Context Of Decision Research Articles

A Benefit-Risk Conceptual Framework for Biologic Use During Pregnancy: A Mini-Review.

Recent reports related to in utero exposure of marketed immunosuppressive biologics led to clinical recommendations to delay live vaccinations for infants due to the concern of reduced vaccine effectiveness and/or increased risk of vaccine-related disease. These delays can increase the risk of children contracting vaccine preventable diseases, yet the alternative cessation of biologics during pregnancy may result in increased autoimmune disease activity for the pregnant person, raising complex benefit-risk (B-R) considerations and trade-offs. Our goal is to develop a conceptual framework for B-R assessment based on the key benefits and risks pregnant people would consider for themselves and their children when continuing (vs. discontinuing) a biologic during pregnancy. The proposed framework defines the decision contexts, key domains and attributes for potential benefits, and risks of biologic use during pregnancy, informed by a literature review of indications for biologics and refined with key clinical stakeholders. The framework includes both the pregnant person taking the biologic and the infant potentially exposed to the biologic in utero, with potential benefit and risk domains and attributes for each participant. To advance this conceptual framework, there are considerations of potential biases and uncertainty of available data that will be imperative to address when quantifying the B-R framework. For these reasons, we recommend the formation of a consortium to ensure development of a robust, validated framework that can be adopted in the healthcare setting.

Plastic pollution as waste colonialism in Aotearoa (New Zealand)

The international discourse around waste colonialism has highlighted the often imbalanced power dynamics evident in the transboundary movement of waste. While plastic pollution as a form of waste colonialism has been explored in some overseas contexts, its impact and resonance in Aotearoa (New Zealand) remains poorly understood. This study addresses this gap through interviews with several specialists in the fields of plastic pollution, zero waste, circular economic development, and Indigenous sovereignty. Taking a full life-cycle approach to understanding plastic pollution, this study explores the interconnections between plastics, power, and people to better understand how Aotearoa could holistically respond to the plastic pollution crisis. The study found that colonial governmental and economic systems have enabled a widespread disconnection between plastic consumption and its socio-ecological consequences. Plastic pollution as waste colonialism also perpetuates significant inequities for Aotearoa’s most disenfranchised communities, including Māori, Pasifika, and low-income households. This demonstrates the need for genuine representation of these communities in all relevant decision-making contexts and holistic, systemic transformation to address plastic pollution as waste colonialism.

Simulation–optimization configurations for real-time decision-making in fugitive interception

Simulation–optimization models are well-suited for real-time decision-support to the control room for search and interception of fugitives by Police on a road network, due to their ability to encode complex behavior while still optimizing the interception.The typical simulation–optimization configuration is simulation model optimization, where the simulation model describes the system to be optimized, and the optimizer attempts to find the combination of decision variables that maximizes the interception probability. However, the repeated evaluation of the simulation model leads to high computation time, thus rendering it inadequate for time-constrained decision contexts. To support police interception operations in real-time, timely calculation of the solution is essential. Sequential simulation–optimization, where the simulation model, with its rich behavior, constructs (part of) the constraints of an optimization problem, could decrease the computation time.We compare the computation time for two configurations of simulation–optimization (typical simulation model optimization and sequential simulation–optimization) for various problem instances of the fugitive interception problem. We show that sequential simulation–optimization reduces the computation time of large instances of the fugitive interception case study ten-fold. This result illustrates the potential of sequential simulation–optimization to mitigate the expensive optimization of simulation models.

An Analytic Hierarchy Process Contingency Analysis of Factors Affecting the Emigration Decision of Medical Doctors in Turkey.

