Heuristic Decision Research Articles

A Heuristic Approach to Explore: The Value of Perfect Information

This research introduces a new heuristic decision model called myopic-value of perfect information (VPI) to study multiarmed bandit (MAB) problems. The myopic-VPI approach only involves ranking the alternatives and computing a one-dimensional integration to obtain the expected future value of exploration. Because myopic-VPI is intuitive and does not involve solving a dynamic programming problem, it has the potential to serve as a useful heuristic approach to model exploration-exploitation tradeoffs. We conduct a series of simulation experiments to study its performance relative to other heuristics under a wide range of parameterizations. We find that myopic-VPI provides significant savings in computational time and decent performance in accumulated utility (although not the strongest) relative to other forward-looking heuristics; this suggests that it is a useful “fast-and-frugal” heuristic. Furthermore, our simulation experiments also reveal the conditions under which myopic-VPI outperforms and underperforms compared with other heuristics. Its empirical performance in the diaper category further shows that myopic-VPI can save estimation time significantly and fit the data on par with index and near-optimal, providing encouraging news that myopic-VPI could be added to the researcher’s or practitioner’s toolkit for MAB problems. This paper was accepted by Gui Liberali, marketing. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2019.00578 .

Marketing Tactics for Sustainable Fashion and the Circular Economy: The Impact of Ethical Labels on Fast Fashion Choice

This study aimed to contribute to the empirical literature on ethical fashion labelling. It investigates if complex, ethical, point-of-sale labels that ‘rate’ products’ ethical status on an ordinal scale influence consumer evaluations of fast-fashion garments (a) in any significant way at all and (b) in a way that is consistent with their reported ethical scores. In an experiment, 400 consumers evaluated a set of four fast-fashion garments with two levels of the Tearfund ordinal ethical label, a generic binary ethical label and garments with no label. Purchase intention was the dependent variable. The presence of a Tearfund label promoted significantly higher fast-fashion garment purchase intention, whatever ethical status the label was indicating. Thus, the rating label did significantly influence fast-fashion garment purchase intention, but not in any useful way. This is a novel and significant finding that indicates that fashion ethical labels are evaluated by using similar subconscious heuristic decision processes to those found in fast-moving consumer goods (FMCG) markets. Ethical labels that rely on cognitive processing by the consumer may therefore be ineffective, and simpler iconic brand-like label systems that can support subconscious processing may be more useful in a fast-fashion setting.

Editorial: Toyota Production System practices asFast‐and‐Frugalheuristics

Editorial: Toyota Production System practices as<scp>Fast‐and‐Frugal</scp>heuristics

Increasing the initiation of antiretroviral therapy through optimal placement of diagnostic technologies for pediatric HIV in Zimbabwe: A modeling analysis

Point-of-care (POC) devices for infant HIV testing provide timely result-return and increase antiretroviral (ART) initiation. We aimed to optimally locate POC devices to increase 30-day ART initiation in Matabeleland South, Zimbabwe. We developed an optimization model to identify the locations for limited POC devices at health facilities, maximizing the number of infants who receive HIV test results and initiate ART within 30 days of testing. We compared location-optimization model results to non-model-based decision heuristics, which are more practical and less data-intensive. Heuristics assign POC devices based on demand, test positivity, laboratory result-return probability, and POC machine functionality. With the current placement of 11 existing POC machines, 37% of all tested infants with HIV were projected to receive results and 35% were projected to initiate ART within 30 days of testing. With optimal placement of existing machines, 46% were projected to receive results and 44% to initiate ART within 30 days, retaining three machines in current locations, moving eight to new facilities. Relocation based on the highest POC device functionality would be the best-performing heuristic decision (44% receiving results and 42% initiating ART withing 30 days); although, it still would not perform as well as the optimization-based approach. Optimal and ad hoc heuristic relocation of limited POC machines would increase timely result-return and ART initiation, without further, often costly, interventions. Location optimization can enhance decision-making regarding the placement of medical technologies for HIV care.

Tracking and handling behavioral biases in active learning frameworks

The computational capabilities of AI engines integrated with human knowledge and experience can help create intelligent human-in-the-loop (HITL) decision systems. Such synergistic frameworks improve the decision-making process and make AI-enabled systems more robust, accurate and smarter. In safety-critical applications that require a certain level of human supervision, human and AI engine errors can be costly. However, modeling human behavior in collaborative human-AI decision setup is not straightforward. Humans use cognitive mechanisms and decision heuristics to process information and make decisions under uncertainty. This paper, for the first time, presents a systematic framework for modeling, tracking and adaptation of behavioral biases in a collaborative decision environment within an “Active Learning” context. The proposed framework is validated using experiments conducted on a real-world pancreatic cancer dataset. We consider five different learning scenarios based on different grades of human experts and compare the performance of bias-aware decision models with naive models. It is observed that bias-aware models improve the classification accuracy of decision models by upto 16%.

