How Cues of Being Watched Promote Risk Seeking in Fund Investment in Older Adults

Social cues, such as being watched, can subtly alter fund investment choices. This study aimed to investigate how cues of being watched influence decision-making, attention allocation, and risk tendencies. Using decision scenarios adopted from the “Asian Disease Problem,” we examined participants’ risk tendency in a financial scenario when they were watched. A total of 63 older and 66 younger adults participated. Eye tracking was used to reveal the decision-maker’s attention allocation (fixations and dwell time per word). The results found that both younger and older adults tend to seek risk in the loss frame than in the gain frame (i.e., framing effect). Watching eyes tended to escalate reckless gambling behaviors among older adults, which led them to maintain their share in the depressed fund market, regardless of whether the options were gain or loss framed. The eye-tracking results revealed that older adults gave less attention to the sure option in the eye condition (i.e., fewer fixations and shorter dwell time). However, their attention was maintained on the gamble options. In comparison, images of “watching eyes” did not influence the risk seeking of younger adults but decreased their framing effect. Being watched can affect financial risk preference in decision-making. The exploration of the contextual sensitivity of being watched provides us with insight into developing decision aids to promote rational financial decision-making, such as human-robot interactions. Future research on age differences still requires further replication.

What are the chances? How descriptions of probability affect risky decisions

<p>People make different decisions when they know the odds of an event occurring, (e.g. told 10% chance of an earthquake that year) than when they draw on only their own experience (e.g. living in a city with, on average, one earthquake every 10 years). It may be that when we make decisions based on our past experience (decisions from experience) we are more likely to choose a risky option when it can lead to the biggest win and avoid it when it can lead to the biggest loss, this effect is called the Extreme-Outcome rule. Across three Experiments we tested the Extreme-Outcome rule by having participants make repeated choices between either safe or risky options which had the same expected value. In each experiment, we varied the magnitude of the reinforcer’s participants could win in both an Experience condition and a condition that had both description and experience information. In Experiment 1 where we had two reinforcer sizes (small and large) we found an Extreme-Outcome effect in the Experience condition, but not the Description-Experience condition. In Experiment 2 we tested a prediction of the Extreme-Outcome rule that participants would be sensitive to the best and worst outcome by adding another reinforcer size (reinforcers were small, medium and large) and therefore on some trials neither alternative included an extreme outcome. We also removed zero as a potential outcome to investigate whether zero aversion might be driving the effect of reinforcer magnitude in the Experience condition. We did not find response patterns consistent with an Extreme-Outcome rule in the Experience condition. Instead, participants were least risk seeking when the reinforcer was small, but there was no difference in levels of risk seeking between the medium and large reinforcer trials. In other words, there was an effect of the low-extreme outcome but not the high-extreme outcome. Like Experiment 1, in the Description-Experience condition risk preference was not influenced by reinforcer size, but the absolute levels were higher. To investigate whether this increase in risk preference was due to removing the zero, in Experiment 3 we manipulated whether zero was present or absent. When zero was absent, risk preference was not influenced by the size of the reinforcer in the Description-Experience condition, but there was an effect of the low-extreme outcome when zero was present. We also found an effect of the low extreme outcome in the Experience condition regardless of whether zero was present or absent. Overall, these findings suggest the Extreme-Outcome rule needs to be modified to take into account the effect of the low extreme but not the high extreme outcome.</p>

