Short-term Decision-making Research Articles

Short-term risk management of electricity retailers under rising shares of decentralized solar generation

Electricity retailers face increasing uncertainty due to the ongoing expansion and self-consumption of unpredictable distributed generation in the residential sector. We analyze how increasing levels of households’ solar PV self-generation affect the short-term decision-making and associated risk exposure of electricity retailers in the German day-ahead and intraday markets. First, we develop a stochastic model accounting for correlations between solar load, residual load and price in sequentially nested wholesale spot markets across seasons and type of day. Second, we develop a computationally tractable two-stage stochastic mixed-integer optimization model to investigate the trading portfolio and risk optimization problem faced by retailers. Through conditional value-at-risk we assess the retailers’ profitability and risk exposure to different levels of PV self-generation by assuming different retail tariff schemes. We find risk-hedging trading strategies and tariffs to have greater impact in Summer and with low levels of residual load in the system, i.e. when the solar generation uncertainty affects more the households’ demand to be served and the wholesale spot prices. The study is innovative in unveiling the potential of dynamic electricity tariffs, which are indexed to spot prices, to sustain a high penetration of renewable energy source while promoting a fair risk sharing between retailers, regular consumers and prosumers (consumers with self-generation). Our findings have implications for electricity retailers facing load and revenue risks in wholesale spot markets, likewise for regulators and policy-makers interested in electricity market design.

Clinical Evaluation of a Fully-Automated High-Throughput Multiplex Screening-Assay to Detect and Differentiate the SARS-CoV-2 B.1.1.529 (Omicron) and B.1.617.2 (Delta) Lineage Variants.

Background: The recently emerged SARS-CoV-2 B.1.1.529 lineage and its sublineages (Omicron variant) pose a new challenge to healthcare systems worldwide due to its ability to efficiently spread in immunized populations and its resistance to currently available monoclonal antibody therapies. RT-PCR-based variant tests can be used to screen large sample-sets rapidly and accurately for relevant variants of concern (VOC). The aim of this study was to establish and validate a multiplex assay on the cobas 6800/8800 systems to allow discrimination between the two currently circulating VOCs, Omicron and Delta, in clinical samples. Methods: Primers and probes were evaluated for multiplex compatibility. Analytic performance was assessed using cell culture supernatant of an Omicron variant isolate and a clinical Delta variant sample, normalized to WHO-Standard. Clinical performance of the multiplex assay was benchmarked against NGS results. Results: In silico testing of all oligos showed no interactions with a high risk of primer-dimer formation or amplification of human DNA/RNA. Over 99.9% of all currently available Omicron variant sequences are a perfect match for at least one of the three Omicron targets included in the multiplex. Analytic sensitivity was determined as 19.0 IU/mL (CI95%: 12.9–132.2 IU/mL) for the A67V + del-HV69-70 target, 193.9 IU/mL (CI95%: 144.7–334.7 IU/mL) for the E484A target, 35.5 IU/mL (CI95%: 23.3–158.0 IU/mL) for the N679K + P681H target and 105.0 IU/mL (CI95%: 80.7–129.3 IU/mL) for the P681R target. All sequence variances were correctly detected in the clinical sample set (225/225 Targets). Conclusion: RT-PCR-based variant screening compared to whole genome sequencing is both rapid and reliable in detecting relevant sequence variations in SARS-CoV-2 positive samples to exclude or verify relevant VOCs. This allows short-term decision-making, e.g., for patient treatment or public health measures.

Comparison of artificial intelligence to the veterinary radiologist's diagnosis of canine cardiogenic pulmonary edema.

