Applied Decision Making With Fast-and-Frugal Heuristics

Age differences in the recruitment of syntactic analysis and semantic plausibility during sentence comprehension

Syntactic analysis and semantic plausibility provide important cues to build the meaningful representation of sentences. The purpose of this research is to explore the age-related differences in the use of syntactic analysis and semantic plausibility during sentence comprehension under different working memory load conditions. A sentence judgment task was implemented among a group of older and younger adults. Semantic plausibility (plausible, implausible) and syntactic consistency (consistent, inconsistent) were manipulated in the experimental stimuli, and working memory load (high, low) was varied by manipulating the presentation of the stimuli. The study revealed a stronger effect of semantic plausibility in older adults than in younger adults when working memory load was low. But no significant age difference in the effect of syntactic consistency was discovered. When working memory load was high, there was a stronger effect of semantic plausibility and a weaker effect of syntactic consistency in older adults than in younger adults, which suggests that older adults relied more on semantic plausibility and less on syntactic analysis than younger adults. The findings indicate that there is an age-related increase in the use of semantic plausibility, and a reduction in the use of syntactic analysis as working memory load increases.

Blinding to Circumvent Human Biases: Deliberate Ignorance in Humans, Institutions, and Machines

Inequalities and injustices are thorny issues in liberal societies, manifesting in forms such as the gender-pay gap; sentencing discrepancies among Black, Hispanic, and White defendants; and unequal medical-resource distribution across ethnicities. One cause of these inequalities is implicit social bias-unconsciously formed associations between social groups and attributions such as "nurturing," "lazy," or "uneducated." One strategy to counteract implicit and explicit human biases is delegating crucial decisions, such as how to allocate benefits, resources, or opportunities, to algorithms. Algorithms, however, are not necessarily impartial and objective. Although they can detect and mitigate human biases, they can also perpetuate and even amplify existing inequalities and injustices. We explore how a philosophical thought experiment, Rawls's "veil of ignorance," and a psychological phenomenon, deliberate ignorance, can help shield individuals, institutions, and algorithms from biases. We discuss the benefits and drawbacks of methods for shielding human and artificial decision makers from potentially biasing information. We then broaden our discussion beyond the issues of bias and fairness and turn to a research agenda aimed at improving human judgment accuracy with the assistance of algorithms that conceal information that has the potential to undermine performance. Finally, we propose interdisciplinary research questions.

Collective pronouns, collective health actions: Predicting pandemic precautionary measures through online first-person plural pronoun usage across U.S. states

The COVID-19 pandemic has underscored the role of group identification in shaping collective health behaviors. Using the novel Pronoun-Influenced Collective Health Model - an integrated framework combining elements from health and social psychology theories - we investigated the relationship between online first-person plural pronoun usage and adherence to COVID-19 preventive measures across the United States. Analyzing weekly Google Trends data on English (Study 1) and Spanish (Study 2) first-person pronoun searches, alongside data on adherence to pandemic precautionary measures from early 2020 to late 2022, we found significant positive associations between relative first-person plural pronoun search volumes and adherence to social distancing, stay-at-home orders, vaccination rates, and proactive disease prevention information seeking. These associations remained robust after adjusting for potential confounding factors. A mini meta-analysis (Study 3) confirmed the consistency of our findings, revealing no significant moderation effects by language context or ecological-socio-cultural factors, suggesting broad generalizability. The implications of this research highlight the potential for tracking online collective language as a valuable indicator of and proxy for societal-level health engagement during crises. This novel digital linguistics approach, synergistically combining applied health and social psychology with big data from digital platforms such as Google, offers powerful tools for monitoring collective health actions across linguistic and cultural boundaries during large-scale health crises.

An Environmental Scan of Tools That Help Individuals Living With Mild Cognitive Impairment or Neurocognitive Disorders Achieve Their Preferred Health or Well-Being

BACKGROUND AND OBJECTIVES

Older adults experiencing neurocognitive disease (NCD) contend with complex care often characterized by high emotional strain. Mitigating complex care with decision support tools can clarify options. When used in conjunction with the practice of shared decision making (SDM), these tools can improve satisfaction and confidence in treatment. The use of these tools for cognitive health has increased, but more is needed to understand how these tools incorporate social needs into treatment plans.

