Temporal Horizon Research Articles

Crisis, temporality and governmental policy agendas: The cases of Finland and Sweden

AbstractCrises transform the temporal orientation of political decision‐making. They demand immediate and decisive action and thus convert time into a means of political control. In these circumstances, assessing the long‐term consequences of proposed policies with respect to welfare, sustainability or justice also becomes demanding. Yet crisis conditions may also contain of elements that can enable positive postcrisis societal changes. Crises can offer opportunities for new developmental measures and building a better future—something political leaders may capitalise on in their agenda‐setting. By analysing the programmes of the post‐2014 Finnish and Swedish governments, this paper seeks to make sense of how the current crisis mode has shaped these countries' politics in terms of their temporal horizons. The analysis focuses on three main perspectives or forms of articulation—innovative stabilisation, long‐termism and synchronisation—by way of which these governments seek to navigate through crises‐afflicted times and offer a desirable future direction for their respective societies. The analysis offers important insights into the (unused) potential of crises to achieve something substantially novel in the Nordic and European political landscape, ultimately advancing the conditions for a future‐regarding democratic system.

Application of multiple linear regression model and long short-term memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variables

Despite the implementation of various initiatives, dengue remains a significant public health concern in Malaysia. Given that dengue has no specific treatment, dengue prediction remains a useful early warning mechanism for timely and effective deployment of public health preventative measures. This study aims to develop a comprehensive approach for forecasting dengue cases in Selangor, Malaysia by incorporating climate variables. An ensemble of Multiple Linear Regression (MLR) model, Long Short-Term Memory (LSTM), and Susceptible-Infected mosquito vectors, Susceptible-Infected-Recovered human hosts (SI-SIR) model were used to establish a relation between climate variables (temperature, humidity, precipitation) and mosquito biting rate. Dengue incidence subject to climate variability can then be projected by SI-SIR model using the forecasted mosquito biting rate. The proposed approach outperformed three alternative approaches and expanded the temporal horizon of dengue prediction for Selangor with the ability to forecast approximately 60 weeks ahead with a Mean Absolute Percentage Error (MAPE) of 13.97 for the chosen prediction window before the implementation of the Movement Control Order (MCO) in Malaysia. Extended validation across subsequent periods also indicates relatively satisfactory forecasting performance (with MAPE ranging from 13.12 to 17.09). This research contributed to the field by introducing a novel framework for the prediction of dengue cases over an extended temporal range.

Future Generations in Climate Litigation: Early Whispers of an Intergenerational Law?

Abstract The “migration” of Future Generations from a moral to a judicial context represents a captivating development in contemporary legal discourse. Recent years have seen a surge in courts across various nations addressing the intersection of future generations and climate litigation. This nexus, far from being coincidental or sporadic, epitomizes a deeper societal and legal dilemma that necessitates a nuanced articulation for effective resolution. The main objective of this Article is to provide preliminary insights for contextualizing this legal evolution. Initially, the Article delineates the journey towards social legitimacy of climate science. Subsequently, it examines the impact of this social legitimation on judicial rulings, particularly observing an emerging trend in climate litigation to expand the temporal scope of legal relevance. The Article culminates in an exploration of the possible interplay between the legal significance of future generations and the extension of the law’s temporal horizon. This conjectural postulation is substantiated through select historical precedents.

Thanato-technics: Temporal Horizons of Death and Dying.

Advances in end-of-life technologies increasingly destabilize received notions of personhood, identity, and ethics. As notions of personhood and identity within such systems are made to conform to discrete, binary and less fluid categories, some in the West have sought guidance in the techniques and views related to the dying process cultivated in other cultures, particularly Tibetan Buddhism. This article considers such dynamics as they unfolded in research focused on the postmortem bodies of Tibetan Buddhist practitioners in India. This article introduces the term thanato-technics to highlight the temporalities, imaginary or otherwise, evoked, enabled, and invested through the use of technologies to ascertain or conjecture about the intrasubjectivity of the dead and dying.

