Normative foundations of competitive markets and their relevance to democracy

SATS ◽  
2015 ◽  
Vol 16 (2) ◽  
Author(s):  
Petri Räsänen
Keyword(s):  
Decision Making ◽  
Economic Theory ◽  
Free Trade ◽  
Real World ◽  
Global Markets ◽  
Competitive Markets ◽  
Core Self ◽  
Normative Foundations ◽  
Critical Consideration ◽  
The Ideal

AbstractThe economic theory of competitive markets is widely accepted as a theoretical framework for the construction of real-world global markets. Most influentially, this is apparent in the commonly accepted ideal of global free trade, with WTO as its primary proponent. The virtue of efficiency related to competitive markets is also often deemed politically neutral or, at least, unworthy of too-critical consideration. This paper argues against this contention. The theoretical axioms behind competitive markets assume a normatively and politically saturated core: self-interested agents (individuals, households and firms) who 1) are attached to consequential rationality, 2) must bypass various moral commitments and 3) are committed to specific interpretations of the concepts of freedom and justice. I will argue that attachment to these notions downplays democratic decision-making. As there is no genuine room for so-called procedural decision-making, the prospects for political innovation will be narrowed. For example, those kinds of interpretations of the concepts of freedom and justice that do not fit within the ideal of competitive markets are precluded from politically feasible options. I will argue that taking note of deliberative rationality and democracy would alleviate this problem.

To What Extent Should We Enrich Law and Economics? On Calabresi and his Future of Law and Economics

Global Jurist ◽  
2019 ◽  
Vol 19 (3) ◽  
Author(s):  
Régis Lanneau
Keyword(s):  
Decision Making ◽  
Economic Theory ◽  
Real World ◽  
Law And Economics ◽  
Point Of View ◽  
The Future ◽  
Epistemological Point ◽  
Laws And Regulations

Abstract In this paper, I argue that the “expanded” economic theory advocated in Calabresi’s book “The Future of Law and Economics” could be interpreted in at least three different ways, all of which are compatible. First, Calabresi’s book could be interpreted as an attempt to incentivize lawyer-economists to explore laws and regulations from different angles or perspectives rather than merely apply neoclassical theories. Second, it could be considered an attempt to justify the introduction of the notion of moral costs into law and economics to better explain some legal realities. Third, it could be considered an attempt to advocate, in a more normative way, the need to incorporate moral costs into real world analysis to better improve upon decision making. This paper will address and discuss each of these possible interpretations. It will be clear that, from an epistemological point of view, if the first interpretation might be more widely accepted because it is less controversial, the second and third interpretations remain more problematic. Admittedly, the concept of moral costs could obscure and even distort our understanding of some legal realities. Moreover, the introduction of such costs for decision making is raising questions which cannot be answered through economic theory alone.

scholarly journals A Study of Adam Smith’s Original Contributions to Economic Theory and Decision Making under Uncertainty

2016 ◽  
Vol 3 (3) ◽  
pp. 39
Author(s):  
Michael Emmett Brady
Keyword(s):  
Decision Making ◽  
Economic Theory ◽  
Real World ◽  
Original System ◽  
Mathematical Concept ◽  
Bank Loans ◽  
Clear Cut ◽  
Tit For Tat ◽  
Risk Takers ◽  
Single Number

The current, dominant belief among economists that Smith had made no original contributions to economic theory, outside of presenting an original system of thought that was composed of the original analysis of other thinkers, is incorrect. Smith made four unique contributions which have been overlooked. The first contribution was clear cut recognition that probabilities could not be calculated exactly using precise, single number answers .Probabilities were indeterminate and/or imprecise. The mathematical laws of the probability calculus can’t be applied, in general, to decision making in the real world. Smith rejects any decision making approach based on fair, or mathematical, lotteries .Secondly, Smith was the first to explicitly recognize that real world decision making was very uncertain, as opposed to being based on the mathematical concept of risk which was based on known probabilities and outcomes leading to expected value and expected utility calculations a la Jeremy Bentham’s rational economic calculator of probable odds model .Third, Smith was the first to recognize that, as long as the probabilities were greater than 0 , retaliatory tariffs might lead a country that had imposed a tariff on another country to rescind that decision .There was room for a set of strategies in conflicts between nations over tariffs involving war ,threats, negotiations, mediation, and retaliation implying tit for tat or chicken strategies .Fourth, Smith recognized that the main threat to economic prosperity originated from projectors, prodigals, and imprudent risk takers (J M Keynes’s speculators and rentiers) ,who were able to obtain bank loans in order to try to leverage their debt position many times over. The result would be that the bank’s deposits would be wasted and destroyed. This will lead to a recession or depression. Smith did not add this last sentence because it is obvious.

scholarly journals Using argumentation theory to identify the challenges of shared decision-making when the doctor and the patient have a difference of opinion

