Debiasing the crowd: selectively exchanging social information improves collective decision making

Collective decision making is ubiquitous across biological systems. However, biases at the individual level can impair the quality of collective decisions. One prime bias is the human tendency to underestimate quantities. We performed estimation experiments in human groups, in which we re-wired the structure of information exchange, favouring the exchange of estimates closest to an overestimation of the median, expected to approximate the truth. We show that this re-wiring of social information exchange counteracts the underestimation bias and boosts collective decisions compared to random exchange. Underlying this result are a human tendency to herd, to trust large numbers more than small numbers, and to follow disparate social information less. We introduce a model that reproduces all the main empirical results, and predicts conditions for optimising collective decisions. Our results show that leveraging existing knowledge on biases can boost collective decision making, paving the way for combating other cognitive biases threatening collective systems

Wise or mad crowds? The cognitive mechanisms underlying information cascades

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.Whether getting vaccinated, buying stocks, or crossing streets, people rarely make decisions alone. Rather, multiple people decide sequentially, setting the stage for information cascades whereby early-deciding individuals can influence others’ choices. To understand how information cascades through social systems, it is essential to capture the dynamics of the decision-making process. We introduce the social drift–diffusion model to capture these dynamics. We tested our model using a sequential choice task. The model was able to recover the dynamics of the social decision-making process, accurately capturing how individuals integrate personal and social information dynamically over time and when their decisions were timed. Our results show the importance of the interrelationships between accuracy, confidence, and response time in shaping the quality of information cascades. The model reveals the importance of capturing the dynamics of decision processes to understand how information cascades in social systems, paving the way for applications in other social systems.German Research Foundation, grant number: KU 3369/1-1Germany’s Excellence Strategy—EXC 2002/1 “Science of Intelligence”—project number 39052313

The effect of boldness on decision-making in barnacle geese is group-size-dependent

In group-living species, decisions made by individuals may result in collective behaviours. A central question in understanding collective behaviours is how individual variation in phenotype affects collective behaviours. However, how the personality of individuals affects collective decisions in groups remains poorly understood. Here, we investigated the role of boldness on the decision-making process in different-sized groups of barnacle geese. Naive barnacle geese, differing in boldness score, were introduced in a labyrinth in groups with either one or three informed demonstrators. The demonstrators possessed information about the route through the labyrinth. In pairs, the probability of choosing a route prior to the informed demonstrator increased with increasing boldness score: bolder individuals decided more often for themselves where to go compared with shyer individuals, whereas shyer individuals waited more often for the demonstrators to decide and followed this information. In groups of four individuals, however, there was no effect of boldness on decision-making, suggesting that individual differences were less important with increasing group size. Our experimental results show that personality is important in collective decisions in pairs of barnacle geese, and suggest that bolder individuals have a greater influence over the outcome of decisions in groups

Boosting medical diagnostics by pooling independent judgments

Collective intelligence refers to the ability of groups to outperform individual decision makers when solving complex cognitive problems. Despite its potential to revolutionize decision making in a wide range of domains, including medical, economic, and political decision making, at present, little is known about the conditions underlying collective intelligence in real-world contexts. We here focus on two key areas of medical diagnostics, breast and skin cancer detection. Using a simulation study that draws on large real-world datasets, involving more than 140 doctors making more than 20,000 diagnoses, we investigate when combining the independent judgments of multiple doctors outperforms the best doctor in a group. We find that similarity in diagnostic accuracy is a key condition for collective intelligence: Aggregating the independent judgments of doctors outperforms the best doctor in a group whenever the diagnostic accuracy of doctors is relatively similar, but not when doctors' diagnostic accuracy differs too much. This intriguingly simple result is highly robust and holds across different group sizes, performance levels of the best doctor, and collective intelligence rules. The enabling role of similarity, in turn, is explained by its systematic effects on the number of correct and incorrect decisions of the best doctor that are overruled by the collective. By identifying a key factor underlying collective intelligence in two important real-world contexts, our findings pave the way for innovative and more effective approaches to complex real-world decision making, and to the scientific analyses of those approaches

Data from: Parasite-infected sticklebacks increase the risk-taking behavior of uninfected group members

Trophically transmitted parasites frequently increase their hosts' risk-taking behaviour, to facilitate transmission to the next host. Whether such elevated risk-taking can spill over to uninfected group members is, however, unknown. To investigate this, we confronted groups of six three-spined sticklebacks, Gasterosteus aculeatus, containing 0, 2, 4 or 6 experimentally infected individuals with a simulated bird attack and studied their risk-taking behaviour. As a parasite, we used the tapeworm Schistocephalus solidus, which increases the risk-taking of infected sticklebacks, to facilitate transmission to its final host, most often piscivorous birds. Before the attack, infected and uninfected individuals did not differ in their risk-taking. However, after the attack, individuals in groups with only infected members, showed lower escape responses and higher risk-taking than individuals from groups with only uninfected members. Importantly, uninfected individuals adjusted their risk-taking behaviour to the number of infected group members, taking more risk with an increasing number of infected group members. Infected individuals, however, did not adjust their risk-taking to the number of uninfected group members. Our results show that behavioural manipulation by parasites does not only affect the infected host, but also uninfected group members, shedding new light on the social dynamics involved in host-parasite interactions

Collective Intelligence Increases Diagnostic Accuracy in a General Practice Setting.

