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In Longoni et al. (2019), we examine how algorithm aversion influences utilization of healthcare delivered by human and artificial intelligence providers. Pezzo and Beckstead's (2020) commentary asks whether resistance to medical AI takes the form of a noncompensatory decision strategy, in which a single attribute determines provider choice, or whether resistance to medical AI is one of several attributes considered in a compensatory decision strategy. We clarify that our paper both claims and finds that, all else equal, resistance to medical AI is one of several attributes (e.g., cost and performance) influencing healthcare utilization decisions. In other words, resistance to medical AI is a consequential input to compensatory decisions regarding healthcare utilization and provider choice decisions, not a noncompensatory decision strategy. People do not always reject healthcare provided by AI, and our article makes no claim that they do. [ABSTRACT FROM AUTHOR]

When scientists study the phenomena they are interested in, they apply sound methods and base their work on theoretical considerations. In contrast, when the fruits of their research are being evaluated, basic scientific standards do not seem to matter. Instead, simplistic bibliometric indicators (i.e., publication and citation counts) are, paradoxically, both widely used and criticized without any methodological and theoretical framework that would serve to ground both use and critique. Recently, however Bornmann and Marewski (2019) proposed such a framework. They developed bibliometrics- based heuristics (BBHs) based on the fast-and-frugal heuristics approach (Gigerenzer; Todd; ABC Research Group, 1999) to decision making, in order to conceptually understand and empirically investigate the quantitative evaluation of research as well as to effectively train end-users of bibliometrics (e.g., science managers, scientists). Heuristics are decision strategies that use part of the available information and ignore the rest. By exploiting the statistical structure of task environments, they can aid to make accurate, fast, effortless, and cost-efficient decisions without that trade-offs are incurred. Because of their simplicity, heuristics are easy to understand and communicate, enhancing the transparency of decision processes. In this commentary, we explain several BBHs and discuss how such heuristics can be employed in practice (using the evaluation of applicants for funding programs as one example). Furthermore, we outline why heuristics can perform well, and how they and their fit to task environments can be studied. In pointing to the potential of research on BBHs and to the risks that come with an under-researched, mindless usage of bibliometrics, this commentary contributes to make research evaluation more scientific. [ABSTRACT FROM AUTHOR]

This paper investigates the interaction effects between the search strategy of software agents and consumers' product class knowledge in the context of consumers seeking to purchase cars on the Internet. The research design used was a 2 × 4, between-groups, completely randomized, two-factor, factorial design. The independent variables that were manipulated were product class knowledge (HIGH KNOWLEDGE, LOW KNOWLEDGE) and agent search strategy [elimination by aspects (EBA STRATEGY), weighted average method (WAD STRATEGY), profile building (PROFILE STRATEGY), simple hypertext (HY- PERTEXT STRATEGY)]. The dependent variables that were measured were satisfaction with the decision process (SATISFACTION), confidence in the decision (CONFIDENCE), trust in the agent's recommendations (TRUST), propensity to purchase (PURCHASE), perceived cost savings (SAVINGS), and cognitive decision effort (EFFORT). Significant differences were found in the affective reactions of the subjects toward the agent/application depending on the level of product class knowledge possessed by the subjects. Subjects with high product class knowledge had more positive affective reactions toward agents/applications that used the WAD and EBA strategies as compared to the PROFILE strategy. Subjects with low product class knowledge had more positive affective reactions to agents/applications that used the PROFILE strategy as compared to the EBA and WAD strategies. [ABSTRACT FROM AUTHOR]

