The Benefit of Additional Opinions

Conformity: the hidden actor in surgical decisions

Conformity in decision making has a relevant impact in surgical environments. This manuscript reveals how group-based opinions can distort the surgeon's individual judgment, even in critical situations. Two key phenomena are analysed: communication cascades and group polarisation, which amplify conformity and lead to extreme and sometimes harmful decisions. It is important to highlight the importance of evidence-based education, critical thinking, and diversification of information sources to counteract the negative effects of conformity. Self-identifying conformist tendencies, encouraging open debate, and implementing constructive dissent strategies can help mitigate the effects of conformity in decision-making. Reviewing authority models and promoting diversity in surgical settings may improve decision-making and the quality of patient care.

Improving the Accuracy of a Biohybrid for Environmental Monitoring

Environmental monitoring should be minimally disruptive to the ecosystems that it is embedded in. Therefore, the project Robocoenosis suggests using biohybrids that blend into ecosystems and use life forms as sensors. However, such a biohybrid has limitations regarding memory-as well as power-capacities, and can only sample a limited number of organisms. We model the biohybrid and study the degree of accuracy that can be achieved by using a limited sample. Importantly, we consider potential misclassification errors (false positives and false negatives) that lower accuracy. We suggest the method of using two algorithms and pooling their estimations as a possible way of increasing the accuracy of the biohybrid. We show in simulation that a biohybrid could improve the accuracy of its diagnosis by doing so. The model suggests that for the estimation of the population rate of spinning Daphnia, two suboptimal algorithms for spinning detection outperform one qualitatively better algorithm. Further, the method of combining two estimations reduces the number of false negatives reported by the biohybrid, which we consider important in the context of detecting environmental catastrophes. Our method could improve environmental modeling in and outside of projects such as Robocoenosis and may find use in other fields.

Anchoring in a Social Context: How the Possibility of Being Misinformed by Others Impacts One's Judgment

Building on research about naïve theories of biases, we propose that people are more likely to engage in critical thinking when assessing others’ reasoning. Hence, anchoring effects should be reduced when anchor values are presented as others’ estimates and people perceive others as less knowledgeable (i.e., more prone to biases) than themselves. Three experiments tested this hypothesis by presenting the same anchors as other participants’ answers or without a specified source. This source manipulation was combined with explicit forewarnings about the anchoring effect, which have been shown to trigger debiasing efforts. In support of our hypothesis, results showed that anchors provided by a social source effectively reduced the anchoring effect and did so in a more reliable way than forewarnings. Furthermore, the response-time analysis in two of the experiments suggests that such attenuation was the result of deliberate adjustment.

An online randomised controlled trial of prognosticating imminent death in advanced cancer patients: Clinicians give greater weight to advice from a prognostic algorithm than from another clinician with a different profession

BACKGROUND

A second opinion or a prognostic algorithm may increase prognostic accuracy. This study assessed the level to which clinicians integrate advice perceived to be coming from another clinician or a prognostic algorithm into their prognostic estimates, and how participant characteristics and nature of advice received affect this.

METHODS

An online double-blind randomised controlled trial was conducted. Palliative doctors, nurses and other types of healthcare professionals were randomised into study arms differing by perceived source of advice (algorithm or another clinician). In fact, the advice was the same in both arms (emanating from the PiPS-B14 prognostic model). Each participant reviewed five patient summaries. For each summary, participants: (1) provided an initial probability estimate of two-week survival (0% 'certain death'-100% 'certain survival'); (2) received advice (another estimate); (3) provided a final estimate. Weight of Advice (WOA) was calculated for each summary (0 '100% advice discounting' - 1 '0% discounting') and multilevel linear regression analyses were conducted.

CLINICAL TRIAL REGISTRATION NUMBER

NCT04568629.