BACKGROUND General practitioners (GPs) work in an ill-defined environment where diagnostic errors are prevalent. Previous research indicates that aggregating independent diagnoses can improve diagnostic accuracy in a range of settings. We examined whether aggregating independent diagnoses can also improve diagnostic accuracy for GP decision making. In addition, we investigated the potential benefit of such an approach in combination with a decision support system (DSS). METHODS We simulated virtual groups using data sets from 2 previously published studies. In study 1, 260 GPs independently diagnosed 9 patient cases in a vignette-based study. In study 2, 30 GPs independently diagnosed 12 patient actors in a patient-facing study. In both data sets, GPs provided diagnoses in a control condition and/or DSS condition(s). Each GP's diagnosis, confidence rating, and years of experience were entered into a computer simulation. Virtual groups of varying sizes (range: 3-9) were created, and different collective intelligence rules (plurality, confidence, and seniority) were applied to determine each group's final diagnosis. Diagnostic accuracy was used as the performance measure. RESULTS Aggregating independent diagnoses by weighing them equally (i.e., the plurality rule) substantially outperformed average individual accuracy, and this effect increased with increasing group size. Selecting diagnoses based on confidence only led to marginal improvements, while selecting based on seniority reduced accuracy. Combining the plurality rule with a DSS further boosted performance. DISCUSSION Combining independent diagnoses may substantially improve a GP's diagnostic accuracy and subsequent patient outcomes. This approach did, however, not improve accuracy in all patient cases. Therefore, future work should focus on uncovering the conditions under which collective intelligence is most beneficial in general practice. HIGHLIGHTS We examined whether aggregating independent diagnoses of GPs can improve diagnostic accuracy.Using data sets of 2 previously published studies, we composed virtual groups of GPs and combined their independent diagnoses using 3 collective intelligence rules (plurality, confidence, and seniority).Aggregating independent diagnoses by weighing them equally substantially outperformed average individual GP accuracy, and this effect increased with increasing group size.Combining independent diagnoses may substantially improve GP's diagnostic accuracy and subsequent patient outcomes

Detection accuracy of collective intelligence assessments for skin cancer diagnosis

IMPORTANCE: Incidence rates of skin cancer are increasing globally, and the correct classification of skin lesions (SLs) into benign and malignant tissue remains a continuous challenge. A collective intelligence approach to skin cancer detection may improve accuracy. OBJECTIVE: To evaluate the performance of 2 well-known collective intelligence rules (majority rule and quorum rule) that combine the independent conclusions of multiple decision makers into a single decision. DESIGN, SETTING, AND PARTICIPANTS: Evaluations were obtained from 2 large and independent data sets. The first data set consisted of 40 experienced dermoscopists, each of whom independently evaluated 108 images of SLs during the Consensus Net Meeting of 2000. The second data set consisted of 82 medical professionals with varying degrees of dermatology experience, each of whom evaluated a minimum of 110 SLs. All SLs were evaluated via the Internet. Image selection of SLs was based on high image quality and the presence of histopathologic information. Data were collected from July through October 2000 for study 1 and from February 2003 through January 2004 for study 2 and evaluated from January 5 through August 7, 2015. MAIN OUTCOMES AND MEASURES: For both collective intelligence rules, we determined the true-positive rate (ie, the hit rate or specificity) and the false-positive rate (ie, the false-alarm rate or 1\u2009-\u2009sensitivity) and compared these rates with the performance of single decision makers. Furthermore, we evaluated the effect of group size on true- and false-positive rates. RESULTS: One hundred twenty-two medical professionals performed 16 029 evaluations. Use of either collective intelligence rule consistently outperformed single decision makers. The groups achieved an increased true-positive rate and a decreased false-positive rate. For example, individual decision makers in study 1, using the pattern analysis as diagnostic algorithm, achieved a true-positive rate of 0.83 and a false-positive rate of 0.17. Groups of 3 individuals achieved a true-positive rate of 0.91 and a false-positive rate of 0.14. These improvements increased with increasing group size. CONCLUSIONS AND RELEVANCE: Collective intelligence might be a viable approach to increase diagnostic accuracy in skin cancer and reduce skin cancer-related mortality

The effect of predation pressure and patch density on exploration and scrounging values.

<p>An increase in predation resulted in a reduction in exploration, but there was no effect on scrounging proportion.</p

Parameters of the simulation (a) and behavioural variables (b).

<p>Parameters of the simulation (a) and behavioural variables (b).</p