Hasil untuk "Economic history and conditions"

L. Sher

Abstract Multiple lines of evidence indicate that the COVID-19 pandemic has profound psychological and social effects. The psychological sequelae of the pandemic will probably persist for months and years to come. Studies indicate that the COVID-19 pandemic is associated with distress, anxiety, fear of contagion, depression, and insomnia in the general population and among health care professionals. Social isolation, anxiety, fear of contagion, uncertainty, chronic stress, and economic difficulties may lead to the development or exacerbation of depressive, anxiety, substance use, and other psychiatric disorders in vulnerable populations including individuals with pre-existing psychiatric disorders and people who reside in high COVID-19 prevalence areas. Stress-related psychiatric conditions including mood and substance use disorders are associated with suicidal behavior. COVID-19 survivors may also be at elevated suicide risk. The COVID-19 crisis may increase suicide rates during and after the pandemic. Mental health consequences of the COVID-19 crisis including suicidal behavior are likely to be present for a long time and peak later than the actual pandemic. To reduce suicides during the COVID-19 crisis it is imperative to decrease stress, anxiety, fears and loneliness in the general population. There should be traditional and social media campaigns to promote mental health and reduce distress. Active outreach is necessary, especially for people with a history of psychiatric disorders, COVID-19 survivors, and older adults. Research studies are needed of how mental health consequences can be mitigated during and after the COVID-19 pandemic.

Nicolas L. Gutierrez, R. Hilborn, O. Defeo

M. Milne, R. Gray

Xingliang Guan, Houkai Wei, Shasha Lu et al.

J. Schumpeter

J. Feagin, Clarence N. Stone

T. Reusch, J. Dierking, H. Andersson et al.

Science-based, multinational management of the Baltic Sea offers lessons on amelioration of highly disturbed marine ecosystems. Coastal global oceans are expected to undergo drastic changes driven by climate change and increasing anthropogenic pressures in coming decades. Predicting specific future conditions and assessing the best management strategies to maintain ecosystem integrity and sustainable resource use are difficult, because of multiple interacting pressures, uncertain projections, and a lack of test cases for management. We argue that the Baltic Sea can serve as a time machine to study consequences and mitigation of future coastal perturbations, due to its unique combination of an early history of multistressor disturbance and ecosystem deterioration and early implementation of cross-border environmental management to address these problems. The Baltic Sea also stands out in providing a strong scientific foundation and accessibility to long-term data series that provide a unique opportunity to assess the efficacy of management actions to address the breakdown of ecosystem functions. Trend reversals such as the return of top predators, recovering fish stocks, and reduced input of nutrient and harmful substances could be achieved only by implementing an international, cooperative governance structure transcending its complex multistate policy setting, with integrated management of watershed and sea. The Baltic Sea also demonstrates how rapidly progressing global pressures, particularly warming of Baltic waters and the surrounding catchment area, can offset the efficacy of current management approaches. This situation calls for management that is (i) conservative to provide a buffer against regionally unmanageable global perturbations, (ii) adaptive to react to new management challenges, and, ultimately, (iii) multisectorial and integrative to address conflicts associated with economic trade-offs.

N. Tuptuk, S. Hailes

A revolution in manufacturing systems is underway: substantial recent investment has been directed towards the development of smart manufacturing systems that are able to respond in real time to changes in customer demands, as well as the conditions in the supply chain and in the factory itself. Smart manufacturing is a key component of the broader thrust towards Industry 4.0, and relies on the creation of a bridge between digital and physical environments through Internet of Things (IoT) technologies, coupled with enhancements to those digital environments through greater use of cloud systems, data analytics and machine learning. Whilst these individual technologies have been in development for some time, their integration with industrial systems leads to new challenges as well as potential benefits. In this paper, we explore the challenges faced by those wishing to secure smart manufacturing systems. Lessons from history suggest that where an attempt has been made to retrofit security on systems for which the primary driver was the development of functionality, there are inevitable and costly breaches. Indeed, today's manufacturing systems have started to experience this over the past few years; however, the integration of complex smart manufacturing technologies massively increases the scope for attack from adversaries aiming at industrial espionage and sabotage. The potential outcome of these attacks ranges from economic damage and lost production, through injury and loss of life, to catastrophic nation-wide effects. In this paper, we discuss the security of existing industrial and manufacturing systems, existing vulnerabilities, potential future cyber-attacks, the weaknesses of existing measures, the levels of awareness and preparedness for future security challenges, and why security must play a key role underpinning the development of future smart manufacturing systems.

Janine Berg

D. Groves, F. Bierlein, L. Meinert et al.

Frits Verhagen, Marco Tomamichel, Erkka Haapasalo

We offer the first operational interpretation of the α-z relative entropies, a measure of distinguishability between two quantum states introduced by Jakšić et al. and Audenaert and Datta. We show that these relative entropies appear when formulating conditions for large-sample or catalytic relative majorization of pairs of flat states and certain generalizations of them. Indeed, we show that such transformations exist if and only if all the α-z relative entropies of the two pairs are ordered. In this setting, the α and z parameters are truly independent from each other. These results also yield an expression for the optimal rate of converting one flat state pair into another. Our methods use real-algebraic techniques involving preordered semirings and certain monotone homomorphisms and derivations on them.

