Dynamic Policy Research Articles

Efficient criticality oriented service brokering policy in cloud datacenters

Cloud service provider (CSP) offers a huge number of datacenters and virtual servers to the users for processing their workloads in an infrastructure as a service (IaaS) cloud computing environment. Due to the heterogeneous volume of these resources and the immense number of user workloads arriving simultaneously in the cloud, it is necessary to use an effective load distribution technique for scheduling the resources to achieve high performance and high user satisfaction. Service brokering policy and load balancing techniques are the two crucial areas to be focused on while selecting the datacenters and virtual machines, respectively. In this study, we have proposed a dynamic efficient criticality-oriented service brokering policy for load allocations among datacenters by considering task criticality, datacenter proximity, and traffic, the size of the datacenter, its present load and makespan value. The proposed methodology is examined against the current policies in the CloudAnalyst simulation tool and the analysis report confirms that our proposed policy gives priority to processing the urgent loads and chooses the optimum datacenter to diminish the load response time, datacenter processing time, minimizes the cost, achieves optimum resource utilization and workload balancing among resources.

Stochastic Economic Lot Scheduling via Self-Attention Based Deep Reinforcement Learning

The Stochastic Economic Lot Scheduling Problem (SELSP) is a difficult dynamic optimization problem with wide industrial applications. Traditional methods such as hyper-heuristics are manually designed based on substantial expert knowledge, which may limit their optimization performance. Recently, Deep Reinforcement Learning (DRL) is shown to be promising in automatically learning scheduling policies for SELSP. However, its performance is still quite far from that of hyper-heuristics, due to the lack of suitable deep models. In this paper, we propose a novel DRL method to learn dynamic scheduling policies for SELSP in an end-to-end fashion. Based on self-attention, our method can effectively extract useful features from raw state information, and is flexible in handling different numbers of products, which is not viable for previous methods. Experiments on a complex biopharmaceutical manufacturing process show that our method outperforms a recent DRL method and state-of-the-art hyper-heuristics. Moreover, the trained policy performs better in environments different from training with demand forecast errors and varying number of products, showing its strong robustness and generalization ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The Stochastic Economic Lot Scheduling Problem (SELSP) is an important problem for manufacturing enterprises, which is to optimally balance the production and inventory so as to minimize the total cost. However, SELSP is very challenging to solve due to the involvement of uncertain factors such as customer demands and machine failures. Traditional methods for solving SELSP, such as heuristic policies and hyper-heuristics, heavily rely on human experiences to design and hence the performance could be limited. This paper proposes a Deep Reinforcement Learning (DRL) based method to automatically learn scheduling policy for solving SELSP, which could alleviate the above limitation through a self-attention based feature extraction mechanism and reward based training. Experimental results on a realistic manufacturing process show that our method can deliver higher revenue than conventional manual policy and an existing DRL based method.

A mini review of reinfection with the SARS-CoV-2 Omicron variant.

COVID-19 has caused severe morbidity and mortality worldwide. After the end of the dynamic zero-COVID policy in China in December, 2022, concerns regarding reinfection were raised while little was known due to the lack of surveillance data in this country. This study reviews the probability, risk factors, and severity of severe acute respiratory syndrome coronavirus 2 Omicron variant reinfection, as well as the interval between infections, risk of onward transmission by reinfected cases, and the role of booster vaccination against reinfection. References for this review were identified through searches of PubMed and Web of Science up to September 24, 2023. The rate of reinfection ranges from 3.1% to 13.0%. Factors associated with a higher risk of reinfection include being female, having comorbidities, and being unvaccinated. Reinfection with the BA.4 or BA.5 variant occurs approximately 180 days after the initial infection. Reinfections are less clinically severe than primary infections, and there is evidence of lower transmissibility. The debate surrounding the effectiveness and feasibility of booster vaccinations in preventing reinfection continues. The reinfection rate during the Omicron epidemic is significantly higher than in previous epidemic periods. However, the symptoms and infectivity of reinfection were weaker than those of the prior infection. Medical staff and individuals at high risk of reinfection should be vigilant. The efficacy of booster vaccinations in reducing reinfection is currently under debate.

