In a series of formal studies and less formal applications, Hong and Page offer a ‘diversity trumps ability’ result on the basis of a computational experiment accompanied by a mathematical theorem as explanatory background (Hong & Page 2004, 2009; Page 2007, 2011). “[W]e find that a random collection of agents drawn from a large set of limited-ability agents typically outperforms a collection of the very best agents from that same set” (2004, p. 16386). The result has been extremely influential as (...) an epistemic justification for diversity policy initiatives. Here we show that the ‘diversity trumps ability’ result is tied to the particular random landscape used in Hong and Page’s simulation. We argue against interpreting results on that random landscape in terms of ‘ability’ or ‘expertise.’ These concepts are better modeled on smother and more realistic landscapes, but keeping other parameters the same those are landscapes on which it is groups of the best performing that do better. Smoother landscapes seem to vindicate both the concept and the value of expertise. Change in other parameters, however, also vindicates diversity. With an increase in the pool of available heuristics, diverse groups again do better. Group dynamics makes a difference as well; simultaneous ‘tournament’ deliberation in a group in place of the ‘relay’ deliberation in Hong and Page’s original model further emphasizes an advantage for diversity. ‘Tournament’ 2 dynamics particularly shows the advantage of mixed groups that include both experts and non-experts. As a whole, our modeling results suggest that relative to problem characteristics and conceptual resources, the wisdom of crowds and the wisdom of the few each have a place. We regard ours as a step toward attempting to calibrate their relative virtues in different modelled contexts of intellectual exploration. (shrink)

The epistemic position of an agent often depends on their position in a larger network of other agents who provide them with information. In general, agents are better off if they have diverse and independent sources. Sullivan et al. [19] developed a method for quantitatively characterizing the epistemic position of individuals in a network that takes into account both diversity and independence; and presented a proof-of-concept, closed-source implementation on a small graph derived from Twitter data [19]. This paper reports on (...) an open-source reimplementation of their algorithm in Python, optimized to be usable on much larger networks. In addition to the algorithm and package, we also show the ability to scale up our package to large synthetic social network graph profiling, and finally demonstrate its utility in analyzing real-world empirical evidence of ‘echo chambers’ on online social media, as well as evidence of interdisciplinary diversity in an academic communications network. (shrink)

Meta-induction, in its various forms, is an imitative prediction method, where the prediction methods and the predictions of other agents are imitated to the extent that those methods or agents have proven successful in the past. In past work, Schurz demonstrated the optimality of meta-induction as a method for predicting unknown events and quantities. However, much recent discussion, along with formal and empirical work, on the Wisdom of Crowds has extolled the virtue of diverse and independent judgment as (...) essential to maintenance of 'wise crowds'. This suggests that meta-inductive prediction methods could undermine the wisdom of the crowd inasmuch these methods recommend that agents imitate the predictions of other agents. In this article, we evaluate meta-inductive methods with a focus on the impact on a group's performance that may result from including meta-inductivists among its members. In addition to considering cases of global accessibility (i.e., cases where the judgments of all members of the group are available to all of the group's members), we consider cases where agents only have access to the judgments of other agents within their own local neighborhoods. (shrink)

In a legendary technical report, the Google founders sketched a wisdom-of-crowds justification for PageRank arguing that the algorithm, by aggregating incoming links to webpages in a sophisticated way, tracks importance on the web. On this reading of the report, webpages that have a high impact as measured by PageRank are supposed to be important webpages in a sense of importance that is not reducible to mere impact or popularity. In this paper, we look at the state of the (...) art regarding the more precise statement of the thesis that PageRank and other similar in-link-based ranking algorithms can be justified by reference to the wisdom of crowds. We argue that neither the influential preferential attachment models due to Barabási and Albert in nor the recent model introduced by Masterton et al. in allows for a satisfactory wisdom-of-crowds justification of PageRank. As a remedy, we suggest that future work should explore “dual models” of linking on the web, i.e., models that combine the two previous approaches. Dual models view links as being attracted to both popularity and importance. (shrink)