RESULTS

A total of 283 clinicians were included in the analysis. Clinicians integrated advice from the algorithm more than advice from another clinician (WOA difference = -0.12 [95% CI -0.18, -0.07], p < 0.001). There was no interaction between study arm and participant profession, years of palliative care or overall experience. Advice of intermediate strength (75%) was given a lower WOA (0.31) than advice received at either the 50% (WOA 0.40) or 90% level (WOA 0.43). The overall interaction between strength of advice and study arm on WOA was significant (p < 0.001).

CONCLUSION

Clinicians adjusted their prognostic estimates more when advice was perceived to come from a prognostic algorithm than from another clinician. Research is needed to understand how clinicians make prognostic decisions and how algorithms are used in clinical practice.

A meta-analysis of the weight of advice in decision-making

The degree to which people take advice, and the factors that influence advice-taking, are of broad interest to laypersons, professionals, and policy-makers. This meta-analysis on 346 effect sizes from 129 independent datasets (N = 17, 296) assessed the weight of advice in the judge-advisor system paradigm, as well as the influence of sample and task characteristics. Information about the advisor(s) that is suggestive of advice quality was the only unique predictor of the overall pooled weight of advice. Individuals adjusted estimates by 32%, 37%, and 48% in response to advisors described in ways that suggest low, neutral, or high quality advice, respectively. This indicates that the benefits of compromise and averaging may be lost if accurate advice is perceived to be low quality, or too much weight is given to inaccurate advice that is perceived to be high quality. When examining the three levels of perceived quality separately, advice-taking was greater for subjective and uncertain estimates, relative to objective estimates, when information about the advisor was neutral in terms of advice quality. Sample characteristics had no effect on advice-taking, thus providing no evidence that age, gender, or individualism influence the weight of advice. The findings contribute to current theoretical debates and provide direction for future research.

Algorithm-based advice taking and clinical judgement: impact of advice distance and algorithm information

Evidence-based algorithms can improve both lay and professional judgements and decisions, yet they remain underutilised. Research on advice taking established that humans tend to discount advice-especially when it contradicts their own judgement ("egocentric advice discounting")-but this can be mitigated by knowledge about the advisor's past performance. Advice discounting has typically been investigated using tasks with outcomes of low importance (e.g. general knowledge questions) and students as participants. Using the judge-advisor framework, we tested whether the principles of advice discounting apply in the clinical domain. We used realistic patient scenarios, algorithmic advice from a validated cancer risk calculator, and general practitioners (GPs) as participants. GPs could update their risk estimates after receiving algorithmic advice. Half of them received information about the algorithm's derivation, validation, and accuracy. We measured weight of advice and found that, on average, GPs weighed their estimates and the algorithm equally-but not always: they retained their initial estimates 29% of the time, and fully updated them 27% of the time. Updating did not depend on whether GPs were informed about the algorithm. We found a weak negative quadratic relationship between estimate updating and advice distance: although GPs integrate algorithmic advice on average, they may somewhat discount it, if it is very different from their own estimate. These results present a more complex picture than simple egocentric discounting of advice. They cast a more optimistic view of advice taking, where experts weigh algorithmic advice and their own judgement equally and move towards the advice even when it contradicts their own initial estimates.

Do Risky Scenarios Affect Forecasts of Savings and Expenses?

Many people do not possess the necessary savings to deal with unexpected financial events. People’s biases play a significant role in their ability to forecast future financial shocks: they are typically overoptimistic, present-oriented, and generally underestimate future expenses. The purpose of this study is to investigate how varying risk information influences people’s financial awareness, in order to reduce the chance of a financial downfall. Specifically, we contribute to the literature by exploring the concept of ‘nudging’ and its value for behavioural changes in personal financial management. While of great practical importance, the role of nudging in behavioural financial forecasting research is scarce. Additionally, the study steers away from the standard default choice architecture nudge, and adds originality by focusing on eliciting implementation intentions and precommitment strategies as types of nudges. Our experimental scenarios examined how people change their financial projections in response to nudges in the form of new information on relevant risks. Participants were asked to forecast future expenses and future savings. They then received information on potential events identified as high-risk, low-risk or no-risk. We investigated whether they adjusted their predictions in response to various risk scenarios or not and how such potential adjustments were affected by the information given. Our findings suggest that the provision of risk information alters financial forecasting behaviour. Notably, we found an adjustment effect even in the no-risk category, suggesting that governments and institutions concerned with financial behaviour can increase financial awareness merely by increasing salience about possible financial risks. Another practical implication relates to splitting savings into different categories, and by using different wordings: A financial advisory institution can help people in their financial behaviour by focusing on ‘targets’, and by encouraging (nudging) people to make breakdown forecasts rather than general ones.

