Decision Policy Research Articles

Competing neural representations of choice shape evidence accumulation in humans.

Making adaptive choices in dynamic environments requires flexible decision policies. Previously, we showed how shifts in outcome contingency change the evidence accumulation process that determines decision policies. Using in silico experiments to generate predictions, here we show how the cortico-basal ganglia-thalamic (CBGT) circuits can feasibly implement shifts in decision policies. When action contingencies change, dopaminergic plasticity redirects the balance of power, both within and between action representations, to divert the flow of evidence from one option to another. When competition between action representations is highest, the rate of evidence accumulation is the lowest. This prediction was validated in in vivo experiments on human participants, using fMRI, which showed that (1) evoked hemodynamic responses can reliably predict trial-wise choices and (2) competition between action representations, measured using a classifier model, tracked with changes in the rate of evidence accumulation. These results paint a holistic picture of how CBGT circuits manage and adapt the evidence accumulation process in mammals.

Public acceptance of nuclear waste disposal sites: a decision-making process utilising the ‘veil of ignorance’ concept

This study demonstrates that a decision-making process utilising ‘the veil of ignorance’ concept, defined in process terms as beginning from a blank slate encompassing the entire country as potential sites and shortlisting candidate sites based on scientific (geological) safety, promotes public acceptance of siting a repository for the geological disposal of high-level radioactive waste and fosters procedural fairness. A hypothetical scenario experiment was conducted in Japan, manipulating the site selection process by setting two conditions—one being the application/proposal condition that the Japanese government currently employs, such as an application by municipalities or a proposal by the government, and the other being the veil of ignorance condition, in which multiple candidate areas are selected from a blank slate for the entire land area based purely on geological factors. Three stages of acceptance were presumed—at the level of general management policy, the site selection process itself with a specified decision policy, and the siting of a repository in their area of residence. Two hypotheses were tested: (a) the veil of ignorance condition will be evaluated as a more acceptable and fairer procedure and will engender increased national consensus than the application/proposal condition at the site selection and repository siting stages, and (b) procedural fairness and national consensus will impact acceptance at each stage; these variables at each stage help shape the same variables in the next stage. The results supported these hypotheses. This study discusses the importance of the site selection process, beginning where any de facto site can be a candidate and shortlisting the candidate sites based on scientific criteria.

Macro-Financial Stability in the COVID-19 Crisis: Some Reflections

The global financial system showed remarkable resilience during the COVID-19 pandemic, despite a sharp decline in economic activity and the initial financial market upheaval in March 2020. This article takes stock of the factors that contributed to this resilience, focusing on the role of monetary and financial policies. Drawing on the existing literature, it argues that the swift and decisive policy actions of major central banks in response to the pandemic-induced crisis played a key role in easing financial conditions and sustaining the flow of credit to the real economy. While the pandemic crisis has underscored the importance of policies in preventing calamitous financial outcomes, it has also brought to the fore some unintended consequences of policy actions—in particular, of providing prolonged monetary policy support and of applying regulation to specific segments of the financial system rather than taking a broader approach—that could undermine financial stability in the future.

Robustness assessment and enhancement of deep reinforcement learning-enabled load restoration for distribution systems

Efficient critical load restoration under extreme natural disasters is a promising solution to establish resilient distribution systems. Deep reinforcement learning (DRL) approaches are widely adopted in the load restoration problem to avoid incorporating the accurate distribution system model and improve online decision efficiency. However, the vulnerability of DRL towards adversarial examples may lead to unpracticable decisions and pose potential threats to load restoration. To address this issue, this paper proposes a robustness assessment and enhancement method for DRL-enabled distribution system load restoration. In particular, the distribution system load restoration problem is formulated as a Markov decision process, and a deep Q-network is adopted to learn the optimal decision policy. Then, an adversarial example generation optimization model incorporating the deep Q-network is established to implement the robustness assessment of the DRL-enabled load restoration against adversarial examples. Furthermore, adversarial training with the experience replay of adversarial examples is adopted to retrain the agent and improve the stability of the load restoration decision-making. Finally, the effectiveness of the proposed method is analyzed and verified in the modified IEEE 33-bus system and IEEE 123-bus system. The results show that robustness evaluation and enhancement significantly reduce the application risk of DRL in load restoration with safety-critical requirements.