While the importance of explicitly identifying and considering contingent factors such as decision content and context is widely accepted as a way to ensure the validity of the decision analysis for the specific task at hand, few studies include this. This research uses a contingency theoretical approach to study factors affecting the emigration decision of medical doctors (MDs) for the specific case of Turkey. The motivation for conducting this study arises from the observation that the growing trend in emigration among MDs from Turkey is having a significant impact on the country's healthcare system. Dealing with the emigration of MDs is crucial for ensuring an effective and sustainable healthcare system, especially in terms of the availability of services, satisfaction, and employment of the healthcare staff. Contextual factors were explicitly identified through consultation with experts, while the generic factors were retrieved from the specialized medical migration literature. The Analytic Hierarchy Process method was utilized to prioritize the factors. Seventy-three participants were surveyed about their intention to either study or work abroad. The findings reveal that low remuneration and anxiety about their future due to the political situation in the country constitute the two most important factors driving the decision to emigrate.

Validating care and treatment scenarios for measuring decisional conflict regarding future care preferences among older adults.

Decisional conflict is used increasingly as an outcome measure in advance care planning (ACP) studies. When the Decisional Conflict Scale (DCS) is used in anticipatory decision-making contexts, the scale is typically tethered to hypothetical scenarios. This study reports preliminary validation data for hypothetical scenarios relating to life-sustaining treatments and care utilisation to inform their broader use in ACP studies. Three hypothetical scenarios were developed by a panel of multidisciplinary researchers, clinicians and community representatives. A convenience sample of 262 older adults were surveyed. Analyses investigated comprehensibility, missing data properties, sample norms, structural, convergent and discriminant validity. Response characteristics suggested that two of the scenarios had adequate comprehensibility and response spread. Missing response rates were unrelated to demographic characteristics. Predicted associations between DCS scores and anxiety (r's = .31-.37, p < .001), and ACP engagement (r's = -.41 to -.37, p < .001) indicated convergent validity. A substantial proportion of older adults reported clinically significant levels of decisional conflict when responding to a range of hypothetical scenarios about care or treatment. Two scenarios showed acceptable comprehensibility and response characteristics. A third scenario may be suitable following further refinement. The scenarios tested here were designed in collaboration with a community representative and were further piloted with two groups of community members with relevant lived experiences; four people with life-limiting conditions and five current or former care partners.

Elevating humanism in high-stakes automation: experts-in-the-loop and resort-to-force decision making

ABSTRACT Artificial intelligence (AI) technologies pervade myriad decision systems, mobilising data at a scale, speed, and scope that far exceed human capacities. Although it may be tempting to displace humans with these automated decision systems, doing so in high-stakes settings would be a mistake. Anchored by the example of states’ resort to force, I argue that human expertise should be elevated—not relegated—within high-stakes decision contexts that incorporate AI tools. This argument builds from an empirical reality in which defence institutions increasingly rely on and invest in AI capabilities, an active debate about how (and if) humans should figure into automated decision loops, and a socio-technical landscape marked by both promise and peril. The argument proceeds through a primary claim about the amplified relevance of expert humans in light of AI, underpinned by the assumed risks of omitting human experts, together motivating a tripartite call to action. The position presented herein speaks directly to the military domain, but also generalises to a broader worldbuilding project that preserves humanism amidst suffusive AI.

Exploring the nexus of artificial intelligence in talent acquisition: Unravelling cost-benefit dynamics, seizing opportunities, and mitigating risks

The rise in talent management complications led organizations to rely on the latest technologies to automate their routine HRM tasks through AI. This study proposed to examine fundamental aspects of AI in talent acquisition (cost-benefit, opportunities, and risk factors) from the context of strategic analysis and decision-making. 52 respondents from HRM and the information technology departments from fifteen large dairy enterprises, each with more than one thousand employees, were included in the focus group discussion. Both departments were included in the focus group discussion as they heavily employ AI in talent acquisition. The opinions were collected in multiple rounds based on the cost, benefit, opportunity, and risk criteria using the analytical hierarchy process, a multi-criteria decision-making framework. The findings demonstrated that most respondents opinioned AI supports talent acquisition with many opportunities (38.7%) that involve the identification of the best applicants (18.7%) and different benefits (33.2%) to the organization in the form of saving time and cost (16.1%) leading to higher efficacy. The study infers that the application of AI in HRM significantly contributes to talent acquisition, streamlining processes, improving efficiency, and enhancing decision-making. The study recommends that implementing AI in talent acquisition requires a strategic approach, and organizations need to consider factors such as data privacy, ethical use of AI, and ongoing training to ensure successful integration into their hiring processes. Additionally, regular monitoring and adjustments are essential to optimize the effectiveness of AI tools in talent acquisition. AcknowledgmentThe authors of this article would like to thank Prince Sultan University for its financial and academic support for this publication.