Application of artificial intelligence (AI) to improve clinical diagnosis is a burgeoning field in human and veterinary medicine. The objective of this prospective, diagnostic accuracy study was to determine the accuracy, sensitivity, and specificity of an AI-based software for diagnosing canine cardiogenic pulmonary edema from thoracic radiographs, using an American College of Veterinary Radiology-certified veterinary radiologist's interpretation as the reference standard. Five hundred consecutive canine thoracic radiographs made after-hours by a veterinary Emergency Department were retrieved. A total of 481 of 500 cases were technically analyzable. Based on the radiologist's assessment, 46 (10.4%) of these 481 dogs were diagnosed with cardiogenic pulmonary edema (CPE+). Of these cases, the AI software designated 42 of 46 as CPE+ and four of 46 as cardiogenic pulmonary edema negative (CPE-). Accuracy, sensitivity, and specificity of the AI-based software compared to radiologist diagnosis were 92.3%, 91.3%, and 92.4%, respectively (positive predictive value, 56%; negative predictive value, 99%). Findings supported using AI software screening for thoracic radiographs of dogs with suspected cardiogenic pulmonary edema to assist with short-term decision-making when a radiologist is unavailable.

A Novel Embedding Model Based on a Transition System for Building Industry-Collaborative Digital Twin

Recently, the production environment has been rapidly changing, and accordingly, correct mid term and short term decision-making for production is considered more important. Reliable indicators are required for correct decision-making, and the manufacturing cycle time plays an important role in manufacturing. A method using digital twin technology is being studied to implement accurate prediction, and an approach utilizing process discovery was recently proposed. This paper proposes a digital twin discovery framework using process transition technology. The generated digital twin will unearth its characteristics in the event log. The proposed method was applied to actual manufacturing data, and the experimental results demonstrate that the proposed method is effective at discovering digital twins.

The Use of Weather Website Data for Construction Project Decision-Making in The Short Term

In most work sites, it is a priority to keep the work going well and to avoid unforeseen incidents. Fluctuations in weather conditions are one of the factors affecting the continuity of work in construction projects. Indeed, for example, the temperature is important in concrete and asphalt works, and wind speed is important in lifting and high construction works. Therefore, taking the appropriate decision, starting and completing the work, is very important to maintain the quality of the project. This research aims to demonstrate the reliability of short-term decision-making through data taken from the weather site five days before the time to work. The data was collected for a month, five days before the intended day and on the same day, day and night, for different weather factors by weather location such as temperature, humidity, possibility of rain, Uv index, wind speed. By analyzing the data, it was found that there was little difference in those predictors of all the factors recorded. To conclude at the end of the study that it is possible to rely on the decision-making on the weather location in small and medium projects, but in large and sensitive projects, they need to rely on more accurate data than relying on weather location data.

Dynamics of short-term and long-term decision-making in English housing associations: A study of using systems thinking to inform policy design

• Disjointed and frequently changing government policy can risk policies’ intended quality of housing and community by disrupting the organisations’ ability to incorporate long-term goals in decision-making. • A causal loop diagram describes the interlinkages between policy design, specifically inconsistencies and changes, and housing organisations’ decision-making. • Systems thinking approach in policy design can support HAs’ decision-making for sustainability and social issues more strongly. Incorporating consideration of causal mechanisms of complex policy issues and goals is critical for policy design, but tools to support exploration of the interconnections, trade-offs and unintended consequences of a focused policy issue are limited. Understanding how to undertake systems-based policy design is crucial for designing effective policy interventions. Through a case study with two housing associations (HAs) in England, this paper explores how group model building (GMB) workshops, as a systems thinking tool, can elicit complex causal mechanisms to inform policy design. The paper presents a causal loop diagram (CLD) describing English HAs’ decision-making around sustainable and healthy housing in response to housing policies. The CLD illustrates how frequent policy changes and disjointed objectives can create disruptive challenges for HA's long-term decision-making, increasing short-term decision-making, and compromising the delivery of housing policy goals as an unintended consequence. We argue that the systems perspective of the interlinkages between policy design, specifically inconsistencies and changes, and housing organisations’ reactions highlights the importance of the systems thinking approach of policy design to support HAs’ organisational decision-making for sustainability and social issues. Policy design elements that facilitate HAs’ long-term decision-making are discussed. Through the case study, we contribute to the housing policy literature by explicitly showing how policy changes affect HA's decision-making. We advance the integration of policy design and soft operational research fields by describing the systems thinking approaches are used not only on the content of policy design to enhance a particular policy, but also on increasing our understanding of its process, by generating insights about the nature of decision-making dynamics and challenges faced. Limitations and implications for future research are discussed.

