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This article investigates the use of dynamic laboratory simulations as a tool for studying decisions to prepare for hurricane threats. A prototype web-based simulation named Stormview is described that allows individuals to experience the approach of a hurricane in a computer-based environment. In Stormview participants can gather storm information through various media, hear the opinions of neighbors, and indicate intentions to take protective action. We illustrate how the ability to exert experimental control over the information viewed by participants can be used to provide insights into decision making that would be difficult to gain from field studies, such as how preparedness decisions are affected by the nature of news coverage of prior storms, how a storm's movement is depicted in graphics, and the content of word-of-mouth communications. Data from an initial application involving a sample of Florida residents reveal a number of unexpected findings about hurricane risk response. Participants who viewed forecast graphics, which contained track lines depicting the most likely path of the storm, for example, had higher levels of preparation than those who saw graphics that showed only uncertainty cones-even among those living far from the predicted center path. Similarly, the participants who were most likely to express worry about an approaching storm and fastest to undertake preparatory action were those who, ironically, had never experienced one. Finally, external validity is evidenced by a close rank-order correspondence between patterns of information use revealed in the lab and that found in previous cross-sectional field studies., (© 2012 Society for Risk Analysis.)

The use of pharmacogenetics and pharmacogenomics in the drug development process, and in the assessment of such data submitted to regulatory agencies by industry, has generated significant enthusiasm as well as important reservations within the scientific and medical communities. This situation has arisen because of the increasing number of exploratory and confirmatory investigations into variations in RNA expression patterns and DNA sequences being conducted in the preclinical and clinical phases of drug development, and the uncertainty surrounding the acceptance of these data by regulatory agencies. This report summarizes the outcome of a workshop cosponsored by the Food and Drug Administration (FDA), the Pharmacogenetics Working Group (PWG), the Pharmaceutical Research and Manufacturers of America (PhRMA), and the PhRMA Preclinical Safety Committee (DruSafe). The specific aim of the workshop was to identify key issues associated with the application of pharmacogenetics and pharmacogenomics, including the feasibility of a regulatory "safe harbor" for exploratory genome-based data, and to provide a forum for industry-regulatory agency dialogue on these important issues.

Many students in the Unites States enter college without having decided on a focus for their studies, and thus are faced with choosing from a large number of potential majors and associated very complex sets of degree requirements which can include many courses in other areas of study. Academic advisors use academic planning tools to help students make decisions about class schedules, selecting an academic major or minor, planning for graduation, and many other academic related activities. There is a dearth of decision support systems for degree planning, mainly due to the complexity of degree requirements, and thus many existing academic planning tools utilize static documents or PDF files for displaying information pertaining to degree requirements and course prerequisites. This work considers the complexity of degree requirements and presents the design and implementation of an efficient interactive decision support system that helps students explore degree completion paths. [The page range cited (pp. 101-116) on the .pdf is incorrect. The correct page range is pp. 101-115.]

An introduction is presented in which the guest editors discuss the theme of the issue, decision making, spending, and consumption in education marketing.

A common assumption made in structural approaches to empirical strategy research in marketing is that firms and consumers satisfy the assumptions of dynamic optimality when making decisions. When faced with problems of how best to allocate resources, firms are assumed consider the future consequences of different strategic options and, in each point in time, choose the option that maximizes long-term utility. The validity of such assumptions, however, is often called into question by behavioral researchers who point to work in psychology that finds that assumptions of optimality are frequently violated in experimental settings. If this is indeed the case, it would lend support to approaches that argue that markets have inefficiencies that can be discovered and exploited by simpler, largely correlational, methods. In this article, the authors attempt to reconcile these contrasting views by proposing a framework for assessing when assumptions of dynamic optimality are likely to be good ones and when they are likely to be untenable in empirical analysis. [ABSTRACT FROM AUTHOR]

