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We examined parental preferences in raising Spanish/English bilingual children. We identified factors influencing their decisions, and the strategies used to promote bilingualism. Focus groups were conducted with Spanish-primary-language parents of children 3 to 7 years old. These groups were audiotaped and transcribed. Three reviewers independently analyzed transcripts for themes using margin-coding and grounded theory; disagreements were resolved by consensus. Thirteen Spanish-primary-language parents participated in two focus groups. The results show that parents wanted their children to be bilingual. Parents also stated that the benefits of bilingualism included better career opportunities, and preservation of culture and native language. Family members, schools, and prior parental experiences influenced the parents' decisions to raise bilingual children. Parents preferred English-only school classes and to teach Spanish at home. Strategies identified for raising bilingual children included reading bilingual books and having children speak only Spanish at home. Schools and pediatricians are used as resources.

The "wisdom of the crowd" phenomenon refers to the finding that the aggregate of a set of proposed solutions from a group of individuals performs better than the majority of individual solutions. Most often, wisdom of the crowd effects have been investigated for problems that require single numerical estimates. We investigate whether the effect can also be observed for problems where the answer requires the coordination of multiple pieces of information. We focus on combinatorial problems such as the planar Euclidean traveling salesperson problem, minimum spanning tree problem, and a spanning tree memory task. We develop aggregation methods that combine common solution fragments into a global solution and demonstrate that these aggregate solutions outperform the majority of individual solutions. These case studies suggest that the wisdom of the crowd phenomenon might be broadly applicable to problem-solving and decision-making situations that go beyond the estimation of single numbers. (Contains 2 notes, 3 tables and 11 figures.)

Bandit problems provide an interesting and widely-used setting for the study of sequential decision-making. In their most basic form, bandit problems require people to choose repeatedly between a small number of alternatives, each of which has an unknown rate of providing reward. We investigate restless bandit problems, where the distributions of reward rates for the alternatives change over time. This dynamic environment encourages the decision-maker to cycle between states of exploration and exploitation. In one environment we consider, the changes occur at discrete, but hidden, time points. In a second environment, changes occur gradually across time. Decision data were collected from people in each environment. Individuals varied substantially in overall performance and the degree to which they switched between alternatives. We modeled human performance in the restless bandit tasks with two particle filter models: one that can approximate the optimal solution to a discrete restless bandit problem, and another simpler particle filter that is more psychologically plausible. It was found that the simple particle filter was able to account for most of the individual differences. (This work was funded by award FA95500710082 from the Air Force Office of Scientific Research.)

Studies of human problem solving have traditionally used deterministic tasks that require the execution of a systematic series of steps to reach a rational and optimal solution. Most real-world problems, however, are characterized by uncertainty, the need to consider an enormous number of variables and possible courses of action at each stage in solving the problem, and the need to optimize the solution subject to multiple interacting constraints. There are reliable individual differences in people's abilities to solve such realistic problems. It also seems likely that people's ability to solve these difficult problems reflects, or depends on, their intelligence. We report on a study of N = 101 adults who completed a series of visual optimization problems (Traveling Salesperson, Minimum Spanning Tree, and Generalized Steiner Tree Problems), as well as a cognitive optimization problem (a version of the Secretary Problem). We also characterized these individuals along three relevant and important cognitive abilities dimensions-fluid ability, visuo-spatial ability, and cognitive processing speed. Modeling of covariance structures indicated that performance on both types of optimization problems relies on general intelligence and raises the possibility that they can be used to assess intelligence.

We study human decision making in a simple forced-choice task that manipulates the frequency and accuracy of available information. Empirically, we find that people make decisions consistent with the advice provided, but that their subjective confidence in their decisions shows 2 interesting properties. First, people's confidence does not depend solely on the accuracy of the advice. Rather, confidence seems to be influenced by both the frequency and accuracy of the advice. Second, people are less confident in their guessed decisions when they have to make relatively more of them. Theoretically, we develop and evaluate a type of sequential sampling process model--known as a self-regulating accumulator--that accounts for both decision making and confidence. The model captures the regularities in people's behavior with interpretable parameter values, and we show its ability to fit the data is not due to excessive model complexity. Using the model, we draw conclusions about some properties of human reasoning under uncertainty.

