4,266 research outputs found

    Interval reliability inference for multi-component systems

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    This thesis is a collection of investigations on applications of imprecise probability theory to system reliability engineering with emphasis on using survival signatures for modelling complex systems. Survival signatures provide efficient representation of system structure and facilitate several reliability assessments by separating the computationally expensive combinatorial part from the subsequent evaluations submitted to only polynomial complexity. This proves useful for situations which also account for the statistical inference on system component lifetime distributions where Bayesian methods require repeated numerical propagation for the samples from the posterior distribution. Similarly, statistical methods involving imprecise probabilistic models composed of sets of precise probability distributions also benefit from the simplification by the signature representation. We will argue the pragmatic benefits of using statistical models based on imprecise probability models in reliability engineering from the perspective of inferential validity and provision of objective guarantees for the statistical procedures. Imprecise probability methods generally require solving an optimization problem to obtain bounds on the assessments of interest, but monotone system structures simplify them without much additional complexity. This simplification extends to survival signature models, therefore many reliability assessments with imprecise (interval) component lifetime models tend to be tractable as will be demonstrated on several examples

    Hospital performance including quality: creating economic incentives consistent with evidence-based medicine

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    This thesis addresses questions of how to incorporate quality of care, represented by disutility-bearing effects such as mortality, morbidity and re-admission, in measuring relative performance of public hospitals. Currently, case-mix funding and performance, measured with costs per case-mix adjusted separation, hold hospitals accountable for costs, but not effects, of care, creating economic incentives for quality of care minimising cost per admission. To allow an appropriate trade-off between the value and cost of quality of care a correspondence is demonstrated between maximising net benefit and minimising costs plus decision makersโ€™ value of disutility events, where effects of care can be represented by disutility events and hospitals face a common comparator. Applying this correspondence to performance measurement, frontier methods specifying disutility events as inputs are illustrated to have distinct advantages over output specifications, allowing estimation of: 1. economic efficiency conditional on the value of avoiding disutility events. 2. technical, scale and congestion sources of net benefit efficiency; 3. best practice peers over potential decision makersโ€™ value of quality; and 4. industry shadow price of avoiding disutility events. The accountability this performance measurement framework provides for effects and cost of quality of care are also illustrated as the basis for moving from case-mix funding towards a funding mechanism based on maximising net benefit. Links to evidence-based medicine in health technology assessment are emphasised in illustrating application of the correspondence to comparison of multiple strategies in the cost-disutility plane, where radial properties as shown to provide distinct advantages over comparison in the cost-effectiveness plane. The identified performance measurement and funding framework allows policy makers to create economic incentives consistent with evidence-based medicine in practice, while avoiding incentives for cream-skimming and cost-shifting. The linear nature of the net benefit correspondence theorem allows simple inclusion of multiple effects of quality, whether expressed as not meeting a standard, functional limitation or disutility directly. In applying the net benefit correspondence theorem to hospitals a clinical activity level is suggested, to allow correspondence conditions to be robustly satisfied in identification of effects with decision analytic methods, adjustment for within DRG risk factors and data linkage to effects beyond separation

    Social Welfare

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    "Social Welfare" offers, for the first time, a wide-ranging, internationally-focused selection of cutting-edge work from leading academics. Its interdisciplinary approach and comparative perspective promote examination of the most pressing social welfare issues of the day. The book aims to clarify some of the ambiguity around the term, discuss the pros and cons of privatization, present a range of social welfare paradoxes and innovations, and establish a clear set of economic frameworks with which to understand the conditions under which the change in social welfare can be obtained