RESEARCH DESIGN AND METHODS

We conducted an environmental scan using a MEDLINE-informed search strategy and feedback from an expert steering committee to characterize current tools and approaches for engaging older adults experiencing NCD. We assessed their application and development, incorporation of social determinants, goals or preferences, and inclusion of caregivers in their design.

RESULTS

We identified 11 articles, 7 of which show that SDM helps guide tool development and that most center on clinical decision making. Types of tools varied by clinical site and those differences reflected patient need. A collective value across tools was their use to forge meaningful conversations. Most tools appeared designed without the explicit goal to elicit patient social needs or incorporate nonclinical strategies into treatment plans.

DISCUSSION AND IMPLICATIONS

Several challenges and opportunities exist that center on strategies to engage patients in the design and testing of tools that support conversations with clinicians about cognitive health. Future work should focus on building and testing adaptable tools that support patient and family social care needs beyond clinical care settings.

The impact of cognitive resource constraints on goal prioritization

Many decisions we face daily entail deliberation about how to coordinate resources shared between multiple, competing goals. When time permits, people appear to approach these goal prioritization problems by analytically considering all goal-relevant information to arrive at a prioritization decision. However, it is not yet clear if this normative strategy extends to situations characterized by resource constraints such as when deliberation time is scarce or cognitive load is high. We evaluated the questions of how limited deliberation time and cognitive load affect goal prioritization decisions across a series of experiments using a gamified experimental task, which required participants to make a series of interdependent goal prioritization decisions. We fit several candidate models to experimental data to identify decision strategy adaptations at the individual subject-level. Results indicated that participants tended to opt for a simple heuristic strategy when cognitive resources were constrained rather than making a general tradeoff between speed and accuracy (e.g., the type of tradeoff that would be predicted by evidence accumulation models). The most common heuristic strategy involved disproportionately weighing information about goal deadlines compared to other goal-relevant information such as the goal's difficulty and the goal's subjective value.

Robust airway microbiome signatures in acute respiratory failure and hospital-acquired pneumonia

Respiratory microbial dysbiosis is associated with acute respiratory distress syndrome (ARDS) and hospital-acquired pneumonia (HAP) in critically ill patients. However, we lack reproducible respiratory microbiome signatures that can increase our understanding of these conditions and potential treatments. Here, we analyze 16S rRNA sequencing data from 2,177 respiratory samples collected from 1,029 critically ill patients (21.7% with ARDS and 26.3% with HAP) and 327 healthy controls, sourced from 17 published studies. After data harmonization and pooling of individual patient data, we identified microbiota signatures associated with ARDS, HAP and prolonged mechanical ventilation. Microbiota signatures for HAP and prolonged mechanical ventilation were characterized by depletion of a core group of microbes typical of healthy respiratory samples, and the ARDS microbiota signature was distinguished by enrichment of potentially pathogenic respiratory microbes, including Pseudomonas and Staphylococcus. Using machine learning models, we identified clinically informative, three- and four-factor signatures that predicted ARDS, HAP and prolonged mechanical ventilation with relatively high accuracy (area under the curve of 0.751, 0.72 and 0.727, respectively). We validated the signatures in an independent prospective cohort of 136 patients on mechanical ventillation and found that patients with microbiome signatures associated with ARDS, HAP or prolonged mechanical ventilation had longer times to successful extubation than patients lacking these signatures (hazard ratios of 1.56 (95% confidence interval (CI) 1.07-2.27), 1.51 (95% CI 1.02-2.23) and 1.50 (95% CI 1.03-2.18), respectively). Thus, we defined and validated robust respiratory microbiome signatures associated with ARDS and HAP that may help to identify promising targets for microbiome therapeutic modulation in critically ill patients.

Narratives, environments, and decision-making: A fascinating narrative, but one to be completed

I encourage Johnson et al. to ground Conviction Narrative Theory in more detail in foundational, earlier decision-making research - first and foremost in Herbert Simon's work. Moreover, I wonder if and how further reflections about narratives could aid tackling two interrelated grand challenges of the decision sciences: To describe decision-making environments; to understand how people select among decision-strategies in environments.