Prescriber perceptions of boxed warnings: A qualitative study.

To explore how boxed warning (BW) information fits within the context of prescribers' overall treatment decision-making and communication with patients. In-depth interviews (N = 52) were conducted with primary care providers and specialists. Participants were presented with one of two prescribing scenarios: (1) estrogen vaginal inserts to treat vulvovaginal atrophy (VVA) associated with menopause; or (2) direct-acting antivirals (DAA) to treat chronic hepatitis C virus infection (HCV). The semi-structured interviews explored participants' treatment decision-making within the scenario, reactions to current prescribing information for a product within the FDA-approved drug class, as well as their perceptions of BWs generally. Across scenarios, providers described that the BW is only one of several factors that influence treatment decision-making. In the VVA scenario, symptom severity, family history, and experience with nonprescription drugs were raised as common factors that influence prescribing considerations; compared to comorbid infections, viral load, and HCV genotype in the HCV scenario. Perceptions of the DAA BW were generally positive or neutral, as many participants found the information important and appropriate. The VVA BW was viewed less favorably, with many participants stating the BW overstates the risk for this drug. Findings suggest that BWs are one of several factors that influence providers' treatment decisions, and BW influence largely depends on context. Providers across scenarios expressed notable differences in their perceptions of the risk information provided in the presented BWs; however, across scenarios participants expressed consideration of how patients may perceive the BW.

Systematic methodological review of health state values in glaucoma cost-utility analyses.

Describing the characteristics and sources of health state utility values and reporting practice in the literature of cost-utility analyses facilitates an understanding of the level of the transparency, validity, and generalizability of cost-utility analyses. Improving the quality of reporting will support investigators in describing the incremental value of emerging glaucoma interventions. To describe the state of practice among published glaucoma cost-utility analysis studies, focusing on valuation of health and the quality of reporting. We searched several databases including Medline, CINHAL, Embase, Web of Science, Scopus, Biosis previews, the Health Economic Evaluations Database, and the NHS Economic Evaluation Database (NHS EED). We included full-text, English, published cost-utility analyses of glaucoma interventions with quality-adjusted life years (QALYs) as the primary outcome measure to calculate incremental cost-utility ratios. Excluded studies were non-English language, reviews, editorials, protocols, or other types of economic studies (cost-benefit, cost-minimization, cost-effectiveness). Study characteristics, operational definitions of glaucoma health states and health state utilities were extracted. The original source of the health utility was reviewed to determine the scale of measurement and the source of preference weighting. Items from the Systematic Review of Utilities for Cost-Effectiveness (SpRUCE checklist) were used to assess the reporting and quality of health utilities in glaucoma CUA. 43 CUAs were included, with 11 unique sources of health utilities. A wide range of health utilities for the same Hodapp-Parrish-Anderson glaucoma health states were reported; ocular hypertension (0.84-0.95), mild (0.68-0.94), moderate (0.57-0.92), advanced (0.58-0.88), severe/blind (0.46-0.76), and bilateral blindness (0.26-0.5). Most studies reported the basis for using health utilities (34, 79%) and any assumptions or adjustments applied to the health utilities (22, 51%). Few studies reported a framework for assessing the relevance of health utilities to a decision context (8, 19%). Even fewer (3, 7%) applied a systematic search strategy to identify health utilities and used a structured assessment of quality for inclusion. Overall, reporting has not improved over time. This review describes that few CUAs describe important rationale for using health state utility values. Including additional details on the search, appraisal, selection, and inclusion process of health utility values improves transparency, generalizability and supports the assessment of the validity of study conclusions. Future investigations should aim to use health utilities on the same scale of measurement across health states and consider the source and relevance to the decision context/purpose of conducting that cost-utility study.