Adapting to the algorithm: how accuracy comparisons promote the use of a decision aid

In three experiments, we sought to understand when and why people use an algorithm decision aid. Distinct from recent approaches, we explicitly enumerate the algorithm’s accuracy while also providing summary feedback and training that allowed participants to assess their own skills. Our results highlight that such direct performance comparisons between the algorithm and the individual encourages a strategy of selective reliance on the decision aid; individuals ignored the algorithm when the task was easier and relied on the algorithm when the task was harder. Our systematic investigation of summary feedback, training experience, and strategy hint manipulations shows that further opportunities to learn about the algorithm encourage not only increased reliance on the algorithm but also engagement in experimentation and verification of its recommendations. Together, our findings emphasize the decision-maker’s capacity to learn about the algorithm providing insights for how we can improve the use of decision aids.

Development of decision making based on internal and external information: A hierarchical Bayesian approach

In decision making, people may rely on their own information as well as on information from external sources, such as family members, peers, or experts. The current study investigated how these types of information are used by comparing four decision strategies: 1) an internal strategy that relies solely on own information; 2) an external strategy that relies solely on the information from an external source; 3) a sequential strategy that relies on information from an external source only after own information is deemed inadequate; 4) an integrative strategy that relies on an integration of both types of information. Of specific interest were individual and developmental differences in strategy use. Strategy use was examined via Bayesian hierarchical mixture model analysis. A visual decision task was administered to children and young adolescents (N=305, ages 9–14). Individual differences but no age-related changes were observed in either decision accuracy or strategy use. The internal strategy was dominant across ages, followed by the integrative and sequential strategy, respectively, while the external strategy was extremely rare. This suggests a reluctance to rely entirely on information provided by external sources. We conclude that there are individual differences but not developmental changes in strategy use pertaining to perceptual decision-making in 9- through 14-year-olds. Generalizability of these findings is discussed with regard to different forms of social influence and varying perceptions of the external source. This study provides stepping stones in better understanding and modeling decision making processes in the presence of both internal and external information.

Who are the "Real" Experts? The Debate Surrounding COVID-19 Health Risk Management: An Israeli Case Study

BACKGROUND

The uncertainty surrounding the COVID-19 crisis and the different approaches taken to manage it have triggered scientific controversies among experts. This study seeks to examine how the fragile nature of Israeli democracy accommodated differences of opinion between experts during the COVID-19 crisis.

OBJECTIVE

To map and analyze the discourse between experts surrounding issues that were the topic of scientific controversy. To examine the viewpoints of the public regarding the positions of the different experts.

METHODS AND SAMPLE

A sequential mixed study design. The qualitative research was a discourse analysis of 435 items that entailed mapping the voices of different experts regarding controversial topics. In the quantitative study, a total of 924 participants answered a questionnaire examining topics that engendered differences of opinion between the experts.

RESULTS

The results showed that there was no dialogue between opposition and coalition experts. Moreover, the coalition experts labeled the experts who criticized them as "coronavirus deniers" and "anti-vaxxers." The coalition changed its opinion on one issue only-the issue of lockdowns. When we asked the public how they see the scientific controversy between the coalition and the opposition experts, they expressed support for opposition policies on matters related to the implications of the lockdowns and to transparency, while supporting government policy mainly on topics related to vaccinations. The research findings also indicate that personal and socio-demographic variables can influence how the public responds to the debate between experts. The main differentiating variables were the personal attribute of conservatism, locus of control, age, and nationality.