Factors which influence the deprescribing decisions of community-living older adults and GPs in Australia

Deprescribing aims to reduce polypharmacy and inappropriate medication use. Both General Practitioners (GPs) and older adults have expressed a willingness to consider deprescribing. However, deprescribing is often deferred in practice. The aim of this study was to identify factors which influence GP and older adult decisions about deprescribing in primary care. Semi-structured interviews were used in this qualitative study, conducted in a regional area in Australia. Participants included GPs and adults aged 65 years or older, using five or more medications and living independently in the community. Data were collected between January 2018 and May 2019. Thematic analysis was used to analyse the verbatim transcribed interviews using NVivo 12. A total of 41 interviews were conducted, 25 with older adults and 16 with GPs. Four key themes influenced deprescribing decisions: views of ageing, shared decision-making, attitudes toward medication use and characteristics of the health care environment. Discussions of deprescribing were limited by the influence of negative stereotypes toward age and ageing, a lack of older adult participation in shared decision-making, a positive attitude towards ongoing medication use and perception of the normality of using medications in older age. Time constraints, poor communication about prescribing information and unclear roles regarding responsibility for deprescribing also prevented discussions. Continuity of care, involvement of older adults in medication reviews and GPs who asserted their generalist role were the main factors which promoted discussion of deprescribing. GPs are well placed to discuss deprescribing with their older patients because they are trusted and can provide continuity of care. Actively encouraging and involving older adults in medication reviews in order to understand their preferences, supports shared decision-making about deprescribing. Active involvement may also reduce the influence of negative views of ageing held by both older adults and GPs.

When Self-Humanization Leads to Algorithm Aversion

Decision support systems are increasingly being adopted by various digital platforms. However, prior research has shown that certain contexts can induce algorithm aversion, leading people to reject their decision support. This paper investigates how and why the context in which users are making decisions (for-profit versus prosocial microlending decisions) affects their degree of algorithm aversion and ultimately their preference for more human-like (versus computer-like) decision support systems. The study proposes that contexts vary in their affordances for self-humanization. Specifically, people perceive prosocial decisions as more relevant to self-humanization than for-profit contexts, and, in consequence, they ascribe more importance to empathy and autonomy while making decisions in prosocial contexts. This increased importance of empathy and autonomy leads to a higher degree of algorithm aversion. At the same time, it also leads to a stronger preference for human-like decision support, which could therefore serve as a remedy for an algorithm aversion induced by the need for self-humanization. The results from an online experiment support the theorizing. The paper discusses both theoretical and design implications, especially for the potential of anthropomorphized conversational agents on platforms for prosocial decision-making.

Climate Adaptation Heuristic Planning Support System (HPSS): Green-Blue Strategies to Support the Ecological Transition of Historic Centres

The issue of climate has posed major and urgent challenges for the global community. The European Green Deal sets out a new growth strategy aimed at turning the European Union into a just and prosperous society, with a modern, resource-efficient, and competitive economy, which will no longer generate net greenhouse gas emissions by 2050. Cities in this context are committed on several fronts to rapid adaptation to improve their resilience capacity. The historic centre is the most vulnerable part of a city, with a reduced capacity for adaptation, but also the densest of values, which increase the complexity of the challenge. This study proposes an integrated tool, Heuristic Planning Support System (HPSS), aimed at exploring green-blue strategies for the historic centre. The tool is integrated with classic Planning Support System (PSS), a decision process conducted from the perspective of heuristic approach and Geographic Information System (GIS). It comprises modules for technical assessment, environmental assessment Life Cycle Assessment (LCA), economic assessment Life Cycle Cost (LCC), Life Cycle Revenues (LCR), and Discounted Cash Flow Analysis (DCFA) extended to the life cycle of specific interventions, the Multi-Attribute Value Theory (MAVT) for the assessment of energy, environmental, identity, landscape, and economic values. The development of a tool to support the ecological transition of historic centres stems from the initiative of researchers at the University of Catania, who developed it based on the preferences expressed by a group of decision makers, that is, a group of local administrators, scholars, and professionals. The proposed tool supports the exploration of green-blue strategies identified by decision makers and the development of the plan for the historic district of Borgata di Santa Lucia in Syracuse.

