong together. In such a situation, and in combination with being “forced” for an action/conscious percept, information processing in the brain will lead to basically two general output alternatives: First, the output is based solely on present sensory data. In case incoming data are with regard to a decision ambiguous the output of the system (e.g., responses) will be random across trials. In this case no bias will be evident. Second, present sensory data are integrated with other data available in the system (e.g., gathered through past experience). In this case a specific bias will be evident. Such a mechanism might reduce decision time in ambiguous or complex situations.A brief overview about recent behavioral and electrophysiological findings in our lab (using SJs and TOJs) will be presented compatible with the view outlined above. I will emphasize how such a view might especially contribute to a better understanding of interindividual differences and individual bias by demonstrating: 1) influence of prior information, 2) a systematic relationship of prior information and the degree of uncertainty about present sensory data, 3) a systematic relation of task complexity (degree of freedom of possible cue or neural networks combinations) and interindividual differences.

A Simulation Study of Managed Qualitative Thinking Support Systems (MQTSS)

The continued growth in the volume of available domain and technical data has been facilitated by a corresponding advancement in and communication technology. This information overload can result in inefficient use of time and resources as well as the creation of recommended courses of action that are overruled by the decision makers' judgment and experience. In order to address these problems, multiple knowledge sources and an inference process capable of mirroring the human thought processes (especially judgment and experience) must be available at the right time to the persons or groups needing the knowledge for decision making. Such a concept can be referred to as MANAGED QUALITATIVE THINKING SUPPORT SYSTEMS (MQTSS). Traditional decision support systems (DSS) rely upon decision maker or staff expertise to render knowledge in support of decision making. If the decision maker or staff has insufficient domain or technical expertise to utilize the DSS's embedded models, interpret results, or implement the recommendations, knowledge delivery may be compromised or rendered ineffective. MQTSS can alleviate these support problems and improve knowledge delivery for decision making by reducing knowledge search times, streamlining decision-making tasks, reducing decision time, and promoting appropriate qualitative thinking,. The MQTSS approach theoretically can enhance the decision making process and decision outcomes. This paper attempts to replicate an earlier study by this author to test the theory. First, the MQTSS approach is presented. Next, an system is created to deliver the technology to management. Finally, a simulation experiment is reported that compares the effectiveness of support rendered by a traditional decision support system and the created MQTSS system. The paper closes with conclusions and implications for systems research.

The influence of training on decision times and errors associated with classifying trunk postures using video-based posture assessment methods

The purpose of this study was to examine the influence of training on the decision times and errors associated with video-based trunk posture classifications. Altogether, 30 amateur and 30 knowledge-based participants completed a three-phase study (pre-training, training, post-training) that required them to classify static trunk postures in images on a computer screen into a posture category that represented the angle of the trunk depicted. Trunk postures were presented in both flexion/extension and lateral bend views and at several distances from the boundaries of the posture categories. Both decision time and errors decreased as distance from the boundaries increased. On average, amateur analysts experienced a larger decrease in decision time per posture classification than knowledge-based analysts (amateur: 0.79 s, knowledge-based: 0.60 s; p <0.05) suggesting that training can have beneficial effects on classification performance. The implications are that the analysis time associated with video-based posture assessment methods can be reduced with appropriate training, making this type of approach feasible for larger-scale field studies. Statement of Relevance:Understanding the role that training can play in reducing errors associated with the use of video-based posture assessment methods may result in more efficient use of these tools by ergonomic practitioners. Reducing decision time and misclassification errors will provide a more efficient, accurate and representative assessment of injury risk.