2012 ◽  
Vol 1 (2) ◽  
pp. 26 ◽  
Author(s):  
Claudia A. Zanini ◽  
Sara Rubinelli
Keyword(s):  
Decision Making ◽  
Dispute Resolution ◽  
Shared Decision Making ◽  
Communication Skills ◽  
Critical Discussion ◽  
Argumentation Theory ◽  
Shared Decision ◽  
Ideal Model ◽  
Difference Of Opinion ◽  
The Ideal

This paper aims to identify the challenges in the implementation of shared decision-making (SDM) when the doctor and the patient have a difference of opinion. It analyses the preconditions of the resolution of this difference of opinion by using an analytical and normative framework known in the field of argumentation theory as the ideal model of critical discussion. This analysis highlights the communication skills and attitudes that both doctors and patients must apply in a dispute resolution-oriented communication. Questions arise over the methods of empowerment of doctors and patients in these skills and attitudes as the preconditions of SDM. Overall, the paper highlights aspects in which research is needed to design appropriate programmes of training, education and support in order to equip doctors and patients with the means to successfully engage in shared decision-making.

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

Real‐world evidence to support regulatory decision making: New or expanded medical product indications

2021 ◽  
Author(s):  
Jessica M. Franklin ◽  
Kai‐Li Liaw ◽  
Solomon Iyasu ◽  
Cathy Critchlow ◽  
Nancy Dreyer
Keyword(s):  
Decision Making ◽  
Real World ◽  
Medical Product ◽  
Regulatory Decision ◽  
Real World Evidence

scholarly journals Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alan Brnabic ◽  
Lisa M. Hess
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Wenjun Jiang ◽  
Jing Chen ◽  
Xiaofei Ding ◽  
Jie Wu ◽  
Jiawei He ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Text Summarization ◽  
Experimental Results ◽  
Product Review ◽  
Comprehensive Review ◽  
Online Systems ◽  
Real World Datasets ◽  
Different Characteristics

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

scholarly journals Postlaunch evidence-generation studies for medical devices in Spain: the RedETS approach to integrate real-world evidence into decision making

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Pedro Serrano-Aguilar ◽  
Iñaki Gutierrez-Ibarluzea ◽  
Pilar Díaz ◽  
Iñaki Imaz-Iglesia ◽  
Jesús González-Enríquez ◽  
...  
Keyword(s):  
Decision Making ◽  
Medical Devices ◽  
Real World ◽  
Policy Decision ◽  
Assessment Process ◽  
The European Union ◽  
Political Commitment ◽  
Real World Evidence ◽  
Health Policy Decision ◽  
Regional Health Authorities

Abstract The Monitoring Studies (MS) program, the approach developed by RedETS to generate postlaunch real-world evidence (RWE), is intended to complement and enhance the conventional health technology assessment process to support health policy decision making in Spain, besides informing other interested stakeholders, including clinicians and patients. The MS program is focused on specific uncertainties about the real effect, safety, costs, and routine use of new and insufficiently assessed relevant medical devices carefully selected to ensure the value of the additional research needed, by means of structured, controlled, participative, and transparent procedures. However, despite a clear political commitment and economic support from national and regional health authorities, several difficulties were identified along the development and implementation of the first wave of MS, delaying its execution and final reporting. Resolution of these difficulties at the regional and national levels and a greater collaborative impulse in the European Union, given the availability of an appropriate methodological framework already provided by EUnetHTA, might provide a faster and more efficient comparative RWE of improved quality and reliability at the national and international levels.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

An algebraic approach to N-soft sets with application in decision-making using TOPSIS

2021 ◽  
pp. 1-21
Author(s):  
Muhammad Shabir ◽  
Rimsha Mushtaq ◽  
Munazza Naz
Keyword(s):  
Decision Making ◽  
Mathematical Models ◽  
Real World ◽  
Algebraic Approach ◽  
Multi Criteria Decision Making ◽  
Commutative Monoids ◽  
Soft Sets ◽  
Algebraic Properties ◽  
Different Types ◽  
Selection Of

In this paper, we focus on two main objectives. Firstly, we define some binary and unary operations on N-soft sets and study their algebraic properties. In unary operations, three different types of complements are studied. We prove De Morgan’s laws concerning top complements and for bottom complements for N-soft sets where N is fixed and provide a counterexample to show that De Morgan’s laws do not hold if we take different N. Then, we study different collections of N-soft sets which become idempotent commutative monoids and consequently show, that, these monoids give rise to hemirings of N-soft sets. Some of these hemirings are turned out as lattices. Finally, we show that the collection of all N-soft sets with full parameter set E and collection of all N-soft sets with parameter subset A are Stone Algebras. The second objective is to integrate the well-known technique of TOPSIS and N-soft set-based mathematical models from the real world. We discuss a hybrid model of multi-criteria decision-making combining the TOPSIS and N-soft sets and present an algorithm with implementation on the selection of the best model of laptop.

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