Stress-related changes in financial risk taking: Considering joint effects of cortisol and affect

Rank, stress, and risk: A conjecture

A perception at the core of studies that consider the link between social rank and stress (typically measured by the so-called stress hormone cortisol) is that the link is direct. Examples of such studies are Bartolomucci (2007), Beery and Kaufer (2015), and Koolhaas et al. (2017). A recent and stark representation of this body of work is a study by Smith-Osborne et al. (2023), who state that "social hierarchies directly influence stress status" (Smith-Osborne et al. p. 1537, italics added). In the present paper, we reflect on this "direct" perspective. We conjecture that the link between social rank and stress involves an intervening variable: an indirect relationship arises when the loss of rank triggers a behavioral response in the form of risk taking aimed at regaining rank, and it is the engagement in risk-taking behavior that is the cause of an elevated level of cortisol. Smith-Osborne et al., as well as others whose papers are cited by Smith-Osborne et al. and who, like Creel (2001) and Avitsur et al. (2006), conducted comprehensive research on the association between rank (social standing) and stress, do not refer to risk taking at all. We present four strands of research that lend support to our conjecture: evidence that in response to losing rank, individuals are stressed; evidence that in response to losing rank, individuals resort to risk-taking behavior aimed at regaining their lost rank; evidence that there exists a link between engagement in risky activities or exposure to risk and elevated levels of cortisol; and an analytical perspective on incidence and intensity, namely a perspective that shows how the willingness to take risks responds to a change in rank, specifically, how a loss of rank triggers a greater willingness to take risks and how this trigger is stronger for individuals whose rank is higher.

Navigating the decision-making landscape of AI in risk finance: Techno-accountability unveiled

The integration of artificial intelligence (AI) systems has ushered in a profound transformation. This conversion is marked by revolutionary extrapolative capabilities, a shift toward data-centric decision-making processes, and the enhancement of tools for managing risks. However, the adoption of these AI innovations has sparked controversy due to their unpredictable and opaque disposition. This study employs the transactional stress model to empirically investigate how six technological stressors (techno-stressors) impact both techno-eustress (positive stress) and techno-distress (negative stress) experienced by finance professionals and experts. To collect data for this research, an e-survey was distributed to a diverse group of 251 participants from various sources. The findings, particularly the identification and development of techno-accountability as a significant factor, contribute to the risk analysis domain by improving the failure mode and effect analysis framework to better fit the rapidly evolving landscape of AI-driven innovations.

Risk Perception and Public Pandemic Fatigue: The Role of Perceived Stress and Preventive Coping

Purpose

This study explores the status of pandemic fatigue, predictors, and their mechanisms of action based on a stress-response framework and a parallel model of future-oriented response.

Patients and methods

Study 1 investigated 8426 Chinese adult residents' understanding of and willingness to cooperate with the pandemic prevention and control policies and Study 2 surveyed 1635 Chinese residents on their activeness of pandemic prevention and control (APPC), pandemic risk perception, perceived stress, and future-oriented coping.

Results

Study 1 found that public understanding of and willingness to cooperate with prevention policies were significantly lower in 2022 than in 2020 and 2021. Study 2 found that risk perception negatively predicted the APPC; perceived stress and preventive coping significantly mediated the relationship between risk perception and APPC; but perceived stress and proactive coping did not significantly mediate the relationship between risk perception and APPC.

Conclusion

This revealed an increase in public fatigue in the third year of the pandemic. Pandemic fatigue can be predicted by pandemic risk perception, but the direct pathway of action is not significant and requires the mediation of perceived stress and preventive coping.

Why take the risk? Exploring the psychosocial determinants of floodwater driving

As anthropogenic climate change progresses, there is an increasing need for individuals to make appropriate decisions regarding their approach to extreme weather events. Natural hazards are involuntary risk environments (e.g., flooded roads); interaction with them cannot be avoided (i.e., a decision must be made about how to engage). While the psychological and sociocultural predictors of engagement with voluntary risks (i.e., risk situations that are sought out) are well-documented, less is known about the factors that predict engagement with involuntary risk environments. This exploratory study assessed whether mental health (depression, anxiety, and stress symptoms), personality traits, and cultural worldviews combine to predict engagement with involuntary risk, using the situation of floodwater driving. An Australian sample (N = 235) was assessed via questionnaire and scenario measures. Results were analyzed in a binomial logistic regression assessing which individual factors predicted decision-making in a proxy floodwater driving scenario. Agreeableness and gender were individually significant predictors of floodwater driving intention, and four factors (named “affect,” “progressiveness,” “insightfulness,” and “purposefulness”) were derived from an exploratory factor analysis using the variables of interest, though only two (“progressiveness” and “insightfulness”) predicted floodwater driving intention in an exploratory binomial logistic regression. The findings highlight the need for further research into the differences between voluntary and involuntary risk. The implication of cultural worldviews and personality traits in interaction with mental health indicators on risk situations is discussed.

Associations between anxiety and the willingness to be exposed to COVID-19 risk among French young adults during the first pandemic wave

The COVID-19 outbreak has generated significant uncertainty about the future, especially for young adults. Health and economic threats, as well as more diffuse concerns about the consequences of COVID-19, can trigger feelings of anxiety, leading individuals to adopt uncertainty-reducing behaviours. We tested whether anxiety was associated with an increase in willingness to be exposed to the risk of COVID-19 infection (WiRE) using an online survey administered to 3,110 French individuals aged between 18 and 35 years old during the first pandemic wave and lockdown period (April 2020). Overall, 56.5% of the sample declared a positive WiRE. A one standard deviation increase in psychological state anxiety raised the WiRE by +3.9 pp (95% CI [+1.6, 6.2]). Unemployment was associated with a higher WiRE (+8.2 percentage points (pp); 95% CI [+0.9, 15.4]). One standard deviation increases in perceived hospitalisation risk and in income (+1160€) were associated with a -4.1 pp (95% CI [-6.2, 2.1]) decrease in the WiRE and +2.7 pp increase (95% CI [+1.1, 4.4]), respectively. Overall, our results suggest that both psychological anxiety and the prospect of economic losses can undermine young adults' adherence to physical distancing recommendations. Public policies targeting young adults must consider both their economic situation and their mental health, and they must use uncertainty-reducing communication strategies.

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.

Neural processes in antecedent anxiety modulate risk-taking behavior

Though real-world decisions are often made in the shadow of economic uncertainties, work problems, relationship troubles, existential angst, etc., the neural processes involved in this common experience remain poorly understood. Here, we randomly assigned participants (N = 97) to either a poignant experience of forecasted economic anxiety or a no-anxiety control condition. Using electroencephalography (EEG), we then examined how source-localized, anxiety-specific neural activation modulated risky decision making and strategic behavior in the Balloon Analogue Risk Task (BART). Previous research demonstrates opposing effects of anxiety on risk-taking, leading to contrasting predictions. On the one hand, activity in the dorsomedial PFC/anterior cingulate cortex (ACC) and anterior insula, brain regions linked with anxiety and sensitivity to risk, should mediate the effect of economic anxiety on increased risk-averse decision-making. On the other hand, activation in the ventromedial PFC, a brain region important in emotion regulation and subjective valuation in decision-making, should mediate the effect of economic anxiety on increased risky decision-making. Results revealed evidence related to both predictions. Additionally, anxiety-specific activation in the dmPFC/ACC and the anterior insula were associated with disrupted learning across the task. These results shed light on the neurobiology of antecedent anxiety and risk-taking and provide potential insight into understanding how real-world anxieties can impact decision-making processes.