Adolescents with Behavior Disorders (BD), Mild-to-Borderline Intellectual Disability (MBID), and with both BD and MBID (BD + MBID) are known to take more risks than normal controls. To examine the processes underlying this increased risk-taking, the present study investigated cool decision-making strategies in 479 adolescents (12-18 years, 55.9 % male) from these four groups. Cool decision-making was assessed with the paper-and-pencil Gambling Machine Task. This task, in combination with advanced latent group analysis, allows for an assessment of decision strategies. Results indicated that adolescents with BD and controls were almost equivalent in their decision-making strategies, whereas adolescents with MBID and adolescents with BD + MBID were characterized by suboptimal decision-making strategies, with only minor differences between these two clinical groups. These findings may have important clinical implications, as they suggest that risk taking in adolescents with MBID and in adolescents with BD + MBID can be (partly) attributed to the strategies that these adolescents use to make their decisions. Interventions may therefore focus on an improvement of these strategies. [ABSTRACT FROM AUTHOR]

In this paper, we present a new approach for the optimal experimental design problem of generating diagnostic choice tasks, where the respondent's decision strategy can be unambiguously deduced from the observed choice. In this new approach, we applied a genetic algorithm that creates a one-to-one correspondence between a set of predefined decision strategies and the alternatives of the choice task; it also manipulates the characteristics of the choice tasks. In addition, this new approach takes into account the measurement errors that can occur when the preferences of the decision makers are being measured. The proposed genetic algorithm is capable of generating diagnostic choice tasks even when the search space of possible choice tasks is very large. As proof-of-concept, we used this novel approach to generate respondent-specific choice tasks with either low or high context-based complexity that we operationalize by the similarity of alternatives and the conflict between alternatives. We find in an experiment that an increase in the similarity of the alternatives and an increase in the number of conflicts within the choice task lead to an increased use of non-compensatory strategies and a decreased use of compensatory decision strategies. In contrast, the size of the choice tasks, measured by the number of attributes and alternatives, only weakly influences the strategy selection. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Achieving ambitious climate goals would require enormous structural modifications in fossil energy-dominated power systems. Unpredictability of the future makes a cautious decision necessary in the low-carbon transition in power systems. Choosing a proper decision method when planning could reduce cost losses when unpredictable emergencies have a disruptive impact on a power system. This is extremely crucial for countries whose power system is still expanding rapidly because their development costs are already high enough and the decarbonisation pathway is uncertain and influenced a lot by the way of decision. Therefore, in this paper, we present a multi-region load despatch transition pathway planning model for power systems with huge demand growth, which can be used for both short-term and long-term decision methods. By comparing the transition costs of various decision-making methods, the decision with lower costs can be obtained. China is taken as a case study. Results show that in order to minimise the cost loss caused by inestimable incidents, China's power system adopts long-term or short-term decisions based on expectations before 2036 or 2041. The cost loss of up to 500 billion CNY can be reduced by selecting a decision method with lower costs according to the rough expectation. • A low-carbon transition optimisation model for rapidly expanding power systems is proposed. • Decision-making strategies that can reduce cost losses are found quantitatively. • Planning strategy of China's power system relates to the forecast before 2036 or 2041. • Up to 500 billion CNY cost loss can be reduced by selecting a suitable decision. • Overly positive renewable power generation construction has little benefit in China. [ABSTRACT FROM AUTHOR]

With the growth of online shopping, the buyers are faced with information and cognitive overload, entailing worse buyers' decisions. Various decision aids, more and more implemented as web services, aim at reducing this overload. Often they implement compensatory strategies that enable desirable and undesirable values of a product attribute to compensate each other. However, increasing the number of options beyond a handful can lead to poor choices, decreasing satisfaction (i.e., paradox of choice). In such a situation, that involves uncertainty, people relies more on heuristics than rationality to arrive at decisions and purchases. Heuristics, or noncompensatory strategies, do not consider a buyer's preference for multiple attributes, such as the satisficing heuristic that compares each attribute value with a predetermined cut-off level, rejecting alternatives that do not meet it. This paper presents a study combining an E-Commerce literature survey, an E-Commerce websites' analysis, and a survey of online buyers opinions. It is pointing to a gap that exists between sellers' services and buyers' expectations. Empirical evidence suggests that it can be bridged turning to noncompensatory strategies implemented as web services. [ABSTRACT FROM AUTHOR]