We consider human performance on an optimal stopping problem where people are presented with a list of numbers independently chosen from a uniform distribution. People are told how many numbers are in the list, and how they were chosen. People are then shown the numbers one at a time, and are instructed to choose the maximum, subject to the constraint that they must choose a number at the time it is presented, and any choice below the maximum is incorrect. We present empirical evidence that suggests people use threshold-based models to make decisions, choosing the first currently maximal number that exceeds a fixed threshold for that position in the list. We then develop a hierarchical generative account of this model family, and use Bayesian methods to learn about the parameters of the generative process, making inferences about the threshold decision models people use. We discuss the interesting aspects of human performance on the task, including the lack of learning, and the presence of large individual differences, and consider the possibility of extending the modeling framework to account for individual differences. We also use the modeling results to discuss the merits of hierarchical, generative and Bayesian models of cognitive processes more generally.

We study the wisdom of the crowd in the context of spatial knowledge, asking participants to identify US states and African countries on unlabeled tile maps. We use two question frames, asking participants to select where the target is present or eliminate where it is absent. Participants generally display overconfidence, often selecting small regions that do not include the target. We find strong wisdom of the crowd effects by aggregating participants' responses, especially by weighting the individual responses according to the size of their selection. The weighted crowd outperforms all but a few participants for the US states and all participants for the African countries. We also find wisdom of the crowd within effects, by aggregating the present and absent frames for the same participant. We discuss the implications of our findings for understanding how people express uncertain spatial knowledge and the potential use of crowd aggregation in real-world applications.

We apply the "wisdom of the crowd" idea to human category learning, using a simple approach that combines people's categorization decisions by taking the majority decision. We first show that the aggregated crowd category learning behavior found by this method performs well, learning categories more quickly than most or all individuals for 28 previously collected datasets. We then extend the approach so that it does not require people to categorize every stimulus. We do this using a model-based method that predicts the categorization behavior people would produce for new stimuli, based on their behavior with observed stimuli, and uses the majority of these predicted decisions. We demonstrate and evaluate the model-based approach in two case studies. In the first, we use the general recognition theory decision-bound model of categorization (Ashby & Townsend, ) to infer each person's decision boundary for two categories of perceptual stimuli, and we use these inferences to make aggregated predictions about new stimuli. In the second, we use the generalized context model exemplar model of categorization (Nosofsky, ) to infer each person's selective attention for face stimuli, and we use these inferences to make aggregated predictions about withheld stimuli. In both case studies, we show that our method successfully predicts the category of unobserved stimuli, and we emphasize that the aggregated crowd decisions arise from psychologically interpretable processes and parameters. We conclude by discussing extensions and potential real-world applications of the approach.

The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performance across groups, and to compare inferred parameters of cognitive models developed for the IGT. Here, we present a Bayesian alternative based on Bayesian repeated-measures ANOVA for comparing performance, and a suite of three complementary model-based methods for assessing the cognitive processes underlying IGT performance. The three model-based methods involve Bayesian hierarchical parameter estimation, Bayes factor model comparison, and Bayesian latent-mixture modeling. We illustrate these Bayesian methods by applying them to test the extent to which differences in intuitive versus deliberate decision style are associated with differences in IGT performance. The results show that intuitive and deliberate decision-makers behave similarly on the IGT, and the modeling analyses consistently suggest that both groups of decision-makers rely on similar cognitive processes. Our results challenge the notion that individual differences in intuitive and deliberate decision styles have a broad impact on decision-making. They also highlight the advantages of Bayesian methods, especially their ability to quantify evidence in favor of the null hypothesis, and that they allow model-based analyses to incorporate hierarchical and latent-mixture structures.

The development of cognitive models involves the creative scientific formalization of assumptions, based on theory, observation, and other relevant information. In the Bayesian approach to implementing, testing, and using cognitive models, assumptions can influence both the likelihood function of the model, usually corresponding to assumptions about psychological processes, and the prior distribution over model parameters, usually corresponding to assumptions about the psychological variables that influence those processes. The specification of the prior is unique to the Bayesian context, but often raises concerns that lead to the use of vague or non-informative priors in cognitive modeling. Sometimes the concerns stem from philosophical objections, but more often practical difficulties with how priors should be determined are the stumbling block. We survey several sources of information that can help to specify priors for cognitive models, discuss some of the methods by which this information can be formalized in a prior distribution, and identify a number of benefits of including informative priors in cognitive modeling. Our discussion is based on three illustrative cognitive models, involving memory retention, categorization, and decision making.