Value analysis as a decision support tool in cruise ship design

In the cruise ship industry product development and production activities are really complex and must follow strict rules imposed by naval registries. Designers are frequently required to choose among several alternative solutions (e.g., materials, components, layouts, etc.). Because of time constraints, as a matter of fact, design decisions are made fast and in a reactive way, according to the particular case, without considering decisions made in the past and without using specific decision support tools. The final choice is often left to a single designer's experience, whose selection criteria are unknown and not formalised. As a consequence there is no shared knowledge justifying the reason why a design solution has been chosen and whether it is the best one. We developed and implemented in Fincantieri S.p.A. – a leading company in the cruise ship industry – an original decision support tool, based on value analysis, designers can use to document and formalise their choices. Value analysis is a well known structured method to increase product value and/or cut costs, thus supporting the selection of the most valuable solution by means of objective parameters. We demonstrate that the proposed tool can also facilitate reuse of the available knowledge base on decisional criteria, increase interactions between people (design staff, buyers, shipyard personnel, etc.) involved in different stages of different value analysis projects, and reduce decision time.

Strategies to adapt to an uncertain climate change

Many decisions concerning long-lived investments already need to take into account climate change. But doing so is not easy for at least two reasons. First, due to the rate of climate change, new infrastructure will have to be able to cope with a large range of changing climate conditions, which will make design more difficult and construction more expensive. Second, uncertainty in future climate makes it impossible to directly use the output of a single climate model as an input for infrastructure design, and there are good reasons to think that the needed climate information will not be available soon. Instead of optimizing based on the climate conditions projected by models, therefore, future infrastructure should be made more robust to possible changes in climate conditions. This aim implies that users of climate information must also change their practices and decision-making frameworks, for instance by adapting the uncertainty-management methods they currently apply to exchange rates or R&D outcomes. Five methods are examined: (i) selecting “no-regret” strategies that yield benefits even in absence of climate change; (ii) favouring reversible and flexible options; (iii) buying “safety margins” in new investments; (iv) promoting soft adaptation strategies, including long-term prospective; and (v) reducing decision time horizons. Moreover, it is essential to consider both negative and positive side-effects and externalities of adaptation measures. Adaptation–mitigation interactions also call for integrated design and assessment of adaptation and mitigation policies, which are often developed by distinct communities.

EXPERIENCE-BASED KNOWLEDGE REPRESENTATION: SOEKS

When managers make decisions, they use previous, similar, or equal experiences to help themselves in a new decision-making situation. Thus, keeping record of previous decision events appears to be of the utmost importance as part of the decision making process. For us, every formal decision event has to be collected and stored as experienced knowledge, and any technology able to do this will allow us to improve the decision-making process by reducing decision time, as well as by avoiding duplication in the process. However, one of the most complicated issues about knowledge is its representation. Developing a knowledge structure that stores and administers experience from the day-to-day decision processes would improve decision-making quality and efficiency. We are proposing such a knowledge structure and have named it set of experience knowledge structure. A set of experience knowledge structure (SOEKS) is a combination of organized information obtained from a formal decision event. Fully applied, the set of experience knowledge structure would advance the notion of administering knowledge in the current decision-making environment.

Strategies to adapt to an uncertain climate change

Abstract Many decisions concerning long-lived investments already need to take into account climate change. But doing so is not easy for at least two reasons. First, due to the rate of climate change, new infrastructure will have to be able to cope with a large range of changing climate conditions, which will make design more difficult and construction more expensive. Second, uncertainty in future climate makes it impossible to directly use the output of a single climate model as an input for infrastructure design, and there are good reasons to think that the needed climate information will not be available soon. Instead of optimizing based on the climate conditions projected by models, therefore, future infrastructure should be made more robust to possible changes in climate conditions. This aim implies that users of climate information must also change their practices and decision-making frameworks, for instance by adapting the uncertainty-management methods they currently apply to exchange rates or RD (ii) favouring reversible and flexible options; (iii) buying “safety margins” in new investments; (iv) promoting soft adaptation strategies, including long-term prospective; and (v) reducing decision time horizons. Moreover, it is essential to consider both negative and positive side-effects and externalities of adaptation measures. Adaptation–mitigation interactions also call for integrated design and assessment of adaptation and mitigation policies, which are often developed by distinct communities.