This article reports an empirical investigation of the cognitive effort required to decide in multiattribute binary choice using a variation of the Additive Difference strategy. In contrast with other studies, this paper focuses on the effect of various context variables (rather than task variables) on cognitive effort. In order to select the context variables to be manipulated, we used the model proposed by Shugan (1980; J. Consumer Res. 75 (1980) 99). Our results indicate that there is a positive relationship between the cognitive effort required to decide and the mean of the differences between the dimensions of the choice alternatives. We have also found an inverse relationship between cognitive effort and the variance of the differences between the dimensions of the choice alternatives. Finally, we have found that in negative correlation contexts the effort needed to decide is greater than in positive and null correlation contexts. [ABSTRACT FROM AUTHOR]

This paper examines how a number of decision context variables affect the cognitive effort required for decision making on dichotomical choice tasks. Subjects are trained in the use of a strategy in which information processing is alternative-based. The correlation between the attributes of the alternatives and the mean and variance of the difference between the attributes is manipulated. The results show that the effort needed for decision making increases as the mean of the differences decreases. Yet, neither the variance of the differences nor the correlation context affect the decision making effort in this type of strategies. [ABSTRACT FROM AUTHOR]

Research on cognitive processes in decision making has identified heuristics that often work well but sometimes lead to serious errors. This paper presents an investigation of the performance of heuristics in a complex dynamic setting, characterized by repeated decisions with feedback. There are three components: (1) A simulated task resembling medical decision problems (diagnosis and treatment) is described. (2) Computer models of decision strategies are developed. These include models based on cognitive heuristics as well as benchmark strategies that indicate the limit of the heuristic strategies' performance. The upper benchmark is based on statistical decision theory, the lower one on random trial and error. (3) Selected task characteristics are systematically varied and their influence on performance evaluated in simulation experiments. Results indicate that task characteristics often studied in past research (e.g., symptom diagonosticity, disease base-rates) have less influence on performance relative to feedback-related aspects of the task. These dynamic characteristics are a major determinant of when heuristics perform well or badly. The results also provide insights about the costs and benefits of various cognitive heuristics. In addition, the possible contribution of this research to the design and evaluation of decision aids is considered. [ABSTRACT FROM AUTHOR]

Introduction: Decision-making is not purely rational but highlighted by the influence of intuitive and emotional processes. Recently, researchers have focused more attention on understanding which environmental and personal features influence decision-making processes, and how. Objective and methods: On this study, we investigate whether Trait Anxiety moderates the impact of Post-Traumatic Stress (PTS) symptoms reported during COVID-19 pandemic on decision-making styles. Results: The study included 1,358 Brazilian participants (80% women) aged between 20 and 74 (M = 41.11; SD = 11.23) who responded to an online survey between May and August of the year 2021 of COVID-19 pandemic to The State-Trait Anxiety Inventory, The Decisions Styles Scale, The Impact of Event Scale - Revised and questions related to COVID-19. Through moderation analysis, we observed that experiencing PTS is associated with a higher tendency to biased/heuristic decision-making processes. Discussion: Trait Anxiety seems to influence how people respond to PTS symptoms on decision-making related processes. Subjects with higher Trait Anxiety reported lower tendency to appeal to rationality, especially under higher reported levels of PTS. Meanwhile, lower Trait Anxiety subjects exhibited more reason-based decision-making under higher rates of PTS. This work contributes to a deeper understanding of the interplay among environmental and individual differences on decision-making styles and helps to identify factors of vulnerability for poorer cognitive functioning on stressful scenarios. [ABSTRACT FROM AUTHOR]

