The capability approach could be looked at in two ways; one may focus on realised functioning or on the real opportunities enjoyed by the person as indicated by the collection of his functionings. The former refers to the ability of the person for his beings and doings, on the other hand, the latter as clear in it, the set of functionings from which he or she can choose from. The capability of the person is expressed in the realised functioning; it is the chosen doing or being from among all the alternatives available to him or her. Therefore, his or her well-being is seen in the present state of being or doing. The focus, in this case, will be on the present state of being or doing as the person himself or herself has chosen the same. The real opportunity approach is a much-expanded version of the capability approach. Its focus is not just the chosen alternative but the group of alternatives from which he or she will be choosing from.
The capability approach is also related to the evaluation of living of person in terms of his actual beings or doings in order to achieve his various valuable functionings. (Sen 1993) According to him, it provides an informational base which could be used for critically seeing the individual or social advantages for the being as part of the institution or society etc. The informational space provided by the capability approach is different from all other approaches such as classical utility approach, absolute or relative opulence, primary goods, equality of resources approaches etc.
Some of the important functionings as mentioned by Sen are being well-nourished, avoiding escapable morbidity and premature mortality, having self-respect, being able to take part in the life of the community, decently clothed, minimally educated, properly sheltered, being in good health, being happy, being free from avoidable disease, to appear in public without shame etc. He pointed out that the assessment of the well-being of the person should be formed in such a way that its primary focus is on all such relevant functionings which are the constituent elements of his or her present beings or doings. The attempt by Martha Nussbaum can be seen in her ten central human capabilities which consists of Life, Bodily Health, Bodily Integrity, Senses, Imagination and Thought, Play, Affiliation, Practical Reasons, Emotion, Species and Control over one’s life. This list has been used worldwide in various studies for the evaluation purpose and one of them was ‘The Capability Approach: developing an instrument for evaluating public health intervention’ at the University of Glasgow. . The study made use of British Household data to capture the capabilities and used ten central human capabilities or the evaluation of public health interventions. But to be specific, Se has clearly disapproved to have a fixed list of capability or a universal list as he argued as above, the selection of the functionings to form a capability set which has to be evaluated should be depending upon the underlying values and objectives of evaluation exercise by the competent authority. Therefore, instead of having a universal list, he advocated for having a separate list which shall emphasize the underlining and constituted factors of the programme to be evaluated. It does not concentrate just on one benefit which may be expressed either in utility or in opulence; consider a factor as assumed to be governed by a particular factor; it goes beyond it and considers all possible factor. All those factors or functionings which are forming the capability of the person are most possibly undertaken in the purview for the assessment of the well-being of the person. There are various applications, himself discussed by Sen in his writings; include poverty, individual differences, inequality, the standard of living, education income, justice, health, social security, human development, women empowerment etc. which are touched upon for their efficacy and extent of deprivations and their role in the well-being for the nation.The poverty has been viewed in the standard criteria i.e. lowliness of income but in the capability framework, since the individual advantage is seen in the capability achievement, it is nothing but the deprivation of basic capability. In this approach, the income is just seen as an instrument of getting away from the basic capability, therefore, seen as one of the functioning of the individual. The level of income of the individual cannot be taken as a cause of poverty as even after having a good sum of money in hand in a rich county, for example, may be causing him to be in poverty as compared to the other people in the country. In terms of capability, it is the deprivation of the same which is affecting his lowness of income. The evaluation of public policy then has to be taken not in terms of increasing the income level of the people but it should be whether it is enhancing the capabilities of the individuals which could further lead him to come out from the poverty i.e. basic capability deprivation. (Sen 1999) . The concept of poverty is viewed as lack of real opportunity which is subject to the social constraints, personal circumstances etc. The level of income and the possession of wealth are not just the determinants of the extent of poverty; they are the factors which causes the deprivation of capability as can be seen in terms of lacking real opportunities.
The inequality and its extent are also seen as having different capability set for every individual. It is not just dependent upon the respective income, wealth and utilities. The concept of inequality is generally studied with income factor but the capability approach sees it in wealth, opportunities, achievements, freedom, education and health etc. (Sen 1992) . The approach insists that every individual has different characteristics with the natural and social environment and other external features; they also differ in physical and mental abilities. These all affect the state of equality in every aspect and hence the inequality should be viewed or evaluated not just on the basis of income, wealth or utility but should be focusing on all such characteristics of the individual. The selection of relevant variables as above gives much-expanded evaluative space which makes the concept more understanding.
