Determining Utility System Value of Demand Flexibility From Grid-interactive Efficient Buildings

This report focuses on ways current methods and practices that establish the value to electric utility systems of distributed energy resource (DER) investments can be enhanced to determine the value of demand flexibility in grid-interactive efficient buildings that can provide grid services. The report introduces key valuation concepts that are applicable to demand flexibility that these buildings can provide and links to other documents that describe these concepts and their implementation in more detail.The scope of this report is limited to the valuation of economic benefits to the utility system. These are the foundational values on which other benefits (and costs) can be built. Establishing the economic value to the grid of demand flexibility provides the information needed to design programs, market rules, and rates that align the economic interest of utility customers with building owners and occupants. By nature, DERs directly impact customers and provide societal benefits external to the utility system. Jurisdictions can use utility system benefits and costs as the foundation of their economic analysis but align their primary cost-effectiveness metric with all applicable policy objectives, which may include customer and societal (non-utility system) impacts.This report suggests enhancements to current methods and practices that state and local policymakers, public utility commissions, state energy offices, utilities, state utility consumer representatives, and other stakeholders might support. These enhancements can improve the consistency and robustness of economic valuation of demand flexibility for grid services. The report concludes with a discussion of considerations for prioritizing implementation of these improvements

Ontology-Based MEDLINE Document Classification

An increasing and overwhelming amount of biomedical information is available in the research literature mainly in the form of free-text. Biologists need tools that automate their information search and deal with the high volume and ambiguity of free-text. Ontologies can help automatic information processing by providing standard concepts and information about the relationships between concepts. The Medical Subject Headings (MeSH) ontology is already available and used by MEDLINE indexers to annotate the conceptual content of biomedical articles. This paper presents a domain-independent method that uses the MeSH ontology inter-concept relationships to extend the existing MeSH-based representation of MEDLINE documents. The extension method is evaluated within a document triage task organized by the Genomics track of the 2005 Text REtrieval Conference (TREC). Our method for extending the representation of documents leads to an improvement of 17% over a non-extended baseline in terms of normalized utility, the metric defined for the task. The SVMlight software is used to classify documents

Leveraging Social Foci for Information Seeking in Social Media

The rise of social media provides a great opportunity for people to reach out to their social connections to satisfy their information needs. However, generic social media platforms are not explicitly designed to assist information seeking of users. In this paper, we propose a novel framework to identify the social connections of a user able to satisfy his information needs. The information need of a social media user is subjective and personal, and we investigate the utility of his social context to identify people able to satisfy it. We present questions users post on Twitter as instances of information seeking activities in social media. We infer soft community memberships of the asker and his social connections by integrating network and content information. Drawing concepts from the social foci theory, we identify answerers who share communities with the asker w.r.t. the question. Our experiments demonstrate that the framework is effective in identifying answerers to social media questions.Comment: AAAI 201

Implementing Behavioral Concepts into Banking Theory: The Impact of Loss Aversion on Collateralization

In standard bank theoretic models agents are assumed to be fully rational expected utility maximizers. This fact ignores the huge amount of evidence for anomalies in human behavior found by psychologists. In this paper we argue that the implementation of behavioral concepts into banking theory might increase the predictive power of the models. As an example we consider a loan market and discuss the impact of loss aversion on the degree of collateralization in equilibrium. The very well established concept loss aversion predicts entrepreneurs to pay much more attention to the potential loss of some of their initial wealth due to a collateralized loan than they would do as expected utility maximizers. This results in a higher effort choice which in turn increases the success probability of the loan financed project. Optimal levels of collateralization are derived for different degrees of loss aversion and the problem of private information about the degree of loss aversion is addressed. It is shown that in specific situations banks can offer self selecting pairs of contracts that costlessly eliminate the private information problem.