What determine livestock feed and marketing? Insights from rural Ethiopia

In sub-Saharan Africa where farmers undergo rapid losses of grazing lands and pasture areas, they face feed scarcity and poor animal marketing. In response, farmers make non-separable decisions on feed production and livestock marketing. Under the context of non-separable decision, however, factors affecting livestock feed collection and marketing are unclear in the literature. With the aim of addressing such a gap, this study has proposed two research questions: (1) what factors determine household's decisions on feed collection and livestock marketing? (2) Does feed collection influence livestock marketing? For the analysis, cross sectional data were gathered from 689 households in rural Ethiopia. While Tobit model was used to determine feed collection, two-stage Heckman model was deployed to identify key factors affecting livestock marketing. The study found that household's access to communal land, size of livestock holding, share-in land and own farmlands had positive and significant influence on feed collection. This urges farmers to involve in livestock marketing via selling their extra yield. Increased feed collection from communal and croplands leads to improve animal productivity. Econometric results further showed that household's education and access to training were influencing feed collection and livestock marketing positively. It also confirmed that feed collection and provision of credit services had significant effects on livestock marketing. The study further showed that livestock marketing was hardly realized without gathering feed from communal and private lands, which also entails the need to maintain the environment. This study is useful because it provides the significant roles of farm education, animal holding, credit services, communal and private lands in improving access to feed sources. The findings suggest the need for augmenting land conservation, introducing improved animal breeds and enhancing farmers’ capacity. These interventions ought to be supplemented with provision of credit services and follow up trainings.

Enabling explainable artificial intelligence capabilities in supply chain decision support making

Explainable artificial intelligence (XAI) has been instrumental in enabling the process of making informed decisions. The emergence of various supply chain (SC) platforms in modern times has altered the nature of SC interactions, resulting in a notable degree of uncertainty. This study aims to conduct a thorough analysis of the existing literature on decision support systems (DSSs) and their incorporation of XAI functionalities within the domain of SC. Our analysis has revealed the influence of XAI on the decision-making process in the field of SC. This study utilizes the SHapley Additive exPlanations (SHAP) technique to analysis the online data using Python machine learning (ML) process. Explanatory algorithms are specifically crafted to augment the lucidity of ML models by furnishing rationales for the prognostications they produce. The present study aims to establish measurable standards for identifying the constituents of XAI and DSSs that augment decision-making in the context of SC. This study assessed prior research with regards to their ability to make predictions, utilization of online dataset, number of variables examined, development of learning capability, and validation within the context of decision-making, emphasizes the research domains that necessitate additional exploration concerning intelligent decision-making under conditions of uncertainty.

The EPA Ecosystem Services Tool Selection Portal.

The dynamics of an environmental decision-making context can be complicated. The use of decision support tools can help better facilitate restoring and maintaining ecosystems that provide environmental benefits (ecosystem services) to people. Although an ecosystem services assessment tool is designed for specific purposes, having access to a comprehensive suite of tools offers the user additional insight and resources to help in decision making. A range of approaches exist to connect ecosystem services to a given decision context ranging from less to more complex: using the best professional judgment; applying examples from other efforts; testing individual tool applications; and using a systematic, decision-tree approach to navigate among relevant tools and frameworks. The U.S. Environmental Protection Agency developed a decision-tree approach for a user to navigate the question of how to choose among a suite of ecosystem services assessment tools for three decision contexts: (1) ecological risk assessments; (2) cleanup of contaminated sites; (3) and generic structured decision-making processes. This tool selection navigator was developed with/for the intended user, including developing crosswalks between tool functionality and the user's language for what they require in a tool. To navigate the tool, the user first chooses one of three decision contexts. Second, the user selects among the different phases of the decision process. Third, the user selects among a few ecosystem-services related tasks relevant to the decision context chosen to identify potential tools. The tool uses simple language to navigate the decision pathways and provides the user with a suite of potential ES resources and tools for their given decision context.