Arc regulates a second-guessing cognitive bias during naturalistic foraging through effects on discrete behavior modules

Foraging in animals relies on innate decision-making heuristics that can result in suboptimal cognitive biases in some contexts. The mechanisms underlying these biases are not well understood, but likely involve strong genetic effects. To explore this, we studied fasted mice using a naturalistic foraging paradigm and discovered an innate cognitive bias called "second-guessing." This involves repeatedly investigating an empty former food patch instead of consuming available food, which hinders the mice from maximizing feeding benefits. The synaptic plasticity gene Arc is revealed to play a role in this bias, as Arc-deficient mice did not exhibit second-guessing and consumed more food. In addition, unsupervised machine learning decompositions of foraging identified specific behavior sequences, or "modules", that are affected by Arc. These findings highlight the genetic basis of cognitive biases in decision making, show links between behavior modules and cognitive bias, and provide insight into the ethological roles of Arc in naturalistic foraging.

Decision heuristics in contexts integrating action selection and execution

Heuristics can inform human decision making in complex environments through a reduction of computational requirements (accuracy-resource trade-off) and a robustness to overparameterisation (less-is-more). However, tasks capturing the efficiency of heuristics typically ignore action proficiency in determining rewards. The requisite movement parameterisation in sensorimotor control questions whether heuristics preserve efficiency when actions are nontrivial. We developed a novel action selection-execution task requiring joint optimisation of action selection and spatio-temporal skillful execution. State-appropriate choices could be determined by a simple spatial heuristic, or by more complex planning. Computational models of action selection parsimoniously distinguished human participants who adopted the heuristic from those using a more complex planning strategy. Broader comparative analyses then revealed that participants using the heuristic showed combined decisional (selection) and skill (execution) advantages, consistent with a less-is-more framework. In addition, the skill advantage of the heuristic group was predominantly in the core spatial features that also shaped their decision policy, evidence that the dimensions of information guiding action selection might be yoked to salient features in skill learning.

Variational Mode Decomposition-Based Simultaneous R Peak Detection and Noise Suppression for Automatic ECG Analysis

Automatic and reliable detection of R peak from electrocardiogram (ECG) signal is essential in both pathological and nonpathological applications. The presence of various kinds of noises and artifacts makes it still a challenging problem in the accurate and reliable extraction of ECG parameters in ambulatory conditions. In this article, we present a straightforward R peak detection and ECG denoising method based on the variational mode decomposition, mode selection, first-order derivative, Shannon energy (SE)-based nonlinear amplification, Hilbert transform (HT), and positive zero-crossing point. Optimal parameters were chosen for suppressing the low- and high-frequency noises and extracting the candidate QRS complex portions. The challenging effect of long P and T waves is suppressed in differentiation. The SE-based amplification process is used to magnify medium-amplitude QRS complexes largely compared to high-amplitude QRS complexes to accurately detect low-amplitude QRS complexes in the presence of high-amplitude QRS complexes. The HT is used to automatically detect the location of candidate R peaks from smoothed SE envelope by processing positive zero-crossing points of HT of the SE waveform. The R peak detection results on the standard MIT-BIH arrhythmia database showed that the method had an average sensitivity of 99.77% and a positive predictivity of 99.91%. This method is straightforward unlike existing R peak detections based on a search-back algorithm with heuristic decision rules to include missed peaks and reject noisy peaks. The denoising results showed the noise removal capability of the proposed algorithm with the preservation of local waves in the ECG signal.

Understanding the Relative Strength of QBF CDCL Solvers and QBF Resolution

QBF solvers implementing the QCDCL paradigm are powerful algorithms that successfully tackle many computationally complex applications. However, our theoretical understanding of the strength and limitations of these QCDCL solvers is very limited. In this paper we suggest to formally model QCDCL solvers as proof systems. We define different policies that can be used for decision heuristics and unit propagation and give rise to a number of sound and complete QBF proof systems (and hence new QCDCL algorithms). With respect to the standard policies used in practical QCDCL solving, we show that the corresponding QCDCL proof system is incomparable (via exponential separations) to Q-resolution, the classical QBF resolution system used in the literature. This is in stark contrast to the propositional setting where CDCL and resolution are known to be p-equivalent. This raises the question what formulas are hard for standard QCDCL, since Q-resolution lower bounds do not necessarily apply to QCDCL as we show here. In answer to this question we prove several lower bounds for QCDCL, including exponential lower bounds for a large class of random QBFs. We also introduce a strengthening of the decision heuristic used in classical QCDCL, which does not necessarily decide variables in order of the prefix, but still allows to learn asserting clauses. We show that with this decision policy, QCDCL can be exponentially faster on some formulas. We further exhibit a QCDCL proof system that is p-equivalent to Q-resolution. In comparison to classical QCDCL, this new QCDCL version adapts both decision and unit propagation policies.