We address the fundamental question why we should use PageRank and similar link-based algorithms in search engines, if at all. In a legendary article from 1998, the Google founders gave an intriguing wisdom-of-crowds justification for PageRank according to which the latter tracks quality online. This striking suggestion stands in contrast to the view that PageRank merely tracks what is popular. However, Masterton and Olsson showed that web-ecologies generated by Google-like assumptions essentially fail to reflect the scale-free structure of (...) the web. They pointed to attraction to popularity or a rich-get-richer effect being the likely main cause of scalefreeness. In this article, we explore dual models of linking behavior, i.e. models that combine attraction to importance with attraction to popularity. Our results, obtained through computer simulation, indicate that there exist dual models that give rise both to a wisdom-of-crowds effect for PageRank and to scale-free web-graphs, thus giving a partial vindication of the wisdom-of-crowds thesis for the real web. Future work should explore larger web-graphs as well as other aspects pertaining to the empirical plausibility of dual linking models. (shrink)

In their simplest form, consensus gentium arguments for theism argue that theism is true on the basis that everyone believes that theism is true. While such arguments may have been popular in history, they have all but fallen from grace in the philosophy of religion. In this short paper, we reconsider the neglected topic of consensus gentium arguments, paying particular attention to the value of such arguments when deployed in the defence of theistic belief. We argue that while consensus gentium (...) arguments are unlikely to offer anything close to overwhelming support for theism, their probative value is nevertheless underappreciated, and that they have been unfairly maligned as a consequence. (shrink)

It is well-known that people will adjust their first-order beliefs based on observations of others. We explore how such adjustments interact with second-order beliefs regarding universalism and relativism in a population. Across a range of simulations, we show that populations where individuals have a tendency toward universalism converge more quickly in coordination problems, and generate higher total payoffs, than do populations where individuals have a tendency toward relativism. Thus, in contexts where coordination is important, belief in universalism is advantageous. However, (...) we also show, across a range of simulations, that universalism will enshrine inequalities and eliminate diversity, and in these cases it seems that relativism has its own advantages. (shrink)

The “wisdom of the crowd” phenomenon refers to the finding that the aggregate of a set of proposed solutions from a group of individuals performs better than the majority of individual solutions. Most often, wisdom of the crowd effects have been investigated for problems that require single numerical estimates. We investigate whether the effect can also be observed for problems where the answer requires the coordination of multiple pieces of information. We focus on combinatorial problems such as the (...) planar Euclidean traveling salesperson problem, minimum spanning tree problem, and a spanning tree memory task. We develop aggregation methods that combine common solution fragments into a global solution and demonstrate that these aggregate solutions outperform the majority of individual solutions. These case studies suggest that the wisdom of the crowd phenomenon might be broadly applicable to problem-solving and decision-making situations that go beyond the estimation of single numbers. (shrink)

Trust in the practice of rational deliberation is widespread and largely unquestioned. This paper uses recent work from business contexts to challenge the view that rational deliberation in a group improves decisions. Pressure to reach consensus can, in fact, lead to phenomena such as groupthink and to suppression of relevant data. Aggregation of individual decisions, rather than deliberation to a consensus, surprisingly, can produce better decisions than those of either group deliberation or individual expert judgment. I argue that dissent is (...) epistemically valuable, not because of the discussion it can provoke (Mill's and Longino's view about the benefit of dissent), but because dissenting positions often are associated with particular data or insights that would be lost in consensus formation. Social epistemologists can usefully pay attention to various methods of aggregation of individual opinion for their effectiveness at realizing epistemic goals. (shrink)

In their paper on the influence of meta-induction to the wisdom of the crowd, Paul Thorn and Gerhard Schurz argue that adding meta-inductive methods to a group influences the group positively, whereas replacing independend methods of a group with meta-inductive ones may have a negative impact. The first fact is due to an improvement of average ability of a group, the second fact is due to an impairment of average diversity within a group by meta-induction. In this paper some (...) critical remarks to meta-inductive group expansion and replacement are made. In particular it is stressed that both ability and diversity are of equal importance to a group’s performance. (shrink)

Does pre-voting group deliberation improve majority outcomes? To address this question, we develop a probabilistic model of opinion formation and deliberation. Two new jury theorems, one pre-deliberation and one post-deliberation, suggest that deliberation is beneficial. Successful deliberation mitigates three voting failures: (1) overcounting widespread evidence, (2) neglecting evidential inequality, and (3) neglecting evidential complementarity. Formal results and simulations confirm this. But we identify four systematic exceptions where deliberation reduces majority competence, always by increasing Failure 1. Our analysis recommends deliberation that (...) is 'participatory', 'neutral', but not necessarily 'equal', i.e., that involves substantive sharing, privileges no evidences, but might privilege some persons. (shrink)