Hybrid-based framework for COVID-19 prediction via federated machine learning models.

The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python’s libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases.

Research on Long Short-Term Decision-Making System for Excavator Market Demand Forecasting Based on Improved Support Vector Machine

Future demand forecasting of the excavators is of great significance to guide the supply and marketing plan. For a long time, market forecasting of the construction machinery is regarded as short-term forecasting, which lacks the analysis of macro-marketing law and cannot reflect the true law of market development. In this paper, a decision-making system based on both long-term and short-term features was proposed. The interval classification and recursive feature elimination were used to select the main factors that affect the demand of excavators. Then a support vector regression model based on decomposition synthesis (DS-SVR) was developed to forecast the long-term features, and a model combined with a seasonal autoregressive integrated moving average model (SARIMA) was developed to forecast the short-term features. Finally, the differential evolution algorithm (DE) was applied to optimize model parameters. The performance of the forecasting model was tested using the marketing data of a typical enterprise. The results showed that the total error rate of the forecasting model for the one-year long-term forecasting is 26.61%, and the classification error of forecasting of the three-month short-term forecasting are 13.65%, 18.83%, and 19.62%, respectively, which are superior to the SVR forecasting model and the SARIMA forecasting model.

Chameleonic knowledge: a study of ex ante analysis in large infrastructure policy processes

Using ex ante analysis to predict policy outcomes is common practice in the world of infrastructure planning. However, accounts of its uses and merits vary widely. Advisory agencies and government think tanks advocate this practice to prevent cost overruns, short-term decision-making and suboptimal choices. Academic studies on knowledge use, on the other hand, are critical of how knowledge can be used in decision making. Research has found that analyses often have no impact at all on decision outcomes or are mainly conducted to provide decision makers with the confidence to decide rather than with objective facts. In this paper, we use an ethnographic research design to understand how it is possible that the use of ex ante analysis can be depicted in such contradictory ways. We suggest that the substantive content of ex ante analysis plays a limited role in understanding its depictions and uses. Instead, it is the process of conducting an ex ante analysis itself that unfolds in such a manner that the analysis can be interpreted and used in many different and seemingly contradictory ways. In policy processes, ex ante analysis is like a chameleon, figuratively changing its appearance based on its environment.

Assessing the performance of deep learning models for multivariate probabilistic energy forecasting

Deep learning models have the potential to advance the short-term decision-making of electricity market participants and system operators by capturing the complex dependences and uncertainties of power system operation. Currently, however, the adoption of global deep learning models for multivariate energy forecasting in power systems is far behind the developments in the deep learning research field. In this context, the objectives of this study are to review recent developments in the field of probabilistic, multivariate, and multihorizon time series forecasting and empirically evaluate the performance of novel global deep learning models for forecasting wind and solar generation, electricity load, and wholesale electricity price for intraday and day-ahead time horizons. Two forecast types, deterministic and probabilistic forecasts, are studied. The evaluation data consist of real-world datasets with hourly resolution at the levels of an individual customer and regional and national electricity market bidding zones. The model evaluation criteria include achievable levels of forecasting accuracy and uncertainty risks, hyperparameter sensitivity, the effect of exogenous variables and fieldwise dataset split, and run-time efficiency factors, such as memory utilization, simulation time, electricity consumption, and convergence rate. We conclude that the performance of the global models is more beneficial for intraday forecasts of heterogeneous datasets with nonuniform patterns of time series, but can be affected by the hyperparameter sensitivity and hardware limitations with the growth of dataset dimensionality. The results can serve as a reference point for the quantitative evaluation of deep learning models for probabilistic multivariate energy forecasting in power systems.