CONCLUSION

Controversy must be encouraged to prevent misconceptions. The internal discourse in the committees that advise the government must be transparent, and coalition experts must be consistently exposed to the views of opposition experts, who must be free to voice their views without fear.

Why does peer instruction benefit student learning?

In peer instruction, instructors pose a challenging question to students, students answer the question individually, students work with a partner in the class to discuss their answers, and finally students answer the question again. A large body of evidence shows that peer instruction benefits student learning. To determine the mechanism for these benefits, we collected semester-long data from six classes, involving a total of 208 undergraduate students being asked a total of 86 different questions related to their course content. For each question, students chose their answer individually, reported their confidence, discussed their answers with their partner, and then indicated their possibly revised answer and confidence again. Overall, students were more accurate and confident after discussion than before. Initially correct students were more likely to keep their answers than initially incorrect students, and this tendency was partially but not completely attributable to differences in confidence. We discuss the benefits of peer instruction in terms of differences in the coherence of explanations, social learning, and the contextual factors that influence confidence and accuracy.

Overcoming the Shadow of Expertise: How Humility and Learning Goal Orientation Help Knowledge Leaders Become More Flexible

Although experts are valuable assets to organizations, they suffer from the curse of knowledge and cognitive entrenchment, which prevents them from being able to adapt to changing situational demands. In this study, I propose that experts' performance goal orientation resulting from pressures to perform contributes to their flexibility, but this mechanism can be moderated by learning goal orientation and humility. Data from a small sample of healthcare professionals suggested that performance goal orientation partially explained the mechanism of why experts may be inflexible. Humility, both as self-report and other-report measures, was found to be the most consistent moderator of this indirect effect. Experts with low levels of humility suffered from the negative effects of performance goal orientation, leading them to be less flexible compared to their counterparts with higher levels of humility. Experts who reported high levels of humility, on the other hand, were perceived to be more flexible as their expertise increased. Meanwhile, learning goal orientation partially moderated the indirect effect of expertise on flexibility through performance goal orientation. These findings lead to new conversations on how to get experts unstuck and highlight the importance of developing humility as both a personal virtue and a strategic advantage for organizations.

Why dyads heed advice less than individuals do

Following up on a recent debate, we examined advice taking in dyads compared to individuals in a set of three studies (total N = 303 dyads and 194 individuals). Our first aim was to test the replicability of an important previous finding, namely that dyads heed advice less than individuals because they feel more confident in the accuracy of their initial judgments. Second, we aimed to explain dyads’ behavior based on three premises: first, that dyads understand that the added value of an outside opinion diminishes when the initial pre-advice judgment is made by two judges rather than one judge (given that the dyad members’ opinions are independent of each other); second, that they fail to recognize when the assumption of independence of opinions does not hold; and third, that the resistance to advice commonly observed in individuals persists in groups but is neither aggravated nor ameliorated by the group context. The results of our studies show consistently that previous findings on advice taking in dyads are replicable. They also support our hypothesis that groups exhibit a general tendency to heed advice less than individuals, irrespective of whether the accuracy of their initial judgments warrants this behavior. Finally, based on the three assumptions mentioned above, we were able to make accurate predictions about advice taking in dyads, prompting us to postulate a general model of advice taking in groups of arbitrary size.

The impact of advice distance on advice taking: Evidence from an ERP study

To use ERPs to investigate the impact of advice distance on advice taking in the Judge-Advisor System (JAS) paradigm, behavioural and ERP data were recorded from 20 subjects. The behavioural discrepancies and EEG characteristics of advice taking, as affected by the advice distances, were compared. The results showed that the relationship between the modification rate of a decision maker's initial estimation and the advice distance exhibited an inverse U-shaped curve. During the advice evaluation stage, different advice distances induced the feedback-related negativity (FRN) and subsequent P300 components. The amplitude of the FRN increased as the advice distance increased, while the amplitude of P300 in the zero distance condition was significantly larger than those in the intermediate and far distance conditions. These findings indicate that the advice evaluation process of a decision maker is related to conflict perception, prediction error and motivational or affective significance.