Deep Reinforcement Learning Empowers Wireless Powered Mobile Edge Computing: Towards Energy-Aware Online Offloading

Deep integration of wireless power transmission and mobile edge computing (MEC) promotes wireless powered MEC to become a new research hotspot in the field of Internet of Things. In this paper, we focus on the joint optimization problem of online offloading decision and charging resource allocation for minimizing task accomplishing time in dynamic time-varying wireless channel scenarios. The optimal solution involves addressing a mixed integer programming problem in real time, which is proved to be NP-hard, and imposes nontrivial challenges to design with conventional optimization methods. To efficiently address this problem, we leverage the deep reinforcement learning (DRL) technology to propose an energy-aware online offloading algorithm called EAOO. EAOO algorithm learns empirically the online offloading decision policies via a well-designed DRL framework, and adopts the feasible solution region analysis method to implement the charging resource allocation. We further propose a novel feasible decision vector generation method, and incorporate the crossover and mutation technology to expand the offloading vector search space with the provable feasibility guarantee. Extensive experimental results show that, our EAOO algorithm outperforms existing baseline algorithms, and achieves near-optimal performance with low CPU execution latency, which well satisfies the practical requirements of real-time and efficiency.

Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process.

Decisions about noisy stimuli are widely understood to be made by accumulating evidence up to a decision bound that can be adjusted according to task demands. However, relatively little is known about how such mechanisms operate in continuous monitoring contexts requiring intermittent target detection. Here, we examined neural decision processes underlying detection of 1 s coherence targets within continuous random dot motion, and how they are adjusted across contexts with weak, strong, or randomly mixed weak/strong targets. Our prediction was that decision bounds would be set lower when weak targets are more prevalent. Behavioural hit and false alarm rate patterns were consistent with this, and were well captured by a bound-adjustable leaky accumulator model. However, beta-band EEG signatures of motor preparation contradicted this, instead indicating lower bounds in the strong-target context. We thus tested two alternative models in which decision-bound dynamics were constrained directly by beta measurements, respectively, featuring leaky accumulation with adjustable leak, and non-leaky accumulation of evidence referenced to an adjustable sensory-level criterion. We found that the latter model best explained both behaviour and neural dynamics, highlighting novel means of decision policy regulation and the value of neurally informed modelling.

In-Network Caching for ICN-Based IoT (ICN-IoT): A Comprehensive Survey

The Internet of Things (IoT) has already emerged as one of the most popular directions in today’s information and communication technology (ICT) domain. With its advancement over different application areas, such as smart home, smart healthcare, industry 4.0, etc., a huge amount of data has been generated by billions of IoT devices, which aggravates the shortcomings of IP-based networks, such as limited expressiveness of IP addressing, inefficient support for mobility and in-network caching. Building IoT on top of information-centric networking (ICN) is believed to be a promising solution to tackle the above challenge, especially the in-network caching of ICN can significantly benefit IoT in terms of reducing data and saving IoT devices’ energy. However, caching IoT data is more challenging than caching traditional Internet content, e.g., video, because IoT data are usually valid within a certain period of time, and IoT devices are typically constrained with battery. Hence, in this survey, we first review the current implementation proposals of ICN-IoT. Next, we present the conventional caching decision policies and replacement policies which could be adopted to mitigate the aforementioned challenges, e.g., reducing IoT traffic, saving energy, reducing data retrieval latency. Further, since leveraging machine learning (ML) techniques have the potential to further improve the caching efficiency by dealing with uncertainties, e.g., predicting unknown information, adaptively interacting with the environment, we also demonstrate the recently proposed ML-based caching schemes for ICN-IoT. In addition, we outline the open research issues and point out the future opportunities of caching in ICN-IoT.

Electrophysiological Markers of Aberrant Cue-Specific Exploration in Hazardous Drinkers.

Hazardous drinking is associated with maladaptive alcohol-related decision-making. Existing studies have often focused on how participants learn to exploit familiar cues based on prior reinforcement, but little is known about the mechanisms that drive hazardous drinkers to explore novel alcohol cues when their value is not known. We investigated exploration of novel alcohol and non-alcohol cues in hazardous drinkers (N = 27) and control participants (N = 26) during electroencephalography (EEG). A normative computational model with two free parameters was fit to estimate participants' weighting of the future value of exploration and immediate value of exploitation. Hazardous drinkers demonstrated increased exploration of novel alcohol cues, and conversely, increased probability of exploiting familiar alternatives instead of exploring novel non-alcohol cues. The motivation to explore novel alcohol stimuli in hazardous drinkers was driven by an elevated relative future valuation of uncertain alcohol cues. P3a predicted more exploratory decision policies driven by an enhanced relative future valuation of novel alcohol cues. P3b did not predict choice behavior, but computational parameter estimates suggested that hazardous drinkers with enhanced P3b to alcohol cues were likely to learn to exploit their immediate expected value. Hazardous drinkers did not display atypical choice behavior, different P3a/P3b amplitudes, or computational estimates to novel non-alcohol cues-diverging from previous studies in addiction showing atypical generalized explore-exploit decisions with non-drug-related cues. These findings reveal that cue-specific neural computations may drive aberrant alcohol-related decision-making in hazardous drinkers-highlighting the importance of drug-relevant cues in studies of decision-making in addiction.