People underestimate the errors made by algorithms for credit scoring and recidivism prediction but accept even fewer errors

This study provides the first representative analysis of error estimations and willingness to accept errors in a Western country (Germany) with regards to algorithmic decision-making systems (ADM). We examine people's expectations about the accuracy of algorithms that predict credit default, recidivism of an offender, suitability of a job applicant, and health behavior. Also, we ask whether expectations about algorithm errors vary between these domains and how they differ from expectations about errors made by human experts. In a nationwide representative study (N = 3086) we find that most respondents underestimated the actual errors made by algorithms and are willing to accept even fewer errors than estimated. Error estimates and error acceptance did not differ consistently for predictions made by algorithms or human experts, but people's living conditions (e.g. unemployment, household income) affected domain-specific acceptance (job suitability, credit defaulting) of misses and false alarms. We conclude that people have unwarranted expectations about the performance of ADM systems and evaluate errors in terms of potential personal consequences. Given the general public's low willingness to accept errors, we further conclude that acceptance of ADM appears to be conditional to strict accuracy requirements.

What Cognitive Mechanism, When, Where, and Why? Exploring the Decision Making of University and Professional Rugby Union Players During Competitive Matches

Over the past 50 years decision making research in team invasion sport has been dominated by three research perspectives, information processing, ecological dynamics, and naturalistic decision making. Recently, attempts have been made to integrate perspectives, as conceptual similarities demonstrate the decision making process as an interaction between a players perception of game information and the individual and collective capability to act on it. Despite this, no common ground has been found regarding what connects perception and action during performance. The differences between perspectives rest on the role of stored mental representations, that may, or may not facilitate the retrieval of appropriate responses in time pressured competitive environments. Additionally, in team invasion sports like rugby union, the time available to players to perceive, access memory and act, alters rapidly between specific game situations. Therefore, the aim of this study was to examine theoretical differences and the mechanisms that underpin them, through the vehicle of rugby union. Sixteen semi-elite rugby union players took part in two post-game procedures to explore the following research objectives; (i) to consider how game situations influence players perception of information; (ii) to consider how game situations influence the application of cognitive mechanisms whilst making decisions; and (iii) to identify the influence of tactics and/or strategy on player decision making. Deductive content analysis and elementary units of meaning derived from self-confrontation elicitation interviews indicate that specific game situations such as; the lineout, scrum or open phases of play or the tackle situation in attack or defence all provide players with varying complexity of perceptual information, formed through game information and time available to make decisions. As time increased, players were more likely to engage with task-specific declarative knowledge-of the game, stored as mental representations. As time diminished, players tended to diagnose and update their knowledge-in the game in a rapid fashion. Occasionally, when players described having no time, they verbalised reacting on instinct through a direct connection between perception and action. From these findings, clear practical implications and directions for future research and dissemination are discussed.

Automatic discovery of interpretable planning strategies

When making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decision-makers, especially professionals such as medical doctors, with decision aids, such as decision trees and flowcharts. Designing effective decision aids is a difficult problem. We propose that recently developed reinforcement learning methods for discovering clever heuristics for good decision-making can be partially leveraged to assist human experts in this design process. One of the biggest remaining obstacles to leveraging the aforementioned methods for improving human decision-making is that the policies they learn are opaque to people. To solve this problem, we introduce AI-Interpret: a general method for transforming idiosyncratic policies into simple and interpretable descriptions. Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule. We evaluate our new AI-Interpret algorithm and employ it to translate information-acquisition policies discovered through metalevel reinforcement learning. The results of three large behavioral experiments showed that providing the decision rules generated by AI-Interpret as flowcharts significantly improved people’s planning strategies and decisions across three different classes of sequential decision problems. Moreover, our fourth experiment revealed that this approach is significantly more effective at improving human decision-making than training people by giving them performance feedback. Finally, a series of ablation studies confirmed that our AI-Interpret algorithm was critical to the discovery of interpretable decision rules and that it is ready to be applied to other reinforcement learning problems. We conclude that the methods and findings presented in this article are an important step towards leveraging automatic strategy discovery to improve human decision-making. The code for our algorithm and the experiments is available at https://github.com/RationalityEnhancement/InterpretableStrategyDiscovery.