Evolution of non-expected utility preferences

We investigate an extension of Dekel, Ely and Yilankaya's (2004) treatment of the evolution of preference to more general, possibly nonexpected utility preferences. Along the lines of their analysis we consider a population of types that is repeatedly and randomly matched to play the mixed extension of any given symmetric two-player normalform game with complete information. In our setup, a type is a generic best-response correspondence that is assumed to satisfy only standard assumptions. Preferences evolve according to the "success" of the player which is determined by the payoff she receives in the game. As in Dekel, Ely and Yilankaya (2004), the players observe the type of their opponent and a Nash equilibrium according to their best responses is played. We show that Dekel, Ely and Yilankaya's result that stability of an outcome implies efficiency is robust in this more general setup. However, in our model we obtain full equivalence between the two concepts for 2x2 games. We show that efficiency of any strategy also implies the stability of the outcome that it induces. This is in contrast to the former work in which only efficiency of a pure strategy leads to a stable outcome. The result implies the existence of a stable outcome in any 2x2 game. Considering the class of rank-dependent expected utility preferences as example we discuss the model's ability to embed specific types of non-expected utility theories. Moreover, we study implications for well-established games like the prisoner's dilemma.evolution of preferences, non-expected utility theory, best-response correspondence, stability, efficient strategy

Thermodynamics as a theory of decision-making with information processing costs

Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here we propose an information-theoretic formalization of bounded rational decision-making where decision-makers trade off expected utility and information processing costs. Such bounded rational decision-makers can be thought of as thermodynamic machines that undergo physical state changes when they compute. Their behavior is governed by a free energy functional that trades off changes in internal energy-as a proxy for utility-and entropic changes representing computational costs induced by changing states. As a result, the bounded rational decision-making problem can be rephrased in terms of well-known concepts from statistical physics. In the limit when computational costs are ignored, the maximum expected utility principle is recovered. We discuss the relation to satisficing decision-making procedures as well as links to existing theoretical frameworks and human decision-making experiments that describe deviations from expected utility theory. Since most of the mathematical machinery can be borrowed from statistical physics, the main contribution is to axiomatically derive and interpret the thermodynamic free energy as a model of bounded rational decision-making.Comment: 26 pages, 5 figures, (under revision since February 2012

Sequential decision making with adaptive utility

Decision making with adaptive utility provides a generalisation to classical Bayesian decision theory, allowing the creation of a normative theory for decision selection when preferences are initially uncertain. The theory of adaptive utility was introduced by Cyert & DeGroot [27], but had since received little attention or development. In particular, foundational issues had not been explored and no consideration had been given to the generalisation of traditional utility concepts such as value of information or risk aversion. This thesis addresses such issues. An in-depth review of the decision theory literature is given, detailing differences in assumptions between various proposed normative theories and their possible generalisations. Motivation is provided for generalising expected utility theory to permit uncertain preferences, and it is argued that in such a situation, under the acceptance of traditional utility axioms, the decision maker should seek to select decisions so asto maximise expected adaptive utility . The possible applications of the theory forsequential decision making are illustrated by some small-scale examples, including examples of relevance within reliability theory

Information-Driven Adaptive Structured-Light Scanners

Sensor planning and active sensing, long studied in robotics, adapt sensor parameters to maximize a utility function while constraining resource expenditures. Here we consider information gain as the utility function. While these concepts are often used to reason about 3D sensors, these are usually treated as a predefined, black-box, component. In this paper we show how the same principles can be used as part of the 3D sensor. We describe the relevant generative model for structured-light 3D scanning and show how adaptive pattern selection can maximize information gain in an open-loop-feedback manner. We then demonstrate how different choices of relevant variable sets (corresponding to the subproblems of locatization and mapping) lead to different criteria for pattern selection and can be computed in an online fashion. We show results for both subproblems with several pattern dictionary choices and demonstrate their usefulness for pose estimation and depth acquisition.United States. Office of Naval Research (Grant N00014-09-1-1051)United States. Army Research Office (Grant W911NF-11- 1-0391)United States. Office of Naval Research (Grant N00014- 11-1-0688