Scaling Learning based Policy Optimization for Temporal Logic Tasks by Controller Network Dropout

This paper introduces a model-based approach for training feedback controllers for an autonomous agent operating in a highly nonlinear (albeit deterministic) environment. We desire the trained policy to ensure that the agent satisfies specific task objectives and safety constraints, both expressed in Discrete-Time Signal Temporal Logic (DT-STL). One advantage for reformulation of a task via formal frameworks, like DT-STL, is that it permits quantitative satisfaction semantics. In other words, given a trajectory and a DT-STL formula, we can compute the robustness , which can be interpreted as an approximate signed distance between the trajectory and the set of trajectories satisfying the formula. We utilize feedback control, and we assume a feed forward neural network for learning the feedback controller. We show how this learning problem is similar to training recurrent neural networks (RNNs), where the number of recurrent units is proportional to the temporal horizon of the agent's task objectives. This poses a challenge: RNNs are susceptible to vanishing and exploding gradients, and naïve gradient descent-based strategies to solve long-horizon task objectives thus suffer from the same problems. To tackle this challenge, we introduce a novel gradient approximation algorithm based on the idea of dropout or gradient sampling. One of the main contributions is the notion of controller network dropout , where we approximate the NN controller in several time-steps in the task horizon by the control input obtained using the controller in a previous training step. We show that our control synthesis methodology, can be quite helpful for stochastic gradient descent to converge with less numerical issues, enabling scalable backpropagation over long time horizons and trajectories over high dimensional state spaces. We demonstrate the efficacy of our approach on various motion planning applications requiring complex spatio-temporal and sequential tasks ranging over thousands of time-steps.

The border as temporal horizon: a borderlands massacre and the contested futures of federalism in eastern Ethiopia

AbstractIn 1995, a coalition of former rebel groups redrew Ethiopia's map, establishing an ethnic‐federal system. By 2017, internal border conflicts signalled federalism's potential unravelling. This article analyses expectations about federalism's future among Somalis in Ethiopia, drawing on anthropologies of time to understand how everyday processes of border‐making orient around the ‘future in the present’. Anthropologists and historians concerned with political time have focused largely on how the ‘past in the present’ shapes state‐building and political identity formation. Meanwhile, political scientists and commentators, as well as many Ethiopians living amid uncertainty, prognosticate about the future of identity politics in Ethiopia and attempt to discern whether decentralization is paving the way to state fragmentation. Foregrounding how people work to manage time at borders and through borderwork, this article analyses the roles borders play in people's collective constructions of political futures – and the role anticipations about the future play in people's engagement with borders.

Time Horizon for Forecasting the Number of Traffic Accidents

On Polish roads, there are more and more automobiles each year. This results in more people using the roadways. Because of this, the threat posed by traffic accidents, which involve the loss of human life and financial resources, is quickly expanding. The rise of motorization and the quickening expansion of the human population are to blame for this. The tiny amount of the dataset that may be utilized for research in this respect is the major problem in anticipating and evaluating data on road accidents. Despite the fact that road accidents result in millions of lives and injuries worldwide each year, they are not very common. Traffic accidents are still quite common despite the pandemic's reduction. The aim of the essay is to evaluate the temporal horizon of the predicted number of traffic accidents. For this reason, an estimate of the expected number of traffic accidents in the future years was made, and its effect on the average percentage error of the projection was computed. Based on the analysis of the input data on the number of road accidents (annual and monthly), the results show that monthly and annual data can be forecast up to six years in advance.

Business failure prediction models with high and stable predictive power over time using genetic programming

This study focuses on the deterioration of the predictive power and the analysis of the predictive stability of business failure prediction models, an aspect not sufficiently analysed in previous research. Insolvency prediction is considered with three temporal horizons (1 year, 3 years and 5 years prior to failure). The Genetic Programming (GP) tool has been used to achieve prediction models with high performance and stability over time, considering a long post-learning period in the stability analysis. In addition, novel scenarios representative of actual model use are proposed and considered, as well as metrics to assess the deterioration of the models’ predictive power. The optimised GP prediction models (in the three temporal horizons) present a higher performance with respect to external references and, more importantly in relation to the objective of our study, the selected GP models substantially improve on the stability reported in previous studies, meeting the pursued requirements of degree of deterioration (less than 5%) and stability (Pearson’s coefficient of variation less than 5%). Thus, the predictions of the GP models after the learning are very stable (period 2008–2019), to a certain extent immune, with respect to their environment, responding adequately in both procyclical and countercyclical modes, all of which is particularly relevant as this period includes a strong recession and a strong recovery. This should help to increase the reliability of business failure prediction models. Moreover, the relevance of including variables other than the usual financial ratios as predictors of failure is confirmed.