Group Dynamics and Public Policy Formation: Insights from Nigerian Society

This paper appraises the theoretical assumptions of group theory within the purview of public policy with a specific focus on the Nigerian context. It begins by providing conceptual clarity on policy, public policy, and public policy analysis to lay the foundation for the subsequent discussion. The paper then examines the underlying assumptions of group theory and critiques its applicability within public policy discourse. Through the analysis of secondary data, this paper explores the role of group theory in shaping policy formulation and implementation processes. It highlights the significant influence of group actions on government policies, illustrating how various interest groups exert pressure and influence decision-making processes. This paper concludes that public policy emerges as a result of collective agitation and interaction within a given society. It recognises that various groups play pivotal roles in shaping policy outcomes, yet their actions are typically governed by established rules, fostering a climate of peaceful competition among competing interests.

Dynamic curriculum learning for conversation response selection

Curriculum learning (CL) has demonstrated its potential to fine-tune pre-trained models for the response selection task. However, existing CL works exhibit a significant reliance on fixed data buckets and a pre-ordained learning schedule. This paper seeks to transcend these constraints by proposing a novel dynamic curriculum learning (DCL) algorithm that employs: (1) a new difficulty measurement that amalgamates linguistic features with model confidence, and (2) an automatic uncertainty-based learning scheduler with a dynamic data sampling policy. This unique approach enables the model to autonomously assemble rational data batches and adjust the curriculum stage by itself. Empirical experiments show that our DCL method reveals its superior performance over three strong baselines and the competitive method on three multi-turn dialogue datasets. The in-depth analysis demonstrates our approach dynamically adapts the CL process, which in turn makes effective use of existing data and alleviates data scarcity.

Spatiotemporal differentiation calendar for car and truck flow on expressways: A case study of Jiangsu, China

The transport objects and demands of cars and trucks differ, leading to different spatiotemporal characteristics in their flow patterns. Existing studies on expressway flow are mainly focused on the overall traffic, however, a decomposition analysis of cars and trucks is necessary to accurately explore traffic prediction, management, and dynamic toll policy. This study presents calendars and spatial distribution of car and truck flow based on toll fee data in Jiangsu Province, China. Additionally, the influencing factors are explored by using Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The results show that: (1) car flow follows a regular pattern of low from Monday–Thursday and high from Friday–Sunday, while high from Monday–Friday and low from Saturday–Sunday for truck flow. This law is not obvious in the total flow calendar. (2) The car flow predominantly accumulates in the 0–20 km range, while the truck flow is concentrated within the 20–40 km range. (3) The spatial distributions are stable for both car and truck flow, characterized by higher flows in the southern part of Jiangsu and lower in the northern region. Gaps in truck flow between cities are greater than those in car flow, narrowing during holidays/festivals than on weekdays or weekends. (4) private vehicle possession and travel time to Shanghai are the main influencing factors on car flow during different periods, the number of research institutions and the added value of tertiary industry are positively correlated on weekdays and weekends, respectively. Total retail sales of consumer goods is the main factor affecting truck flow during different periods, total import and export goods can be attributed to weekdays, while per capita disposable income affects weekends and festivals. Finally, the findings of this study offer insights for the optimal implementation of toll policies.

FairPlay: A Multi-Sided Fair Dynamic Pricing Policy for Hotels

In recent years, popular touristic destinations face overtourism. Local communities suffer from its consequences in several ways. Among others, overpricing and profiteering harms local societies and economies deeply. In this paper we focus on the problem of determining fair hotel room prices. Specifically, we put forward a dynamic pricing policy where the price of a room depends not only on the demand of the hotel it belongs to but also on the demand of: (i) similar rooms in the area and (ii) their hotels. To this purpose, we model our setting as a cooperative game and exploit an appropriate game theoretic solution concept that promotes fairness both on the customers' and the providers' side. Our simulation results involving price adjustments across real-world hotels datasets, confirm that ours is a fair dynamic pricing policy, avoiding both over- and under-pricing hotel rooms.

Divergence-Guided Simultaneous Speech Translation

To achieve high-quality translation with low latency, a Simultaneous Speech Translation (SimulST) system relies on a policy module to decide whether to translate immediately or wait for additional streaming input, along with a translation model capable of effectively handling partial speech input. Prior research has tackled these components separately, either using ``wait-k'' policies based on fixed-length segments or detected word boundaries, or dynamic policies based on different strategies (e.g., meaningful units), while employing offline models for prefix-to-prefix translation. In this paper, we propose Divergence-Guided Simultaneous Speech Translation (DiG-SST), a tightly integrated approach focusing on both translation quality and latency for streaming input. Specifically, we introduce a simple yet effective prefix-based strategy for training translation models with partial speech input, and develop an adaptive policy that makes read/write decisions for the translation model based on the expected divergence in translation distributions resulting from future input. Our experiments on multiple translation directions of the MuST-C benchmark demonstrate that our approach achieves a better trade-off between translation quality and latency compared to existing methods.

