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Hot temperatures drive excessive energy use for space-cooling in built environments. In a building, a system operator could save costs by making better decisions under the uncertainties associated with urban temperature and future energy demands. In this paper, we assess the impact of urban weather modeling on energy cost, using a value of information (VoI) analysis, in a day-ahead (DA) electricity market. To do that, we combine two probabilistic models: (a) a model for forecasting urban temperature and (b) a model for forecasting hourly net electric load of a building given ambient urban temperature. We then quantify the impact of better urban weather modeling by propagating the uncertainty from the temperature model to the load forecasting model. We perform a numerical case study on residential building prototypes located in the city of Pittsburgh. The result indicates that using a better weather model could save 4.34-8.22% of the electricity costs for space-cooling. 

We assess the Value of Information (VoI) for inspecting components in systems managed by multiple agents, using game theory and Nash equilibrium analysis. We focus on binary systems made up by binary components which can be either intact or damaged. Agents taking maintenance actions are responsible for the repair costs of their own components, and the penalty for system failure is shared among all agents. The precision of inspection is also considered, and we identify the prior and posterior Nash equilibrium with perfect or imperfect inspections. The VoI is assessed for the individual agents as well as for the whole set of agents, and the analysis consider series, parallel and general systems. A negative VoI can trigger the phenomenon of Information Avoidance (IA), where rational agents prefer not to collect free information. We discuss whether it is possible that the VoI is negative for one or for all agents, for the agents with inspected or uninspected components, and for the total sum of VoIs. 

A state-space model (SSM) integrating physical parameters is proposed and developed in this work, to describe the increase of global average temperature and the subsequent changes in regional climate and hydrology. This SSM approach aims at providing updated and improved forecasts, based on observations and using Bayesian inference, and at facilitating flexible engineering decision-making schemes. Global climate model simulations are used for informing the distribution of the parameters of the SSM. The case study of the Colorado River Basin serves as a preliminary application of the method, to forecast changes in the upper basin natural flow. The method projects that the post-2000 low flow volume will continue, or become even lower on average, although such projections are subject to large uncertainty. Given the increasing need of climate projections in the design, operation, and management of infrastructure, the SSM approach can serve as a useful tool, informed by historical records, to facilitate engineering applications. 

Management scientists recognize that decision making depends on the information people have but lack a unified behavioral theory of the demand for (and avoidance of) information. Drawing on an existing theoretical framework in which utility depends on beliefs and the attention paid to them, we develop and test a theory of the demand for information encompassing instrumental considerations, curiosity, and desire to direct attention to beliefs one feels good about. We decompose an individual’s demand for information into the desire to refine beliefs, holding attention constant, and the desire to focus attention on anticipated beliefs, holding these beliefs constant. Because the utility of resolving uncertainty (i.e., refining beliefs) depends on the attention paid to it and more important or salient questions capture more attention, demand for information depends on the importance and salience of the question(s) it addresses. In addition, because getting new information focuses attention on one’s beliefs and people want to savor good news and ignore bad news, the desire to obtain or avoid information depends on the valence (i.e., goodness or badness) of anticipated beliefs. Five experiments (n = 2,361) test and find support for these hypotheses, looking at neutrally valenced as well as ego-relevant information. People are indeed more inclined to acquire information (a) when it feels more important, even if it cannot aid decision making (Experiments 1A and 2A); (b) when a question is more salient, manipulated through time lag (Experiments 1B and 2B); and (c) when anticipated beliefs have higher valence (Experiment 2C).