This article examines how multiattribute impressions are formed in riskless choice and judgment when there is uncertainty or ambiguity associated with attribute-importance weights. Building on previous results in the modeling of ambiguity in probabilities, a mathematical theory of multiattribute judgments under weight uncertainty is developed. The theory incorporates framing effects for ambiguous weights in multiattribute judgments, similar to the gain-loss framing effects found in studies of preferences for lotteries. Specifically, it is shown that the importance of an attribute depends on whether it is seen as enhancing or preserving an expected level of utility in a category. The results of three experiments provide support for the predicted dependency of the effects of weight uncertainty on an attribute's framing. The data also suggest that, within frames, the effects of increasing uncertainty may not be additive across attributes. In particular, given pairs of uncertain attributes with different frames, subjects acted as if they were applying a single frame to both attributes, resulting in a common direction for the weight-uncertainty effect. [ABSTRACT FROM AUTHOR]

The article comments on the 1987 paper "Effects of Quality and Quantity on Decision Effectiveness," by professors Rick Staelin and Kevin Keller, which discussed whether it was possible to provide consumers with too much information when making choices. The article states that Keller and Staelin found that when information quality was held constant, increases in information had a strong negative effect on decision accuracy. The article's critique centers on Keller and Staelin's use of inferred choice errors when identifying consumer error in a given choice.

A new approach to inferring hierarchical models of consumer choice is described. A classification algorithm is used to estimate decision trees at an individual level without requiring prior assumptions about tree form. Derived models are analyzed within a modeling system that summarizes the diversity of decision rules in a sample as well as their implications for aggregate market shares. An application to the analysis of panel data and a comparison with disaggregate logit analysis are reported. [ABSTRACT FROM AUTHOR]

A multiattribute judgment model is advanced which is purported to characterize how consumers form evaluations of products when information about products' attributes is limited and variability or unreliability is present within attributes. The model is based on the hypothesis that the subjective value consumers associate with a product attribute can be represented in terms of a weighted average of the value of the information they have received about that attribute and the perceived mean value of that attribute within the product class. This average is integrated with the consumer's perception of the amount of variability both within and between other alternatives on that attribute--a measure of uncertainty. A series of laboratory experiments testing aspects of the model are described. Results support both the model's predictions and aspects of its structure. [ABSTRACT FROM AUTHOR]

Although the field has seen great advances in hurricane prediction and response, the economic toll from hurricanes on U.S. communities continues to rise. We present data from Hurricanes Earl (2010), Irene (2011), Isaac (2012), and Sandy (2012) to show that individual and household decisions contribute to this vulnerability. From phone surveys of residents in communities threatened by impending hurricanes, we identify five decision biases or obstacles that interfere with residents' ability to protect themselves and minimize property damage: (1) temporal and spatial myopia, (2) poor mental models of storm risk, (3) gaps between objective and subjective probability estimates, (4) prior storm experience, and (5) social factors. We then discuss ways to encourage better decision making and reduce the economic and emotional impacts of hurricanes, using tools such as decision defaults (requiring residents to opt out of precautions rather than opt in) and tailoring internet-based forecast information so that it is local, specific, and emphasizes impacts rather than probability. [ABSTRACT FROM AUTHOR]

Parental misconceptions and even “demand” for unnecessary antibiotics were previously viewed as contributors to overuse of these agents. We conducted focus groups to explore the knowledge and attitudes surrounding common infections and antibiotic use in the current era of more judicious prescribing. Among diverse groups of parents, we found widespread use of home remedies and considerable concern regarding antibiotic resistance. Parents generally expressed the desire to use antibiotics only when necessary. There was appreciation of inherent error in the diagnosis of common infections, with most trust placed in providers with whom parents had long-standing relationships. While some parents had experience with “watchful waiting” for otitis media, there was little enthusiasm for this approach. While there may still be room for further education, it appears that parents have become more informed and sophisticated regarding appropriate uses of antibiotics. This has likely contributed to the declines seen in their use nationally. [ABSTRACT FROM PUBLISHER]