A development framework for decision support systems in high-performance sport

Abstract Decision making in sport involves forecasting and selecting choices from different options of action, care, or management. These processes are conditioned by the available information (sometimes limited, fallible, or excessive), the cognitive limitations of the decision-maker (heuristics and biases), the finite amount of available time to make the decision, and the levels of risk and reward. Decision support systems have become increasingly common in sporting contexts such as scheduling optimization, skills evaluation and classification, decision-making assessment, talent identification and team selection, or injury risk assessment. However no specific, formalised framework exists to help guide either the development or evaluation of these systems. Drawing on a variety of literature, this paper proposes a decision support system development framework for specific use in high-performance sport. It proposes three separate criteria for this purpose: 1) Context Satisfaction, 2) Output Quality, and 3) Process Efficiency. Underpinning these criteria there are six specific components: Feasibility, Delivered knowledge, Decisional guidance, Data quality, System error, and System complexity. The proposed framework offers a systematic approach for users to ensure that each of the six components are considered and optimised before, during, and after developing the system. A DSS development framework for high-performance sport should help to improve both short and long term decision-making in a variety of sporting contexts.

A spatio-temporal Bayesian Network approach for deforestation prediction in an Amazon rainforest expansion frontier

In the last decade, Brazil has successfully managed to reduce deforestation in the Amazon forest. However, continued increases in annual deforestation rates call for environmental modeling to support short-term decision-making. This paper presents the functioning of a stepwise spatio-temporal Bayesian Network approach for spatially explicit analysis of deforestation risk based on observation data. The study area comprises a deforestation expansion frontier located in the southwest of Pará state, Brazil. The proposed approach has been successful in estimating deforestation risk over the years. Among the selected variables to compose the Bayesian Network model, distance from hot spots and distance from degraded areas present the highest contribution, while protected areas variable present a significant mitigation effect on the phenomenon. Accuracy assessment indices corroborate the agreement between deforestation events and predictions.

A Framework for the Sustainability Assessment of (Micro)transit Systems

The increasing global focus on sustainability is bringing the question of the sustainability of transport systems—which are still exhibiting numerous negative effects as evidence of their unsustainability—to the fore. While sustainability is an often-discussed concept, tools to guide the practical implementation thereof are limited. This paper presents a framework for an inventory of indicators against which to measure the sustainability of transport systems. While the framework is validated for urban transport systems for increased mobility (here referenced as microtransit systems), the concept is investigated in the context of transport systems in general. A systematic review of the literature was used to develop a framework of 12 areas and 50 indicators of sustainability. Expert reviews, an Analytical Hierarchical Process (AHP), and an Equally Weighted Average (EWA) method were employed to allocate weights to the indicators and to validate the framework for microtransit systems. The framework contributes to the literature by identifying, categorizing, and integrating concepts related to sustainability in transport systems. It is intended to aid short-term decision-making in the design of urban transport systems, to continuously monitor the long-term progress of transport systems against sustainability goals, and to guide policy development. Future work would include enhanced empirical validation of the framework in the context of other types of transport systems, beyond microtransit.

Deepening Supply-Side Structural Reforms in Coal Power with a Power Market

ABSTRACT The new round of reforms in power system gradually liberalizes the wholesale market for power generation, but the current average dispatch model does not support the new market system. We use Guangdong Province as a case study to analyze the cost of new entry (CONE) and net revenue of new and old coal power plants. We find that the real CONE of low-efficiency plants that were put into production earlier is significantly lower than that of the new high-efficiency plants. Meanwhile, the fact that net revenues are higher for low-efficiency plants than high-efficiency plants has led to distortion in the incentives aimed at retiring old low-efficiency plants from the market, and new investment is economically justified under existing market rules. By simulating a market system, we find that the “scarce returns” of peak plants are depressed because of excessive capacity, which leads to losses from all coal power plants. Accelerating market reforms, including reshaping the market mechanism of short-term operations and long-term investment decision-making is key to the successful implementation of supply-side reform policies. They are important for ensuring both economic operations in the short-run wholesale market and the long-term sufficiency of the power supply via careful regulatory mechanisms.