Why Does Advice Discounting Occur? The Combined Roles of Confidence and Trust

Judges tend to discount the opinions of others even though advice is often helpful in improving their accuracy. The present research proposes that this phenomenon of advice discounting results from the judges' confidence in their initial decision and little trust in advice. Furthermore, the degree of advice discounting may be predicted by the combined roles of confidence and trust. Three studies provide evidence for these hypotheses. Participants were very confident in their initial estimation and had little trust in the advice (study 1). The degree of advice discounting decreased when participants felt less confidence in performing difficult tasks compared with easy tasks (study 2) or when participants placed more trust in advice because the advice was from an expert rather than from a novice (study 3). In addition, confidence and trust predicted the degree of advice discounting across three studies. These findings shed new light on the mechanism underlying advice discounting and advice taking by indicating the combined roles of confidence and trust.

Dear Abby: Should I Give Advice or Receive It?

Typically, individuals struggling with goal achievement seek advice. However, in the present investigation ( N = 2,274), struggling individuals were more motivated by giving advice than receiving it. In a randomized, controlled, double-blind field experiment, middle-school students who gave motivational advice to younger students spent more time on homework over the following month than students who received motivational advice from expert teachers (Experiment 1). This phenomenon was replicated across self-regulatory domains: Strugglers who gave advice, compared with those who received expert advice, were more motivated to save money, control their tempers, lose weight, and seek employment (Experiments 2 and 3). Nevertheless, across domains, people erroneously predicted the opposite, expecting themselves and others to be less motivated by giving advice than receiving it (Experiments 2 and 3). Why are people blind to the motivational power of giving? Giving advice motivated givers by raising their confidence-a reality that predictors fail to anticipate (Experiment 4).

Demographically diverse crowds are typically not much wiser than homogeneous crowds

Averaging independent numerical judgments can be more accurate than the average individual judgment. This "wisdom of crowds" effect has been shown with large, diverse samples, but the layperson wishing to take advantage of this may only have access to the opinions of a small, more demographically homogeneous "convenience sample." How wise are homogeneous crowds relative to diverse crowds? In simulations and survey studies, we demonstrate three necessary conditions under which small socially diverse crowds can outperform socially homogeneous crowds: Social identity must predict judgment, the effect of social identity on judgment must be at least moderate in size, and the average estimates of the social groups in question must "bracket" the truth being judged. Seven survey studies suggest that these conditions are rarely met in real judgment tasks. Comparisons between the performances of diverse and homogeneous crowds further confirm that social diversity can make crowds wiser but typically by a very small margin.

On the Inability to Ignore Useless Advice

Research in the judge-advisor-paradigm suggests that advice is generally utilized less than it should be according to its quality. In a series of four experiments, we challenge this widely held assumption. We hypothesize that when advice quality is low, the opposite phenomenon, namely overutilization of advice, occurs. We further assume that this overutilization effect is the result of anchoring: advice serves as an anchor, thus causing an adjustment toward even useless advice. The data of our four experiments support these hypotheses. Judges systematically adjusted their estimates toward advice that we introduced to them as being useless, and this effect was stable after controlling for intentional utilization of this advice. Furthermore, we demonstrate that anchoring-based adjustment toward advice is independent of advice quality. Our findings enhance our understanding of the processes involved in advice taking and identify a potential threat to judgment accuracy arising from an inability to discount useless advice.

Taking Advice for Vocational Decisions: Regulatory Fit Effects

This study examined whether taking advice is influenced by regulatory fit and whether this effect is reduced or disappears within certain attribution conditions during vocational decision making. Experiment 1 created a vocational decision setting to compare differences in decision makers’ weight of advice (WOA) between ‘eager strategy’ and ‘vigilant strategy’ advice conditions. Results showed no significant main effect of regulatory orientation or advice strategy, but there was a significant interaction. The WOA value, with fit between regulatory focus and advice strategy, was higher than with a fit violation. Experiment 2 examined whether the regulatory fit effect is reduced or disappears within attribution conditions during vocational decision making. Results showed job seekers more easily take others’ advice under the fit condition, and a significant interaction existed between regulatory fit and attribution. Thus, attribution could reduce the influence of the regulatory fit effect. Implications for vocational consultants, job seekers, and advisors are also discussed.