We propose a novel approach, which we call machine learning strategy identification (MLSI), to uncovering hidden decision strategies. In this approach, we first train machine learning models on choice and process data of one set of participants who are instructed to use particular strategies, and then use the trained models to identify the strategies employed by a new set of participants. Unlike most modeling approaches that need many trials to identify a participant's strategy, MLSI can distinguish strategies on a trial-by-trial basis. We examined MLSI's performance in three experiments. In Experiment I, we taught participants three different strategies in a paired-comparison decision task. The best machine learning model identified the strategies used by participants with an accuracy rate above 90%. In Experiment II, we compared MLSI with the multiple-measure maximum likelihood (MM-ML) method that is also capable of integrating multiple types of data in strategy identification, and found that MLSI had higher identification accuracy than MM-ML. In Experiment III, we provided feedback to participants who made decisions freely in a task environment that favors the non-compensatory strategy take-the-best. The trial-by-trial results of MLSI show that during the course of the experiment, most participants explored a range of strategies at the beginning, but eventually learned to use take-the-best. Overall, the results of our study demonstrate that MLSI can identify hidden strategies on a trial-by-trial basis and with a high level of accuracy that rivals the performance of other methods that require multiple trials for strategy identification. [ABSTRACT FROM AUTHOR]

In Longoni et al. (2019), we examine how algorithm aversion influences utilization of healthcare delivered by human and artificial intelligence providers. Pezzo and Beckstead’s (2020) commentary asks whether resistance to medical AI takes the form of a noncompensatory decision strategy, in which a single attribute determines provider choice, or whether resistance to medical AI is one of several attributes considered in a compensatory decision strategy. We clarify that our paper both claims and finds that, all else equal, resistance to medical AI is one of several attributes (e.g., cost and performance) influencing healthcare utilization decisions. In other words, resistance to medical AI is a consequential input to compensatory decisions regarding healthcare utilization and provider choice decisions, not a noncompensatory decision strategy. People do not always reject healthcare provided by AI, and our article makes no claim that they do.

Understanding human behavior remains a grand challenge across disciplines. We used eye tracking to investigate how visual perception is associated with a strategic behavior in the decision process. Gaze activity and eye movement patterns were measured in 14 human participants with different decision strategies. We also employed a social domain to force strategic behavior. We find that social interaction significantly improves the level of cooperation, prosocial decisions, and overall cooperative strategy in experiment participants. Gaze behavior in individuals with a cooperative strategy is characterized by a greater number of fixations and frequent gaze returns to the scanned areas. On the contrary, individuals with a non-cooperative strategy approach decision-making task stimuli in a distinct way with long-duration fixations and a low number of gaze returns to the areas already scanned. Social domain, which enhances cooperation and prosocial behavior, makes participants more attentive to the task stimuli in our experiments. Moreover, prolonged gaze at the area of cooperative choice testifies in favor of the cooperative decision. [ABSTRACT FROM AUTHOR]