    Entrepreneurial Action and Entrepreneurial Rents

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    This dissertation is comprised of three independently standing research papers (chapters 2, 3 and 4) that come together in the common theme of investigating the relationship between entrepreneurial action and performance. The introduction chapter argues that this theme is the main agenda of an entrepreneurial approach to strategy, and provides some background and context for the core chapters. The entrepreneurial approach to strategy falls in line with an array of action-based theories of strategy that trace their economic foundations to the Austrian school of economics. Chapters 2 and 3 take a game theoretical modeling and computer simulation approach and represent one of the first attempts at formal analysis of the Austrian economic foundations of action-based strategy theory. These chapters attempt to demonstrate ways in which formal analysis can begin to approach compatibility with the central tenets of Austrian economics (i.e., subjectivism, dynamism, and methodological individualism). The simulation results shed light on our understanding of the relationship between opportunity creation and discovery, and economic rents in the process of moving towards and away from equilibrium. Chapter 4 operationalizes creation and discovery as exploration and exploitation in an empirical study using data from the Kauffman Firm Survey and highlights the trade-offs faced by start-ups in linking action to different dimensions of performance (i.e., survival, profitability, and getting acquired). Using multinomial logistic regression for competing risks analysis and random effects panel data regression, we find that high technology start-ups face a trade-off between acquisition likelihood and profitability-given-survival while low and medium technology start-ups face a trade-off between survival and profitability-given-survival. Chapter 5 concludes the dissertation by highlighting some of the overall contributions and suggesting avenues for future research