Explaining reshoring in the context of Asian competitiveness: evidence from a Swedish firm

Purpose The purpose of this paper is to arrive at a different understanding of reshoring in Asia vis-à-vis the Western context of competitiveness, through a case study of the Swedish company FM Mattsson. Design/methodology/approach Empirical studies with semi-structured interviews have been conducted both in Sweden and China to gain an in-depth understanding of the case company’s reshoring activities. Findings The findings point at reshoring as a competitive means to respond to the dynamics of internal (firm-specific) and external (country-specific) factors. Reshoring comes as a dynamic process by reshuffling resources inside and outside of the firm that strives for continuous competitiveness. Organizations need to meet the challenges of changing environment, especially the dynamic business competition in Asia, and reshoring is a way. Originality/value This paper contributes to the literature by perceiving reshoring as a dynamic process of competitiveness development. Reshoring is not seen as one-off short-term decision-making on cost and location but as a long-term process in response to the dynamic internal and external challenges ahead.

Forecasting innovations in science, technology, and education

Human survival depends on our ability to predict future outcomes so that we can make informed decisions. Human cognition and perception are optimized for local, short-term decision-making, such as deciding when to fight or flight, whom to mate, or what to eat. For more elaborate decisions (e.g., when to harvest, when to go to war or not, and whom to marry), people used to consult oracles—prophetic predictions of the future inspired by the gods. Over time, oracles were replaced by models of the structure and dynamics of natural, technological, and social systems. In the 21st century, computational models and visualizations of model results inform much of our decision-making: near real-time weather forecasts help us decide when to take an umbrella, plant, or harvest; where to ground airplanes; or when to evacuate inhabitants in the path of a hurricane, tornado, or flood (1). Long-term weather and climate forecasts predict a future with increasing torrential rains, stronger winds, and more frequent drought, landslides, and forest fires as well as rising sea levels, enabling decision makers to prepare for these changes by building dikes, moving cities and roads, and building larger water reservoirs and better storm sewers (2). Computational models are particularly useful if they are combined with high-quality data and if they are widely used and understood. As early as 1960, Buckminster Fuller proposed the “World Game” to address the world’s problems through a holistic and anticipatory systems approach (3, 4). The game used Fuller's Dymaxion Map to visualize resources, trends, and scenarios. It was meant to be accessible to everyone (not just experts); therefore, decisions could be made collectively, and results could be used by anyone. In the 1970s, “The limits to growth: A report to the club of Rome” (5) used simulations to forecast future states of the … [↵][1]1To whom correspondence should be addressed. Email: katy{at}indiana.edu. [1]: #xref-corresp-1-1

Introducing COSMOS: a Web Platform for Multimodal Game-Based Psychological Assessment Geared Towards Open Science Practice