In this paper, we explain and showcase the promising methodology of testimonial network analysis and visualization for experimental epistemology, arguing that it can be used to gain insights and answer philosophical questions in social epistemology. Our use case is the epistemic community that discusses vaccine safety primarily in English on Twitter. In two studies, we show, using both statistical analysis and exploratory data visualization, that there is almost no neutral or ambivalent discussion of vaccine safety on Twitter. Roughly half the (...) accounts engaging with this topic are pro-vaccine, while the other half are con-vaccine. We also show that these two camps rarely engage with one another, and that the con-vaccine camp has greater epistemic reach and receptivity than the pro-vaccine camp. In light of these findings, we question whether testimonial networks as they are currently constituted on popular fora such as Twitter are living up to their promise of delivering the wisdom of crowds. We conclude by pointing to directions for further research in digital social epistemology. (shrink)

We evaluate the asymptotic performance of boundedly-rational strategies in multi-armed bandit problems, where performance is measured in terms of the tendency (in the limit) to play optimal actions in either (i) isolation or (ii) networks of other learners. We show that, for many strategies commonly employed in economics, psychology, and machine learning, performance in isolation and performance in networks are essentially unrelated. Our results suggest that the appropriateness of various, common boundedly-rational strategies depends crucially upon the social context (if any) (...) in which such strategies are to be employed. (shrink)

We study the wisdom of the crowd in three sequential decision‐making tasks: the Balloon Analogue Risk Task (BART), optimal stopping problems, and bandit problems. We consider a behavior‐based approach, using majority decisions to determine crowd behavior and show that this approach performs poorly in the BART and bandit tasks. The key problem is that the crowd becomes progressively more extreme as the decision sequence progresses, because the diversity of opinion that underlies the wisdom of the crowd is lost. (...) We also consider model‐based approaches to each task. This involves inferring cognitive models for each individual based on their observed behavior, and using these models to predict what each individual would do in any possible task situation. We show that this approach performs robustly well for all three tasks and has the additional advantage of being able to generalize to new problems for which there are no behavioral data. We discuss potential applications of the model‐based approach to real‐world sequential decision problems and discuss how our approach contributes to the understanding of collective intelligence. (shrink)

Many social media platforms enable (nearly) anyone to post (nearly) anything. One clear downside of this permissiveness is that many people appear bad at determining who to trust online. Hacks, quacks, climate change deniers, vaccine skeptics, and election deniers have all gained massive followings in these free markets of ideas, and many of their followers seem to genuinely trust them. At the same time, there are many cases in which people seem to reliably determine who to trust online. Consider, for (...) example, Do It Yourself (DIY) content about how to play guitar, bake, fix one’s plumbing, or repair one’s car. For these topics, those who have the largest accounts and the most popular content typically possess significant expertise. That is, social media users seem to reliably pick out DIY experts. We thus have a puzzle: why are social media users seemingly competent at identifying DIY experts, but not climate science or vaccine experts? In what follows, we solve this puzzle. We begin by identifying a novel wisdom of the crowds phenomenon: specifically we argue that the crowd (in combination with social media search and recommendation algorithms) reliably picks out DIY experts and serves as a credentialing institution for DIY content. Next, we argue that (a) there are five epistemic factors that determine whether the crowd can succeed at recognizing experts on social media platforms, and (b) while many of those factors are satisfied to a sufficiently high degree by DIY content, they are mostly lacking for content about climate change or vaccines. (shrink)

asked individuals to make predictions about the duration or extent of everyday events (e.g., cake baking times), and reported that predictions were optimal, employing Bayesian inference based on veridical prior distributions. Although the predictions conformed strikingly to statistics of the world, they reflect averages over many individuals. On the conjecture that the accuracy of the group response is chiefly a consequence of aggregating across individuals, we constructed simple, heuristic approximations to the Bayesian model premised on the hypothesis that individuals have (...) access merely to a sample of k instances drawn from the relevant distribution. The accuracy of the group response reported by Griffiths and Tenenbaum could be accounted for by supposing that individuals each utilize only two instances. Moreover, the variability of the group data is more consistent with this small‐sample hypothesis than with the hypothesis that people utilize veridical or nearly veridical representations of the underlying prior distributions. Our analyses lead to a qualitatively different view of how individuals reason from past experience than the view espoused by Griffiths and Tenenbaum. (shrink)