    ๊ด€์ธกํ•  ์ˆ˜ ์—†๋Š” ๋ณ€๋Ÿ‰ ํšจ๊ณผ์— ๊ด€ํ•œ ํ†ต๊ณ„์  ์ถ”๋ก 

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ†ต๊ณ„ํ•™๊ณผ, 2023. 8. ์ž„์š”ํ•œ.This thesis is composed of six topics related to statistical inference on unobserved random effects, each centered around the concept of extended likelihood that incorporates information about the random unknowns. The first two topics focus on the theoretical properties of confidence distribution, whose density can be interpreted as an extended likelihood. The latter four topics reformulate the hierarchical likelihood, as an extended likelihood at specific scale, and investigate its theoretical properties, as well as its applications to deep learning. In the first topic, an epistemic confidence of the observed confidence intervals is introduced. Furthermore, the relevant subset problem associated is explained by incorporating the existence of betting markets into the Ramsey-De Finetti's Dutch book argument. It is demonstrated that the epistemic confidence is free from such issues. In the second topic, it is revealed that the existence of a point mass in confidence distribution plays an important role in maintaining the essential properties of the confidence distribution. The point mass has been considered paradoxical in Stein's paradox and satellite collision problems, but in fact, it gives an advantage to the confidence distribution. The proposed confidence distribution is free from the false confidence for the parameter of interest, and it maintains the confidence feature in both Stein's problem and the satellite conjunction problem. The third topic introduces the reformulation of hierarchical likelihood (h-likelihood) and establishes the theoretical properties for h-likelihood inference. This novel hierarchical likelihood can provide maximum likelihood estimators for fixed parameters and asymptotically the best unbiased estimators for random parameters, resolving the ambiguity of Lee and Nelder's (1996) original hierarchical likelihood. The last three topics deal with applications of the h-likelihood approach to deep learning models. While the most deep learning models implicitly assume the independence of data, real-world large scale data is often clustered with temporal-spatial correlations. In such cases, prediction performance of deep learning models can be improved by introducing random effects via h-likelihood. The fourth topic deals with deep learning models for continuous data with temporal-spatial correlations, and the fifth topic focuses on deep learning models for count data with non-Gaussian random effects. The sixth topic proposes h-likelihood approach to semi-parametric deep neural networks with gamma frailty for analyzing clustered censored data. In all three topics, the proposed methods improve prediction performance by introducing random effects into the existing deep learning models.