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Traditionally, economic production models consider pollution as bads that may be modeled as either outputs or inputs in economic models. The purpose of this paper is to examine the implications of these modeling choices on the measurements of productive efficiency and private costs of pollution control.

The authors apply the hyperbolic distance functions to measure trucking efficiency and the private costs of pollution control.

The results show: (i) regardless of the choice of modeling, when only one bad was incorporated in hyperbolic distance functions, the efficiency loss and private abatement cost measures derived from the two models were equivalent, but potential pollution reduction and good output expansion differed; (ii) when more than one bad were introduced, the equivalence of efficiency loss measure in (i) did not hold; and (iii) the potential amounts of pollution reduction and good output expansion were larger when bads were modeled as inputs. With multiple bads, private abatement costs varied considerably under the two modeling treatments.

From a policy standpoint, the results suggest that one should consider the modeling options with caution when multiple economic bads are involved, because the resulting measures of economic burden of pollution control differ.

The paper shows that the traditional conceptual framework for modeling pollution in hyperbolic distance functions could yield inconsistent results.

This paper proposes the use of a gauge function as a measure of technical efficiency. The measure of technical inefficiency from a gauge function is desirable as the estimation of a gauge function is not subject to the endogeneity problem under the behavioral assumption of profit maximization in the competitive market.

The authors address three important properties of a gauge function, i.e. linear homogeneity, monotonicity and convexity in inputs and outputs, and show how such properties are utilized in its estimation. Then, the authors apply the estimation of a gauge function to US Blacksmiths in 1850 and 1880 to show that a failure to satisfy such properties may lead to an incorrect inference on the technical efficiency.

The authors find that the Blacksmiths in the 1850s were technically more efficient than the ones in the 1880s, indicating technical regress in Blacksmithing when the properties are satisfied.

This paper introduces a measure of technical inefficiency from a gauge function and shows how to estimate the gauge function parametrically for the measure. The authors show McFadden's gauge function and its properties, which differ from the properties of other distance functions. The authors emphasize linear homogeneity as well as monotonicity and convexity in inputs and outputs, which must be satisfied in the estimation of a gauge function.

The purpose of this paper is to investigate the efficiency of the banking business of Japan’s agricultural cooperatives (JAs), which depend heavily on financial business with non-farmers, contradictory to cooperative principles.

The authors construct a panel data set over 2005–2016 from the financial statements of JAs’ prefectural-level federations and use the input distance stochastic frontier model with a time-variant inefficiency effect for analysis. Both the flow and stock measures of the banking output are used in identical models and the efficiency results are compared. The authors also investigate the determinants of efficiency by using the Tobit and ordinary least squares regression models.

There is strong evidence of significant prefectural differences in efficiency values. The ratio of lending to non-members to total loans is positively related to efficiency. In contrast, the higher reliance on a central organization and credit business leads to lower efficiency.

Apart from banking, JAs provide mutual insurance business services. As the authors investigate only the efficiency of JAs’ banking business in this study, it would be necessary to investigate the efficiency of their insurance business as well when evaluating JAs’ overall financial business.

There are few studies that investigate the efficiency of JAs’ banking business and its determinants, although significant attention has been paid to their excessive dependence on the financial business.

A mathematical gas dynamic model for semiconductor devices is numerically analysed. The well‐known ballistic diode problem is taken as an example.

The paper aims to describe and compare multiple methods for estimating the technical efficiency of 113 US oil refineries in operation in 2006 and 2007, considering undesirable output in a production process.

A technology that satisfies weak disposability between desirable and undesirable outputs is constructed by allowing different abatement factors across all refineries. Several measures based on data envelopment analysis approaches are implemented and compared to study the impact of disposability assumptions and to investigate the effects of using non‐uniform abatement factors. A hyperbolic efficiency measure is used to analyze the potential output loss of each refinery due to environmental regulations.

The results indicate that domestic refineries can improve efficiencies regardless of the disposability assumptions and that environmental regulations reduce the amount of potentially desirable outputs produced by some facilities. However, refineries in the western USA appear to be the most affected by regulations. In general, efficient refineries are less likely to be affected.

Undesirable outputs are limited to toxic release. Undesirable outputs generated from refining crude oil, such as greenhouse gases, can be used when data are available. The desirable outputs in this paper do not include premium products, such as lubricants, which could raise the efficiency estimates of complex refineries.