Koen Ponse, Aske Plaat, Niki van Stein et al.

Accurate economic simulations often require many experimental runs, particularly when combined with reinforcement learning. Unfortunately, training reinforcement learning agents in multi-agent economic environments can be slow. This paper introduces EconoJax, a fast simulated economy, based on the AI economist. EconoJax, and its training pipeline, are completely written in JAX. This allows EconoJax to scale to large population sizes and perform large experiments, while keeping training times within minutes. Through experiments with populations of 100 agents, we show how real-world economic behavior emerges through training within 15 minutes, in contrast to previous work that required several days. We additionally perform experiments in varying sized action spaces to test if some multi-agent methods produce more diverse behavior compared to others. Here, our findings indicate no notable differences in produced behavior with different methods as is sometimes suggested in earlier works. To aid further research, we open-source EconoJax on Github.

Thiago C. Silva, Paulo V. B. Wilhelm, Diego R. Amancio

This study examines the effects of de-globalization trends on international trade networks and their role in improving forecasts for economic growth. Using section-level trade data from nearly 200 countries from 2010 to 2022, we identify significant shifts in the network topology driven by rising trade policy uncertainty. Our analysis highlights key global players through centrality rankings, with the United States, China, and Germany maintaining consistent dominance. Using a horse race of supervised regressors, we find that network topology descriptors evaluated from section-specific trade networks substantially enhance the quality of a country's GDP growth forecast. We also find that non-linear models, such as Random Forest, XGBoost, and LightGBM, outperform traditional linear models used in the economics literature. Using SHAP values to interpret these non-linear model's predictions, we find that about half of most important features originate from the network descriptors, underscoring their vital role in refining forecasts. Moreover, this study emphasizes the significance of recent economic performance, population growth, and the primary sector's influence in shaping economic growth predictions, offering novel insights into the intricacies of economic growth forecasting.

Avichai Snir, Dudi Levy, Dian Wang et al.

Many experimental studies report that economics students tend to act more selfishly than students of other disciplines, a finding that received widespread public and professional attention. Two main explanations that the existing literature offers for the differences found in the behavior between economists and noneconomists are the selection effect, and the indoctrination effect. We offer an alternative, novel explanation. We argue that these differences can be explained by differences in the interpretation of the context. We test this hypothesis by conducting two social dilemma experiments in the US and Israel with participants from both economics and non-economics majors. In the experiments, participants face a tradeoff between profit maximization, that is the market norm and workers welfare, that is the social norm. We use priming to manipulate the cues that the participants receive before they make their decision. We find that when participants receive cues signaling that the decision has an economic context, both economics and non-economics students tend to maximize profits. When the participants receive cues emphasizing social norms, on the other hand, both economics and non-economics students are less likely to maximize profits. We conclude that some of the differences found between the decisions of economics and non-economics students can be explained by contextual cues.

Manasi Sharma, Melkam Alemayehu, Engida Girma et al.

Background: The COVID-19 pandemic has profoundly impacted the economic, psychological, and social well-being of people in Ethiopia. Pandemic-related fears can exacerbate anxiety and depression symptoms among those with pre-existing physical and mental health conditions as well as those with prior exposure to traumatic events. Methods: We used data from the Ethiopia NeuroGAP-Psychosis study (898 cases and 941 controls with and without a diagnosis of psychosis respectively, 66% male, mean age = 37 years). Data was collected between November 2021 and June 2022 during the COVID-19 pandemic from four hospitals in Ethiopia (three in Addis Ababa and one in Jimma city). Structural equation modeling analysis was conducted to examine the associations between trauma exposure, physical health conditions (like arthristis, neurological disorders, diabetes), COVID-19 stress, and psychological distress (depression and anxiety symptoms). We assessed direct and indirect effects for mediation, and conducted multigroup analysis to examine moderation by case control status. Results: We found evidence that the impact of greater trauma exposure and physical health conditions on higher psychological distress was mediated through higher COVID-19 stress. Sociodemographic characteristics (older age and being maried) were associated with higher psychological distress, with these associations mediated through greater trauma, physical health conditions, and COVID-19 stress. Case-control status also moderated the associations between these variables, with the mediation effects being stronger in cases and weaker in controls. Further, cases reported greater trauma and psychological distress, while controls reported more physical health conditions and COVID-19 stress. Implications: Our findings uniquely assess the interaction of health and emergency related factors in understudied settings like Ethiopia. They underscore the importance of including daily hardships and environmental stressors, along with prior trauma exposure, as risk factors for the assessment of mental health symptoms. This study has key implications for mental health screening and intervention research in response to complex emergency contexts like Ethiopia with a history of armed conflict in addition to the COVID-19 pandemic. Our findings can aid the development of targeted services that address the mental health of at-risk groups with pre-existing mental and physical health conditions.