As per the capability approach, there are some of the social variables which significantly contributes to achieving the well-being and give an expanded set of opportunities for all individuals in the country. It is argued that the main cause behind the poor economies is deprivations of basic social achievement viz. education and health, for all of the developing and underdeveloped countries. The economic opportunities and all other relevant spaces could be widened by expanding the capabilities by extending and promoting good education and health facilities for all. These social variables broaden the person’s effective freedom and hence carries intrinsic importance in itself. They are also instrumental in developing the economic opportunities which are valued most by all the individuals. The capabilities and the sets of functionings are significantly expanded with the widened social values as in education and health.
The discussion ends up in the employment of a rational framework which overcomes the biases of traditional methodologies in evaluating the individual or population wellbeing. The assessment takes place not by caring about the satisfaction or the possession of commodities or wealth but attributes the well-being to the real freedom enjoyed by the person or the entire population. The operationalisation of the framework depends on its objectives; it requires to derive the capability set. This capability set determines the real freedom enjoyed by the target population. This set thus unveils the real opportunities for the said population. Therefore this set is taken into consideration for evaluation and then a required methodology is applied for the finding out the facts about the well-being of the said population. The evaluation exercise also requires the sourcing of data to supplement the value of the capability set to be evaluated. This data or information could then be processed then the reliability of the chosen capability set might be established and therefore the conclusions might be drawn in its bases.
Though there's no attempt by Sen within the construction of index which could address to grasp the image of the state of being of the person whether gaining or losing, there are other endeavours like by Paul Anand and Paula Lorgelly, as discussed above, which may well be the solution for putting in place the framework for using in assessment work of any of the programmes and policies. The prominent use of the thought may well be traced within the construction of human development index by the United Nation which is employed to rank the countries in terms of the event criterion supported the essential human capabilities like education, health and standard of living etc. Though the approach remains on its infant stage, there are various developments are going down in swotting the employment and application of the potential framework across the world. |
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It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .
With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business for distributed digital and AI innovation.
QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.
Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.
Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.
Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.
Let’s deliver on the promise of technology from strategy to scale.
Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.
The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.
To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.
Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.
Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.
The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.
Join our colleagues Jessica Lamb and Gayatri Shenai on April 8, as they discuss how companies can navigate the ever-changing world of gen AI.
By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.
The following are examples of new skills needed for the successful deployment of generative AI tools:
The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).
It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.
While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.
To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.
While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built. They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).
For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.
Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.
Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:
The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture are needed to maximize the future strategic benefits of gen AI:
Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.
One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.
Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.
Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.
While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.
Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.
In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.
The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.
This article was edited by Barr Seitz, an editorial director in the New York office.
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Validation scores to evaluate the detection capability of sensor systems used for autonomous machines in outdoor environments.
Objectives of the work, 2. an overview of environment perception for agricultural machinery, 2.1. standardization, 2.1.1. machine-related standards, 2.1.2. sensor-related standards, 2.2. approaches to evaluate the detection capability, 2.2.1. machine-related evaluation, 2.2.2. sensor-related evaluation, 2.3. methodology of real environment detection area (reda), 2.3.1. reda terminology.
3.1. challenges for the detection capability in agricultural environments, 3.1.1. high variance of possible objects, 3.1.2. constantly changing environment and concealments, 3.1.3. environment related influences, 3.2. terminology used to describe the detection capability, 3.2.1. detection goal, 3.2.2. detection information.
4. parameters of interest for real environment detection capability, 4.1. proposal for a parameter-based description of the environment, 4.2. parameter-based test designs.
4.4.1. systematic error evaluation.
5.1. classification for the parameter-based real environment detection capability.
6. conclusions.
‘A statement on the system-under-test ( test criteria ) that is expressed quantitatively ( metric ) under a set of specified conditions ( test scenario ) with the use of knowledge of an ideal result ( reference ).’
Data availability statement, acknowledgments, conflicts of interest, abbreviations.