In The Price Is Right game show, players compete to win a prize, by placing bids on its price. We ask whether it is possible to achieve a "wisdom of the crowd" effect, by combining the bids to produce an aggregate price estimate that is superior to the estimates of individual players. Using data from the game show, we show that a wisdom of the crowd effect is possible, especially by using models of the decision-making processes involved in bidding. The key insight is that, because of the competitive nature of the game, what people bid is not necessarily the same as what they know. This means better estimates are formed by aggregating latent knowledge than by aggregating observed bids. We use our results to highlight the usefulness of models of cognition and decision-making in studying the wisdom of the crowd, which are often approached only from non-psychological statistical perspectives.

Social robots in the home will need to solve audio identification problems to better interact with their users. This article focuses on the classification between (a) natural conversation that includes at least one co-located user and (b) media that is playing from electronic sources and does not require a social response, such as television shows. This classification can help social robots detect a user's social presence using sound. Social robots that are able to solve this problem can apply this information to assist them in making decisions, such as determining when and how to appropriately engage human users. We compiled a dataset from a variety of acoustic environments that contained either natural or media audio, including audio that we recorded in our own homes. Using this dataset, we performed an experimental evaluation on a range of traditional machine learning classifiers and assessed the classifiers' abilities to generalize to new recordings, acoustic conditions, and environments. We conclude that a C-Support Vector Classification (SVC) algorithm outperformed other classifiers. Finally, we present a classification pipeline that in-home robots can utilize, and we discuss the timing and size of the trained classifiers as well as privacy and ethics considerations. [ABSTRACT FROM AUTHOR]

Item response tree (IRTree) models are theorized to extract response styles from self-report data by utilizing multidimensional item response theory (IRT) models based on theoretical decision processes. Despite the growing popularity of the IRTree framework, there has been little research that has systematically examined the ability of its most popular models to recover item parameters across sample size and test length. This Monte Carlo simulation study explored the ability of IRTree models to recover item parameters based on data created from the midpoint primary process model. Results indicate the IRTree model can adequately recover item parameters early in the decision process model, specifically the midpoint node. However, as the model progresses through the decision hierarchy, item parameters have increased associated error variance. The authors ultimately recommend caution when employing the IRTree framework. [ABSTRACT FROM AUTHOR]

We study whether experts and novices differ in the way they make predictions about National Football League games. In particular, we measure to what extent their predictions are consistent with five environmental regularities that could support decision making based on heuristics. These regularities involve the home teamwinning more often, the team with the better win-loss record winning more often, the team favored by the majority of media experts winning more often, and two others related to surprise wins and losses in the teams' previous game. Using signal detection theory and hierarchical Bayesian analysis, we show that expert predictions for the 2017 National Football League (NFL) season generally follow these regularities in a near optimal way, but novice predictions do not. These results support the idea that using heuristics adapted to the decision environment can support accurate predictions and be an indicator of expertise. [ABSTRACT FROM AUTHOR]

The use of key performance indicators (KPIs) to assist on-farm decision making has long been seen as a promising strategy to improve operational efficiency of agriculture. The potential benefit of KPIs, however, is heavily dependent on the economic relevance of the metrics used, and an overabundance of ambiguously defined KPIs in the livestock industry has disincentivised many farmers to collect information beyond a minimum requirement. Using high-resolution sheep production data from the North Wyke Farm Platform, a system-scale grazing trial in southwest United Kingdom, this paper proposes a novel framework to quantify the information values of industry recommended KPIs, with the ultimate aim of compiling a list of variables to measure and not to measure. The results demonstrated a substantial financial benefit associated with a careful selection of metrics, with top-ranked variables exhibiting up to 3.5 times the information value of those randomly chosen. When individual metrics were used in isolation, ewe weight at lambing had the greatest ability to predict the subsequent lamb value at slaughter, surpassing all mid-season measures representing the lamb's own performance. When information from multiple metrics was combined to inform on-farm decisions, the peak benefit was observed under four metrics, with inclusion of variables beyond this point shown to be detrimental to farm profitability regardless of the combination selected. The framework developed herein is readily extendable to other livestock species, and with minimal modifications to arable and mixed agriculture as well. [ABSTRACT FROM AUTHOR]