To the authors' knowledge, this paper is the first implementation of the weakly disposable technology constructed by different uniform abatement factors. Further, the paper investigates the effects of various disposability assumptions on efficiency estimation. The result clearly identifies refineries that use their resources efficiently. The paper suggests that the data may be used to augment managerial decision‐making regarding benchmarking and best practices.

This chapter conducts a systematic comparison of behavioral economics’s challenges to the standard accounts of economic behaviors within three dimensions: under risk, over time, and regarding other people. A new perspective on two underlying methodological issues, i.e., inter-disciplinarity and the positive/normative distinction, is proposed by following the entanglement thesis of Hilary Putnam, Vivian Walsh, and Amartya Sen. This thesis holds that facts, values, and conventions have inter-dependent meanings in science which can be understood by scrutinizing formal and ordinary language uses. The goal is to provide a broad and self-contained picture of how behavioral economics is changing the mainstream of economics.

The relevance of present consumption bias on personal finance has been confirmed in several studies and has important theoretical and practical implications. It has important, measurable implications when analyzing commitment or self-control, adherence to healthy habits (e.g. exercising or dieting), procrastination tendencies or savings. The purpose of this paper is to contribute to our understanding of these issues by postulating a model of income uncertainty within a hyperbolic discounting framework that measures the cost of financial intertemporal inconsistencies related to this bias. The emphasis is on the analysis of this cost. We also propose experimental designs and consistent estimation methods, as well as agent-based modelling extensions.

The authors develop a finite-horizon model with hyperbolic preferences. Individuals have a present bias distinct from their discount rate so their choices face intertemporal inconsistencies. The authors further extend the analysis with uncertainty about future incomes. Specifically, individuals live for three periods, and the authors find the optimal consumption levels in the perfect-information benchmark by backward induction. They then proceed to add biases and uncertainty to characterize their implications and measure the costs of the intertemporal inconsistencies they cause.

The authors measure how an agent's utility is greater when they “tie their hands” than when they are free to re-evaluate and change their consumption schedule. This “cost of being vulnerable to falling into temptation” only depends (increasingly) on the measure of the present bias and (decreasingly) on the discount factor. They analyze the varying effects on utility and consumption of changes in impatience and optimism. They conclude by discussing theoretical and practical implications; they also propose agent-based simulations, as well as empirical and experimental designs, to further test the relevance and applications of the results.

This model has important, measurable implications when analyzing commitment or self-control, adherence to healthy habits (e.g. exercising or dieting), procrastination tendencies or savings.

The results enhance the estimation of the costs of present biases such that employers can better identify the incentives required to acquire and retain human capital. The authors provide evidence that workers are vulnerable to contract renegotiations and about the need for a regulator that restores ex-ante efficiency. Similarly, in the private sector, firms could recognize the postulated consumer profiles and focus their resources on anxious, too-optimistic or potentially addictive consumers; this, again, provides some justification about the need for a regulator.

In traditional exponential discounting, the marginal rate of substitution of consumption between two points depends only on their distance; thus, it allows none of the intertemporal inconsistencies we often observe in real life. Therefore, hyperbolic discounting better fits the data. The authors model choice under uncertainty and focus on the costs caused when present biases (ex-post) push behaviour away from ex-ante optimality. They conclude by proposing experimental designs to further enhance the estimation and implications of these costs. The postulated refinements have the potential to improve previous analyses on commitment devices and commitment-related regulation.

This paper models the benefits of Islamic banking on the efficiency of the banking sector and on societal happiness. This paper aims to examine how the adoption of Islamic banking to various degrees affects economics outcomes.

This study uses machine-learning tools to build a happiness function and integrate it in an agent-based model to test for the direct and indirect welfare effects of implementing Islamic banking principles.

This study shows that even though Islamic banking systems tend to reduce economic activity, financial stability and societal happiness is improved. Additionally, a banking sector using Islamic principles across all its members is better equipped to handle banking crises because contagion to both economic activity and societal welfare is greatly reduced. At the same time, adoption of the profit-and-loss sharing (PLS) paradigm by banks may also slow down economic growth.

The findings extend existing literature on the advantages of Islamic banking, by quantifying the welfare benefits of the PLS paradigm on happiness and financial stability.

To the best of the authors’ knowledge, this paper is the first to combine agent-based modelling with machine learning tools to examine the benefits of the Islamic banking model on financial stability, social welfare and unemployment.