Evidence for arrogance: On the relative importance of expertise, outcome, and manner

Arrogant behavior is as old as human nature. Nonetheless, the factors that cause people to be perceived as arrogant have received very little research attention. In this paper, we focused on a typical manifestation of arrogance: dismissive behavior. In particular, we explored the conditions under which a person who dismissed advice would be perceived as arrogant. We examined two factors: the advisee's competence, and the manner in which he or she dismissed the advice. The effect of the advisee's competence was tested by manipulating two competence cues: relative expertise, and the outcome of the advice dismissal (i.e., whether the advisee was right or wrong). In six studies (N = 1304), participants made arrogance judgments about protagonists who dismissed the advice of another person while the advisees' relative expertise (compared to the advisor), their eventual correctness, and the manner of their dismissal were manipulated in between-participant designs. Across various types of decisions and advisee-advisor relationships, the results show that less expert, less correct, and ruder advisees are perceived as more arrogant. We also find that outcome trumps expertise, and manner trumps both expertise and outcomes. In two additional studies (N = 101), we examined people's naïve theories about the relative importance of the aforementioned arrogance cues. These studies showed that people overestimate the role of expertise information as compared to the role of interpersonal manner and outcomes. Thus, our results suggest that people may commit arrogant faux pas because they erroneously expect that their expertise will justify their dismissive behavior.

When Advisors' True Intentions Are in Question. How Do Bank Customers Cope with Uncertainty in Financial Consultancies?

When making financial decisions bank customers are confronted with two types of uncertainty: first, return on investments is uncertain and there is a risk of losing money. Second, customers cannot be certain about their financial advisor’s true intentions. This might decrease customers’ willingness to cooperate with advisors. However, the uncertainty management model and fairness heuristic theory predict that in uncertain situations customers are willing to cooperate with financial advisors when they perceive fairness. In the current study, we investigated how perceived fairness in the twofold uncertain situations increased people’s intended future cooperation with an advisor. We asked customers of financial consultancies about their experienced uncertainty regarding both the investment decision and the advisor’s intentions. Moreover, we asked them about their perceived fairness, as well as their intention to cooperate with the advisor in the future. A three-way moderation analysis showed that customers who faced high uncertainty regarding the investment decision and high uncertainty regarding the advisor’s true intentions indicated the lowest intended cooperation with the advisor but high fairness increased their cooperation. Interestingly, when people were only uncertain about the advisor’s intentions (but certain about the decision) they indicated less cooperation than when they were only uncertain about the decision (but certain about the advisor’s intentions). A mediated moderation analysis revealed that this relationship was explained by customers’ lower trust in their advisors.

The Potential of Collective Intelligence in Emergency Medicine: Pooling Medical Students' Independent Decisions Improves Diagnostic Performance

BACKGROUND

Evidence suggests that pooling multiple independent diagnoses can improve diagnostic accuracy in well-defined tasks. We investigated whether this is also the case for diagnostics in emergency medicine, an ill-defined task environment where diagnostic errors are rife.

METHODS

A computer simulation study was conducted based on empirical data from 2 published experimental studies. In the computer experiments, 285 medical students independently diagnosed 6 simulated patients arriving at the emergency room with dyspnea. Participants' diagnoses (n = 1,710), confidence ratings, and expertise levels were entered into a computer simulation. Virtual groups of different sizes were randomly created, and 3 collective intelligence rules (follow-the-plurality rule, follow-the-most-confident rule, and follow-the-most-senior rule) were applied to combine the independent decisions into a final diagnosis. For different group sizes, the performance levels (i.e., percentage of correct diagnoses) of the 3 collective intelligence rules were compared with each other and against the average individual accuracy.