The Influence of Housing Policy on People's House Buying - A Case Study of China

This research examines the influence of housing policy on individuals' decisions to purchase houses in China. The study considers various factors, including disposable income, GDP, national property tax, and the urban registered unemployment rate, and analyzes their impact on housing purchase decisions through regression analysis. The findings indicate that disposable income is the primary driving factor behind the decision to buy a house in China. Higher per capita disposable income among urban and rural residents reflects increased financial capability, leading to a greater propensity to purchase houses. Moreover, an increase in GDP demonstrates a positive influence on housing purchase decisions, as economic growth and higher income levels correspond to increased willingness to buy houses. However, the introduction of national property tax has shown a certain negative impact on the decision to buy a house, potentially due to the increased cost associated with purchasing a property. Notably, the relationship between the urban registered unemployment rate and housing purchase decisions remains less clear. Overall, these findings contribute to a better understanding of the role of housing policy and various socioeconomic factors in shaping people's decisions to buy houses in China.

Solving nonstationary Markov decision processes via contextual decomposition: A military air battle management application

Reinforcement learning for nonstationary problems is a subject of widespread research given that most realistic problems do not exist within static environments. Approaching these problems can require significant effort in feature engineering to provide a learning algorithm with enough useful information about the state space to uncover complex system dynamics. As an alternative for problems with sufficient data describing the nonstationary environment, we propose the contextual decomposition Markov decision process (CDMDP) as a collection of stationary sub-problems intended to approximate nonstationary problem dynamics using a linear combination of value functions. We demonstrate the effectiveness of the CDMDP approach with an application in military air battle management. We use a designed computational experiment and analysis of variance to show that a complex, nonstationary learning problem can be effectively approximated with a small set of stationary sub-problems, and that the CDMDP solution significantly improves solution quality over a baseline approach without the need for additional feature engineering. If a researcher suspects that a complex and continuously varying environment can be approximated by a small number of stationary contexts, the CDMDP framework may save significant computational resources and yield decision policies that are much easier to visualize and implement.

Prevalence and patterns of intimate partner violence among antenatal clinic attendees at federal medical center, Abuja.

Intimate partner violence (IPV) has been a source of increasing concern to the government of nations as well as their citizens despite measures taken to reduce it. This is supported by recent data published by the World Health Organization and other development partners. In health care facilities, intimate partner violence in pregnancy has not been screened routinely. Community-based findings have been the source of most data informing policies for decisions. These were to determine the prevalence and patterns of IPV among antenatal clinic attendees at the Federal Medical Center (FMC), Abuja. Following ethical clearance, a health facility-based cross-sectional study was conducted at the FMC, Abuja to determine the prevalence and correlates of IPV during pregnancy among attendees of antenatal clinics. It was conducted between 26th June and 17th September 2021. A total of 450 questionnaires were administered among consecutive consenting clients and 407 were returned filled giving a non-response rate of 9.6%. The questionnaire collected data on respondents' sociodemographics; experience of and types of IPV; and health problems arising from IPV. Results were presented in tables and charts and analysis was done using IBM SPSS (International Business Machines' Statistical product and service solutions) version 25 software. The mean age was 29.37 ± standard deviation 4.43 years and the predominant ethnic group was Igbo (46.5%); 96.56% were married; 68.06% had tertiary education and 49.14% were in their third trimesters. The combined incidence of intimate partner violence among the participants was 17.69% with physical violence contributing 3.19%; the head region (40%) being the most affected body part. While 34% experienced intrauterine foetal death in the past following IPV. In Miller's landmark study, 27 of 1300 sexually active young women, one in five reported partner pregnancy non promoting behaviors, such as intimidation, threats to leave the relationship if the woman did not become pregnant or actual violence. The two most significant factors for IPV were age and marital status both at P values of P = 0.0001. Medical doctors should feel more open discussing issues around IPV with their clients during antenatal visits.