Henry James and the “Laboratory Brain”: The Neuroscience of Cognitive Time-Travel

Abstract: Henry James’s skepticism about the “laboratory brain” is shared by much contemporary neuroscience. Both reject the notion that a brain detached from its embodied engagement with the social world could duplicate the experience of what it is like to be conscious. James’s understanding of how consciousness can cross temporal horizons and support collaborative interactions is consistent with and can be elucidated through scientific theories of embodied cognition. These theories help to explain how works of art and literature can cross historical distance and enable the past to speak to the present and the future.

Through the Lens of Merleau-Ponty

This article discusses how Merleau-Ponty’s existential phenomenology may serve as the lens and theoretical frame in a qualitative research study of the lived experience of patients, their families, and nurses during critical illness in the intensive care unit. Merleau-Ponty's unique existential concepts of corporeality, spatiality, temporality, and relationality provide a useful foundation and serve as a unifying philosophy of science in understanding this nursing phenomenon that is grounded in philosophical beliefs about humans, and the holistic nature of professional nursing that is not amenable to experimental investigative research methods. The research sheds light on the patients, families, and nurses’ embodied temporal, spatial, and relational horizons, and reveals a new conception of critical illness that will provide caregivers with knowledge about effective humanistic, and more holistic care, that opens new ways for coping amongst patients and their families, as well as caregiving possibilities for the nurses. Findings from this qualitative inquiry can help contribute knowledge about family engagement that would impact the provision of care in the intensive care unit, thus improving patient, family, and organizational outcomes.

Episodic future thinking improves intertemporal choice and food choice in individuals with higher weight: A systematic review and meta-analysis.

Episodic future thinking (EFT) strengthens self-regulation abilities by increasing the perceived value of long-term reinforcements and reducing impulsive choice in delay discounting tasks. As such, EFT interventions have the potential to improve dietary and eating-related decision-making in individuals with obesity or binge eating symptoms, conditions associated with elevated delay discounting. Here, we meta-analyzed evidence from 12 studies that assessed whether EFT interventions improve delay discounting and real-world food choice compared to control interventions. Included studies involved 951 adults with overweight or obesity (body mass index [BMI] ≥25). There were no studies involving participants with binge eating disorder. EFT intervention pooled effects were significant, improving delay discounting with a medium effect, g = 0.55, p < 0.0001, and subsequent food choice outcomes with a small effect, g = 0.31, p < 0.01. Notably, our review is the first to analyze mechanisms of effect in this population, demonstrating that improvements were greater when temporal horizons of EFT episodes were aligned with delay discounting tasks and more distant horizons predicted far-transfer to subsequent dietary and eating-related choices. Our findings thus show that EFT is an effective intervention for individuals with higher weight at risk of adverse health consequences.

Exploring the impact of entrepreneurial experience on future entrepreneurship aspirations

This paper evaluates how different types of direct and vicarious experience in entrepreneurship determine the perceived entrepreneurial intention in short, medium, and long-term horizons. The paper concludes with a discussion of the importance of considering both personal experiences and social influences in designing curriculum. A survey was conducted on four types of entrepreneurial experience and four intention horizons. Two of the experiences were direct experiences: current and previous, whilst two were vicarious: family and knowing someone. The four intention horizons were now, short-term, long-term and no intention. The study looks at 679 agricultural students from universities in both China and the United Kingdom. The paper uses the integrated model of entrepreneurial intentions together with a temporal horizon model of intention as the theoretical underpinning for this approach. The study’s results reinforce the positive relationship between family experience and long-term intentions, thus supporting the idea that familial exposure to entrepreneurship can shape individuals’ long-term entrepreneurial aspirations. The lack of a significant association between current direct experience and long-term intentions deviates from previous research and may be attributed to other factors influencing individuals’ long-term intentions, such as changing circumstances or evolving entrepreneurial opportunities. The cohorts available, whilst from China and UK, as participants were limited which presents several opportunities to expand upon this work and challenge its central premise. An understanding of the role of family in shaping long-term entrepreneurial intention has widespread social implications. The discovery that individuals with entrepreneurial experience, be it direct or through vicarious means, generally show heightened entrepreneurial intentions, coupled with the insight that knowing an entrepreneur correlates with shorter-term intention horizons, whereas family-influenced experiences align with longer-term venture creation ambitions, offers valuable new perspectives for those involved in entrepreneurial education and support.