RESULTS

For all collective intelligence rules, combining independent decisions substantially increased performance relative to average individual performance. For groups of 4 or fewer, the follow-the-most-confident rule outperformed the other rules; for larger groups, the follow-the-plurality rule performed best. For example, combining 5 independent decisions using the follow-the-plurality rule increased diagnostic accuracy by 22 percentage points. These results were robust across case difficulty and expertise level. Limitations of the study include the use of simulated patients diagnosed by medical students. Whether results generalize to clinical practice is currently unknown.

CONCLUSION

Combining independent decisions may substantially improve the quality of diagnoses in emergency medicine and may thus enhance patient safety.

Influence of event characteristics on assessing credibility and advice-taking

Purpose

The purpose of this paper is to examine how the environment surrounding a decision-making event affects whether decision-makers consider the credibility of their advisors and take their advice.

Design/methodology/approach

In two experiments, the characteristics of the event and credibility of the advisor were manipulated, the extent to which participants considered the information from the advisor was measured, and whether participants took advice was determined.

Findings

Decision-makers are more likely to take advice from advisors when the decision-making event is of low urgency or high criticality because they are more likely to consider information provided by high-credibility advisors.

Practical implications

Within organizations, decision-makers may be making suboptimal decisions when faced with highly urgent decisions or decisions with low criticality. This study suggests that under these conditions, decision-makers are less likely to consider the information provided by high-credibility advisors. Organizations may consider encouraging decision-makers to override their tendency to disregard advice from credible advisors.

Originality/value

This study introduces a contextual factor relevant to managers, event characteristics, which has an effect on whether decision-makers take advice. A unique experimental design was utilized in which credibility was manipulated across two studies with an explicit (Study 1: resume) vs implicit (Study 2: video) method, and advice-taking was measured with a decision that was clearly right or wrong.

Effects of the Advisor and Environment on Requesting and Complying With Automated Advice

Given the rapid technological advances in our society and the increase in artificial and automated advisors with whom we interact on a daily basis, it is becoming increasingly necessary to understand how users interact with and why they choose to request and follow advice from these types of advisors. More specifically, it is necessary to understand errors in advice utilization. In the present study, we propose a methodological framework for studying interactions between users and automated or other artificial advisors. Specifically, we propose the use of virtual environments and the tarp technique for stimulus sampling, ensuring sufficient sampling of important extreme values and the stimulus space between those extremes. We use this proposed framework to identify the impact of several factors on when and how advice is used. Additionally, because these interactions take place in different environments, we explore the impact of where the interaction takes place on the decision to interact. We varied the cost of advice, the reliability of the advisor, and the predictability of the environment to better understand the impact of these factors on the overutilization of suboptimal advisors and underutilization of optimal advisors. We found that less predictable environments, more reliable advisors, and lower costs for advice led to overutilization, whereas more predictable environments and less reliable advisors led to underutilization. Moreover, once advice was received, users took longer to make a final decision, suggesting less confidence and trust in the advisor when the reliability of the advisor was lower, the environment was less predictable, and the advice was not consistent with the environmental cues. These results contribute to a more complete understanding of advice utilization and trust in advisors.

Seeking advice: A sampling approach to advice taking

The present research addresses advice taking from a holistic perspective covering both advice seeking and weighting. We build on previous theorizing that assumes that underweighting of advice results from biased samples of information. That is, decision makers have more knowledge supporting their own judgment than that of another person and thus weight the former stronger than the latter. In the present approach, we assume that participants reduce this informational asymmetry by the sampling of advice and that sampling frequency depends on the information ecology. Advice that is distant from the decision maker’s initial estimate should lead to a higher frequency of advice sampling than close advice. Moreover, we assume that advice distant from the decision maker’s initial estimate and advice that is supported by larger samples of advisory estimates are weighted more strongly in the final judgment. We expand the classical research paradigm with a sampling phase that allows participants to sample any number of advisory estimates before revising their judgments. Three experiments strongly support these hypotheses, thereby advancing our understanding of advice taking as an adaptive process.