The my-side bias is a well-documented cognitive bias in the evaluation of arguments, in which reasoners in a discussion tend to overvalue arguments that confirm their prior beliefs, while undervaluing arguments that attack their prior beliefs. The first part of this paper develops and justifies a Bayesian model of myside bias at the level of individual reasoning. In the second part, this Bayesian model is implemented in an agent-based model of group discussion among myside-biased agents. The agent-based model is then (...) used to perform a number of experiments with the objective to study whether the myside bias hinders or enhances the ability of groups to collectively track the truth, that is, to reach the correct answer to a given binary issue. An analysis of the results suggests the following: First, whether the truth-tracking ability of groups is helped or hindered by myside bias crucially depends on how the strength of myside bias is differentially distributed across subgroups of discussants holding different beliefs. Second, small groups are more likely to track the truth than larger groups, suggesting that increasing group size has a detrimental effect on collective truth-tracking through discussion. (shrink)

We give a probabilistic justification of the shape of one of the probability weighting functions used in Prospect Theory. To do so, we use an idea recently introduced by Herzog and Hertwig. Along the way we also suggest a new method for the aggregation of probabilities using statistical distances.

We apply the “wisdom of the crowd” idea to human category learning, using a simple approach that combines people's categorization decisions by taking the majority decision. We first show that the aggregated crowd category learning behavior found by this method performs well, learning categories more quickly than most or all individuals for 28 previously collected datasets. We then extend the approach so that it does not require people to categorize every stimulus. We do this using a model‐based method that (...) predicts the categorization behavior people would produce for new stimuli, based on their behavior with observed stimuli, and uses the majority of these predicted decisions. We demonstrate and evaluate the model‐based approach in two case studies. In the first, we use the general recognition theory decision‐bound model of categorization (Ashby & Townsend, ) to infer each person's decision boundary for two categories of perceptual stimuli, and we use these inferences to make aggregated predictions about new stimuli. In the second, we use the generalized context model exemplar model of categorization (Nosofsky, ) to infer each person's selective attention for face stimuli, and we use these inferences to make aggregated predictions about withheld stimuli. In both case studies, we show that our method successfully predicts the category of unobserved stimuli, and we emphasize that the aggregated crowd decisions arise from psychologically interpretable processes and parameters. We conclude by discussing extensions and potential real‐world applications of the approach. (shrink)

Purpose Children are becoming heavy users of communication and information technologies from an early age. These technologies carry risks to which children may be exposed. In collaboration with the Israel Ministry of Education, the authors launched a week-long safe online awareness program for school children in 257 elementary and middle schools in Israel. Each class independently composed a safe and ethical code of online behavior following two classroom debate sessions. The purpose of this study was to analyze these codes and (...) learn how school children perceive and understand the proper use of the network using thematic analysis. Design/methodology/approach A total of 8,181 students between the ages of 8 and 14 years in 303 classes from 257 schools participated in the program. These classes composed 303 ethical codes, which were decomposed into 2,201 elements. Using mixed-methods research combining quantitative and qualitative methodologies, the elements of the ethical codes were analyzed, interpreted, and classified to identify the dominant themes and discourses used by the students when addressing issues related to safe online use. Findings Findings indicate that Israeli students are aware of the dangers and risks of the internet, and these concerns are reflected in their own ethical codes. The students discouraged online self-exposure and encouraged precautions and wariness towards members of out-groups. The themes included sentences which asked for responsible, appropriate, and lawful use, expressed concern about privacy issues, and stated the need for adult involvement. Most of them reflected an “us against them ” perspective. Originality/value The current study presents an innovative “bottom-up” program based on wisdom of the crowd approach, that can be implemented in schools internationally in order to encourage reflexivity and teach children the necessary skills for safe online experiences. In addition, this study analyses the school children’s own views of the dangers of social media and learn about their perspective and understanding of internet use. (shrink)

I hypothesize that re-occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This “core-periphery learning” theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery-driven innovation, and a number of recent (...) reports on the adaptation of protein, neuronal, and social networks. The core-periphery learning theory may increase our understanding of signaling, memory formation, information encoding and decision-making processes. Moreover, the power of network periphery-related “wisdom of crowds” inventing creative, novel responses indicates that deliberative democracy is a slow yet efficient learning strategy developed as the success of a billion-year evolution. Also see the video abstract here: https://youtu.be/IIjP7zWGjVE. The network core triggers fast responses to previously experienced stimuli. Innovations require the network periphery that encodes a novel attractor by core-remodeling. This core-periphery learning theory is introduced as a general adaptation, learning, decision-making, and forgetting mechanism of all complex systems including protein, metabolic, signaling, neuronal, social networks, and ecosystems. (shrink)

A computer simulation is used to study collective judgements that an expert panel reaches on the basis of qualitative probability judgements contributed by individual members. The simulated panel displays a strong and robust crowd wisdom effect. The panel's performance is better when members contribute precise probability estimates instead of qualitative judgements, but not by much. Surprisingly, it doesn't always hurt for panel members to interpret the probability expressions differently. Indeed, coordinating their understandings can be much worse.

