In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

https://www.edegan.com/pdfs/Gans%20Stern%20(2003)%20-%20The%20Product%20Market%20And%20The%20Market%20For%20Ideas%20Commercialization%20Strategies%20For%20Technology%20Entrepreneurs.pdf

The product market and the market for “ideas”: commercialization strategies for technology entrepreneurs

The purpose of this course is to acquaint students with the wide variety of regulatory processes, policies, and institutions in this country. Furthermore, students will develop a sophisticated and rigorous understanding of selected elements of regulatory politics, processes, and policies involving a regulatory agency of the U.S. government. To accomplish this, a variety of materials will be presented. These will involve reading, discussion and integration of the concepts of regulation, as well as understanding the processes by which regulatory policy is developed and implemented. Also, the impacts or results of such regulatory policies will be treated. In addition, students will gain a good deal of experience and develop techniques for conducting a diverse set of research and writing skills.

The politics of regulation

Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques

Over the past decades, detailed individual building energy models (BEM) on the one side and regional and country-level building stock models on the other side have become established modes of analysis for building designers and energy policy makers, respectively. More recently, these two toolsets have begun to merge into hybrid methods that are meant to analyze the energy performance of neighborhoods, i.e. several dozens to thousands of buildings. This paper reviews emerging simulation methods and implementation workflows for such bottom-up urban building energy models (UBEM). Simulation input organization, thermal model generation and execution, as well as result validation, are discussed successively and an outlook for future developments is presented.

Urban building energy modeling – A review of a nascent field

Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

Identifying key variables and interactions in statistical models of building energy consumption using regularization

How much information disclosure of building energy performance is necessary

Estimating Energy Savings from Benchmarking Policies in New York City

Energy benchmarking for buildings has become increasingly important in government policy and industry practice for energy efficiency. The questions of how energy benchmarking is currently conducted, and how it might be improved using rapidly growing quantities of self-reported data, are examined. A case study of commercial office buildings in New York City demonstrates how the rapid growth in self-reported data presents both new opportunities and challenges for energy benchmarking for buildings. A critique is presented for the scoring methodology and data sources for Energy Star, one of the largest and most successful benchmarking certification schemes. Findings from recent studies are examined to illustrate how this certification currently works in the marketplace. Self-reported building energy data are rapidly growing in Portfolio Manager (the user interface to Energy Star) due to mandatory energy benchmarking laws, and can be used to improve Energy Star's current scoring methods. These self-reported da...

David Hsu

Papers

Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

Identifying key variables and interactions in statistical models of building energy consumption using regularization

How much information disclosure of building energy performance is necessary

Estimating Energy Savings from Benchmarking Policies in New York City

Improving energy benchmarking with self-reported data