THE DYNAMICS OF POLICY CHANGE AND AGILE GOVERNANCE: HOW INDONESIAN GOVERNMENT RESPONSE TO COVID-19 PANDEMIC

Indonesia and other countries must deal with the COVID-19 pandemic with limited information control amid uncertainty. Handling a pandemic that has become a crisis places great demands on the government to provide agile and dynamic policies. Variations of policies that the Indonesian government has issued raise doubts in the public sphere. The government changed several policies quickly and easily, giving the impression that they did not go through a thorough formulating process. It shows that the handling of COVID-19 requires a policy system that is quite complex due to the many interactions of various sectors. By using qualitative methods, this study attempts to discuss the dynamics of changing public policies by using the concept of incremental policy amid demands for agile governance implementation by looking at the cases of policies issued by the Indonesian government in response to COVID-19. The dynamic of the policy network will be explored further to dismantle the discussion of policies for handling COVID-19 in Indonesia. Therefore, the results of this research show the challenges in implementing agile principles simply by changing and adapting policies to empirical conditions. In its application, agile governance shows several dominant actors who gather resources and support, shape policy outcomes, and advance their interests.

Maximizing the net present value of a project under uncertainty: Activity delays and dynamic policies

We study a project with stochastic activity durations and cash flows; we model the uncertainty using discrete scenarios. The project entails precedence-related activities, each of which incurs a cash flow that may be positive (inflow) or negative (outflow). The problem is to find a scheduling policy that maximizes the expected net present value of the project. A scheduling policy decides the starting time of each activity under every possible realization of the unknown parameters. Ideally, one wants to expedite the inflows (e.g., incoming payments), while delaying the outflows (e.g., costs) as much as possible, without violating the project deadline. In this article, we devise an exact and a heuristic method to define policies within two new classes of scheduling policies. The first policy class generalizes all existing static policies in the literature and further illustrates the importance of intentional activity delays from both a theoretical as well as an empirical point of view. Whereas the literature on project scheduling has mainly focused on static policies, we also propose a second class of dynamic policies. We show that dynamic policies outperform static policies by means of extensive computational experiments.

The exceptions to child exceptionalism: Racialised migrant ‘deservingness’ and the UK's free school meal debates

Free School Meals (FSM) have been the site of renewed contestation and extensive campaigning in the last half-decade. Until recently, children in families with ‘no recourse to public funds’ because of their immigration status were excluded from accessing FSMs, despite being some of the most destitute in Britain. Through an analysis of campaign materials and interviews with advocates, we consider this dynamic policy terrain in light of the UK's ‘hostile environment’ and consider lessons for campaigners. By exploring imaginaries of childhood, poverty, and nation shaping FSM policies, we offer a warning that campaigns can end up justifying exclusions at the same time as aiming for more expansive support. How campaigns represent their causes has implications, which, in this case, is often through the exceptionalism and hyper-deservingness of childhood. We argue these representations reduce discussion to technical questions about who is ‘deserving’, thereby risking shoring up an exclusionary and hostile state.

Dynamic Pricing and Route Guidance: A Multi-Agent System for Effective Parking and Traffic Management

In a city center that is usually flooded with traffic, finding a parking space is a challenge for most commuters. Searching for parking generates additional urban traffic, increases energy consumption and vehicle emissions, and wastes time. This study examines how parking systems can be used as an effective tool for reducing traffic congestion. It proposes a new, smart approach to parking using real-time dynamic pricing and route guidance in a multi-agent system to better align supply and demand, thereby reducing both congestion and the amount of time spend by drivers searching for parking. To examine the effects of dynamic parking pricing on road traffic, we conducted a multi-agent open-source framework to simulate spatiotemporal distributions of daily individual activity. By extending MATSim, we were able to model parking usage more realistically. We developed several micro-simulation scenarios to evaluate strategy performance for both on-street and off-street parking in the city center of Tunis, Tunisia. The results show that dynamic pricing policies can be used to plan and manage parking areas more efficiently, minimizing the dispersion of parking occupancy lots. Variations in parking prices were shown to increase parking lot vacancies and reduce traffic congestion. Moreover, the system can also generate increased revenue for government and private-sector parking authorities while improving driver satisfaction. Finally, the parking dynamic pricing strategy promises a convenient mobility experience, while increasing efficiency and reducing the negative externalities of urban transportation.

