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In this chapter, I take a symbolic interactionist look at the anthropomorphic projections of 38 people in four large Canadian cities who report having encountered ghosts. As part of a larger project on perceived encounters with ghosts, I interviewed these people using audiotape, transcribed the interviews, and identified themes within them. Together, they mentioned 195 ghosts and described their appearances and actions. I identified anthropomorphic projections in four areas: (1) Physicality – pertaining to the bodies of the ghosts, (2) Relational – pertaining to kinship ties, e.g., mothers, fathers, grandmothers, (3) Rational – the ghosts' motivations and justifications, and (4) Affective – pertaining to the ghosts' emotions. These anthropomorphic projections were the respondents' efforts at sense making after having encountered what they believed to be phantasmal.

This chapter addresses one of the most crucial areas for critical thinking: the morality of turbulent markets around the world. All of us are overwhelmed by such turbulent markets. Following Nassim Nicholas Taleb (2004, 2010), we distinguish between nonscalable industries (ordinary professions where income grows linearly, piecemeal or by marginal jumps) and scalable industries (extraordinary risk-prone professions where income grows in a nonlinear fashion, and by exponential jumps and fractures). Nonscalable industries generate tame and predictable markets of goods and services, while scalable industries regularly explode into behemoth virulent markets where rewards are disproportionately large compared to effort, and they are the major causes of turbulent financial markets that rock our world causing ever-widening inequities and inequalities. Part I describes both scalable and nonscalable markets in sufficient detail, including propensity of scalable industries to randomness, and the turbulent markets they create. Part II seeks understanding of moral responsibility of turbulent markets and discusses who should appropriate moral responsibility for turbulent markets and under what conditions. Part III synthesizes various theories of necessary and sufficient conditions for accepting or assigning moral responsibility. We also analyze the necessary and sufficient conditions for attribution of moral responsibility such as rationality, intentionality, autonomy or freedom, causality, accountability, and avoidability of various actors as moral agents or as moral persons. By grouping these conditions, we then derive some useful models for assigning moral responsibility to various entities such as individual executives, corporations, or joint bodies. We discuss the challenges and limitations of such models.

This chapter explores other theoretical explanations to the commonly observed phenomenon of negatively skewed performance outcomes and inverse risk-return relationships in empirical firm data. The analysis conducted in many prior studies have implicated direct causal dependencies between performance and risk, or vice versa, with the possibility of simultaneous two-way relationships that are harder to discern. It is also shown how spurious artifacts deriving from the arithmetic links between mean and variance associate left-skewed distributions with negative mean variance correlations. However, the heterogeneous display of response capabilities among firms that compete in the same industry contexts may provide an alternative explanation for the observed performance characteristics. This is expressed as strategic responsiveness where performance outcomes with high negative skewness and excess kurtosis derive from heterogeneous adaptive processes among firms as they respond to a dynamic environment with different degrees of success. We test these results in different simulated competitive contexts disrupted by major unexpected events and find robust results across different environmental scenarios. The analysis looks at two different response processes, one modeled as conventional adaptive planning following an annual budget cycle, and another modeled as interactive updating where executives have frequent informative budget discussions with operating managers in the firm. The computational simulations show that interactive updating generates outcomes with higher returns and lower performance risk for moderate learning levels and restructuring costs. However, the resulting performance distributions are not as left-skewed as those observed in the empirical data that show higher resemblance to the adaptive planning outcomes.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

Forestry is an integral part of an economy. It not only maintains the balance of the ecosystem but provides sustainable livelihood to the forest-fringe dwellers, apart from being a huge source of revenue for the government. Hence, proper management of the forest resources is an important concern for the economists. On the other hand, convergence of natural resources is one of the hot cakes of discussion for the economists. In this study, the authors have considered three forest regions of West Bengal; these three being the largest forest areas hold an important position in West Bengal’s economy. Here, the authors have considered the whole of dryland forestry that covers the arid and semi-arid districts of western part of the state. They have also considered the forestry of Jalpaiguri and Cooch Behar districts that cover the sub-Himalayan forestry of North Bengal and mangrove forestry of South 24 Parganas district. The authors have used time-series data for the time period 1980–2019 and performed the Absolute Beta Convergence and Sigma Convergence Analysis as well as Conditional Beta Convergence of the total forest products of these three regions. In this study, the authors have found the existence of both forms of beta convergence but for variations, a divergence has been observed. The authors have also used the data from 2000 to 2019 on various aspects of income from forestry and performed the three forms of tests, like that of total forest products and observed similar type of results, that is, beta convergence of both forms but sigma divergence. The findings of this study ask for serious steps by the government bodies, since the decreasing rates are the causes of worries.

This chapter tests theoretically and empirically the existence of a stable relationship between energy consumption and CO2 emissions. Based on microeconomics and physics, a model has been specified and applied to annual data for twenty countries, which representing 61 percent of the world’s population in 2018, over the period 1995–2015. The data are from the International Energy Agency (2019) and econometric techniques including panel data and causality tests have been used. The results indicate that there is a causal relationship between energy consumption and CO2 emissions. In general, consumers cannot directly change emissions caused by production processes, but they can act on emissions caused by their own domestic energy consumption. Approximately three quarters of domestic energy consumption is due to heating and domestic hot water consumption. Taking into account the lower emissions and the lower economic cost of the initial investment, four potential energy systems have been selected for use in heating and domestic hot water. Their social returns have been assessed across nine of the twenty countries in the sample over a lifecycle of 25 years from 2018: France, Portugal, Ireland, Spain, Iceland, Germany, United Kingdom, Morocco and the United States. Cost-benefit analysis techniques have been used for this purpose and the results indicate that the use of thermal water, where applicable, is the most socially profitable system among the proposed systems, followed by natural gas. The least socially profitable systems are those using electricity.

The objective of this chapter is to discuss how different techniques in Regional Science and Peace Science and the emerging techniques in Management Science can be used in analysing Disaster Management and Global pandemic with special reference to developing countries. It is necessary for me to first discuss the subjects of Disaster Management, Regional Science, Peace Science and Management Science. The objective of this chapter is to emphasise that the studies of Disaster Management should be more integrated with socioeconomic and geographical factors. The greatest disaster facing the world is the possibility of war, particularly nuclear war, and the preparation of the means of destruction through military spending.

More than 30 legal recyclables were proclaimed by Environmental Protection Administration (EPA) in Taiwan of those producers and importers are liable for paying a Resource Recycling Fee (RRF). The Resource Recycling Management Fund determines both the tariff of RRF and the subsidy rate for recycling activities based on a predetermined pricing formula and collects the revenue to finance its collection and disposal. While contemplating on whether to proclaim waste mattress as a legal recyclable, EPA is facing several critical challenges, particularly the lack of data required for setting a tariff–subsidy mix. In this chapter we critically review the formula and propose an innovative pricing rule. Also, we develop a science-based approach to demonstrate how a tariff–subsidy mix could be determined under the circumstance of data deficiency. By doing so, we avoid not only the difficulty in solving the nonhomogeneous and nonautonomous first-order difference equation that governs the stock accumulation of waste mattress but also the distributed lag model of multiperiods linking quantity of mattress discarded and the quantity of new mattress sold. Such an approach could be applied to the durables for recycling pricing particularly when relevant data are limited.