Testing the effects of moral intensity news frames on consumer boycott intention

PurposeThis exploratory research examined how emphasizing a brand’s unethical behaviour through high moral intensity news framing influences consumer boycott intention.Design/methodology/approachThe hypotheses were tested and validated using two experimental studies that expose customers of real retail and personal care product brands to news articles that have high and low moral intensity news frames.FindingsThe results showed high moral intensity news framing’s positive effect on consumer boycott intention. The frame’s influence is moderated by moral awareness and partially mediated by perceived moral intensity and moral judgement. The findings suggest that consumers’ perception of the frame and their attitude towards the brand will have a substantial role in boycott intention.Practical implicationsThese research outcomes aid in the understanding of news framing effects on boycott intention, providing both insights for consumer activists and managerial implications for stewards of brands.Originality/valueWhile previous research have examined the impact of news frames on the typical audience, there has been relatively little focus on news framing’s impact on consumers and their decision to boycott brands. This study addresses this gap by applying the work on emphasis framing to a consumer decision-making context. It also introduces moral intensity framing to the news frame classification. In addition, this study expands current conceptualizations of individual ethical decision-making to help explain consumer boycott intent.

Reliability of AI Algorithms in Safety Applications

The integration of Artificial Intelligence (AI) algorithms into safety-critical applications has become increasingly prevalent across various domains, including autonomous vehicles, medical diagnosis, industrial automation, and aerospace systems. These applications rely heavily on AI to make decisions that directly affect human safety, economic stability, and operational efficiency. Given the critical nature of these tasks, it is essential to rigorously assess the reliability of AI algorithms to ensure they perform consistently and accurately under all conditions. Reliability, in this context, refers to the AI system's ability to function without failure over a specific period, under defined operational conditions. In safety-critical domains, even minor errors or inconsistencies in AI decision-making can lead to catastrophic outcomes, such as traffic accidents involving autonomous vehicles, incorrect medical diagnoses leading to improper treatments, or failures in industrial processes that may cause costly downtime or even human casualties. The increasing complexity and deployment of AI technologies in these domains highlight the urgent need for a comprehensive understanding and evaluation of AI reliability. This paper provides a detailed analysis of the design considerations and methodologies for enhancing the reliability of AI algorithms. The discussion begins by exploring the underlying principles of reliability in AI systems, focusing on both theoretical and practical perspectives. We examine key factors that influence reliability, including data quality, algorithmic robustness, model interpretability, and system integration. The paper then delves into various reliability assessment techniques, such as fault tolerance mechanisms, error detection and correction methods, redundancy, and validation processes. To provide a deeper understanding of reliability in AI, we introduce mathematical models and statistical evaluation techniques that quantify reliability metrics. For instance, reliability modeling using exponential distribution, Monte Carlo simulations for probabilistic reliability analysis, and error propagation studies using Jacobian matrices are presented. We also explore the use of machine learning-specific reliability metrics, such as the Area Under the Curve (AUC) in Receiver Operating Characteristic (ROC) analysis, which helps evaluate the performance of AI in critical decision-making contexts. Furthermore, this paper addresses the current challenges and limitations in ensuring AI reliability, including computational complexity, ethical considerations, and regulatory compliance issues. We highlight the difficulties in developing AI models that can maintain their reliability across diverse and unpredictable real-world scenarios. The potential for bias, lack of transparency in AI decision-making, and difficulties in explaining complex AI models also present significant hurdles that need to be addressed to enhance reliability. The findings and methodologies discussed in this paper aim to contribute to a deeper understanding of the complex landscape of AI reliability, providing a framework for researchers, practitioners, and policymakers to develop safer, more reliable AI systems that can be trusted to operate in environments where safety is paramount.