Formal and empirical work on the Wisdom of Crowds has extolled the virtue of diverse and independent judgment as essential to the maintenance of ‘wise crowds’. In other words, com-munication and imitation among members of a group may have the negative effect of decreasing the aggregate wisdom of the group. In contrast, it is demonstrable that certain meta-inductive methods provide optimal means for predicting unknown events. Such meta-inductive methods are essentially imitative, where the predictions of other (...) agents are imitated to the extent that those agents have proven successful in the past. Despite the (self-serving) optimality of meta-inductive methods, their imitative nature may undermine the ‘wisdom of the crowd’, since these methods recommend that agents imitate the predictions of other agents. In this paper, I present a replication of selected results of Thorn and Schurz, illustrating the effect on a group’s performance that may result from having members of a group adopt meta-inductive methods. I then expand on the work of Thorn and Schurz by considering three simple measures by which meta-inductive prediction methods may improve their own performance, while simultaneously mitigating their negative impact on group performance. The effects of adopting these maneuvers are investigated using computer simulations. (shrink)

This paper considers pick-the-winners contests as a simple method for harnessing the wisdom of crowds to produce probability forecasts. Pick-the-winners contests are those in which players pick the outcomes of selected future binary events with a prize going to the player with the most correct picks. In contrast to soliciting probability forecasts from experts, this paper shows that competition among players is to be encouraged because it improves the accuracy of the resulting crowd probability forecasts. This improvement comes (...) because the competition not only discourages the overbetting of favorites that occurs if prizes are awarded based on absolute performance, but also helps mitigate the public knowledge bias that occurs if everyone reports truthfully. In addition to the theoretical arguments, the paper analyzes picks from 6 years of pick-the-winners contests involving hundreds of players and 1037 college lacrosse games to find that the crowd proportions outperformed two benchmark sets of probabilities. (shrink)

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological (...) research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from ‘the sequence tells the structure tells the function’ fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader. (shrink)

We consider a situation in which individuals search for accurate decisions without direct feedback on their accuracy, but with information about the decisions made by peers in their group. The “wisdom of crowds” hypothesis states that the average judgment of many individuals can give a good estimate of, for example, the outcomes of sporting events and the answers to trivia questions. Two conditions for the application of wisdom of crowds are that estimates should be independent and (...) unbiased. Here, we study how individuals integrate social information when answering trivia questions with answers that range between 0% and 100%. We find that, consistent with the wisdom of crowds hypothesis, average performance improves with group size. However, individuals show a consistent bias to produce estimates that are insufficiently extreme. We find that social information provides significant, albeit small, improvement to group performance. Outliers with answers far from the correct answer move toward the position of the group mean. Given that these outliers also tend to be nearer to 50% than do the answers of other group members, this move creates group polarization away from 50%. By looking at individual performance over different questions we find that some people are more likely to be affected by social influence than others. There is also evidence that people differ in their competence in answering questions, but lack of competence is not significantly correlated with willingness to change guesses. We develop a mathematical model based on these results that postulates a cognitive process in which people first decide whether to take into account peer guesses, and if so, to move in the direction of these guesses. The size of the move is proportional to the distance between their own guess and the average guess of the group. This model closely approximates the distribution of guess movements and shows how outlying incorrect opinions can be systematically removed from a group resulting, in some situations, in improved group performance. However, improvement is only predicted for cases in which the initial guesses of individuals in the group are biased. (shrink)