We study the wisdom of the crowd in three sequential decision‐making tasks: the Balloon Analogue Risk Task (BART), optimal stopping problems, and bandit problems. We consider a behavior‐based approach, using majority decisions to determine crowd behavior and show that this approach performs poorly in the BART and bandit tasks. The key problem is that the crowd becomes progressively more extreme as the decision sequence progresses, because the diversity of opinion that underlies the wisdom of the crowd is lost. We also consider model‐based approaches to each task. This involves inferring cognitive models for each individual based on their observed behavior, and using these models to predict what each individual would do in any possible task situation. We show that this approach performs robustly well for all three tasks and has the additional advantage of being able to generalize to new problems for which there are no behavioral data. We discuss potential applications of the model‐based approach to real‐world sequential decision problems and discuss how our approach contributes to the understanding of collective intelligence. [ABSTRACT FROM AUTHOR]

For over 100 years impulsiveness has been cited as a key factor in why some people that think about killing themselves go on to attempt suicide. Yet prior studies are limited by not using experimental groups that can test this hypothesis and by treating impulsiveness as a unidimensional construct. To overcome these limitations, we compared suicide ideators and suicide attempters on several dimensions of impulsiveness. In Study 1 we compared inpatient suicide attempters who made an attempt within the prior two weeks (n = 30), current inpatient suicide ideators (n = 31), and community controls (n = 34) on several dimensions of impulsiveness using self-report and behavioral measures. In Study 2 (n = 346), we compared three similar groups based on lifetime and past year suicidal behaviors on several of the measures in Study 1. In Study 1, we found only that negative urgency was clearly elevated among attempters compared with ideators. In Study 2, there were no significant differences on any impulsiveness constructs, including negative urgency. Results from the two studies suggest that attempters may not have significantly elevated trait impulsiveness, compared to ideators; however, attempters may have higher impulsiveness when in a negative state. [ABSTRACT FROM AUTHOR]

We consider the recently-developed "surprisingly popular" method for aggregating decisions across a group of people (Prelec, Seung and McCoy, 2017). The method has shown impressive performance in a range of decision-making situations, but typically for situations in which the correct answer is already established. We consider the ability of the surprisingly popular method to make predictions in a situation where the correct answer does not exist at the time people are asked to make decisions. Specifically, we tested its ability to predict the winners of the 256 US National Football League (NFL) games in the 2017-2018 season. Each of these predictions used participants who self-rated as "extremely knowledgeable" about the NFL, drawn from a set of 100 participants recruited through Amazon Mechanical Turk (AMT). We compare the accuracy and calibration of the surprisingly popular method to a variety of alternatives: the mode and confidence-weighted predictions of the expert AMT participants, the individual and aggregated predictions of media experts, and a statistical Elo method based on the performance histories of the NFL teams. Our results are exploratory, and need replication, but we find that the surprisingly popular method outperforms all of these alternatives, and has reasonable calibration properties relating the confidence of its predictions to the accuracy of those predictions. [ABSTRACT FROM AUTHOR]

Take-the-best is a decision-making strategy that chooses between alternatives, by searching the cues representing the alternatives in order of cue validity, and choosing the alternative with the first discriminating cue. Theoretical support for take-the-best comes from the 'fast and frugal' approach to modeling cognition, which assumes decision-making strategies need to be fast to cope with a competitive world, and be simple to be robust to uncertainty and environmental change. We contribute to the empirical evaluation of take-the-best in two ways. First, we generate four new environments-involving bridge lengths, hamburger prices, theme park attendances, and US university rankings-supplementing the relatively limited number of naturally cue-based environments previously considered. We find that take-the-best is as accurate as rival decision strategies that use all of the available cues. Secondly, we develop 19 new data sets characterizing the change in cities and their populations in four countries. We find that take-the-best maintains its accuracy and limited search as the environments change, even if cue validities learned in one environment are used to make decisions in another. Once again, we find that take-the-best is as accurate as rival strategies that use all of the cues. We conclude that these new evaluations support the theoretical claims of the accuracy, frugality, and robustness for take-the-best, and that the new data sets provide a valuable resource for the more general study of the relationship between effective decision-making strategies and the environments in which they operate. [ABSTRACT FROM AUTHOR]