์ด ๋…ผ๋ฌธ์€ ๊ด€์ธกํ•  ์ˆ˜ ์—†๋Š” ๋ณ€๋Ÿ‰ ํšจ๊ณผ์— ๋Œ€ํ•œ ํ†ต๊ณ„์  ์ถ”๋ก ์— ๊ด€๋ จ๋œ ์—ฌ์„ฏ ๊ฐœ์˜ ์ฃผ์ œ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ์œผ๋ฉฐ, ๊ฐ๊ฐ์˜ ์ฃผ์ œ๋Š” ๊ด€์ธกํ•  ์ˆ˜ ์—†๋Š” ๋ณ€๋Ÿ‰์— ๊ด€ํ•œ ์ •๋ณด๋ฅผ ๊ฐ–๊ณ  ์žˆ๋Š” ํ™•์žฅ๋œ ๊ฐ€๋Šฅ๋„(extended likelihood)๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ๋‹ค. ์ „๋ฐ˜๋ถ€์˜ ๋‘ ์ฃผ์ œ๋Š” ์‹ ๋ขฐ๋„(confidence) ์ด๋ก ์— ๊ด€ํ•œ ๋‚ด์šฉ์œผ๋กœ, ์‹ ๋ขฐ๋ถ„ํฌ์˜ ๋ฐ€๋„ํ•จ์ˆ˜(confidence density)๋ฅผ ํ™•์žฅ๋œ ๊ฐ€๋Šฅ๋„๋กœ ํ•ด์„ํ•˜์—ฌ, ์‹ ๋ขฐ๋„์— ๊ด€ํ•œ ์ด๋ก ์  ์„ฑ์งˆ์„ ๊ทœ๋ช…ํ•˜์˜€๋‹ค. ํ›„๋ฐ˜๋ถ€์˜ ๋„ค ์ฃผ์ œ๋Š” ํŠน์ˆ˜ํ•œ ์Šค์ผ€์ผ์—์„œ์˜ ํ™•์žฅ๋œ ๊ฐ€๋Šฅ๋„๋กœ ์ •์˜๋˜๋Š” ๊ณ„์ธต์  ๊ฐ€๋Šฅ๋„(hierarchical likelihood)์— ๊ด€ํ•œ ์ด๋ก ์  ์„ฑ์งˆ๊ณผ ๋”ฅ๋Ÿฌ๋‹์œผ๋กœ์˜ ํ™•์žฅ ๋ฐ ์‘์šฉ์— ๊ด€ํ•œ ๋‚ด์šฉ์„ ๋‹ค๋ฃฌ๋‹ค. ์ฒซ๋ฒˆ์งธ ์ฃผ์ œ์—์„œ๋Š” ๊ด€์ธก๋œ ์‹ ๋ขฐ๊ตฌ๊ฐ„์— ๋Œ€ํ•ด ์ธ์‹๋ก ์  ์‹ ๋ขฐ๋„(epistemic confidence)๋ฅผ ์ •์˜ํ•˜๊ณ  ์ด๋ฅผ ๊ณ„์‚ฐํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋นˆ๋„์ฃผ์˜์  ๊ด€์ ์—์„œ ์ •์˜๋˜๋Š” ์‹ ๋ขฐ๋„๊ฐ€ ๊ฐ–๋Š” relevant subset ๋ฌธ์ œ๋ฅผ Ramsey-De Finetti์˜ Dutch book ๋…ผ์˜์— betting market์˜ ์กด์žฌ๋ฅผ ๋„์ž…ํ•จ์œผ๋กœ์จ ์„ค๋ช…ํ•˜๊ณ , ์ธ์‹๋ก ์  ์‹ ๋ขฐ๋„๊ฐ€ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋กœ๋ถ€ํ„ฐ ์ž์œ ๋กœ์šธ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ๋‘๋ฒˆ์งธ ์ฃผ์ œ์—์„œ๋Š” Stein์˜ ์—ญ์„ค๊ณผ ์ธ๊ณต์œ„์„ฑ ์ถฉ๋Œ ๋ฌธ์ œ๋ฅผ ํ†ตํ•ด ์‹ ๋ขฐ๋ถ„ํฌ๊ฐ€ ํŠน์ • ์ง€์ ์—์„œ point mass๋ฅผ ๊ฐ–๋Š” ๋ฌธ์ œ๋ฅผ ์ƒˆ๋กœ์šด ๊ด€์ ์—์„œ ํ•ด์„ํ•˜์—ฌ, ์—ญ์„ค์ ์œผ๋กœ ์—ฌ๊ฒจ์ง€๋˜ point mass์˜ ์กด์žฌ๊ฐ€ ์‹ ๋ขฐ๋ถ„ํฌ์˜ ํ•ต์‹ฌ์ ์ธ ์„ฑ์งˆ์„ ์œ ์ง€ํ•˜๋„๋ก ๋งŒ๋“ค์–ด์ฃผ๋Š” ๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐํ˜”๋‹ค. ์ด์™€ ๋”๋ถˆ์–ด, ์ œ์•ˆํ•œ ํ˜•ํƒœ์˜ ์‹ ๋ขฐ ๋ถ„ํฌ๊ฐ€ ํ™•๋ฅ  ํ˜•ํƒœ์˜ ์ถ”๋ก ์ด ๊ฐ–๋Š” ๊ทผ๋ณธ์ ์ธ ํ•œ๊ณ„์ ์œผ๋กœ ์ง€์ ๋œ ๊ฑฐ์ง“ ์‹ ๋ขฐ(false confidence) ๋ฌธ์ œ์—์„œ (์ ์–ด๋„ ๋ชฉํ‘œ ๊ฐ€์„ค์— ํ•œํ•ด) ์ž์œ ๋กญ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐํžˆ๊ณ , ๊ธฐ์กด์˜ ๋‹ค๋ฅธ ๋ฐฉ๋ฒ•๋ก ๋“ค๊ณผ ๋‹ฌ๋ฆฌ Stein ๋ฌธ์ œ ๋ฐ ์ธ๊ณต์œ„์„ฑ ์ถฉ๋Œ ๋ฌธ์ œ์—์„œ ์ ์ • ์‹ ๋ขฐ๋„๋ฅผ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ์„ธ๋ฒˆ์งธ ์ฃผ์ œ์—์„œ๋Š” ๊ณ„์ธต ๊ฐ€๋Šฅ๋„๋ฅผ ์ƒˆ๋กญ๊ฒŒ ์ •์˜ํ•˜๊ณ  ์ด๋ก ์  ์„ฑ์งˆ์„ ๊ทœ๋ช…ํ•˜์˜€๋‹ค. ์ƒˆ๋กœ์šด ๊ณ„์ธต ๊ฐ€๋Šฅ๋„๋Š” ๊ณ ์ • ํšจ๊ณผ์˜ ์ตœ๋Œ€ ๊ฐ€๋Šฅ๋„ ์ถ”์ •๋Ÿ‰๊ณผ ๋ณ€๋Ÿ‰ ํšจ๊ณผ์˜ ์ ๊ทผ์  ์ตœ์†Œ๋ถ„์‚ฐ ๋ถˆํŽธ์ถ”์ •๋Ÿ‰์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๊ธฐ์กด์˜ ๊ณ„์ธต ๊ฐ€๋Šฅ๋„๊ฐ€ ๊ฐ–๊ณ  ์žˆ๋˜ ์ด๋ก ์  ๋ชจํ˜ธ์„ฑ์„ ํ•ด์†Œํ•  ์ˆ˜ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰ ์„ธ ์ฃผ์ œ๋Š” ์ƒˆ๋กญ๊ฒŒ ์ •์˜ํ•œ ๊ณ„์ธต ๊ฐ€๋Šฅ๋„๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๋”ฅ๋Ÿฌ๋‹ ๋ชจํ˜•์„ ๋‹ค๋ฃจ๊ณ  ์žˆ๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ ๋”ฅ๋Ÿฌ๋‹ ๋ชจํ˜•๋“ค์ด ๋ฐ์ดํ„ฐ์˜ ๋…๋ฆฝ์„ฑ์„ ์•”๋ฌต์ ์œผ๋กœ ๊ฐ€์ •ํ•˜๊ณ  ์žˆ์ง€๋งŒ, ์‹ค์ œ ๋ฐ์ดํ„ฐ๋Š” ์‹œ๊ณต๊ฐ„์  ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฐ–๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. ๋”ฅ๋Ÿฌ๋‹ ๋ชจํ˜•์— ๋ณ€๋Ÿ‰ ํšจ๊ณผ๋ฅผ ๋„์ž…ํ•จ์œผ๋กœ์จ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๊ณ„์ธต ๊ฐ€๋Šฅ๋„ ๊ธฐ๋ฐ˜ ์ ‘๊ทผ๋ฒ•์€ ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋ก ์— ๋น„ํ•ด ์—ฌ๋Ÿฌ๊ฐ€์ง€ ์žฅ์ ์„ ๊ฐ–๋Š”๋‹ค. ๋„ค๋ฒˆ์งธ ์ฃผ์ œ์—์„œ๋Š” ์‹œ๊ณต๊ฐ„ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฐ–๋Š” ์—ฐ์†ํ˜• ๋ฐ์ดํ„ฐ๋ฅผ ๋‹ค๋ฃจ๊ธฐ ์œ„ํ•œ ๋”ฅ๋Ÿฌ๋‹ ๋ชจํ˜•์„ ๋‹ค๋ฃจ์—ˆ๊ณ , ๋‹ค์„ฏ๋ฒˆ์งธ ์ฃผ์ œ์—์„œ๋Š” ๋น„์ •๊ทœ ๋ณ€๋Ÿ‰ ํšจ๊ณผ๋ฅผ ๊ฐ–๋Š” ๊ฐ€์‚ฐํ˜• ๋ฐ์ดํ„ฐ๋ฅผ ๋‹ค๋ฃจ๊ธฐ ์œ„ํ•œ ๋”ฅ๋Ÿฌ๋‹ ๋ชจํ˜•์„ ๋‹ค๋ฃจ์—ˆ๋‹ค. ์—ฌ์„ฏ๋ฒˆ์งธ ์ฃผ์ œ์—์„œ๋Š” ๊ตฐ์ง‘ํ™”๋œ ์ ˆ๋‹จ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•œ ๊ณ„์ธต ๊ฐ€๋Šฅ๋„ ๊ธฐ๋ฐ˜ ์ค€๋ชจ์ˆ˜์ (semi-parameteric) ์ ‘๊ทผ๋ฒ•์— ๋Œ€ํ•ด ๋‹ค๋ฃจ์—ˆ๋‹ค. ์„ธ ์ฃผ์ œ ๋ชจ๋‘, ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๋”ฅ๋Ÿฌ๋‹ ๋ชจํ˜•์— ๋ณ€๋Ÿ‰ ํšจ๊ณผ๋ฅผ ๋„์ž…ํ•จ์œผ๋กœ์จ ๊ธฐ์กด ๋ฐฉ๋ฒ•๋ก ์˜ ์˜ˆ์ธก ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์—ˆ๋‹ค.Abstract i List of Figures viii List of Tables xii 1 Introduction 1 2 Epistemic confidence in the observed confidence interval 6 2.1 Introduction 6 2.2 Main theory 10 2.2.1 Relevant subsets 10 2.2.2 Confidence distribution 12 2.2.3 Main theorem 15 2.2.4 Ancillary statistics 20 2.2.5 Computation of epistemic confidence 23 2.3 Examples 24 2.3.1 Simple model 24 2.3.2 Location family model 25 2.3.3 Exponential family model 26 2.4 Discussion 35 2.5 Appendix 37 2.5.1 Curved exponential model: N(ฮธ, ฮธ2) 37 2.5.2 Discrete case 41 2.5.3 When maximal ancillaries are not unique 44 3 Point mass in confidence distributions 47 3.1 Introduction 47 3.2 Ambiguity in confidence of an observed CI 50 3.3 GFD and probability dilution 54 3.4 CD and related methods 57 3.4.1 CD and confidence level of an observed CI 59 3.4.2 CD versus GFD 62 3.4.3 CD versus RP 64 3.5 On false confidence. 66 3.5.1 False confidence and probability dilution 69 3.6 Hypothesis testing 71 3.7 Concluding remarks 74 3.8 Appendix 75 3.8.1 Proof of Theorem 3 175 4 Foundations of h-likelihood inference for random unknowns 77 4.1 Introduction 77 4.2 Hierarchical likelihood 79 4.2.1 Reformulation of H-Likelihood 82 4.2.2 Bartlizable scale of random effects 86 4.3 Main Results 91 4.4 H-likelihood theory for irregular cases 95 4.4.1 Missing data problem when v โˆ’ v = Op(1) 96 4.4.2 Exponential-exponential HGLM when ฮธ โˆ’ ฮธ = Op(1) 102 4.5 When the h-likelihood is not explicit. 