What are the chances? How descriptions of probability affect risky decisions

<p>People make different decisions when they know the odds of an event occurring, (e.g. told 10% chance of an earthquake that year) than when they draw on only their own experience (e.g. living in a city with, on average, one earthquake every 10 years). It may be that when we make decisions based on our past experience (decisions from experience) we are more likely to choose a risky option when it can lead to the biggest win and avoid it when it can lead to the biggest loss, this effect is called the Extreme-Outcome rule. Across three Experiments we tested the Extreme-Outcome rule by having participants make repeated choices between either safe or risky options which had the same expected value. In each experiment, we varied the magnitude of the reinforcer’s participants could win in both an Experience condition and a condition that had both description and experience information. In Experiment 1 where we had two reinforcer sizes (small and large) we found an Extreme-Outcome effect in the Experience condition, but not the Description-Experience condition. In Experiment 2 we tested a prediction of the Extreme-Outcome rule that participants would be sensitive to the best and worst outcome by adding another reinforcer size (reinforcers were small, medium and large) and therefore on some trials neither alternative included an extreme outcome. We also removed zero as a potential outcome to investigate whether zero aversion might be driving the effect of reinforcer magnitude in the Experience condition. We did not find response patterns consistent with an Extreme-Outcome rule in the Experience condition. Instead, participants were least risk seeking when the reinforcer was small, but there was no difference in levels of risk seeking between the medium and large reinforcer trials. In other words, there was an effect of the low-extreme outcome but not the high-extreme outcome. Like Experiment 1, in the Description-Experience condition risk preference was not influenced by reinforcer size, but the absolute levels were higher. To investigate whether this increase in risk preference was due to removing the zero, in Experiment 3 we manipulated whether zero was present or absent. When zero was absent, risk preference was not influenced by the size of the reinforcer in the Description-Experience condition, but there was an effect of the low-extreme outcome when zero was present. We also found an effect of the low extreme outcome in the Experience condition regardless of whether zero was present or absent. Overall, these findings suggest the Extreme-Outcome rule needs to be modified to take into account the effect of the low extreme but not the high extreme outcome.</p>

Gain-loss asymmetry in human experiential tasks

<p>Prospect Theory models behaviour in one-off decisions where outcomes are described. Prospect Theory describes risk aversion when the choice is between gains and risk seeking when the choice is between losses. This asymmetry is known as the reflection effect. In choices about experienced outcomes, individuals show risk seeking for gains and risk aversion for losses. This change in the direction of gain-loss asymmetry is known as the description-experience gap. Across eight experiments, we examined gain-loss asymmetry in two experiential choice procedures. We compared the obtained results with predictions derived from Prospect Theory and the description-experience gap literature. In Study 1, we evaluated the predictions of the reversed reflection effect in probability discounting. Probability discounting is loss in reinforcer value as a function of uncertainty. In typical tasks measuring discounting, participants choose between smaller, certain amounts and a larger amount at one of several probabilities. In choice from description, most participants show a gain-loss asymmetry consistent with the predictions of the reflection effect, discounting gains more steeply than losses. Across three experiments, we examined whether gain-loss asymmetry also occurred when participants experienced the outcomes they chose, when they chose between two uncertain options, and when these two contexts were combined. Across all of the above contexts, no consistent mean difference in discounting of gains and losses was observed. Rather, in most of the tasks that provided experienced outcomes, the participants showed steeper discounting in the first condition completed, whether it involved choices about gains or losses. Furthermore, subsequent conditions produced shallower discounting, but notably, not shallower than choice based on the expected value of the options. In Studies 2 and 3, we followed-up on this order effect by providing the participants with experience of probabilistic outcomes before the discounting tasks. Participants discounted losses more steeply than gains, consistent with the predictions of a reversed reflection effect. In Study 2, we examined gain-loss asymmetry in a rapid-acquisition choice procedure using concurrent variable-interval schedules – the Auckland Card Task. Participants repeatedly chose between two decks of cards that varied in the frequency or magnitude of available gains or losses. Participants were more sensitive to changes in gain than loss frequency between the two decks, consistent with the predictions of a reversed reflection effect, while sensitivity to gain and loss magnitude did not show an asymmetry. We found a novel asymmetry in the local effects of gains and losses. In the frequency tasks, gains disrupted the general pattern of responding more than losses. In the magnitude tasks, varying the magnitude of losses had a bigger effect on local-level patterns following outcomes than varying the magnitude of gains. Across the two tasks we observed patterns of gain-loss asymmetry consistent with the predictions of a reversed reflection effect. We also observed several inconsistencies, particularly when comparing behaviour to choices that would maximize the expected returns. Our research suggested that sufficient exposure to chance outcomes and ensuring delivery of scheduled events are key challenges in further refinement of experiential choice in human operant tasks.</p>