The less-is-more effect predicts that people can be more accurate making paired-comparison decisions when they have less knowledge, in the sense that they do not recognize all of the items in the decision domain. The traditional theoretical explanation is that decisions based on recognizing one alternative but not the other can be more accurate than decisions based on partial knowledge of both alternatives. I present new data that directly test for the less-is-more effect, coming from a task in which participants judge which of two cities is larger and indicate whether they recognize each city. A group-level analysis of these data provides evidence in favor of the less-is-more effect: there is strong evidence people make decisions consistent with recognition, and that these decisions are more accurate than those based on knowledge. An individual-level analysis of the same data, however, provides evidence inconsistent with a simple interpretation of the less-is-more effect: there is no evidence for an inverse-U-shaped relationship between accuracy and recognition, and especially no evidence that individuals who recognize a moderate number of cities outperform individuals who recognize many cities. I suggest a reconciliation of these contrasting findings, based on the systematic change of the accuracy of recognition-based decisions with the underlying recognition rate. In particular, the data show that people who recognize almost none or almost all cities make more accurate decisions by applying the recognition heuristic, when compared to the accuracy achieved by people with intermediate recognition rates. The implications of these findings for precisely defining and understanding the less-is-more effect are discussed, as are the constraints our data potentially place on models of the learning and decision-making processes involved. [ABSTRACT FROM AUTHOR]

In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the names of the cities, but they were able to collect different kinds of cues for both response alternatives (e.g., "Does this city have a university?") before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, 2011) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take-the-best or the Weighted Additive heuristics. [ABSTRACT FROM AUTHOR]

Heuristic decision-making models, like Take-the-best, rely on environmental regularities. They conduct a limited search, and ignore available information, by assuming there is structure in the decision-making environment. Take-the-best relies on at least two regularities: diminishing returns, which says that information found earlier in search is more important than information found later; and correlated information, which says that information found early in search is predictive of information found later. We develop new approaches to determining search orders, and to measuring cue discriminability, that make the reliance of Take-the-best on these regularities clear, and open to manipulation. We then demonstrate, in the well-studied German cities environment, and three new city environments, when and how these regularities support Take-the-best. To do this, we focus not on the accuracy of Take-the-best, as most previous studies have, but on a measure of its coherence as a decision-making process. In particular, we consider whether Take-the-best decisions, based on a single piece of information, can be justified because an exhaustive search for information is unlikely to yield a different decision. Using this measure, we show that when the two environmental regularities are present, the decisions made by limited search are unlikely to have changed after exhaustive search, but that both regularities are often necessary. [ABSTRACT FROM AUTHOR]

Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key ''sampling'' assumption about how the available data were generated. Previous models have considered two extreme possibilities, known as strong and weak sampling. In strong sampling, data are assumed to have been deliberately generated as positive examples of a concept, whereas in weak sampling, data are assumed to have been generated without any restrictions. We develop a more general account of sampling that allows for an intermediate mixture of these two extremes, and we test its usefulness. In two experiments, we show that most people complete simple one-dimensional generalization tasks in a way that is consistent with their believing in some mixture of strong and weak sampling, but that there are large individual differences in the relative emphasis different people give to each type of sampling. We also show experimentally that the relative emphasis of the mixture is influenced by the structure of the available information. We discuss the psychological meaning of mixing strong and weak sampling, and possible extensions of our modeling approach to richer problems of inductive generalization. [ABSTRACT FROM AUTHOR]