Abstract: We present a statistical methodology for clustering decision makers according to similar choice behavior. We apply a p‐median clustering algorithm that identifies an “exemplar” for each cluster, a decision maker who best represents that cluster. We demonstrate that information about group behavior can be inferred by examining the behavior of each cluster's exemplar. The method is exploratory, providing information about the prevalence of decision strategies without researchers needing to specify candidate strategies a priori. The method is also very general and can be applied to a wide range of decision‐making data structures. We illustrate our method by re‐analyzing two existing choice data sets. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Individuals are known to make systematically different decisions when the probabilities in risky choice problems are described or experienced. This difference, known as the description-experience gap, has been reliably replicated across several studies using binary choice gambles. Yet little is known whether these differences exist in more complex gambles in the absence of rare outcomes, and whether they are associated with systematic differences in the use of decision heuristics and strategies across formats. Using three-outcome mixed gambles, we found that participants showed a strong preference for alternatives that maximized the overall probability of winning when such an option was available in the description condition, and chose more randomly otherwise. In the experience condition, preferences were more homogenous across trials types, with participants choosing the alternative with extreme values more often relative to the description condition. However, when we controlled for the experienced outcomes, both natural mean heuristic (choosing the alternative with highest sampled mean or expected value) and overall probability of winning heuristic reliably predicted choice on each trial. In fact, expected value was the strongest predictor of preferences in a conditional logistic regression model that included extreme values, expected value, and overall probability of winning variables simultaneously. Yet expected value did not predict preferences in decisions from description. Together, these findings provide evidence for an explicit dissociation in decision strategies across description and experience formats. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This research investigates what information German Fairtrade coffee consumers search for during pre-purchase information seeking and to what extent information is retrieved. Furthermore, the sequence of the information search as well as the degree of cognitive involvement is highlighted. The role of labeling, the importance of additional ethical information and its quality in terms of concreteness as well as the importance of product price and organic origin are addressed. A set of information relevant to Fairtrade consumers was tested by means of the Information Display Matrix (IDM) method with 389 Fairtrade consumers. Results show that prior to purchase, information on product packages plays an important role and is retrieved rather extensively, but search strategies that reduce the information processing effort are applied as well. Furthermore, general information is preferred over specific information. Results of two regression analyses indicate that purchase decisions are related to search behavior variables rather than to socio-demographic variables and purchase motives. In order to match product information with consumers' needs, marketers should offer information that is reduced to the central aspects of Fairtrade. [ABSTRACT FROM AUTHOR]

In the management literature, heuristics are often conceived of as a source of systematic error, whereas logic and statistics are regarded as the sine qua non of good decision making. Yet, this view can be incorrect for decisions made under uncertainty, as opposed to risk. Research on fast and frugal heuristics shows that simple heuristics can be successful in complex, uncertain environments and also when and why this is the case. This article describes the conceptual framework of heuristics as adaptive decision strategies and connects it with the managerial literature. We review five classes of heuristics, analyze their common building blocks, and show how these are applied in managerial decision making. We conclude by highlighting some prominent opportunities for future research in the field. In the uncertain world of management, simple heuristics can lead to better and faster decisions than complex statistical procedures. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

While it is well established that the search for information after a decision is biased toward supporting that decision, the case of preference-supporting search before the decision remains open. Three studies of consumer choices consistently found a complete absence of a pre-choice bias toward searching for preference-supporting information. The absence of this confirming search bias occurred for products that were both hedonic and utilitarian, both expensive and inexpensive, and both high and low in expected brand loyalty. Experiment 3 also verified the presence of the expected post-choice search bias to support the chosen alternative. Therefore the absence of a pre-choice search bias in all three studies was not likely to be due to our using a method that was so insensitive that a search bias would not be observed under any circumstances. In addition to the absence of an effect of prior preferences on information selection, subjects' self-reported search strategies exhibited a clear tendency toward a balance of positive and negative information. Across the three studies, we also tested for the presence of a preference-supporting bias in the evaluation of the information acquired in the search process. This evaluation bias was found both pre- and post-choice. [ABSTRACT FROM AUTHOR]

Non-compensatory preferences are investigated by incorporating attribute cutoffs into models of consumer choices for food with health-related attributes (omega-3 content) that may be associated with genetic modification. We find empirical evidence that some individuals tend to use attribute cutoffs in decision-making, that incorporating these into the modelling of consumer choices significantly improves model fit, that some respondents are willing to take a utility penalty rather than eliminate an alternative when a cutoff violation occurs and that there is considerable heterogeneity in willingness to violate cutoffs. The study also provides some support to the hypothesis that ignoring cutoff endogeneity in model estimation may generate biased estimates. [ABSTRACT FROM PUBLISHER]