Global Dynamics and Optimal Policy in the Ak Model with Anticipated Future Consumption

Abstract This paper analyzes the Ak model with anticipated future consumption. In the model with internal anticipation individual’s utility depends on current consumption and a forward-looking reference level which is formed from individual’s own future consumption. The global dynamics of the economy is characterized by means of a qualitative phase diagram analysis. In the model with external anticipation the consumption reference level is formed from economy-wide average future consumption, which is taken as given by individuals and causes the competitive equilibrium to be inefficient. Characterizing the global dynamics of the economy is required to characterize an optimal fiscal policy that corrects the inefficiency brought about by this external effect.

How communication infrastructure impacted New York City restaurants’ ability to access resources during the COVID-19 pandemic

ABSTRACT During the COVID-19 pandemic, many restaurants were forced to temporarily close or reduce operations. Drawing on Communication Infrastructure Theory (CIT), this study explored how communication infrastructure helped restaurant owners and chefs in New York City navigate resources and understand the dynamic policy responses to COVID-19 that impacted their industry (e.g. whether indoor dining was allowed, at what capacity, etc.). The results of this study demonstrate that New York City restaurants possess an incredible capacity for resilience, in part due to their communication networks. Even with inadequate relief funds and a mixed bag of regulations and initiatives, most participants’ restaurants were able to adapt their businesses to meet the challenges of the pandemic. Additionally, this work shows how dramatically unsupported the restaurant industry was during the height of the pandemic, and at the same time, how important restaurants can be to the communities they serve.

Sensitivity analysis for a dynamic macroeconomic policy game in a monetary union

In this paper, we examine the sensitivity of the results of an earlier paper which presented and analyzed a dynamic game model of a monetary union with coalitions between governments (fiscal policy makers) and a common central bank (monetary policy maker). Here we examine alternative values of the parameters of the underlying model to show how the earlier results depend on the numerical parameter values chosen, which were obtained by calibration instead of econometric estimation. We demonstrate that the main results are qualitatively the same as in the original model for plausible smaller and larger values of the parameters. For the few cases where they differ, we interpret the deviations in economic terms and illustrate the policies and their macroeconomic effects resulting from the change to the parameter under consideration for one of these cases.

Age of correlated information-optimal dynamic policy scheduling for sustainable Green IoT devices: A multi-agent deep reinforcement learning approach

The rapid progress in communication technologies and the widespread adoption of connected devices have given rise to various information-centric Internet-of-Things (IoT) systems, typically necessitating timely updates of information. Green IoT (G-IoT) strives to enhance the environment by reducing the power consumption of billions of devices engaged in extensive data exchange, addressing the substantial energy demand in the process. The Age of Correlated Information (AoCI) measures the freshness of information shared among two or more devices that contribute to the same decision-making process. Optimizing AoCI using Deep Reinforcement Learning (DRL) in IoT reduces energy consumption, optimizes resource utilization, and promotes environmentally conscious communication, contributing to the development of a sustainable G-IoT system. This paper focuses on scheduling the transmission of status update packets among interconnected G-IoT devices to minimize the application-specific long-term average AoCI. The problem is modeled as an NP-hard episodic Markov Decision Process (MDP), highlighting its computational complexity. To handle correlations and the curse of dimensionality, a multi-agent deep reinforcement learning algorithm, specifically the Multi-Agent Deep Deterministic Policy Grading (MADDPG) algorithm, is developed. The training progress displays episode rewards, with an environment designed to penalize multiple agents transmitting simultaneously or none at all, promoting cooperative behavior and minimizing the average age of correlated information. We provide comprehensive simulation results, including reward convergence, the learning process of actors and critics, and the resulting average AoCI with the number of episodes, demonstrating the effectiveness of the MADDPG algorithm.