ASIMO: Agent-centric scene representation in multi-object manipulation

Vision-based reinforcement learning (RL) is a generalizable way to control an agent because it is agnostic of specific hardware configurations. As visual observations are highly entangled, attempts for vision-based RL rely on scene representation that discerns individual entities and establishes intuitive physics to constitute the world model. However, most existing works on scene representation learning cannot successfully be deployed to train an RL agent, as they are often highly unstable and fail to sustain for a long enough temporal horizon. We propose ASIMO, a fully unsupervised scene decomposition to perform interaction-rich tasks with a vision-based RL agent. ASIMO decomposes agent-object interaction videos of episodic-length into the agent, objects, and background, predicting their long-term interactions. Further, we explicitly model possible occlusion in the image observations and stably track individual objects. Then, we can correctly deduce the updated positions of individual entities in response to the agent action, only from partial visual observation. Based on the stable entity-wise decomposition and temporal prediction, we formulate a hierarchical framework to train the RL agent that focuses on the context around the object of interest. We demonstrate that our formulation for scene representation can be universally deployed to train different configurations of agents and accomplish several tasks that involve pushing, arranging, and placing multiple rigid objects.

Past Happiness and Broken Future Horizon of Oncological Patients during Chemotherapy-A Quantitative Exploration of a Phenomenological Hypothesis.

Understanding the impact of cancer on the experience of time is crucial in the context of hope and recovery. This study, a follow-up to a previous qualitative study of ovarian cancer patients - explored two types of such experiences-the memory of past happiness and the limited future planning. A sociodemographic questionnaire with nine questions about the experience of time was used on a convenience sample of 202 patients with various cancers, predominantly women with breast, ovarian, and cervical cancer. It was found that the respondents experienced increased focus on the present, decreased focus on the future, and a sense of unpredictability, with a relatively short temporal horizon measured in weeks and months, not years. Almost half of the respondents (46%) measured time during treatment by the rhythm of chemotherapy and check-ups, which thus appeared as the most meaningful events. The increase in the frequency with which patients underwent chemotherapy mildly affected their focus on the present (R = 0.25, p < 0.05), likely because of the discomfort of the side effects. The correlations between age and time in treatment, on the one hand, and the experience of time, on the other, were negligible. Changed temporal experience during chemotherapy is a factor that can have an impact on patients' well-being and ability to cope with the disease. It thus should be taken into account when planning oncology care.

Asset Allocation Strategies: A Case Study in Mutual Fund and Inaurance Portfolios in Wallet Wealth LLP

This case study explores effective asset allocation strategies, focusing on mutual funds and insurance portfolios, with an emphasis on incorporating a de-risking factor. It underscores the importance of strategic asset allocation customised to each person’s risk profiles, Investing objectives, and temporal horizons . By utilizing mutual funds and insurance products, investors can construct diversified portfolios to mitigate risk and optimize returns. The study examines how different combinations of these instruments can enhance portfolio resilience and performance. Moreover, the integration of a de-risking factor adds a dynamic element to distribution of assets. This entails modifying the asset in the portfolio. mix depending on the state of the market and risk preferences to minimize downside risk and capture upside potential. Through historical analysis and scenario simulations, the study evaluates the efficacy of various asset allocation strategies, highlighting the benefits of incorporating a de-risking approach. It demonstrates how this strategy can protect capital during market downturns while capitalizing on growth opportunities in favorable market conditions.

Standing upright here: critical disaster studies viewed from the Antipodes

ABSTRACT This article seeks to reinvigorate disaster scholarship, given the disastrous times we find ourselves in. In order to do so, we extend the spatial and temporal horizons of disasters, and consider them as normal processes as well as aberrant events. Knowledge need not exclusively emanate from the metropolitan centres of the Global North. We begin by examining the field’s ‘threshold concepts’, subjecting them to an urgent Indigenous evaluation. Second, as cities are the Anthropocene’s primary terrain, we illustrate the numerous ways in which the recovery from the Canterbury Earthquake Sequence offers global lessons. Third, leveraging understandings from the preceding sections, we orient towards a more hopeful intellectual frontier – a decolonised disaster studies as seen from the perspective of Aotearoa New Zealand. Since much disaster scholarship emphasises failure and loss, here we consider what will be gained through a fuller appreciation of mātauranga Māori.