Altruism or self-interest in tomorrow's veterinarians? A metric conjoint experiment and cluster analysis.

Altruism is considered a trait of veterinary and other health professionals, but the level of altruism in the veterinary profession is unknown. We designed a metric conjoint experiment to reveal other-orientation (an individual's caring concern for the wellbeing of others) and self-interest. We draw on the 'Theory of Other-Orientation', which states that individuals' decision-making heuristics can be impacted by their other-orientation independent of their self-interest. In patient-focused contexts, highly other-oriented or altruistic (veterinary) professionals may care too much for others and suffer immediate or cumulative financial and personal costs of such caring. At the same time, other-orientation can enhance job-related attitudes and outcomes, such as job satisfaction. In a metric conjoint experiment, Australian final-year veterinary, science, nursing, entrepreneurship, and engineering students rated eight job scenarios with orthogonally arranged high and low levels of three job characteristics (n = 586) to provide observed measures of other-orientation and self-interest. A two-way MANOVA showed other-orientation or self-interest differed per discipline, but not gender. Veterinary (and engineering) respondents were less other-oriented than nursing respondents. Veterinary (and entrepreneurship) respondents were more self-interested than nursing respondents. K-Means cluster analysis confirmed four distinct profile groupings-altruistic/self-sacrificing, 'both other-self', self-interested and selfish-aligning with the discourse in the literature. Human nursing respondents stood out for the most members (50%) in the 'both other-self' profile compared to veterinary respondents (28%). Respondents of one of three veterinary schools stood out for the most members (19%) in the altruistic/self-sacrificing group. Our metric conjoint experiment illustrates an alternative to 'self-report' items with Likert-scaled responses. Our finding of the 'both other-self' group adds to the literature, which considers that other-orientation and self-interest are separate constructs that are difficult to co-exist in individuals. This mix of traits is deemed helpful by organizational psychology scholars, for sustainability and wellbeing, especially for healthcare professionals involved in high-frequency and intense, patient-focused interactions. Our findings highlight the need for more research on the potential role of other-orientation and self-interest in veterinary school admissions processes, the hidden or taught curricula, job-related attitudes and beliefs, and wellbeing and professional sustainability in the veterinary sector.

Understanding hubris and heuristics in CEO decision-making: Implications for management

Understanding hubris and heuristics in CEO decision-making: Implications for management

A Network Flow Model for Operational Planning in an Underground Gold Mine

In underground mines, the problem of efficiently scheduling and allocating weekly operations has a major impact on the long-term productivity of the mine. The problem of selecting the optimal locations for operations in an underground gold mine is a complex task. It is not solved by simply selecting the levels with the richest grade because the transportation network for ore in an underground mine has a diverse set of capacity constraints that can frustrate immediate mining of all the richest levels. To solve this scheduling difficulty, we formulated a new mixed-integer network flow model of the problem of weekly allocating mining operations in an underground goldmine such that the total gold mined (in ounces) was maximized subject transportation capacity constraints. The model was applied an underground gold mine in Red Lake, Ontario, Canada. The results were compared to those of a two greedy heuristic models that were designed to represent the decision-making heuristics that are currently used at the mine. It was found that the new model yielded solutions that improved upon the two greedy heuristics by 14.7% and 6.0%, respectively. The results of this research illustrate that the development of this optimization model can support decisions to improve a gold mine’s productivity.

Fast-and-frugal heuristics for decision-making in uncertain and complex settings in construction

Fast-and-frugal heuristics are simple, task-specific decision strategies that form part of a decision maker's repertoire of cognitive strategies for solving judgment and decision tasks. They have been shown to deliver better decision outcomes than statistical (including probabilistic and stochastic) approaches in uncertain and complex settings or where there is only a small sample from which to draw conclusions. Yet, they have received limited attention in the construction-related literature. This paper aims to raise awareness of the important role that fast-and-frugal heuristics play in decision-making as alternatives to statistical models in uncertain and complex settings or where there is only a small sample from which to conclude. Additionally, it is suggested that there is a need for research to identify and develop an adaptive toolbox of ecologically rational heuristics to enhance decision-making within varying project settings in construction. This paper's contributions are twofold: (1) challenges the prevailing reliance on statistical approaches being used when making judgments under uncertainty; and (2) identifies a new line of inquiry to harness the benefits of using fast-and-frugal heuristics for decision-making.