Modelling Influence and Opinion Evolution in Online Collective Behaviour

Opinion evolution and judgment revision are mediated through social influence. Based on a large crowdsourced in vitro experiment (n = 861), it is shown how a consensus model can be used to predict opinion evolution in online collective behaviour. It is the first time the predictive power of a quantitative model of opinion dynamics is tested against a real dataset. Unlike previous research on the topic, the model was validated on data which did not serve to calibrate it. This avoids to favor more complex models over more simple ones and prevents overfitting. The model is parametrized by the influenceability of each individual, a factor representing to what extent individuals incorporate external judgments. The prediction accuracy depends on prior knowledge on the participants’ past behaviour. Several situations reflecting data availability are compared. When the data is scarce, the data from previous participants is used to predict how a new participant will behave. Judgment revision includes unpredictable variations which limit the potential for prediction. A first measure of unpredictability is proposed. The measure is based on a specific control experiment. More than two thirds of the prediction errors are found to occur due to unpredictability of the human judgment revision process rather than to model imperfection.

Consider the source: adolescents and adults similarly follow older adult advice more than peer advice

Individuals learn which of their actions are likely to be rewarded through trial and error. This form of learning is critical for adapting to new situations, which adolescents frequently encounter. Adolescents are also greatly influenced by their peers. The current study tested the extent to which adolescents rely on peer advice to guide their actions. Adolescent and young adult participants completed a probabilistic learning task in which they chose between four pairs of stimuli with different reinforcement probabilities, with one stimulus in each pair more frequently rewarded. Participants received advice about two of these pairs, once from a similarly aged peer and once from an older adult. Crucially, this advice was inaccurate, enabling the dissociation between experience-based and instruction-based learning. Adolescents and adults learned equally well from experience and no age group difference was evident in the overall influence of advice on choices. Surprisingly, when considering the source of advice, there was no evident influence of peer advice on adolescent choices. However, both adolescents and adults were biased toward choosing the stimulus recommended by the older adult. Contrary to conventional wisdom, these data suggest that adolescents may prioritize the advice of older adults over that of peers in certain decision-making contexts.

Young children heed advice selectively

A rational strategy to update and revise one's uncertain beliefs is to take advice by other agents who are better informed. Adults routinely engage in such advice taking in systematic and selective ways depending on relevant characteristics such as reliability of advisors. The current study merged research in social and developmental psychology to examine whether children also adjust their initial judgment to varying degrees depending on the characteristics of their advisors. Participants aged 3 to 6 years played a game in which they made initial judgments, received advice, and subsequently made final judgments. They systematically revised their judgments in light of the advice, and they did so selectively as a function of advisor expertise. They made greater adjustments to their initial judgment when advised by an apparently knowledgeable informant. This suggests that the pattern of advice taking studied in social psychology has its roots in early development.

The effectiveness of imperfect weighting in advice taking

We investigate decision-making in the Judge-Advisor-System where one person, the “judge”, wants to estimate the number of a certain entity and is given advice by another person. The question is how to combine the judge’s initial estimate and that of the advisor in order to get the optimal expected outcome. A previous approach compared two frequently applied strategies, taking the average or choosing the better estimate. In most situations, averaging produced the better estimates. However, this approach neglected a third strategy that judges frequently use, namely a weighted mean of the judges’ initial estimate and the advice. We compare the performance of averaging and choosing to weighting in a theoretical analysis. If the judge can, without error, detect ability differences between judge and advisor, a straight-forward calculation shows that weighting outperforms both of these strategies. More interestingly, after introducing errors in the perception of the ability differences, we show that such imperfect weighting may or may not be the optimal strategy. The relative performance of imperfect weighting compared to averaging or choosing depends on the size of the actual ability differences as well as the magnitude of the error. However, for a sizeable range of ability differences and errors, weighting is preferable to averaging and more so to choosing. Our analysis expands previous research by showing that weighting, even when imperfect, is an appropriate advice taking strategy and under which circumstances judges benefit most from applying it.