The purpose of this paper is to comparatively examine the cost and the overlooked revenue efficiency of Islamic and commercial banks in the aftermath of the crisis, operating in nine MENA-based countries during the 2010-2017 financial period, where the established empirical work is relatively limited. The authors also update the research where they use recent data sets and they provide for a targeted, structured literature review pre- and post-crisis in the Gulf region.

The authors examine cost and revenue efficiency of 25 major Islamic banks (IBs) and 25 major conventional banks (CBs). They conduct tests on the determinants of such variables. In the first stage of the analysis, they measure efficiency by using the data envelopment analysis (DEA) technique. The analysis performs regressions where these also reveal that the bank efficiency index is influenced by various bank type-specific attributes. It also seems that tighter restrictions on bank activities are negatively associated with bank efficiency. Second stage analysis, which accounts for banking environment and bank-level characteristics, confirms these results.

Conventional banks are both more cost and revenue efficient than Islamic banks over the period under examination. The analysis also reveals that the bank efficiency index is influenced by bank-type attributes. Greater presence of fixed capital resources has positive effects on growth in both Islamic and conventional banking. The major constraints impeding Islamic banking growth include labour costs. The authors examine whether and how bank-type orientation affects the cost and revenue efficiency of conventional and Islamic banks. They find that post-crisis Islamic banks underperform their conventional counterparts on both accounts within a mixed banking system.

This study did not include comparative data before the 2008 financial crisis. There is also a great deal of heterogeneity among Islamic banks in the samples that have been examined here and by other researchers and the constructed efficiency scores should be interpreted cautiously as divergent Islamic banks are pooled in the same samples.

This study identified factors that may help bank managers to improve their financial outlook by controlling revenue and cost efficiency profitability. These factors could as well help to understand how some indicators affect both cost and revenue efficiency, particularly in Islamic banking. It also seems that tighter restrictions on Islamic bank activities are negatively associated with bank efficiency. Islamic banks that directly compete with their conventional counterparts in the aftermath of the crisis are less efficient on both the cost and revenue frontiers. They are potentially hindered by the differential regulations of supervising authorities in dual banking systems.

The authors provide recommendations regarding regulatory and other issues that are relevant to Islamic banking and further research is suggested. Findings are relevant to a variety of stakeholders (managers, policymakers and regulators). Islamic banking authorities could re-examine the benefits of partially moving to a more standardized/conventional system of banking by lifting some trading restrictions. In addition, developing and maintaining managerial skills is an indispensable instrument for the long-term endurance of any system. A related aspect is thus an effort to determine the holistic efficiency (including managerial) of Islamic banks as a guide for policymakers to improve managerial performance.

There is relatively limited empirical work that investigates the efficiency between Islamic and conventional banking in the aftermath of the crisis in the Gulf region despite the growing importance of this region on political and economic levels. The authors also examine the revenue efficiency measure often under-researched in the literature and particularly important for comparative studies. Overseas-owned banks have attained much higher infiltration levels in middle-eastern countries over the past decade. It has also been suggested that market penetration differences may also be related to bank efficiency concerns among countries and their financial systems as opposed to types of banks.

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a comprehensive overview pertaining to the background and history of the classification algorithms. This will be followed by an extensive discussion regarding various techniques of classification algorithm in machine learning (ML) hence concluding with their relevant applications in data analysis in medical science and health care. To begin with, the initials of this chapter will deal with the basic fundamentals required for a profound understanding of the classification techniques in ML which will comprise of the underlying differences between Unsupervised and Supervised Learning followed by the basic terminologies of classification and its history. Further, it will include the types of classification algorithms ranging from linear classifiers like Logistic Regression, Naïve Bayes to Nearest Neighbour, Support Vector Machine, Tree-based Classifiers, and Neural Networks, and their respective mathematics. Ensemble algorithms such as Majority Voting, Boosting, Bagging, Stacking will also be discussed at great length along with their relevant applications. Furthermore, this chapter will also incorporate comprehensive elucidation regarding the areas of application of such classification algorithms in the field of biomedicine and health care and their contribution to decision-making systems and predictive analysis. To conclude, this chapter will devote highly in the field of research and development as it will provide a thorough insight to the classification algorithms and their relevant applications used in the cases of the healthcare development sector.