This article investigates the use of dynamic laboratory simulations as a tool for studying decisions to prepare for hurricane threats. A prototype web-based simulation named Stormview is described that allows individuals to experience the approach of a hurricane in a computer-based environment. In Stormview participants can gather storm information through various media, hear the opinions of neighbors, and indicate intentions to take protective action. We illustrate how the ability to exert experimental control over the information viewed by participants can be used to provide insights into decision making that would be difficult to gain from field studies, such as how preparedness decisions are affected by the nature of news coverage of prior storms, how a storm's movement is depicted in graphics, and the content of word-of-mouth communications. Data from an initial application involving a sample of Florida residents reveal a number of unexpected findings about hurricane risk response. Participants who viewed forecast graphics, which contained track lines depicting the most likely path of the storm, for example, had higher levels of preparation than those who saw graphics that showed only uncertainty cones-even among those living far from the predicted center path. Similarly, the participants who were most likely to express worry about an approaching storm and fastest to undertake preparatory action were those who, ironically, had never experienced one. Finally, external validity is evidenced by a close rank-order correspondence between patterns of information use revealed in the lab and that found in previous cross-sectional field studies. [ABSTRACT FROM AUTHOR]

This paper explores the question of whether there are inherent limits to our ability to learn from experience about the value of protection against low-probability, high-consequence, events. Findings are reported from two controlled experiments in which participants have a monetary incentive to learn from experience making investments to protect against hurricane risks. A central finding is that investments display a short-term forgetting effect consistent with the use of reinforcement learning rules, where a significant driver of investments in a given period is whether storm losses were incurred in the precious period. Given the relative rarity of such losses, this reinforcement process produces a mean investment level below that which would be optimal for most storm threats. Investments are also found to be insensitive to the censoring effect of protection itself, implying that the size of experienced losses-rather than losses that are avoided-is the primary driver of investment decisions. [ABSTRACT FROM AUTHOR]

Game theoretic models of marketing channels typically rely on simplifying assumptions that, from a behavioral perspective, often appear naïve. However, behavioral researchers have produced such an abundance of behavioral regularities that they are impossible to incorporate into game theoretic models. We believe that a focus on three core findings would benefit both fields; these are: first, beliefs that are held by the various players regarding profit consequences of different actions are incomplete and often biased; second, players’ preferences and optimization objectives are not commonly known; and third, players have insufficient cognitive abilities to achieve optimization objectives. Embracing these three findings shifts the focus from rational decision making to how decision makers learn to improve their decision-making skills. Concluding, we believe that greater convergence of game theoretic modeling and behavioral research in marketing channels would lead to new insights for both fields. [ABSTRACT FROM AUTHOR]

We review the discussion at a workshop whose goal was to achieve a better integration among behavioral, economic, and statistical approaches to choice modeling. The workshop explored how current approaches to the specification, estimation, and application of choice models might be improved to better capture the diversity of processes that are postulated to explain how consumers make choices. Some specific challenges include how to capture and parsimoniously describe heterogeneous mixes of heuristic choice rules, methods for building realistic models of choice, and nontraditional methods for estimating models. An agenda for important future work in these areas is also proposed. [ABSTRACT FROM AUTHOR]

The article presents chapter 14 of the book "Yearbook of the National Society for the Study of Education." This chapter, entitled "Evidence and Decision Making in Education Systems," discusses educational indicator systems which focuses on how the evidence delivered by these systems can be used to support professional learning and decision making in the future.

A study was conducted to determine the extent to which attributional complexity influences juror decision-making. Using Fletcher’s (1986) attributional complexity scale, mock jurors (N = 186), who were shown a videotape of a simulated armed robbery, then rendered individual judgments on the case. The results showed that attributionally-simple participants found the defendant guilty more often both before and after presentation of the evidence, reported more confidence in their final decision, and attributed more personal causes to the defendant’s behavior. Attributionally-complex participants, while not excluding the role of internal causes, were more likely to consider external causes as possible influences on the defendant’s behavior. The findings demonstrate the existence of clear individual differences in attributional style and the potential importance of this variable for jury selection. [ABSTRACT FROM AUTHOR]

