Faculty and Staff

Interdependent systems providing water and energy services are necessary for agriculture. Climate change and increased resource demands are expected to cause frequent and severe strains on these systems. Arizona is especially vulnerable to such strains due to its hot and arid climate. However, its climate enables year-round agricultural production, allowing Arizona to supply most of the country's winter lettuce and vegetables. In addition to Phoenix and Tucson, cities including El Paso, Las Vegas, Los Angeles, and San Diego rely on Arizona for several types of agricultural products such as animal feed and livestock, meaning that disruptions to Arizona's agriculture also disrupt food supply chains to at least six major cities.

Arizona's predominately irrigated agriculture relies on water imported through an energy intensive process from water-stressed regions. Most irrigation in Arizona is electricity powered, so failures in energy or water systems can cascade to the food system, creating a food-energy-water (FEW) nexus of vulnerability. We construct a dynamic simulation model of the FEW nexus in Arizona to assess the potential impacts of increasing temperatures and disruptions to energy and water supplies on crop irrigation requirements, on-farm energy use, and yield.

We use this model to identify critical points of intersection between energy, water, and agricultural systems and quantify expected increases in resource use and yield loss. Our model is based on threshold temperatures of crops, USDA and US Geological Survey data, Arizona crop budgets, and region-specific literature. We predict that temperature increase above the baseline could decrease yields by up to 12.2% per 1 °C for major Arizona crops and require increased irrigation of about 2.6% per 1 °C. Response to drought varies widely based on crop and phenophase, so we estimate irrigation interruption effects through scenario analysis. We provide an overview of potential adaptation measures farmers can take, and barriers to implementation.

We integrate multiple domains of psychological science to identify, better understand, and manage the effects of subtle but powerful biases in forensic mental health assessment. This topic is ripe for discussion, as research evidence that challenges our objectivity and credibility garners increased attention both within and outside of psychology. We begin by defining bias and provide rich examples from the judgment and decision making literature as they might apply to forensic assessment tasks. The cognitive biases we review can help us explain common problems in interpretation and judgment that confront forensic examiners. This leads us to ask (and attempt to answer) how we might use what we know about bias in forensic clinicians’ judgment to reduce its negative effects.

We conducted an international survey in which forensic examiners who were members of professional associations described their two most recent forensic evaluations (N=434 experts, 868 cases), focusing on the use of structured assessment tools to aid expert judgment. This study describes:

1. The relative frequency of various forensic referrals.
2. What tools are used globally.
3. Frequency and type of structured tools used.
4. Practitioners’ rationales for using/not using tools.

We provide general descriptive information for various referrals. We found most evaluations used tools (74.2%) and used several (on average 4). We noted the extreme variety in tools used (286 different tools). We discuss the implications of these findings and provide suggestions for improving the reliability and validity of forensic expert judgment methods. We conclude with a call for an assessment approach that seeks structured decision methods to advance greater efficiency in the use and integration of case-relevant information.

This chapter integrates from cognitive neuroscience, cognitive psychology, and social psychology the basic science of bias in human judgment as relevant to judgments and decisions by forensic mental health professionals. Forensic mental health professionals help courts make decisions in cases when some question of psychology pertains to the legal issue, such as in insanity cases, child custody hearings, and psychological injuries in civil suits. The legal system itself and many people involved, such as jurors, assume mental health experts are “objective” and untainted by bias. However, basic psychological science from several branches of the discipline suggest the law’s assumption about experts’ protection from bias is wrong. Indeed, several empirical studies now show clear evidence of (unintentional) bias in forensic mental health experts’ judgments and decisions. In this chapter, we explain the science of how and why human judgments are susceptible to various kinds of bias. We describe dual-process theories from cognitive neuroscience, cognitive psychology, and social psychology that can help explain these biases. We review the empirical evidence to date specifically about cognitive and social psychological biases in forensic mental health judgments, weaving in related literature about biases in other types of expert judgment, with hypotheses about how forensic experts are likely affected by these biases. We close with a discussion of directions for future research and practice.

Cities in the Global South face rapid urbanization challenges and often suffer an acute lack of infrastructure and governance capacities. Smart Cities Mission, in India, launched in 2015, aims to offer a novel approach for urban renewal of 100 cities following an area‐based development approach, where the use of ICT and digital technologies is particularly emphasized. This article presents a critical review of the design and implementation framework of this new urban renewal program across selected case‐study cities. The article examines the claims of the so‐called “smart cities” against actual urban transformation on‐ground and evaluates how “inclusive” and “sustainable” these developments are. We quantify the scale and coverage of the smart city urban renewal projects in the cities to highlight who the program includes and excludes. The article also presents a statistical analysis of the sectoral focus and budgetary allocations of the projects under the Smart Cities Mission to find an inherent bias in these smart city initiatives in terms of which types of development they promote and the ones it ignores. The findings indicate that a predominant emphasis on digital urban renewal of selected precincts and enclaves, branded as “smart cities,” leads to deepening social polarization and gentrification. The article offers crucial urban planning lessons for designing ICT‐driven urban renewal projects, while addressing critical questions around inclusion and sustainability in smart city ventures.`

During much of 2009 and 2010, the alternative energy marketplace has been in a state of unpredictable change. The financial landscape is uncertain and profits are slim. Many companies are wondering if the journey to find alternative means for energy production is worth the investment. Wind, hydro and solar projects are all struggling to make ends meet in an economic landscape that predicts success but presents challenges. Strong, lean and agile companies seem to be able to maintain a slim competitive advantage afloat, while larger, slower companies are drowning in a myriad of intricate supply chains and non-existent credit. This paper evaluates innovation opportunities for alternative energy companies, with a focus on large scale, highly integrated solar providers.