107 4.6 Appendix 108 5 DNN with temporal-spatial random effects via h-likelihood 119 5.1 Introduction 119 5.2 Integrated likelihood approach for LMMs 121 5.3 H-likelihood approach for LMMs 124 5.4 Learning algorithm with the h-likelihood 128 5.4.1 REML procedure 132 5.4.2 Adjustments for random effects 133 5.5 Comparison with existing methods 134 5.6 Numerical studies 136 5.7 Real data analysis. 139 5.8 Concluding remarks 141 5.9 Appendix 143 5.9.1 The computation of h-likelihood when q1 is large 143 5.9.2 Methods-of-moments estimators 144 5.9.3 Technical details 144 6 DNN for clustered count data via h-likelihood 150 6.1 Introduction 150 6.2 Model Descriptions 152 6.2.1 Poisson DNN 152 6.2.2 Poisson-gamma DNN 153 6.3 Construction of h-likelihood 155 6.4 Learning algorithm with the h-likelihood 158 6.4.1 Loss function for online learning 158 6.4.2 The local minima problem 159 6.4.3 Pretraining variance components 160 6.5 Experimental Studies 161 6.6 Real Data Analysis 166 6.7 Concluding Remarks 168 6.8 Appendix 168 6.8.1 Convergence of the method-of-moments estimator 168 6.8.2 Technical details 170 7 DNN for semi-parametric frailty models via h-likelihood 173 7.1 Introduction 173 7.2 A review of DNN-Cox model 174 7.2.1 DNN-Cox model 174 7.2.2 Prediction measures 176 7.3 Proposed DNN for frailty model 178 7.3.1 DNN-FM 179 7.3.2 Construction of h-likelihood 179 7.4 Learning algorithm using the profiled h-likelihood 182 7.4.1 Local minima problem 184 7.4.2 ML learning algorithm 184 7.5 Experimental studies 186 7.5.1 Experimental design 186 7.5.2 Experimental results 188 7.6 Multi-center bladder cancer data 194 7.7 Concluding remarks 197 7.8 Appendix 198 7.8.1 Derivation for the predictive likelihood 198 7.8.2 Evaluation measures for DNN-FM 199 7.8.3 Online learning for the DNN-FM 201 7.8.4 Proofs 203 Bibliography 208 Abstract in Korean 224๋ฐ•

    Financial market dynamics: essay in agent-based exploration

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    Futures Studies in the Interactive Society

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    This book consists of papers which were prepared within the framework of the research project (No. T 048539) entitled Futures Studies in the Interactive Society (project leader: ร‰va Hideg) and funded by the Hungarian Scientific Research Fund (OTKA) between 2005 and 2009. Some discuss the theoretical and methodological questions of futures studies and foresight; others present new approaches to or procedures of certain questions which are very important and topical from the perspective of forecast and foresight practice. Each study was conducted in pursuit of improvement in futures fields
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