Resolving the practical factors in the healthcare system management by considering a combine approach of AHP and ANP methods

Healthcare System Management (HSM) is a technique for integrating technology in public hospitals. Knowledge of strategies and decision-making issues is crucial for improving hospital services during the HSM implementation since it directly impacts the efficiency of the hospital and the level of the patient healthcare system. To address the research gap, this study presents a hybrid decision making model that combines the Analysis Hierarchical Process (AHP) method and the Analytic Network Process (ANP) to select Balanced Scorecard Policy (BSP) indicators for evaluating HSM application in a resource-limited background. The hybrid modeling approach not only assists in allocating resources for the execution of HSM but also in determining the significance of the relationship between each analyzed aspect. The hierarchical structure used in this study's quantitative and qualitative relations assessment can also assist various medical facilities in determining how the evaluation criteria and the resources needed to provide HSM services are related. In addition, the management decision policy is shown by the BSP in a healthcare center by combining the two methods mentioned satisfy the goal of strategy weights and limited resources. Finally, the article results indicate that the proposed method increases the efficiency of decisions and maximizes the can improve the reliability in HSM.

Data-driven personalized assortment optimization by considering customers’ value and their risk of churning: Case of online grocery shopping

Online shopping has enabled customers (shoppers) to access a wide variety of products from different online retailers (e-tailers). While beneficial to customers, it puts an extra burden on e-tailers to accurately predict the demand and optimize their inventory. Failure to do so will result in the e-tailer having an imbalanced inventory which could result in customer dissatisfaction and churn. In this paper, we propose a solution to this problem by using the notion of an e-tailer using personalized assortment planning to manage expected customer demand. Our proposed method (termed as Customized Assortment by considering customer Churn – CA&C) assists the e-tailer to identify customers who are at risk of churning, determine the assortment of products they need, and if these products are low in stock, then prevent them from being shown to the regular customers. Doing so, ensures that the assortment which is in limited stock is available for high valued at-risk customers since they could not replenish during the selling periods. CA&C uses mixed multinomial logit (MNL), survival analysis and dynamic programming (DP) techniques to estimate customer preferences, determine the risk of customer churn, the value of each customer and personalized assortment planning, respectively. Customers are grouped based on demographic information. In this study, we discuss how to use Customized Assortment as a lever not only to increase the revenue but also to maintain customers who are at risk of churning. We demonstrate the superiority of the CA&C policy, in saving at-risk customers while maintaining profitability compared to the two other policies, namely, the customized assortment (CA) and myopic decision policy by using a real-world data set.

Exploratory analysis using discrete event simulation modelling of the wait times and service costs associated with the maximum wait time guarantee policy applied in a rheumatology central intake clinic

ABSTRACT Adherence to wait time benchmark targets for the diagnosis and initiation of interventions for rheumatoid arthritis is crucial in altering the disease trajectory. We analysed the impact of the maximum wait time guarantee (MWTG) policy for routing referrals for the initial rheumatologist consults on the waiting and service costs. We modelled a central intake system for a rheumatology clinic as a discrete event simulation (DES) model. Using data from a central intake and rheumatology clinic as input to the model of the system, we simulated the arrival of referrals and rheumatologist visits of patients. We demonstrated the impact of the referral policy on system performance and compared the system costs in an MWTG policy and first-available-appointment policy scenarios. MWTG policy is an option for a wait time management strategy but comes with essential cost considerations. Healthcare managers and policymakers should consider the DES approach to support referral decision policy choices.

RDNF Oriented Analytics to Random Boolean Functions

Dominant areas of computer science and computation systems are intensively linked to the hypercube-related studies and interpretations. This article presents some transformations and analytics for some example algorithms and Boolean domain problems. Our focus is on the methodology of complexity evaluation and integration of several types of postulations concerning special hypercube structures. Our primary goal is to demonstrate the usual formulas and analytics in this area, giving the necessary set of common formulas often used for complexity estimations and approximations. The basic example under considered is the Boolean minimization problem, in terms of the average complexity of the so-called reduced disjunctive normal form (also referred to as complete, prime irredundant, or Blake canonical form). In fact, combinatorial counterparts of the disjunctive normal form complexities are investigated in terms of sets of their maximal intervals. The results obtained compose the basis of logical separation classification algorithmic technology of pattern recognition. In fact, these considerations are not only general tools of minimization investigations of Boolean functions, but they also prove useful structures, models, and analytics for constraint logic programming, machine learning, decision policy optimization and other domains of computer science.

