Search results

One crucial but sometimes overlooked fact regarding the difference between observation in the cross-section and observation over time must be stated before proceeding further. Tempting though it is to draw conclusions about the dynamics of a process from cross-sectional observations taken as a snapshot of that process, it is a fallacious practice except under a very precise condition that is highly unlikely to obtain in processes of interest to the social scientist. That condition is known as ergodicity.

Process-oriented guided-inquiry learning (POGIL) is a student-centered instructional strategy to actively engage students in the classroom in promoting content mastery, critical thinking, and process skills. The students organize into groups of three to four, and each group member works collaboratively to construct their understanding as they proceed through the embedded learning cycle in the POGIL activity. Each group member has a specific role and actively engages in the learning process. The roles rotate periodically, and each student has the opportunity to develop essential process skills, such as leadership skills, oral and written communication skills, team-building skills, and information-processing skills. The student groups are self-managed, and the instructor serves as a facilitator of student learning. A POGIL activity typically contains a model that the students deconstruct using a series of guided, exploratory questions. The students develop concepts (concept invention) as the group members reach a valid, consensus conclusion. The students apply their concepts to new problems completing the learning cycle. The authors implemented POGIL instruction in several chemistry courses at Jackson State University and Tuskegee University. They share their initial findings, experiences, and insights gained using a new instructional strategy.

The increasing prevalence and availability of big data represent a potentially revolutionary development for human resource management (HRM) scholars. Despite this, the current literature provides eclectic and often contradictory guidance for scholars attempting to conceptualize big data and subsequently incorporate it into relevant theoretical frameworks. The authors attempt to bridge this gap by discussing key considerations relevant to understanding and integrating big data into the existing theoretical landscape. Building on a novel, integrative definition of big data, the authors propose a parsimonious theoretical framework utilizing the established dimensions of complexity and dynamism as meta-attributes to bring order to the various attributes that have been proposed as central to defining big data (e.g., volume, variety, velocity, and variability). Throughout, the authors highlight numerous theoretical and empirical opportunities and considerations that this perspective holds for future HRM scholarship.

Certain elements of Hayek’s work are prominent precursors to the modern field of complex adaptive systems, including his ideas on spontaneous order, his focus on market processes, his contrast between designing and gardening, and his own framing of complex systems. Conceptually, he was well ahead of his time, prescient in his formulation of novel ways to think about economies and societies. Technically, the fact that he did not mathematically formalize most of the notions he developed makes his insights hard to incorporate unambiguously into models. However, because so much of his work is divorced from the simplistic models proffered by early mathematical economics, it stands as fertile ground for complex systems researchers today. I suggest that Austrian economists can create a progressive research program by building models of these Hayekian ideas, and thereby gain traction within the economics profession. Instead of mathematical models the suite of techniques and tools known as agent-based computing seems particularly well-suited to addressing traditional Austrian topics like money, business cycles, coordination, market processes, and so on, while staying faithful to the methodological individualism and bottom-up perspective that underpin the entire school of thought.

The paper published below was prepared by Taylor Ostrander for Frank Knight’s course, Economic Theory, Economics 301, during the Fall 1933 quarter.

Currently heating and cooling in buildings is responsible for over 30% of the primary energy consumption in the United Kingdom with a similar amount in China. We analyze heat pumps and district thermal energy network for efficient buildings. Their advantages are examined (i.e., flexibility in choosing heat sources, reduction of fuel consumption and increased environmental quality, enhanced community energy management, reduced costs for end users) together with their drawbacks, when they are intended as means for efficient building heating and cooling.

A literature review observed a range of operating conditions and challenges associated with the efficient operation of district heating and cooling networks, comparing primarily the UK’s and China’s experiences, but also acknowledging the areas of expertise of European, the United States, and Japan. It was noted that the efficiency of cooling networks is still in its infancy but heating networks could benefit from lower distribution temperatures to reduce thermal losses. Such temperatures are suitable for space heating methods provided by, for example, underfloor heating, enhanced area hydronic radiators, or fan-assisted hydronic radiators. However, to use existing higher temperature hydronic radiator systems (typically at a temperatures of >70°C) a modified heat pump was proposed, tested, and evaluated in an administrative building. The results appears to be very successful.

District heating is a proven energy-efficient mechanism for delivering space heating. They can also be adaptable for space cooling applications with either parallel heating and cooling circuits or in regions of well-defined seasons, on flow and return circuit with a defined change-over period from heating to cooling. Renewable energy sources can provide either heating or cooling through, for example, biomass boilers, photovoltaics, solar thermal, etc. However, for lower loss district heating systems, lower distribution temperatures are required. Advanced heat pumps can efficiently bridge the gap between lower temperature distribution systems and buildings with higher temperature hydronic heating systems

This chapter presents a case for district heating (and cooling). It demonstrates the benefits of reduced temperatures in district heating networks to reduce losses but also illustrates the need for temperature upgrading where building heating systems require higher temperatures. Thus, a novel heat pump was developed and successfully tested.

Introduction: Big data in the insurance industry can be defined as structured or unstructured data that can affect the rating, marketing, pricing, or underwriting. The five Vs of big data provide insurers with a valuable framework for converting their raw data into actionable information. These five Vs are specifically: (1) Volume: The need to look at the type of data and the internal systems; (2) Velocity: The speed at which big data is generated, collected, and refreshed; (3) Variety: Refers to both the structured and unstructured data; (4) Veracity: Refers to trustworthiness and confidence in data; and (5) Value: Refers to whether the data collected are good or bad.

Purpose: Insurance companies face many data challenges. However, the administration of big data has allowed insurers to acknowledge the demand of their customers and develop more personalised products. In addition, it can be used to make correct decisions about insurance operations such as risk selection and pricing.

Methodology: We do this by conducting a systematic literature review on big data. Our emphasis is on gathering information on the five Vs of the big data and the insurance market. Specifically, how big data can help in data-driven decisions.

Findings: Big data technology has created an endless series of opportunities, which have ensured a surge in its usage. It has helped businesses make the process more systematic, cost-effective, and helped in the reduction in fraud and risk prediction.

Using the case of the Deepwater Horizon blowout in the Gulf of Mexico in 2010, I argue that the catastrophe was less an example of a low probability-high catastrophe event than an instance of socially produced risks and insecurities associated with deepwater oil and gas production during the neoliberal period after 1980. The disaster exposes the deadly intersection of the aggressive enclosure of a new technologically risky resource frontier (the deepwater continental shelf) with what I call a frontier of neoliberalized risk, a lethal product of cut-throat corporate cost-cutting, the collapse of government oversight and regulatory authority and the deepening financialization and securitization of the oil market. These two local pockets of socially produced risk and wrecklessness have come to exceed the capabilities of what passes as risk management and energy security. In this sense, the Deepwater Horizon disaster was produced by a set of structural conditions, a sort of rogue capitalism, not unlike those which precipitated the financial meltdown of 2008. The forms of accumulation unleashed in the Gulf of Mexico over three decades rendered a high-risk enterprise yet more risky, all the while accumulating insecurities and radical uncertainties which made the likelihood of a Deepwater Horizon type disaster highly overdetermined.