Gain-loss asymmetry in human experiential tasks

<p>Prospect Theory models behaviour in one-off decisions where outcomes are described. Prospect Theory describes risk aversion when the choice is between gains and risk seeking when the choice is between losses. This asymmetry is known as the reflection effect. In choices about experienced outcomes, individuals show risk seeking for gains and risk aversion for losses. This change in the direction of gain-loss asymmetry is known as the description-experience gap. Across eight experiments, we examined gain-loss asymmetry in two experiential choice procedures. We compared the obtained results with predictions derived from Prospect Theory and the description-experience gap literature. In Study 1, we evaluated the predictions of the reversed reflection effect in probability discounting. Probability discounting is loss in reinforcer value as a function of uncertainty. In typical tasks measuring discounting, participants choose between smaller, certain amounts and a larger amount at one of several probabilities. In choice from description, most participants show a gain-loss asymmetry consistent with the predictions of the reflection effect, discounting gains more steeply than losses. Across three experiments, we examined whether gain-loss asymmetry also occurred when participants experienced the outcomes they chose, when they chose between two uncertain options, and when these two contexts were combined. Across all of the above contexts, no consistent mean difference in discounting of gains and losses was observed. Rather, in most of the tasks that provided experienced outcomes, the participants showed steeper discounting in the first condition completed, whether it involved choices about gains or losses. Furthermore, subsequent conditions produced shallower discounting, but notably, not shallower than choice based on the expected value of the options. In Studies 2 and 3, we followed-up on this order effect by providing the participants with experience of probabilistic outcomes before the discounting tasks. Participants discounted losses more steeply than gains, consistent with the predictions of a reversed reflection effect. In Study 2, we examined gain-loss asymmetry in a rapid-acquisition choice procedure using concurrent variable-interval schedules – the Auckland Card Task. Participants repeatedly chose between two decks of cards that varied in the frequency or magnitude of available gains or losses. Participants were more sensitive to changes in gain than loss frequency between the two decks, consistent with the predictions of a reversed reflection effect, while sensitivity to gain and loss magnitude did not show an asymmetry. We found a novel asymmetry in the local effects of gains and losses. In the frequency tasks, gains disrupted the general pattern of responding more than losses. In the magnitude tasks, varying the magnitude of losses had a bigger effect on local-level patterns following outcomes than varying the magnitude of gains. Across the two tasks we observed patterns of gain-loss asymmetry consistent with the predictions of a reversed reflection effect. We also observed several inconsistencies, particularly when comparing behaviour to choices that would maximize the expected returns. Our research suggested that sufficient exposure to chance outcomes and ensuring delivery of scheduled events are key challenges in further refinement of experiential choice in human operant tasks.</p>

Irrationality of Attitudes toward Safety under Complexity and Uncertainty Leading to Asymmetry of Information

This study investigated how complexity and uncertainty, the probability of accidents, and the probability of financial trouble affected individuals’ recognition of validity of irrational risk-seeking decisions. As a result of conducting a multiple regression analysis on the validation score for irrational risk-seeking alternative obtained by a questionnaire survey, we found that the validity score for an irrational risk-seeking alternative was higher when both complexity and uncertainty were high than when both complexity and uncertainty were low, which means that high complexity and high uncertainty in the situation of decision making more readily leads to an irrational risk-seeking behavior that might trigger a major accident. Beyond complexity and uncertainty, the damage of major accident α, the decrease of the probability of major accidents and the increase of the probability of financial trouble (economic factor) were also found to promote the choice of irrational risk-seeking alternatives. Some implications for safety management under high complexity and uncertainty are discussed.

EXPRESS: Lay, professional, and artificial intelligence perspectives on risky medical decisions and COVID-19: How does the number of lives matter in clinical trials framed as gains versus losses?