Conventional wisdom holds two seemingly opposed beliefs. One is that communities are often much better than individuals at dealing with certain situations or solving certain problems. The other is that crowds are usually, and some say always, at best as intelligent as their least intelligent members and at worst even less. Consistency would seem to be easily re-established by distinguishing between advanced, sophisticated social organizations which afford the supporting communities a high level of collective performance, and primitive, mob-like (...) structures which pull the group towards the lower end of the achievement scale. But this reconciliation meets with some objections. The most familiar ones concern the mixed record of elaborate social systems, which are said to occasionally or even, according to some accounts, systematically produce wrong decisions, poor assessments, disastrous plans, counterproductive measures, etc. A more recent set of objections rests on cases where ‘crowds’, i,e, groups not organized in a sophisticated way, produce good results, in fact, results which better those of most, or even all, members of the group. Such cases are collected in James Surowiecki’s book The Wisdom of Crowds, which argues more generally in favor of an ‘order out of chaos’ view of collective thinking: whether sophisticated or simple, social organizations for the production of knowledge or problem-solving can benefit from the absence of certain individualistc constraints which are traditionally thought to foster excellence in cognitive tasks. This flavor of paradox is enhanced by Surowiecki’s choice of phrase: at the surface level, ‘wisdom of crowds’ conflicts with the well-entrenched cliché of the folly1 of crowds; but at a deeper level, Surowiecki seems to appeal to one frequent connotation of ‘wisdom’ which is precisely its paradoxical character. Whether Surowiecki actually intended to exploit this.. (shrink)

In the debate of epistemic peer disagreement the equal-weight view suggests to split the difference between one's own and one's peer's opinions. An argument in favour of this view---which is prominently held by Adam Elga---is that by such a difference-splitting one may make some use of a so-called wise-crowd effect. In this paper it is argued that such a view faces two main problems: First, the problem that the standards for making use of a wise-crowd effect are quite low. And (...) second, the problem that following the equal-weight view decreases such effects and by this the argument's own basis is defeated. We therefore come to the conclusion that an argument for the equal-weight view with the help of wise-crowd effects as provided more or less explicitly by Elga does not succeed. (shrink)

There has been much interest in group judgment and the so-called 'wisdom of crowds'. In many real world contexts, members of groups not only share a dependence on external sources of information, but they also communicate with one another, thus introducing correlations among their responses that can diminish collective accuracy. This has long been known, but it has-to date-not been examined to what extent different kinds of communication networks may give rise to systematically different effects on accuracy. We (...) argue that equations that relate group accuracy, individual accuracy, and group diversity are useful theoretical tools for understanding group performance in the context of research on group structure. In particular, these equations may serve to identify the kind of group structures that improve individual accuracy without thereby excessively diminishing diversity so that the net positive effect is an improvement even on the level of collective accuracy. Two experiments are reported where two structures are investigated from this perspective. It is demonstrated that the more constrained network outperforms the network with a free flow of information. (shrink)

A Time for Wisdom is for a beleaguered audience that wants to cultivate this virtue and elevate themselves above the noise and toxicity of the modern world. Written by a pair of psychologists, it unpacks the research that has been conducted on the subject in recent years but that hasn't been communicated to readers in a relevant way. What's more, the book takes our current scientific understanding and integrates it with timeless concepts of wisdom that have, for millennia, (...) guided men and women through life's troubles. With this as their foundation, the authors lay out four practices to pursue wisdom in our daily lives. These include receiving knowledge; practicing detachment; experiencing tranquility, and cultivating transcendence. These are profound and spiritual ideas that can quiet the shallow cacophony of the crowd and bring us inner peace. The authors make these practices clear and accessible and show us the immense satisfaction we can enjoy by aspiring to live by them. A Time for Wisdom is an invitation to step outside the circus of contemporary life, to break free from its fanfare of foolishness. It commends a course of action towards the Good, the True, and the Beautiful, towards calm and clear moral reasoning. And it gives us a recipe for self-transcendence so that we-like the great sages and scientists before us-can rise above the immediacy of the moment and a taste of the numinous and the infinite. (shrink)

Democratic decision-making is often defended on grounds of the ‘wisdom of crowds’: decisions are more likely to be correct if they are based on many independent opinions, so a typical argument in social epistemology. But what does it mean to have independent opinions? Opinions can be probabilistically dependent even if individuals form their opinion in causal isolation from each other. We distinguish four probabilistic notions of opinion independence. Which of them holds depends on how individuals are causally affected (...) by environmental factors such as commonly perceived evidence. In a general theorem, we identify causal conditions guaranteeing each kind of opinion independence. These results have implications for whether and how ‘wisdom of crowds’ arguments are possible, and how truth-conducive institutions can be designed. (shrink)

We apply a cognitive modeling approach to the problem of measuring expertise on rank ordering problems. In these problems, people must order a set of items in terms of a given criterion (e.g., ordering American holidays through the calendar year). Using a cognitive model of behavior on this problem that allows for individual differences in knowledge, we are able to infer people's expertise directly from the rankings they provide. We show that our model-based measure of expertise outperforms self-report measures, taken (...) both before and after completing the ordering of items, in terms of correlation with the actual accuracy of the answers. These results apply to six general knowledge tasks, like ordering American holidays, and two prediction tasks, involving sporting and television competitions. Based on these results, we discuss the potential and limitations of using cognitive models in assessing expertise. (shrink)