Marlon Azinovic, Jan Žemlička

Contemporary deep learning based solution methods used to compute approximate equilibria of high-dimensional dynamic stochastic economic models are often faced with two pain points. The first problem is that the loss function typically encodes a diverse set of equilibrium conditions, such as market clearing and households' or firms' optimality conditions. Hence the training algorithm trades off errors between those -- potentially very different -- equilibrium conditions. This renders the interpretation of the remaining errors challenging. The second problem is that portfolio choice in models with multiple assets is only pinned down for low errors in the corresponding equilibrium conditions. In the beginning of training, this can lead to fluctuating policies for different assets, which hampers the training process. To alleviate these issues, we propose two complementary innovations. First, we introduce Market Clearing Layers, a neural network architecture that automatically enforces all the market clearing conditions and borrowing constraints in the economy. Encoding economic constraints into the neural network architecture reduces the number of terms in the loss function and enhances the interpretability of the remaining equilibrium errors. Furthermore, we present a homotopy algorithm for solving portfolio choice problems with multiple assets, which ameliorates numerical instabilities arising in the context of deep learning. To illustrate our method we solve an overlapping generations model with two permanent risk aversion types, three distinct assets, and aggregate shocks.

Teddy Lazebnik, Ariel Rosenfeld, Labib Shami

Coffee leaf rust is a prevalent botanical disease that causes a worldwide reduction in coffee supply and its quality, leading to immense economic losses. While several pandemic intervention policies (PIPs) for tackling this rust pandemic are commercially available, they seem to provide only partial epidemiological relief for farmers. In this work, we develop a high-resolution economical-epidemiological model that captures the rust pandemic's spread in coffee tree farms and its associated economic impact. Through extensive simulations for the case of Colombia, a country that consists mostly of small-size coffee farms and is the second-largest coffee producer in the world, our results show that it is economically impractical to sustain any profit without directly tackling the rust pandemic. Furthermore, even in the hypothetical case where farmers perfectly know their farm's epidemiological state and the weather in advance, any rust pandemic-related efforts can only amount to a limited profit of roughly 4% on investment. In the more realistic case, any rust pandemic-related efforts are expected to result in economic losses, indicating that major disturbances in the coffee market are anticipated.

Yiting Chen, Tracy Xiao Liu, You Shan et al.

As large language models (LLMs) like GPT become increasingly prevalent, it is essential that we assess their capabilities beyond language processing. This paper examines the economic rationality of GPT by instructing it to make budgetary decisions in four domains: risk, time, social, and food preferences. We measure economic rationality by assessing the consistency of GPT's decisions with utility maximization in classic revealed preference theory. We find that GPT's decisions are largely rational in each domain and demonstrate higher rationality score than those of human subjects in a parallel experiment and in the literature. Moreover, the estimated preference parameters of GPT are slightly different from human subjects and exhibit a lower degree of heterogeneity. We also find that the rationality scores are robust to the degree of randomness and demographic settings such as age and gender, but are sensitive to contexts based on the language frames of the choice situations. These results suggest the potential of LLMs to make good decisions and the need to further understand their capabilities, limitations, and underlying mechanisms.

Finn H. Eriksen

Artikkelen handler om en viktig yrkesgruppe i Postverket som eksisterte fra ca. 1890 til ca. 1980. Formålet med artikkelen er å peke på hvorfor yrkesgruppen postpakkmesteres framvekst og avansement skulle bli vanskelig å gjennomføre. Utfordringen var avansementskampen og den faglige konkurransen mellom postpakkmesterne og en annen yrkesgruppe i etaten, postekspeditørene. Mens postpakkmesterne utgikk fra postbudgruppen, var postekspeditørene blant begynnerstillingene innenfor Postverkets administrative ledelse. De to gruppene var organisert i hver sin fagorganisasjon, henholdsvis Postpakkmesternes Landsforbund senere opptatt i Norsk Postforbund og Postmannslaget. Pakkmesterne oppnådde egen fagutdanning, og det ble planlagt en mer felles etatsutdanning for begge grupper. Utviklingen gikk mot at toppstillinger og bunnstillinger i de to yrkesgruppene ble samordnet. Etter rasjonalisering i 1960–1970 årene med postnummer og automatisering av sortering, ble yrkesgruppen postpakkmester overflødig og yrkesinnehaverne fikk nye stillinger. Pakkmesterne mistet sin yrkesberettigelse og selve yrket ble rasjonalisert bort sammen med tittelen.

Matteo Straccamore, Matteo Bruno, Bernardo Monechi et al.

Over the years, the growing availability of extensive datasets about registered patents allowed researchers to better understand technological innovation drivers. In this work, we investigate how the technological contents of patents characterise the development of metropolitan areas and how innovation is related to GDP per capita. Exploiting worldwide data from 1980 to 2014, and through network-based techniques that only use information about patents, we identify coherent distinguished groups of metropolitan areas, either clustered in the same geographical area or similar from an economic point of view. We also extend the concept of coherent diversification to patent production by showing how it represents a decisive factor in the economic growth of metropolitan areas. These results confirm a picture in which technological innovation can lead and steer the economic development of cities, opening, in this way, the possibility of adopting the tools introduced here to investigate the interplay between urban development and technological innovation.