Modeling repair demand in existence of a nonstationary installed base

Life cycles of products consist of 3 phases, namely growth, maturity, and decline phases. Modeling repair demand is particularly difficult in the growth and decline stages due to nonstationarity. In this study, we suggest respective stochastic models that capture the dynamics of repair demand in these two phases. We apply our theory to two different operations management problems. First, using the moments of spare parts demand, we suggest an algorithm that selects a parametric distribution from the hypergeometric family (Ord, 1967) for each period in time. We utilize the algorithm in a single echelon inventory control problem. Second, we focus on investment decisions of Original Equipment Manufacturers (OEMs) to extend economic lifetimes of products with technology upgrades. Our results indicate that the second moment is sufficient for growing customer bases, whereas using the third moment doubles the approximation quality of theoretical distributions for a declining customer base. From a cost minimization perspective, using higher moments of demand leads to savings up to 13.6% compared to the single-moment approach. Also, we characterize the optimal investment policy for lifetime extension decisions from risk-neutral and risk-averse perspectives. We find that there exists a critical level of investment cost and installed base size for profitability of lifetime extension for OEMs. From a managerial point of view, we find that a risk-neutral decision maker finds the lifetime extension problem profitable. In contrast, even a slight risk aversion can make the lifetime extension decision economically undesirable.

Quantifying the human cost of global warming

AbstractThe costs of climate change are often estimated in monetary terms, but this raises ethical issues. Here we express them in terms of numbers of people left outside the ‘human climate niche’—defined as the historically highly conserved distribution of relative human population density with respect to mean annual temperature. We show that climate change has already put ~9% of people (>600 million) outside this niche. By end-of-century (2080–2100), current policies leading to around 2.7 °C global warming could leave one-third (22–39%) of people outside the niche. Reducing global warming from 2.7 to 1.5 °C results in a ~5-fold decrease in the population exposed to unprecedented heat (mean annual temperature ≥29 °C). The lifetime emissions of ~3.5 global average citizens today (or ~1.2 average US citizens) expose one future person to unprecedented heat by end-of-century. That person comes from a place where emissions today are around half of the global average. These results highlight the need for more decisive policy action to limit the human costs and inequities of climate change.

Multi-Stage Robust Dynamic Unit Commitment Based on Pre-Extended -Fast Robust Dual Dynamic Programming

To naturally characterize the long-time sequence coupled equipment such as energy storage systems (ESS) and to enforce the non-anticipativity of dispatch decisions under uncertain renewable energy, a multi-stage robust dynamic unit commitment (MRDUC) model is proposed in this paper. MRDUC transforms the multi-stage robust unit commitment into a dynamic programming form, which is the first attempt to combine day-ahead optimization with intra-day multi-stage robust dynamic dispatch. The unit commitment problem is solved in the pre-stage, and a simulation of intra-day multi-stage economic dispatch is done thereafter, which further refines the UC decision in an iterative way. A pre-extended -fast robust dual dynamic programming (PE-FRDDP) method is proposed to efficiently solve the MRDUC problem, which simultaneously optimizes the day-ahead UC and the intra-day decision policies. Case studies on the modified IEEE 6-Bus and 4 large-scale systems from 300 to 1888 buses demonstrate the effectiveness of the proposed model and the solution methodology.

Decision heuristics in contexts integrating action selection and execution

Heuristics can inform human decision making in complex environments through a reduction of computational requirements (accuracy-resource trade-off) and a robustness to overparameterisation (less-is-more). However, tasks capturing the efficiency of heuristics typically ignore action proficiency in determining rewards. The requisite movement parameterisation in sensorimotor control questions whether heuristics preserve efficiency when actions are nontrivial. We developed a novel action selection-execution task requiring joint optimisation of action selection and spatio-temporal skillful execution. State-appropriate choices could be determined by a simple spatial heuristic, or by more complex planning. Computational models of action selection parsimoniously distinguished human participants who adopted the heuristic from those using a more complex planning strategy. Broader comparative analyses then revealed that participants using the heuristic showed combined decisional (selection) and skill (execution) advantages, consistent with a less-is-more framework. In addition, the skill advantage of the heuristic group was predominantly in the core spatial features that also shaped their decision policy, evidence that the dimensions of information guiding action selection might be yoked to salient features in skill learning.

Determinan Nilai Perusahaan Manufaktur di Indonesia

This study aims to determine the effect of profitability, funding decisions and dividend policies on the value of manufacturing companies listed on the Indonesia Stock Exchange. It is a quantitative study with annual data on profitability, funding decisions and dividend policies on the value of manufacturing companies listed on the Indonesia Stock Exchange. The data analysis method uses panel data linear regression with the E-Views application 9. The results of the partial test with statistical panel data analysis show that the profitability (Return on Assets) has no significant effect on manufacturing company value, the funding decision policy (Debt to Equity Ratio) partially has a positive and significant effect and dividend policy (Dividend Payout Ratio) partially has no significant effect. While simultaneously profitability, funding decisions and dividend policy have a positive and significant effect on firm value.