We review current state-of-the-art practices for combining preference data from multiple sources and discuss future research possibilities. A central theme is that any one data source (e.g., a scanner panel source) is often insufficient to support tests of complex theories of choice and decision making. Hence, analysts may need to embrace a wider variety of data types and measurement tools than traditionally have been considered in applied decision making and choice research. We discuss the viability of preference-stationarity assumptions usually made when pooling data, as well as random-utility theory-based approaches for combining data sources. We also discuss types of models and data sources likely to be required to make inferences about and estimate models that describe choice dynamics. The latter discussion is speculative insofar as the body of literature on this topic is small. [ABSTRACT FROM AUTHOR]

The ability of consumers to make rational sequential purchase quantity decisions under imperfect knowledge about future prices in a product category is explored. Normatively, a consumer should make such decisions by defining a series of reservation prices which define how many buyingperiods' supply should be held given an observed price. An experiment is reported in which consumers make sequential purchase quantity decisions under variations in the shape of the distribution of prices and its trend over time. Results suggest a number of systematic deviations from optimality. When facing stationary uniform and bimodal price distributions, for example, subjects tend to systematically overbuy when small purchases are called for and underbuy when large purchases are called for. Likewise, when facing ascending or descending series, behavior is the opposite of that predicted by the normative analysis: there is an increasing tendency to defer purchases given increasing prices and accelerate purchases given descending prices. An explanation for the findings in terms of a Prospect Theory-type loss function in expenditures is offered. (Stockpiling; Price Expectations; Decision Making under Uncertainty; Sequential Decisions) [ABSTRACT FROM AUTHOR]

The processes of consumer preference formation during product learning are analyzed. A hypothesis about how individuals form attribute expectations is used to derive a dynamic multinomial logit model of individual choice which endogenously recognizes product learning. The model and its underlying assumptions are then tested in the context of an interactive grocery store learning game. The results support most elements of the proposed model structure. An assumption of temporal stationarity in attribute salience, however, could not be supported. Approaches to implementation of the model are discussed, and implications for marketing management and research in individual choice modeling are addressed. (Choice Models; New Product Forecasting; Decision Making under Uncertainty) [ABSTRACT FROM AUTHOR]

An algebraic model of the consumer information search process is described. The model is one which seeks to describe the nature of decision making during search, including how consumers form expectations with respect to sets of potential choice alternatives, decide which alternatives to "focus in on" during the search, and update expectations in light of gathered information. A series of experiments are then reported in which several aspects of the model are tested empirically. Results uniformly support the predictions of the model. A discussion of the implications of the research for work in information search behavior is provided. [ABSTRACT FROM AUTHOR]

Research examining the process of individual decision making over time isbriefly reviewed. We focus on two major areas of work in choice dynamics:research that has examined how current choices are influenced by the historyof previous choices, and newer work examining how choices may be made toexploit expectations about options available in the future. A central themeof the survey is that if a general understanding of choice dynamics is toemerge, it will come through the development of boundedly-rational models ofdynamic problem solving that lie on the interface between economics andpsychology. [ABSTRACT FROM AUTHOR]

The process by which individuals learn from feedback when making recurrent choices among ambiguous alternatives is explored. We describe an experiment in which subjects solve a variant of the classic armed-bandit problem of dynamic decision theory, set in the context of airline choice. Subjects are asked to make repeated choices between two hypothetical airlines, one having an on-time departure probability which is known a priori, and the other has an ambiguous probability whose true value can only be discovered by making sample trips on the airline. Subjects attempt to make choices in such a way as to maximize the total number of one-time departures over a fixed planning horizon. We examine the extent to which actual choice patterns over time are consistent with those which would be made by a decision maker acting as an optimal Bernoulli sampler. The data offer support for a number of expected--and some unexpected--departures from optimality, including a tendency to underexperiment with promising options and overexperiment with unpromising options, and a tendency to increasingly switch between airlines as the average base rate of departures decreases. Implications of the work for the descriptive validity of normative dynamic decision models is explored, as well as for the generalizability of previous findings about choice under ambiguity to dynamic settings. [ABSTRACT FROM AUTHOR]