AgPL | Agricultural Performance Level |
eSDA | expected Specified Detection Area |
MTBF | Mean Time Between Failure |
ODD | Operational Design Domain |
ODS | Object Detection System |
PL | Performance Level |
RED | Real Environment Detection |
REDA | Real Environment Detection Area |
REDAM | Real Environment Detection Area Matrix |
ROI | Region of Interest |
SDA | Specified Detection Area |
SRS | Safety Related Sensors |
Click here to enlarge figure
Sensor A | Sensor B | |
---|---|---|
Usability-Score | 94% | 91% |
Reliability-Score | 99% | 87% |
Availability-Score | 89% | 95% |
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Komesker, M.; Meltebrink, C.; Ebenhöch, S.; Zahner, Y.; Vlasic, M.; Stiene, S. Validation Scores to Evaluate the Detection Capability of Sensor Systems Used for Autonomous Machines in Outdoor Environments. Electronics 2024 , 13 , 2396. https://doi.org/10.3390/electronics13122396
Komesker M, Meltebrink C, Ebenhöch S, Zahner Y, Vlasic M, Stiene S. Validation Scores to Evaluate the Detection Capability of Sensor Systems Used for Autonomous Machines in Outdoor Environments. Electronics . 2024; 13(12):2396. https://doi.org/10.3390/electronics13122396
Komesker, Magnus, Christian Meltebrink, Stefan Ebenhöch, Yannick Zahner, Mirko Vlasic, and Stefan Stiene. 2024. "Validation Scores to Evaluate the Detection Capability of Sensor Systems Used for Autonomous Machines in Outdoor Environments" Electronics 13, no. 12: 2396. https://doi.org/10.3390/electronics13122396
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covered in their papers. Formulations of capability have two parts: valuable beings and. doings (functionings), and freedom. Sen's significant contribution has. been to unite the two concepts ...
The merits and challenges of the capability approach in social work research are discussed. Four main applications of the capability approach were found: as a tool for social workers in practice, to explore the subjective sense of well-being, to address social inequalities on a structural level, and as a way of evaluating social practices.
* Global Poverty Research Group, Institute for Development Policy and Management, University of Manchester, UK. This paper is forthcoming under the title ‚Capability Approach™ in Clark, D. A. (ed.) (2006), The Elgar Companion to Development Studies, Cheltenham: Edward Elgar. The author would
et al. 2008; Kuklys 2005; Robeyns 2006). This paper aims to show that Sen's capability approach could be used to address multiple issues in economics and could also provide a framework for evaluation. For this purpose, we propose a non-exhaustive overview of. quantitative applications based on the capability approach.
Abstract. This paper aims to present a theoretical survey of the capability approach in an interdisciplinary and accessible way. It focuses on the main conceptual and theoretical aspects of the capability approach, as developed by Amartya Sen, Martha Nussbaum, and others.
The paper specifies the core elements of Amartya Sen's capability approach to socio-economic valuation. It analyzes recent formulations by some of Sen's close associates, in addition to his ...
Abstract This paper aims to present a theoretical survey of the capability approach in an interdisciplinary and accessible way. It focuses on the main conceptual and theoretical aspects of the capability approach, as developed by Amartya Sen, Martha Nussbaum, and others. The capability approach is a broad normative framework for the evaluation and
Uyan-Semerci, P. (2004) 'Reconsidering the Capability Approach: Poverty, Tradition and Capabilities', Paper presented at the 4th Conference on the Capability Approach: Enhancing Human Security, Pavia, Italy, 5-7 September. Google Scholar
1. Introduction. The Capability Approach (CA) was designed as a normative framework to evaluate the quality of human life and the process of development (Robeyns, Citation 2006, p. 352). Footnote 1 It places emphasis on people as ends rather than means of development, where development is understood as an expansion of humans' ability and freedom to live the life they value (Stewart ...
To deepen this line of research, the capability approach (Sen, 1985) in connection with the new Sociology of children and childhood constitutes a valuable tool since it provides essential elements for an analysis of well-being from a multidisciplinary, relational and interconnected perspective on childhood.
The capability approach is an approach used in well-being assessment developed by Amartya Sen [1] in "Equality of what" and expanded in his later works (see, for example, Sen [2], [3], [4] ). Sen [2] argued that well-being consists of "functionings," which are the things someone achieves to do or be, and "capability," which are ...
The research on disability, health and the capability approach has been diverse in the topics it covers, and the conceptual frameworks and methodologies it uses, beginning over a decade and a half ago in health (Ruger 1998) and more than a decade ago in disability (Baylies 2002).1 We are pleased to share a set of articles in these two areas.
2014. TLDR. In recent years, the human development and capability approach to development studies has gained increased attention from academics, practitioners and policy-makers and rendered the capability approach a wider and more comprehensive framework for designing and assessing development policies. Expand. 18.