Outcomes of clinical trials need to be communicated effectively to make decisions that save lives. We investigated whether framing can bias these decisions and if risk preferences shift depending on the number of patients. Hypothetical information about two medicines used in clinical trials having a sure or a risky outcome was presented in either a gain frame (people would be saved) or a loss frame (people would die). The number of patients who signed up for the clinical trials was manipulated in both frames in all the experiments. Using an unnamed disease, lay participants (experiment 1) and would-be medical professionals (experiment 2) were asked to choose which medicine they would have administered. For COVID-19, lay participants were asked which medicine should medical professionals (experiment 3), artificially intelligent software (experiment 4), and they themselves (experiment 5) favor to be administered. Broadly consistent with prospect theory, people were more risk-seeking in the loss frames than the gain frames. Risk-aversion in gain frames was sensitive to the number of lives with risk-neutrality at low magnitudes and risk-aversion at high magnitudes. In the loss frame, participants were mostly risk-seeking. This pattern was consistent across laypersons and medical professionals, further extended to preferences for choices that medical professionals and artificial intelligence programs should make in the context of COVID-19. These results underscore how medical decisions can be impacted by the number of lives at stake and reveal inconsistent risk preferences for clinical trials during a real pandemic.

P082 Gender moderates the effects of total sleep deprivation and sleep restriction on risk preference

Introduction Total sleep deprivation (TSD) affects risk preference in decision-making. However, little work has examined the effects of sleep restriction (SR), or the potentially moderating role of gender, on risk preference. Here, we investigate the effects of TSD, SR, and gender on risky decision-making. Methods 47 healthy adults (age=24.57±5.26 years, 24F) were randomly assigned to either of 2 counterbalanced protocols: 1) well-rested (WR: 9-hours time-in-bed for 6 nights) and 30hours TSD; or 2) WR and SR (4-hours time-in-bed for 4 nights). Participants performed the Lottery Choice Task (LCT) on the last day of each week. LCT requires a series of choices between two risky gambles with different risk levels. In one block, participants sought to maximise monetary gain (GAINS), and in another block, they sought to minimise losses (LOSSES). A trial-level analysis evaluated participants’ likelihood of choosing the “safer” gamble under influence of each sleep condition. Results The version*condition*gender interaction was significant. GAINS: everyone became more risk averse during TSD. Females also became more risk averse during SR, but males did not. LOSSES: everyone became more risk seeking during SR. During TSD, females became relatively more risk averse, while males became relatively more risk seeking. Conclusion TSD and SR had similar impacts on risk preference. However, gender moderated some effects. Women generally became more risk averse during sleep loss for both GAINS and LOSSES. Men were more risk averse for GAINS and risk seeking for LOSSES. This has implications for real-world situations where individuals are required to make risky decisions.

Evolutionary Game Analysis of Online Shopping Quality Control: The Roles of Risk Attitude and Government Supervision

The product quality issues of online shopping have not been effectively solved with the rapid development of electronic commerce in China. Faced with the quality issues of online shopping, the main entities, such as online platforms and online sellers, should cooperate to control and assure compliant quality. The adverse selection of buyers caused by information asymmetry and the moral hazard of sellers aggravated by bilateral markets make the quality management of online shopping have a high degree of uncertainty and complexity. This study constructs an evolutionary game model to discuss the dynamic process of the quality behaviours of members and analyse the key triggers of the evolutionary directions in online shopping. By using numerical simulation analysis, the influences of government supervision intensities on the evolution trend of members’ quality behaviours are analysed with various risk attitude combinations in various cases. The current research finds that (1) the decrease of the quality control cost and the increase of the government’s benchmark fine are conducive to increasing the platform’s controlling probability; (2) the decrease of the proportion of rent and the increase of sellers’ fraud cost and the government’s benchmark fines are conducive to increasing the seller’s compliance probability; (3) when both the platform and seller are unwilling to make efforts for quality assurance, risk-seeking members make the seller gradually choose to provide compliant products; and (4) when the members are all risk-seeking, strengthening government supervision will help promote the transition of members’ behavioural choices in a benign direction. This paper enriches the theoretical research studies on both online product quality control and government-led social governance system of quality. Suggestions for government supervision and e-commerce entities are also provided for quality assurance and improvement.