Emergency department patients with weakness or fatigue: Can physicians predict their outcomes at the front door? A prospective observational study

BACKGROUND

Generalized weakness and fatigue are underexplored symptoms in emergency medicine. Triage tools often underestimate patients presenting to the emergency department (ED) with these nonspecific symptoms (Nemec et al., 2010). At the same time, physicians' disease severity rating (DSR) on a scale from 0 (not sick at all) to 10 (extremely sick) predicts key outcomes in ED patients (Beglinger et al., 2015; Rohacek et al., 2015). Our goals were (1) to characterize ED patients with weakness and/or fatigue (W|F); to explore (2) to what extent physicians' DSR at triage can predict five key outcomes in ED patients with W|F; (3) how well DSR performs relative to two commonly used benchmark methods, the Emergency Severity Index (ESI) and the Charlson Comorbidity Index (CCI); (4) to what extent DSR provides predictive information beyond ESI, CCI, or their linear combination, i.e., whether ESI and CCI should be used alone or in combination with DSR; and (5) to what extent ESI, CCI, or their linear combination provide predictive information beyond DSR alone, i.e., whether DSR should be used alone or in combination with ESI and / or CCI.

METHODS

Prospective observational study between 2013-2015 (analysis in 2018-2020, study team blinded to hypothesis) conducted at a single center. We study an all-comer cohort of 3,960 patients (48% female patients, median age = 51 years, 94% completed 1-year follow-up). We looked at two primary outcomes (acute morbidity (Bingisser et al., 2017; Weigel et al., 2017) and all-cause 1- year mortality) and three secondary outcomes (in-hospital mortality, hospitalization and transfer to ICU). We assessed the predictive power (i.e., resolution, measured as the Area under the ROC Curve, AUC) of the scores and, using logistic regression, their linear combinations.

FINDINGS

Compared to patients without W|F (n = 3,227), patients with W|F (n = 733) showed higher prevalences for all five outcomes, reported more symptoms across both genders, and received higher DSRs (median = 4; interquartile range (IQR) = 3-6 vs. median = 3; IQR = 2-5). DSR predicted all five outcomes well above chance (i.e., AUCs > ~0.70), similarly well for both patients with and without W|F, and as good as or better than ESI and CCI in patients with and without W|F (except for 1-year mortality where CCI performs better). For acute morbidity, hospitalization, and transfer to ICU there is clear evidence that adding DSR to ESI and/or CCI improves predictions for both patient groups; for 1-year mortality and in-hospital mortality this holds for most, but not all comparisons. Adding ESI and/or CCI to DSR generally did not improve performance or even decreased it.

CONCLUSIONS

The use of physicians' disease severity rating has never been investigated in patients with generalized weakness and fatigue. We show that physicians' prediction of acute morbidity, mortality, hospitalization, and transfer to ICU through their DSR is also accurate in these patients. Across all patients, DSR is less predictive of acute morbidity for female than male patients, however. Future research should investigate how emergency physicians judge their patients' clinical state at triage and how this can be improved and used in simple decision aids.

Human-like machines: Transparency and comprehensibility

Artificial intelligence algorithms seek inspiration from human cognitive systems in areas where humans outperform machines. But on what level should algorithms try to approximate human cognition? We argue that human-like machines should be designed to make decisions in transparent and comprehensible ways, which can be achieved by accurately mirroring human cognitive processes.

A Review of Consequences of Poverty on Economic Decision-Making: A Hypothesized Model of a Cognitive Mechanism

This review focuses on the issue of poverty affecting economic decision-making. By critically evaluating existing studies, the authors propose a structural model detailing the cognitive mechanism involved in how poverty negatively impacts economic decision-making, and explores evidence supporting the basis for the formation of this model. The suggested mechanism consists of a relationship between poverty and four other factors: (1) cognitive load (e.g., experiencing negative affect and stress); (2) executive functions (e.g., attention, working memory, and self-control); (3) intuition/deliberation in decision-making; and (4) economic decision-making (e.g., time-discounting and risk preference), with a final addition of financial literacy as a covariate. This paper focuses on shortfalls in published research, and delves further into the proposed model.