Hierarchical Bayesian methods offer a principled and comprehensive way to relate psychological models to data. Here we use them to model the patterns of information search, stopping and deciding in a simulated binary comparison judgment task. The simulation involves 20 subjects making 100 forced choice comparisons about the relative magnitudes of two objects (which of two German cities has more inhabitants). Two worked-examples show how hierarchical models can be developed to account for and explain the diversity of both search and stopping rules seen across the simulated individuals. We discuss how the results provide insight into current debates in the literature on heuristic decision making and argue that they demonstrate the power and flexibility of hierarchical Bayesian methods in modeling human decision-making. [ABSTRACT FROM AUTHOR]

Analyses of multi-attribute decision problems are dominated by accounts which assume people select from a repertoire of cognitive strategies to make decisions. This paper explores an alternative account based on sequential sampling and evidence accumulation. Two experiments varied aspects of a decision environment to examine competing models of decision behavior. The results highlighted the intra-participant consistency but inter-participant differences in the amount of evidence considered in decisions. This pattern was best captured by a sequential evidence accumulation model (SEQ) which treated pure Take-The-Best (TTB) and pure 'rational' (RAT) models as special cases of a single model. The SEQ model was also preferred by the minimum description length (MDL) criterion to a naive strategy-selection model (NSS) which assumed that TTB or RAT could be selected with some probability for each decision. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: In bandit problems, a decision-maker must choose between a set of alternatives, each of which has a fixed but unknown rate of reward, to maximize their total number of rewards over a sequence of trials. Performing well in these problems requires balancing the need to search for highly-rewarding alternatives, with the need to capitalize on those alternatives already known to be reasonably good. Consistent with this motivation, we develop a new psychological model that relies on switching between latent exploration and exploitation states. We test the model over a range of two-alternative bandit problems, against both human and optimal decision-making data, comparing it to benchmark models from the reinforcement learning literature. By making inferences about the latent states from optimal decision-making behavior, we characterize how people should switch between exploration and exploitation. By making inferences from human data, we begin to characterize how people actually do switch. We discuss the implications of these findings for understanding and measuring the competing demands of exploration and exploitation in sequential decision-making. [Copyright &y& Elsevier]

Over the last decade, the popularity of Bayesian data analysis in the empirical sciences has greatly increased. This is partly due to the availability of WinBUGS, a free and flexible statistical software package that comes with an array of predefined functions and distributions, allowing users to build complex models with ease. For many applications in the psychological sciences, however, it is highly desirable to be able to define one's own distributions and functions. This functionality is available through the WinBUGS Development Interface (WBDev). This tutorial illustrates the use of WBDev by means of concrete examples, featuring the expectancy-valence model for risky behavior in decision making, and the shifted Wald distribution of response times in speeded choice. [ABSTRACT FROM AUTHOR]

Abstract: Research indicates that practicing managers in the oil and gas industry invariably superimpose personal experience onto results of normative decision analysis tools. Decision quality therefore substantially depends upon the decision-makers level of expertise: an insufficient level, by implication, results in value destruction. Shanteau and colleagues have reported substantial work in the area of expertise, arguing that only people who are able to (a) differentiate between similar, but not identical, cases, and (b) repeat their judgment with consistency, qualify as ‘experts’. Methodologically, these researchers suggest a ratio of discrimination and inconsistency to distinguish experts. However, they acknowledge in their methodology it is possible (even for non-experts) to obtain inappropriate high scores using a consistent, but incorrect rule. While drawing inspiration from Shanteau''s work, we suggest three important modifications based on our experiments, for use in the oil and gas industry: 1) Discrimination be defined as ability to discriminate correctly, as judged against a ‘gold standard’, not relative only to the cases presented, 2) Consistency be defined as consistent application of correct, not incorrect, rules, and 3) Inclusion of domain knowledge along with discrimination and consistency to assess expertise. Our initial experiments in distinguishing experts have been encouraging, even though results are only indicative given a small sample of twenty participants. Analysis shows expertise to be highly correlated with the breadth of experience (represented by basins or regions worked) and poorly with experience in the same geological environment (same basin, but multiple fields). The three cognitive abilities of domain knowledge, discrimination and consistency are also highly correlated with breadth of experience. Years of experience, our experiments confirm, is indeed an imperfect indicator of expertise. [Copyright &y& Elsevier]