Reflections on SES modeling: Stop me if you’ve heard this

Key lessons about and limits to social-ecological systems (SES) modeling are widely available and frustratingly consistent over time. Prominent challenges include outdated perspectives about systems and models along with persistent disciplinary hegemony. The inherent complexity in SES means that an emphasis on discrete prediction is misplaced and has potentially reduced model efficacy for decision-making. Although computer models are definitely the tool to use to identify the complex relationships within SES, humans are messy and hence the ‘social’ in SES is often ignored, glossed over, or reduced to simplistic economic or demographic variables. This combination of factors has perpetuated biases in what is worth pursuing and/or publishing. In (re)visiting issues in SES modeling, including debates about model capabilities, data selection, and challenges in working across disciplinary lines, this reflection explores how the author’s experience aligns with extant literature as well as raises issues about what is absent from that body of work. The available lessons suggest that scholars and practitioners need to re-think how, why, and when to employ SES modeling in regulatory or other decision-making contexts.

How do consumers make behavioural decisions on social commerce platforms? The interaction effect between behaviour visibility and social needs

AbstractThe online phenomenon of social commerce (i.e., s‐commerce) platforms has emerged as a combination of online social networking and e‐commerce. On s‐commerce platforms, consumers can observe others' behavioural decisions and can distinguish those made by their friends from those made by their followees (i.e., the people a focal consumer follows but who do not follow that consumer back). Given this distinction, our study examines how consumers' behavioural decisions—regarding, for example, purchases, ratings, or “likes”—are made on s‐commerce platforms, with a focus on how they are influenced by prior decisions of friends and followees. Combining panel data from a large s‐commerce platform and two controlled experiments, we identify a strong normative social influence pattern in which consumers tend to follow others' prior decisions to gain social approval. Because the occurrence of normative social influence depends on both consumer behaviours with high public visibility and strong consumer needs to establish social ties, the unique information concerning behaviour visibility and consumers' social needs in the panel data allows us to identify normative social influence and to distinguish it from informational confounding mechanisms. Our panel data results show that on a friend network, where consumers' behavioural decisions are visible, females exhibit a greater tendency to follow others' prior decisions than males. We attribute this result to the stronger social needs of females. However, on a followee network, where behavioural decisions are invisible, these differences become less evident. Moreover, the two experiments demonstrate that making decision contexts private or activating social needs via a priming procedure can thwart (or even turn off) normative social influence. Our findings challenge prior research that identifies informational social influence as the predominant driver of conformity behaviours and thus have important implications for practice related to normative social influence, such as the development of techniques for satisfying consumers' different social needs depending on their gender or any other situational factors on s‐commerce platforms.

Understanding the influence of stakeholders on the implementation of front-of-pack nutrition labelling in the absence of public debate: the case of Portugal

In the context of the European Commission’s proposal for the harmonization of front-of-pack nutrition labels (FoPLs) across the European Union scheduled for 2023, Portugal’s position on the preferred format to be adopted remains unclear. Our study aims to (1) describe the context of the political decision regarding FoPL’s implementation in Portugal, (2) identify and characterize the actors involved in the debate and (3) evaluate their influence in the decision-making process and their position on the various formats of FoPLs considered. A document review complemented by a qualitative study with eight experts revealed the actors involved in the implementation of FoPLs in Portugal. The stakeholder theory was used to qualify their level of influence on the decision. To complement the experts’ analysis, the views of 1014 Portuguese consumers on stakeholders’ influence were collected via an online questionnaire. The results show a strong mobilization of the Portuguese Ministry of Health on the issue through the production of several technical and scientific reports. The background of the political decision related to FoPLs in Portugal highlights the commitment of some influential stakeholders in favour of the traffic light format, including one of the major Portuguese retailers, Continente. Nevertheless, in the face of public support for the Nutri-Score from a growing number of actors, Portugal has not yet adopted a specific format, partly due to some resistance from the agricultural sector. Portugal, by lack of consensus and agenda setting, is now awaiting formal harmonization of FoPLs by the European Commission. This study provides useful insights for public policy actors when promoting interventional measures.