This volume showcases the best of recent research in the philosophy of science. A compilation of papers presented at the EPSA 13, it explores a broad distribution of topics such as causation, truthlikeness, scientific representation, gender-specific medicine, laws of nature, science funding and the wisdom of crowds. Papers are organised into headings which form the structure of the book. Readers will find that it covers several major fields within the philosophy of science, from general philosophy of science to (...) the more specific philosophy of physics, philosophy of chemistry, philosophy of the life sciences, philosophy of psychology, and philosophy of the social sciences and humanities, amongst others. This volume provides an excellent overview of the state of the art in the philosophy of science, as practiced in different European countries and beyond. ​It will appeal to researchers with an interest in the philosophical underpinnings of their own discipline, and to philosophers who wish to explore the latest work on the themes explored. (shrink)

This article comments on the article of Thorn and Schurz in this volume and focuses on, what we call, the problem of parasitic experts. We discuss that both meta- induction and crowd wisdom can be understood as pertaining to absolute reliability rather than comparative optimality, and we suggest that the involvement of reliability will provide a handle on this problem.

This article examines two questions about scientists’ search for knowledge. First, which search strategies generate discoveries effectively? Second, is it advantageous to diversify search strategies? We argue pace Weisberg and Muldoon, “Epistemic Landscapes and the Division of Cognitive Labor”, that, on the first question, a search strategy that deliberately seeks novel research approaches need not be optimal. On the second question, we argue they have not shown epistemic reasons exist for the division of cognitive labor, identifying the errors that led (...) to their conclusions. Furthermore, we generalize the epistemic landscape model, showing that one should be skeptical about the benefits of social learning in epistemically complex environments. (shrink)

The Condorcet Jury Theorem formalises the “wisdom of crowds”: binary decisions made by majority vote are asymptotically correct as the number of voters tends to infinity. This classical result assumes like-minded, expected utility maximising voters who all share a common prior belief about the right decision. Ellis : 865–895, 2016) shows that when voters have ambiguous prior beliefs—a set of priors—and follow maxmin expected utility, such wisdom requires that voters’ beliefs satisfy a “disjoint posteriors” condition: different private (...) signals lead to posterior sets with disjoint interiors. Both the original theorem and Ellis’s generalisation assume symmetric penalties for wrong decisions. If, as in the jury context, errors attract asymmetric penalties then it is natural to consider voting rules that raise the hurdle for the decision carrying the heavier penalty for error. In a classical model, Feddersen and Pesendorfer :23–35, 1998) have shown that, paradoxically, raising this hurdle may actually increase the likelihood of the more serious error. In particular, crowds are not wise under the unanimity rule: the probability of the more serious error does not vanish as the crowd size tends to infinity. We show that this “Jury Paradox” persists in the presence of ambiguity, whether or not juror beliefs satisfy Ellis’s “disjoint posteriors” condition. We also characterise the strictly mixed equilibria of this model and study their properties. Such equilibria cannot exist in the absence of ambiguity but may exist for arbitrarily large jury size when ambiguity is present. In addition to uninformative strictly mixed equilibria, analogous to those exhibited by Ellis : 865–895, 2016), there may also exist strictly mixed equilibria which are informative about voter signals. (shrink)

Two crucial distinctions regarding political competence must be made. First, the mere probability that you will make a morally right decision (reliability) is distinct from your ability to skillfully make a decision (competence). Empirical and normative accounts have focused primarily on reliability, but competence is more important if we take central normative commitments seriously. Second, the competence you have on your own (direct competence) is distinct from the competence you have in contributing to some collective enterprise (contributory competence). Direct competence (...) is likely to be extremely demanding in a democratic context, so we should move to a contributory account of competence. (shrink)

While epistemic democrats have claimed that majority rule recruits the wisdom of the crowd to identify correct answers to political problems, the conjecture remains abstract. This article illustrates how majority rule leverages the epistemic capacity of the electorate to practically enhance the instrumental value of elections. To do so, we identify a set of sufficient conditions that effect such a majority rule mechanism, even when the decision in question is multidimensional. We then look to the case of sociotropic economic (...) voting in US presidential elections to provide empirical tractability for these conditions. We find that absent such an epistemic capacity a number of presidential elections might well have been decided differently. By generating clear conditions for the plausibility of claims made by epistemic democrats, and demonstrating their correspondence to empirical data, this article strengthens the broader instrumental grounds recommending democracy. (shrink)