A deep learning-based approach for predicting in-flight estimated time of arrival

Predictability is key for efficient and safe air traffic management. In particular, accurately estimating time of arrival for current passenger flights may help terminal controllers to plan ahead and optimize airport operations in terms of safety and resource allocation. While traditional physics-based simulations are still widely used, they are complex to model and often fail to include many factors affecting the progress of a flight. In this paper, we propose a deep learning approach based on LSTM that leverages the 4D trajectory of the flight and weather data at the destination airport, to accurately predict estimated time of arrival. We evaluate our model on flights arriving at Adolfo Suárez-Madrid Barajas airport (Spain), in the first three quarters of 2022, achieving a mean absolute error of 2.65 min over the entire flight and reporting competitive short- and long-term predictions at different spatial and temporal horizons.

Boosting Housing Markets: Amplifying Reserve Requirements for Greater Economic Growth, Insights from China and Indonesia

Boosting Housing Markets: Amplifying Reserve Requirements for Greater Economic Growth, Insights from China and Indonesia

Nexus among Exchange Rate Volatility, Inflation, and Economic Growth: A Panel Data Analysis

This study undertakes an examination of the intricate relationships among exchange rate fluctuations, inflation, and economic growth within South Asian nations, namely Pakistan, India, Bangladesh, Sri Lanka, and Nepal. Spanning the temporal horizon from 1990 to 2021, the research endeavors to unveil the repercussions of exchange rate instability on economic growth—a formidable challenge confronted by monetary authorities and policymakers in South Asia. The central focus lies on comprehending the dynamics of exchange rate volatility, inflationary pressures, and their collective impact on economic growth. In the pursuit of these objectives, GDP per capita serves as the yardstick for economic growth, while independent variables encompass a spectrum including broad money supply, exchange rate volatility, inflation, government consumption expenditures, and gross fixed capital formation. The quantification of exchange rate volatility entails the utilization of both the standard deviation and the GARCH (1,1) models. Subsequent to an in-depth analysis, the initial unit test results undergo reevaluation, incorporating the Breusch-Pagan LM, Pesaran CD, and Pesaran scaled LM tests to discern cross-sectional dependence. To address potential data non-stationarity while considering cross-sectional dependence, the Pesaran-CIPS and Pesaran's CADF tests are applied. A pivotal aspect of the research involves the application of a Feasibly Generalized Least Squares (FGLS) regression model. The outcomes of the FGLS panel data regression illuminate a noteworthy finding: exchange rate volatility, quantified through both techniques, significantly and adversely influences economic growth. In contrast, inflation exhibits a statistically insignificant negative impact on economic growth. The study culminates by proffering specific recommendations to policymakers, urging concerted efforts to ensure exchange rate stability and foster sustained economic growth.

Transformer-based anomaly detection in P-LEO constellations: A dynamic graph approach

Successful management of large space systems, such as the most recent Proliferated Low Earth Orbit (P-LEO) constellations, demands an increased level of autonomy and irregular behavior detection capability. Existing techniques for satellite network management rely on monitoring satellites individually, potentially failing to capture events shared across multiple units. In this work, we propose a pipeline for identifying anomalous connections in proliferated satellite networks. Our pipeline relies on representing a satellite constellation as a dynamic graph. Dynamic graphs can be conveniently exploited to represent P-LEO networks, as they allow to capture meaningful structural and temporal correlations. Such spatial and temporal information are first projected into an embedding space; next, this encoded representation is processed by a transformer-encoder network for the anomaly detection task. We conduct extensive analyses of the main problem parameters, including the temporal horizon, the structure of the graph, and the size of the constellation. To assess the general performance of the method, we perform a Monte Carlo analysis on 960 ground node pair connections, providing empirical evidence of the algorithm’s generalization capability. The obtained results encourage the extension of the method to more complex, realistic modeling and kindred applications such as the on-edge detection problem.