A novel routing method for dynamic control in distributed computing power networks

Driven by diverse intelligent applications, computing capability is moving from the central cloud to the edge of the network in the form of small cloud nodes, forming a distributed computing power network. Tasked with both packet transmission and data processing, it requires joint optimization of communications and computing. Considering the diverse requirements of applications, we develop a dynamic control policy of routing to determine both paths and computing nodes in a distributed computing power network. Different from traditional routing protocols, additional metrics related to computing are taken into consideration in the proposed policy. Based on the multi-attribute decision theory and the fuzzy logic theory, we propose two routing selection algorithms, the Fuzzy Logic-Based Routing (FLBR) algorithm and the low-complexity Pairwise Multi-Attribute Decision-Making (lPMADM) algorithm. Simulation results show that the proposed policy could achieve better performance in average processing delay, user satisfaction, and load balancing compared with existing works.

N-Level Hierarchy-Based Optimal Control to Develop Therapeutic Strategies for Ecological Evolutionary Dynamics Systems.

This article mainly proposes an evolutionary algorithm and its first application to develop therapeutic strategies for ecological evolutionary dynamics systems (EEDS), obtaining the balance between tumor cells and immune cells by rationally arranging chemotherapeutic drugs and immune drugs. First, an EEDS nonlinear kinetic model is constructed to describe the relationship between tumor cells, immune cells, dose, and drug concentration. Second, the N-level hierarchy optimization (NLHO) algorithm is designed and compared with five algorithms on 20 benchmark functions, which proves the feasibility and effectiveness of NLHO. Finally, we apply NLHO into EEDS to give a dynamic adaptive optimal control policy and develop therapeutic strategies to reduce tumor cells, while minimizing the harm of chemotherapy drugs and immune drugs to the human body. The experimental results prove the validity of the research method.

Securing financial data storage: A review of cybersecurity challenges and solutions

In an era where the digital transformation of the financial sector has become both a boon and a battleground, this paper delves into the intricate dynamics of cybersecurity within financial data storage. With the relentless advancement of technology and the concomitant rise in cyber threats, the safeguarding of financial data emerges as a paramount concern. This study sets out with the aim to dissect the current cybersecurity challenges, scrutinize the effectiveness of existing protective measures, and explore the potential of emerging technologies in fortifying financial data against cyber incursions. Employing a qualitative analysis that draws upon a wide array of scholarly contributions, the paper meticulously evaluates the landscape of cybersecurity threats, regulatory frameworks, and the transformative impact of technological innovations like artificial intelligence on financial data security. The findings reveal a significant gap between the sophistication of cyber threats and the current cybersecurity measures, underscoring the urgent need for financial institutions to adopt more robust and advanced defensive strategies. The study further highlights the critical role of regulatory bodies in shaping a resilient cybersecurity framework that not only addresses current vulnerabilities but is also adaptable to future challenges. Conclusively, the paper advocates for a collaborative approach towards cybersecurity, emphasizing the synergy between financial institutions, technology firms, and regulatory agencies in crafting a secure financial ecosystem. Recommendations include the adoption of cutting-edge technologies, the formulation of dynamic regulatory policies, and the fostering of a culture of continuous learning and adaptation to emerging cyber threats. This comprehensive exploration aims to serve as a beacon for stakeholders in the financial sector, guiding them towards a more secure and trustworthy digital future.

Behavioral economics and consumer protection in the U.S.: A review: Understanding how psychological factors shape consumer policies and regulations

This study explores the integration of behavioral economics into consumer protection policies, focusing on the influence of psychological factors on consumer behavior and the implications for market efficiency and consumer welfare. Employing a systematic literature review and content analysis, the research scrutinizes peer-reviewed articles, policy reports, and case studies from 2008 onwards to identify the impact of cognitive biases and heuristics on consumer decisions and the effectiveness of behavioral interventions in enhancing consumer protection. The methodology hinges on a comprehensive search strategy, with inclusion and exclusion criteria meticulously applied to ensure the relevance and quality of the literature reviewed. Key findings reveal that psychological factors significantly affect consumer choices, often leading to outcomes that deviate from optimal welfare and efficiency. Behavioral economics, through its insights into consumer behavior, offers innovative approaches to policy-making that can mitigate these effects, enhancing consumer protection and market functionality. The study identifies challenges and opportunities in the evolving landscape of consumer protection, emphasizing the need for adaptive regulatory frameworks and the potential of digital technologies to understand and influence consumer behavior. Final recommendations advocate for dynamic policies, consumer education, and collaboration among stakeholders to address emerging consumer risks effectively. The study concludes that behavioral economics holds transformative potential for consumer protection, urging continued exploration and innovation in policy development to safeguard consumer interests in a rapidly changing market environment.