Your mother was wise to teach you that just because everybody’s doing it, that doesn’t make it right. She would have been wise to add that just because everybody thinks it, that doesn’t make it right, either. On the other hand, she would not have been wise to add (and probably did not) that when everybody agrees, that is no evidence whatsoever. When nearly everybody believes something, that’s a reason in its favor. . . . I shall look at a (...) hard case, where I claim radical change is needed, but popular opinion is against me. (shrink)

This paper offers the perspective of a behavioural scientist advising and providing evidence for Ireland's government during the coronavirus pandemic. It describes how behavioural research informed the public response in the early months of the crisis, but lost influence as political conflict increased. It proposes some broader lessons for managing public health crises, one of which is to recognise the potential wisdom of crowds.

The surprisingly high reliability of Wikipedia has often been seen as a beneficial effect of the aggregation of diverse contributors, or as an instance of the wisdom of crowds phenomenon; additional factors such as elite contributors, Wikipedia’s policy or its administration have also been mentioned. We adjudicate between such explanations by modelling and simulating the evolution of a Wikipedia entry. The main threat to Wikipedia’s reliability, namely the presence of epistemically disruptive agents such as disinformers and trolls, turns (...) out to be offset only by a combination of factors: Wikipedia’s administration and the possibility to instantly revert entries, both of which are insufficient when considered in isolation. Our results suggest that the reliability of Wikipedia should receive a pluralist explanation, involving factors of different kinds. (shrink)

The adoption of an individualistic perspective on reasoning, choice and decision is a spring of paradoxes of conflicts. Usually the agents immerse in conflicts are drawn or modelled as rational individuals with targets well defined and full capabilities to access to information, without both temporal limitations and perfect reasoning abilities to obtain their preferences are taken account.However, other models of agent, in the bounded rationality perspective, could help to understand better the interrelationships. I adopt embedded argumentative reasoning processes as satisfying (...) criteria to analyze the expert function in a new socio technical environment that has changed deeply the mechanism and tools to access and to aggregate information. The open access to information and institutional arrangements addressed towards team knowledge could offer other kind of tools to affront the conflict, even its possible benefits.The “crowd expertise” is emerging as an actual possibility and it must be incorporated to affront with conflicts. The very possibility of obtaining knowledge generated by “many minds”, collective wisdoms, brings up a real challenge to the conservative or elitist conception of the masses, because masses now emerge as a smart collective user, with new mechanisms to select and produce quality knowledge. These new collective actions differ deeply from the traditional modes of social organization. A new mass society is emerging now as a hybrid one that breaks some conceptual traditional models, such as Ortega y Gasset’s ones, and induces a structured way of flourishing both new practices and new knowledge with transforming capabilities. (shrink)

Democratic decision-making is often defended on grounds of the ‘wisdom of crowds’: decisions are more likely to be correct if they are based on many independent opinions, so a typical argument in social epistemology. But what does it mean to have independent opinions? Opinions can be probabilistically dependent (threatening the ‘wisdom of crowds’) even if individuals form their opinion in causal isolation from each other. We distinguish four probabilistic notions of opinion independence. Which of them holds (...) depends on how individuals are causally affected by environmental factors such as commonly perceived evidence. In a general theorem, we identify causal conditions guaranteeing each kind of opinion independence. These results have implications for whether and how ‘wisdom of crowds’ arguments are possible, and how truth-conducive institutions can be designed. (shrink)

We tend to applaud those who think for themselves: the ever-curious student, for example, or the grownup who does their own research. Even as we’re applauding, however, we ourselves often don’t think for ourselves. This book argues that’s completely OK. -/- In fact, it’s often best just to take other folks’ word for it, allowing them to do the hard work of gathering and evaluating the relevant evidence. In making this argument, philosopher Jonathan Matheson shows how 'expert testimony' and 'the (...) wisdom of crowds' are tested and provides convincing ideas that make it rational to believe something simply because other people believe it. Matheson then takes on philosophy’s best arguments against his thesis, including the idea that non-self-thinkers are free-riding on the work of others, Socrates’ claim that 'the unexamined life isn’t worth living,' and that outsourcing your intellectual labor makes you vulnerable to errors and manipulation. Matheson shows how these claims and others ultimately fail -- and that when it comes to thinking, we often need not be sheepish about being sheep. (shrink)