Impacts of decision support systems on cognition and performance for intelligence-gathering path planning

ABSTRACT Decision Support Systems (DSS) are tools designed to help operators make effective choices in workplace environments where discernment and critical thinking are required for effective performance. Path planning in military operations and general logistics both require individuals to make complex and time-sensitive decisions. However, these decisions can be complex and involve the synthesis of numerous tradeoffs for various paths with dynamically changing conditions. Intelligence collection can vary in difficulty, specifically in terms of the disparity between locations of interest and timing restrictions for when and how information can be collected. Furthermore, plans may need to be changed adaptively mid-operation, as new collection requirements appear, increasing task difficulty. We tested participants in a path planning decision-making exercise with scenarios of varying difficulty in a series of two experiments. In the first experiment, each map displayed two paths simultaneously, relating to two possible routes for the two available trucks. Participants selected the optimal path plan, representing the best solution across multiple routes. In the second experiment, each map displayed a single path, and participants selected the best two paths sequentially. In the first experiment, utilizing the DSS was predictive of adoption of more heuristic decision strategies, and that strategic approach yielded more optimal route selection. In the second experiment, there was a direct effect of the DSS on increased decision performance and a decrease in perceived task workload.

Creativity as privilege

Organizational gatekeepers rely on tacit proxy signals of quality to evaluate creative work: status and status characteristics, elite networks, cultural capital, and a set of signals we refer to as symbolic dexterity. We argue that this reliance is due to the “push” of uncertainty and the “pull” of the culturally dominant person-centered view of creativity. Evaluators are “pushed” toward these proxy signals because the quality of creative work is fundamentally uncertain. Evaluators are “pulled” toward these proxy signals because the person-centered view of creativity makes these signals legitimate and easily available decision heuristics. Since members of privileged social groups are advantaged in producing and understanding the importance of such signals, we argue that access to creative work and success within it are largely a privilege. Given that privilege-based selection in creative work is both entrenched and ethically problematic, we explore its implications for organizational performance and organizational reputation and propose strategies that may help organizations reduce its discriminatory impact. We conclude by presenting questions for future research arising at the intersection of the literatures on evaluation in creative work and on social inequality.

Do cognition and emotion matter? A study of COVID-19 vaccination decision-making in college students.

The unparalleled speed of COVID-19 vaccine development has necessitated an expansion of existing knowledge on vaccination decision-making. The current study explored (1) how cognitions and emotions shaped college students' COVID-19 vaccination decisions, and (2) where vaccination-inclined and vaccination-hesitant students converged and diverged in their decision-making process. Seventy-seven students participated in 26 focus groups to discuss their complex thoughts and feelings regarding COVID-19 vaccination, offering a more nuanced understanding of COVID-19 vaccination decision-making that has not been fully captured by quantitative studies. Thematic analysis found that vaccination-inclined participants and their hesitant counterparts reported differential patterns of positive and negative emotions, systematic appraisals, and heuristics in decision-making. Future research should investigate the roles of hope and relief, non-health-related benefits of vaccination, social trust, and interpersonal influence in vaccination decision-making.

Live-Lab GSD – Generational Sheet Metal Development: a validation environment for methodological design support in sheet metal development

This paper describes the development of a Live-Lab for generational sheet metal development, which can be used to validate methods, processes, and tools. For this purpose, requirements regarding the design task, development process, participants, equipment, and data collection for a Live-Lab are derived based on a literature review and expert interviews. Subsequently, a Live-Lab is developed and presented in which the test subjects develop and manufacture a gas grill over six months. Based on the concept of the Live-Lab, which was implemented and carried out in three years, the validation of three different approaches for methodological design support is described. These approaches address (1) engineering generations and variation types, (2) sheet metal design concerning sustainability, and (3) decision heuristics in agile development.

Supplemental Material for Heuristic Decision-Making Across Adulthood

Supplemental Material for Heuristic Decision-Making Across Adulthood

Ingroup bias in a social learning experiment

The online version contains supplementary material available at 10.1007/s10683-022-09788-1.

Heuristic price theory: A model of pluralistic price evaluations

AbstractEconomic price theory assumes that consumers' responses to prices can be characterized by stable demand curves and price elasticities. The author posits that this assumption lacks descriptive validity because the demand curve is rather unstable; subtle changes in framing and contextual cues can change the demand curve. The article outlines heuristic price theory, which posits that price evaluations are pluralistic in nature. Each price evaluation entails several heuristic decision rules (or decision criteria) that are activated by conscious and unconscious evaluative responses to price and the contextual cues. The extant literature identifies six types of evaluative responses that influence these heuristic decision rules: the pain of paying, price comparisons, price–quality inferences, price negotiability judgments, price fairness judgments, and price–feature tradeoff. To predict how prices influence consumer behavior in a particular context, it is important to identify the heuristic decision rules being used in that context. This implies that managers and researchers, instead of focusing only on estimating price elasticities using stylized demand curves, should also study the heuristic decision rules that shoppers use to evaluate prices.