Stated-Preference Survey Design and Testing in Health Applications.

Following the conceptualization of a well-formulated and relevant research question, selection of an appropriate stated-preference method, and related methodological issues, researchers are tasked with developing a survey instrument. A major goal of designing a stated-preference survey for health applications is to elicit high-quality data that reflect thoughtful responses from well-informed respondents. Achieving this goal requires researchers to design engaging surveys that maximize response rates, minimize hypothetical bias, and collect all the necessary information needed to answer the research question. Designing such a survey requires researchers to make numerous interrelated decisions that build upon the decision context, selection of attributes, and experimental design. Such decisions include considering the setting(s) and study population in which the survey will be administered, the format and mode of administration, and types of contextual information to collect. Development of a survey is an interactive process in which feedback from respondents should be collected and documented through qualitative pre-test interviews and pilot testing. This paper describes important issues to consider across all major steps required to design and test a stated-choice survey to elicit patient preferences for health preference research.

Mobilising transformative community-based climate change adaptation

Under-resourced and marginalised communities often have limited government support, and therefore, must decide by themselves if and how they adapt to the uncertainties of climate change. Community-based climate change adaptation (CbA) forms a plausible approach here. In practice, however, CbA projects often fail to address the underlying causes of vulnerability like power relations and sociocultural structures that influence adaptation measures adopted and outcomes. In response, this perspective paper explores the concept of transformative community-based climate change adaption (TCbA), a reflexive approach that focuses on empowering communities by reframing the decision-making context. It discusses pathways of mobilising this shift to transformative CbA in three steps: (1) specifying the characteristics of a TCbA; (2) exploring the leverage points to mobilise it, and (3) recommending a set of actions and processes that facilitate co-decided interventions at the identified leverage points. These leverage points include "the structure of information flow", "rules of the system" and “power to change system structure or self-organise". The paper concludes by emphasising that the shift to transformative CbA begins when facilitators and members of the community undertaking a CbA project are reflexive about how the societal context of decision processes, like power relations, influence community adaptation decision-making, planning and implementation.

Environmental Impact Assessment United States of India Using GRA Method

An environmental impact assessment examines a project's potential effects on the environment while taking into consideration natural, social, and economic factors. More than half of the nations in the world require EIA in some capacity. EIAs had much less of an impact than their initial supporters anticipated. This study institutionalizes the difference, offers methodological justifications, and suggests approaches for future EIA use that will be more successful. Extensive knowledge of environmental impact assessments (EIA) as a tool for management of the ecosystem in the future Its goals and level of success have sparked a lot of discussion. It is evaluated based on the "Performance" of EIA. In particular, the debate shifts away from issues of actual implementation and toward the most crucial EIA objectives and their placement in broader decision-making contexts. EIA's implementation of planning choices is generally weak. Empirical research has supported the effect. The goal of this paper is to address Scale weights concern COPRAS interval-valued integers. The COPRAS method is extended to MCDM issues with unknowable knowledge. It essentially establishes some optimization models based on the traditional COPRAS method's fundamental concept, the determination of scale weights. This alternative in this method has been adopted by Delhi, Ladakh, Jammu and Kashmir, Andaman and Nicobar, Chandigarh, Lakshadweep, and the assessment criteria are Terrestrial, Aquatic, Economic, Social, and Cultural, as well as Air, Water, Soil, Noise, and Solid Waste pollution. As a result of this research Principles of the GRA technique uses the short-range and negative-best solutions to calculate the long-range answer, but the comparison of these distances is not thought to be important. Delhi has therefore been given the top ranking; Ladakh has received a poor ranking. This study demonstrates Delhi's elevated levels of air pollution.