The prioritisation of literature searches aims to order the large numbers of articles returned by a simple search so that the ones most likely to be relevant are at the top of the list. Prioritisation relies on having a good model of human decision-making that can learn from the articles users select as being relevant to make predictions about which of the remaining articles will be relevant. We develop and evaluate two psychological decision-making models for prioritisation: A "rational" model that considers all of the available information, and a "one reason" model that uses limited information to make decisions. The models are evaluated in an experiment where users rate the relevance of every article returned by PsycINFO for a number of different research topics, with the results showing that both models achieve a level of prioritisation that significantly improves upon the default ordering of PsycINFO. The one reason model is shown to be superior to the rational model, especially when there are only a few relevant articles. The implications of the results for developing prioritisation systems in applied settings are discussed, together with implications for the general modeling of human decision-making. [ABSTRACT FROM AUTHOR]

One of the more puzzling questions encountered in public investment decision making (especially in the health and transport fields) concerns the value placed on improvements (or reductions) in the safety of human life. Schelling (1968) appears to have been the first to suggest that any potential change in the fatal (or nonfatal) accident rate may be regarded as a (typically small) change in the probability of death (or nonfatal accident) for any particular individual. Mishan (1971) takes a similar view of the problem and argues that, if alternative resource allocations are to be compared solely on the basis of "potential Pareto improvements,'' then public investment projects which increase the safety of human life wilI be recommended only if the total of sums which individuals would be prepared to forfeit to effect such probability changes (and the other desirable consequences of the investment) exceeds the capital costs of the projects concerned. Given the manner in which Schelling and Mishan formalize the problem, the central question is therefore the nature of the Hicksian "compensating variations" in wealth[1] for various changes in probability of death or injury. In fact, even if Mishan's potential Pareto improvement criterion is rejected as a basis for allocative decisions, information concerning these compensating variations will still be relevant for all but those public investment decisions which completely ignore private preferences. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop and test a model of the promotional expenditure decision for a firm operating under conditions of combined uncertainty and oligopoly. The uncertainty is assumed to exist in respect of the impact of any particular level of promotional expenditure upon the demand conditions facing the firm.
The analysis forms part of a wider study of advertising by confectionery manufacturers and arose largely as a result of conversations with advertising executives in the confectionery market. In the course of these discussions it became clear that the existing body of theory concerning the promotional expenditure decision is almost completely incapable of accommodating the extensive uncertainty which is an integral part of the promotional decision in a market such as that under consideration. It also became clear (at least in the case of the confectionery market) (a) that decision makers regard the pricing and promotional expenditure decisions as being largely independent and (b) that in a large number of cases decision makers think in terms of a minimum or 'threshold' level of promotional expenditure. While these factors are predominantly in the nature of impressionistic informal observations and therefore of doubtful validity as bases for a scientific enquiry, it was nonetheless felt worthwhile to attempt to develop a theoretical framework capable of accommodating them. [ABSTRACT FROM AUTHOR]

Enhancing consumer satisfaction and well-being is an important objective of companies, retailers and public policy makers. In the current debate on climate change, a consistent theme is that consumers in developed countries must learn to consume less. The present study (based on representative data sets from the US, N = 1,017, and Germany, N = 1030) addresses these issues by using a scenario-based experiment to analyze how satisfied voluntary simplifiers (people who voluntarily abstain from consumption) are with their purchase decisions in the case of a muesli brand. The research question is whether people who follow a sustainable, simple lifestyle are more satisfied with their daily consumption choices than people who have a more consumerist lifestyle. If so, it would be easier for many people to change their lifestyles and consume less. In addition, this scenario experiment manipulates consumer empowerment and decision complexity since both factors are supposed to influence purchase satisfaction. The results are consistent across both countries and indicate that voluntary simplifiers experience a higher level of purchasing satisfaction than non-simplifiers, whereby empowerment and decision complexity play different roles. • This paper links the lifestyle of refraining from consumption with personal well-being and shopping satisfaction. • A scenario-based online experiment on the satisfaction of voluntary simplifiers with their purchase decisions is conducted. • Empowerment and decision complexity moderate the voluntary simplicity-decision satisfaction relationship in different ways. • Voluntary simplifiers are more satisfied with their purchasing decisions than consumption-driven individuals. • Consumer empowerment also leads to greater decision satisfaction. [ABSTRACT FROM AUTHOR]