We have established the COgnitive Science Metrics Online Survey (COSMOS) platform that contains a digital psychometrics toolset in the guise of applied games measuring a wide range of cognitive functions. Here, we are outlining this online research endeavor designed for automatized psychometric data collection and scalable assessment: once set up, the low costs and expenditure associated with individual psychometric testing allow substantially increased study cohorts and thus contribute to enhancing study outcome reliability. We are leveraging gamification of the data acquisition method to make the tests suitable for online administration. By putting a strong focus on entertainment and individually tailored feedback, we aim to maximize subjects’ incentives for repeated and continued participation. The objective of measuring repeatedly is obtaining more revealing multitrial average scores and measures from various operationalizations of the same psychological construct instead of relying on single-shot measurements. COSMOS is set up to acquire an automatically and continuously growing dataset that can be used to answer a wide variety of research questions. Following the principles of the open science movement, this data set will also be made accessible to other publicly funded researchers, given that all precautions for individual data protection are fulfilled. We have developed a secure hosting platform and a series of digital gamified testing instruments that can measure theory of mind, attention, working memory, episodic long- and short-term memory, spatial memory, reaction times, eye-hand coordination, impulsivity, humor appreciation, altruism, fairness, strategic thinking, decision-making, and risk-taking behavior. Furthermore, some of the game-based testing instruments also offer the possibility of using classical questionnaire items. A subset of these gamified tests is already implemented in the COSMOS platform, publicly accessible and currently undergoing evaluation and calibration as normative data is being collected. In summary, our approach can be used to accomplish a detailed and reliable psychometric characterization of thousands of individuals to supply various studies with large-scale neurocognitive phenotypes. Our game-based online testing strategy can also guide recruitment for studies as they allow very efficient screening and sample composition. Finally, this setup also allows to evaluate potential cognitive training effects and whether improvements are merely task specific or if generalization effects occur in or even across cognitive domains.

Looking to the long-term for management of tropical tunas? An assessment of special requirements arguments at WCPFC14

ABSTRACTThe 14th Session of the Western and Central Pacific Fisheries Commission (WCPFC) convened in Manila, Philippines from 3 to 8 December 2017. At the meeting, Commission members adopted eight legally binding measures for the purposes of conserving and managing tuna and tuna-like species in the Western and Central Pacific Ocean. Closing at three am on the longest day of negotiations in WCPFC history, the session advanced complex negotiations on a bridging measure to regulate the region’s three highly valuable tropical tuna stocks. A key factor in the meeting’s late close was the negotiating positions taken by three member countries to alter the measure based on claims concerning their special requirements. Subsequent debate revealed that the Commission currently lacks a systematic approach to addressing the special requirements of its member countries. Furthermore, negotiations showed the extent to which members leverage ambiguity concerning special requirements in order to broker compromises within the Commission’s ad-hoc, short-term decision-making framework. Outcomes from WCPFC14 demonstrate that while Commission members have made long-term promises to evolve their approach to decision-making through tools like harvest strategies and an allocation framework, at the session itself they continued to rely on short-term decision-making that did not reflect scientifically advised limits.

Examples of Long-Term and Short-Term Decision-Making in the UK, Delaware and Germany – Gap-Filling Exercise in the Context of the Shareholder v. Stakeholder Debate and Share Ownership Structure o

This paper explores the extent to which the law in the UK , Delaware and Germany imposes an obligation on directors of solvent public companies to take into account the long-term consequences of their decisions while establishing the content and scope of long-termism in these three legal systems. This research draws on the academic literature and performs a gap-filling exercise by identifying examples of long-term decision-making in these jurisdictions, as well as examples of decision-making and conduct that is not long-term in nature. In the gap-filling exercise, case studies are presented in the context of (i) the contemporary shareholder v. stakeholder debate in corporate governance scholarship and (ii) the relevance of the share ownership structure of the company. These two important debates are used as variables to cast light on the ambit of the notion of long-termism, and the structural differences and similarities between the corporate governance systems and concepts of long-termism in the UK , Delaware and Germany.

Can investor attention predict oil prices?

This paper sets out to investigate the predictive power of investor attention onto oil prices. We firstly construct investor attention index by using the Google search volume index (SVI) based on a broad set of words related to oil-related variables and terms that are directly linked to real economy to measure investor attention. Then the empirical work is performed via a novel hybrid approach and WN model (Westerlund and Narayan, 2012, 2014) that account for characteristics of persistency, endogeneity, and heteroskedasticity. The empirical results show that investor attention does exhibit statistically and economically significant in-sample and out-of-sample forecasting power to directly forecast oil prices for both daily data and weekly data. In addition, the results exhibit the term structure character, which are helpful for understanding the financial phenomena that irrational attentions have more effect in short-term decision-making.