It traces some important avenues along which the Human Development Reports and other empirical studies have operationalized certain aspects of Sen's capability approach. The paper then articulates further developments that might be expected, arguing that such developments must also build upon cutting edge research in other fields.
Martha Nussbaum's 16,17 capabilities approach to quality of life, which has been widely used to analyse social disadvantage in multiple settings, satisfies both of the aims outlined above. First ...
on the Capability Approach, Pavia, Italy, on 6 September 2003. Please do not quote or reproduce without my permission. I would like to thank all those friends and colleagues who commented on earlier papers on which this text is based, and in particular Amartya Sen who taught me much about the capability approach during my doctoral research.
Ingrid Robeyns. This paper aims to present a theoretical survey of the capability approach in an interdisciplinary and accessible way. It focuses on the main conceptual and theoretical aspects of the capability approach, as developed by Amartya Sen, Martha Nussbaum, and others. The capability approach is a broad normative framework for the ...
Here is the easiest ambiguity to clarify: 'the capability approach' refers to Sen's work, and 'the capabilities approach' to Nussbaum's (see e.g. Nussbaum 2000, Gasper 1997). Yet even their close associate Hilary Putnam writes of the 'capabilities approach' (2002: vii) when he in fact refers to Sen's work.
David A. Clark & University of Manchester, 2005. " The Capability Approach: Its Development, Critiques and Recent Advances ," Economics Series Working Papers GPRG-WPS-032, University of Oxford, Department of Economics. Over the last decade Amartya`s Sen`s Capability Approach (CA) has emerged as the leading alternative to standard economic ...
Introduction. Although Sen's Capability Approach (CA) has recently given much attention to disability studies (Mitra, 2006, Nussbaum, 2006, Terzi, 2005, Trani and Bakhshi, 2008), there is little research into how the CA applies to disability and its consequences in terms of public policy - i.e. looking at improving the circumstances and inclusion of persons with disabilities (Dubois and ...
This book demonstrates how the capability approach to human development can contribute to the realisation of the 2015 United Nations Sustainable Development Goals (SDGs). The capability approach dictates that success should not be measured by economic indicators but by people leading meaningful, free, fulfilled, happy, or satisfied lives. Drawing from a range of disciplinary perspectives, this ...
The capability approach, by Amartya Sen, has laid the novel foundation in the field of Economics and measuring and evaluating the well-being in all the aspects of the human life. The nature of development of economics has moved towards its central feature of the process of development and has become more inclusive. The development or the well-being of a person is now seen to
It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement. ... About the research. The online survey was in the field from February 22 to March 5 ...
Multiverse analyses offer a systematic approach to testing a large range of models. We used daily data on 16 government responses in 181 countries in 2020-2021, and 4 outcomes—cases, infections, COVID-19 deaths, and all-cause excess deaths—to construct 99,736 analytic models. ... The current paper presents the results of nearly 100,000 ...
Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale.
This paper focuses on the thermophysical property mixture model approach that makes use of NASA's polynomial fits of species and another cryogenic model chosen from NIST's Refprop program. Presented in this paper are results of a 1D denotation CFD simulation of stoichiometric Hydrogen and Oxygen mixture at a cryogenic upstream condition.
As we shared in our research paper last month, Meta Chameleon is a family of models that can combine text and images as input and output any combination of text and images with a single unified architecture for both encoding and decoding. While most current late-fusion models use diffusion-based learning, Meta Chameleon uses tokenization for text and images.
1 Sen's treatment of human rights reflects the ideas set out in the Dewey Lectures, which highlighted the ways in which '[m]inimal demands of well‐being (in the form of basic functionings, e.g. not to be hungry), and of well‐being freedom (in the form of minimal capabilities, e.g. having the means of avoiding hunger)' can be viewed as rights that 'command attention and call for ...
The approach is based on the combination of two algorithms, which have been introduced recently in the research literature. The first algorithm is the ERT algorithm, which enhances the capability of DT and Random Forest (RF) algorithms to perform classification in data sets, which are unbalanced and of large size as IDS data sets usually are ...
The characterization of the detection capability assumes significance when the reliable monitoring of the region of interest by a non-contact sensor is a safety-relevant function. This paper introduces new validation scores that evaluate the detection capability of non-contact sensors intended to be applied to outdoor machines. The scores quantify, in terms of safety, the suitability of the ...