Objective: Two field studies assessed the effects of critical thinking instruction on training and transfer of a complex decision-making skill.Background: Critical thinking instruction is based on studies of how experienced decision makers approach complex problems.Method: Participants conducted scenario-based exercises in both simplified (Study 1) and high-fidelity (Study 2) training environments. In both studies, half of the participants received instruction in critical thinking. The other half conducted the same exercises but without critical thinking instruction. After the training, test scenarios were administered to both groups.Results: The first study showed that critical thinking instruction enhanced decision outcomes during both training and the test. In the second study, critical thinking instruction benefited both decision outcomes and processes, specifically on the transfer to untrained problems.Conclusion:The results suggest that critical thinking instruction improves decision strategy and enhances understanding of the general principles of the domain.Application: The results of this study warrant the implementation of critical thinking instruction in training programs for professional decision makers that have to operate in complex and highly interactive, dynamic environments. [ABSTRACT FROM PUBLISHER]

E-learning markets have been expanding very rapidly. As a result, the involved senior managers are increasingly being confronted with the need to make significant investment decisions related to the e-learning business activities. Real options applications to risk management and investment evaluation of Information and Communication Technologies (ICT) have mainly focused on a single and a priori known option. However, these options are not inherent in any ICT investment. Actually, they must be carefully planned and intentionally embedded in the ICT investment in order to mitigate its risks and increase its return. Moreover, when an ICT investment involves multiple risks, by adopting different series of cascading options we may achieve risk mitigation and enhance investment performance. In this article, we apply real options to the e-learning investments evaluation. Given the investment's requirements, assumptions and risks, the goal is to maximize the investment's value by identifying a good way to structure it using carefully chosen real options. [ABSTRACT FROM AUTHOR]

An adaptive noncontact gesture-based system for augmentative communication is described. The system detects movement of any anatomical site through the analysis of reflected speckle. This movement is converted into two-dimensional (2-D) cursor coordinates and an adaptive software interface provides click actions and decision strategies. The system requires no accessory to be placed on the user. The system was developed in conjunction with user groups, who participated in the evaluation of the system. The usability results obtained illustrate the utility of the system. The system also compared favorably with other interface solutions [ABSTRACT FROM PUBLISHER]

The use of high resolution graphic display technology provides the control system designer with numerous options for displaying process plant information. This research evaluated the effectiveness of alphanumeric and graphic display formats for presenting system information in a dynamic process plant environment Thirty subjects, divided equally into three groups, were tested on three separate display formats: graphical, digital and multiple representations. The operators' task was to optimize the performance of a simulated fluid processing plant and to detect system failures. Results showed that the multiple display resulted in the best optimization performance and minimized the time required to detect failures. In general, the graphic display produced the worst performance with the digital display exhibiting intermediate results. The decision strategies used by the operators were found to have the greatest impact on performance. The results are discussed in terms of the merits of different display formats for representing system information in a manner appropriate to the operator's task. [ABSTRACT FROM PUBLISHER]

An experiment was conducted to investigate the effect of a priori probability of false alarms and time pressure on decision-making behaviour in a dynamic task environment. In order to assess whether strategy selection in a dynamic task environment would be adaptive, we modelled the task mathematically, and compared actual decision strategies to the optimal ones. In addition to the selected strategy, we also studied decision-making behaviour at a lower operational level, reflecting the amount of effort subjects are willing to spend on the decision process. Subjects were required to monitor the fitness level of a simulated athlete, who was running a race, and had to provide treatments whenever the athlete's fitness level suggested a real physiological problem. When a decline of the athlete's fitness was caused by a false alarm, a spontaneous recovery would occur after some time, without any need for intervention. Time pressure was manipulated by the rate at which the athlete's fitness level declined. Overall, subjects did not select the most efficient strategy: they dominantly selected information before applying an action, even though it would have been more profitable, and less effortful, just to apply actions. At the operational level, subjects appeared to invest less effort when the probability of false alarms increased and to invest more effort when time pressure increased. However, in contrast to the outcomes of our mathematical model, subjects adjusted the amount of intervention to the a priori probability of false alarms and not to time pressure. Together, the results indicate that the selection of a decision strategy in a dynamic task is less adaptive then is generally concluded from studies with static tasks. [ABSTRACT FROM AUTHOR]