Scientists collect evidence in order to confirm or falsify scientific theories. Unfortunately, scientific evidence may sometimes be false or deceiving and as a consequence lead individuals to believe in a false theory. By interaction between scientists, such false beliefs may spread through the entire community. There is currently a debate about the effect of various network configurations on the epistemic reliability of scientific communities. To contribute to this debate from a logical perspective, this paper introduces an epistemic logical framework of (...) observation, interaction and belief revision in scientific communities. The presented sound and complete system provides the formal tools for qualitative analysis of the social dynamics of scientific inquiry. Furthermore, this paper includes detailed suggestions for future applications of the framework. (shrink)

The paper presents and defends a Bayesian theory of trust in social networks. In the first part of the paper, we provide justifications for the basic assumptions behind the model, and we give reasons for thinking that the model has plausible consequences for certain kinds of communication. In the second part of the paper we investigate the phenomenon of overconfidence. Many psychological studies have found that people think they are more reliable than they actually are. Using a simulation environment that (...) has been developed in order to make our model computationally tractable we show that in our model inquirers are indeed sometimes better off from an epistemic perspective overestimating the reliability of their own inquiries. We also show, by contrast, that people are rarely better off overestimating the reliability of others. On the basis of these observations we formulate a novel hypothesis about the value of overconfidence. (shrink)

Evolutionary anthropologists and archaeologists have been considerably successful in modelling the cumulative evolution of culture, of technological skills and knowledge in particular. Recently, one of these models has been introduced in the philosophy of science by De Cruz and De Smedt (Philos Stud 157:411–429, 2012), in an attempt to demonstrate that scientists may collectively come to hold more truth-approximating beliefs, despite the cognitive biases which they individually are known to be subject to. Here we identify a major shortcoming in that (...) attempt: De Cruz & De Smedt’s mathematical model makes one particularly strong tractability assumption that causes the model to largely miss its target (namely, truth accumulation in science), and that moreover conflicts with empirical observations. The second, more constructive part of the paper presents an alternative, agent-based model, which allows one to much better examine the conditions for scientific progress and decline. (shrink)

The generalizability crisis is compounded, or even partially caused, by a lack of specificity in psychological theories. Expanding the use of mechanistic models among psychologists is therefore important, but faces numerous hurdles. A cultural evolutionary approach can help guide and evaluate interventions to improve modeling efforts in psychology, such as developing standards and implementing them at the institutional level.

This paper presents an account of how to evaluate formal models of science: models and simulations in social epistemology designed to draw normative conclusions about the social structure of scientific research. I argue that such models should be evaluated according to their representational and predictive accuracy. Using these criteria and comparisons with familiar models from science, I argue that most formal models of science are incapable of supporting normative conclusions.

Scientific realists have suggested that changes in our scientific communities over the course of their history have rendered those communities progressively less vulnerable to the problem of unconcieved alternatives over time. I argue in response not only that the most fundamental historical transformations of the scientific enterprise have generated steadily mounting obstacles to revolutionary, transformative, or unorthodox scientific theorizing, but also that we have substantial independent evidence that the institutional apparatus of contemporary scientific inquiry fosters an exceedingly and increasingly theoretically (...) conservative form of that inquiry. I conclude that contemporary scientific communities are actually more vulnerable to the problem of unconceived alternatives than their historical predecessors, and I briefly suggest how we might seek to pursue scientific inquiry in a less theoretically conservative way. (shrink)

This paper focuses on the question of how to resolve disagreement and uses the Lehrer-Wagner model as a formal tool for investigating consensual decision-making. The main result consists in a general definition of when agents treat each other as epistemic peers (Kelly 2005; Elga 2007), and a theorem vindicating the “equal weight view” to resolve disagreement among epistemic peers. We apply our findings to an analysis of the impact of social network structures on group deliberation processes, and we demonstrate their (...) stability with the help of numerical simulations. (shrink)

In a seminal book, Alvin I. Goldman outlines a theory for how to evaluate social practices with respect to their “veritistic value”, i.e., their tendency to promote the acquisition of true beliefs in society. In the same work, Goldman raises a number of serious worries for his account. Two of them concern the possibility of determining the veritistic value of a practice in a concrete case because we often don't know what beliefs are actually true, and even if we did, (...) the task of determining the veritistic value would be computationally extremely difficult. Neither problem is specific to Goldman's theory and both can be expected to arise for just about any account of veritistic value. It is argued here that the first problem does not pose a serious threat to large classes of interesting practices. The bulk of the paper is devoted to the computational problem, which, it is submitted, can be addressed in promising terms by means of computer simulation. In an attempt to add vividness to this proposal, an up-and-running simulation environment is presented and put to some preliminary tests. (shrink)

Public discussions of political and social issues are often characterized by deep and persistent polarization. In social psychology, it’s standard to treat belief polarization as the product of epistemic irrationality. In contrast, we argue that the persistent disagreement that grounds political and social polarization can be produced by epistemically rational agents, when those agents have limited cognitive resources. Using an agent-based model of group deliberation, we show that groups of deliberating agents using coherence-based strategies for managing their limited resources tend (...) to polarize into different subgroups. We argue that using that strategy is epistemically rational for limited agents. So even though group polarization looks like it must be the product of human irrationality, polarization can be the result of fully rational deliberation with natural human limitations. (shrink)

We motivate a picture of social epistemology that sees forgetting as subject to epistemic evaluation. Using computer simulations of a simple agent-based model, we show that how agents forget can have as large an impact on group epistemic outcomes as how they share information. But, how we forget, unlike how we form beliefs, isn’t typically taken to be the sort of thing that can be epistemically rational or justified. We consider what we take to be the most promising argument for (...) this claim and find it lacking. We conclude that understanding how agents forget should be as central to social epistemology as understanding how agents form beliefs and share information with others. (shrink)

We motivate a picture of social epistemology that sees forgetting as subject to epistemic evaluation. Using computer simulations of a simple agent-based model, we show that how agents forget can have as large an impact on group epistemic outcomes as how they share information. But, how we forget, unlike how we form beliefs, isn’t typically taken to be the sort of thing that can be epistemically rational or justified. We consider what we take to be the most promising argument for (...) this claim and find it lacking. We conclude that understanding how agents forget should be as central to social epistemology as understanding how agents form beliefs and share information with others. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with the (...) goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

The epistemic attitudes of scientists, such as epistemic tolerance and authoritarianism, play important roles in the discourse about rivaling theories. Epistemic tolerance stands for the mental attitude of an epistemic agent, e.g., a scientist, who is open to opposing views, while epistemic authoritarianism represents the tendency to uncritically accept views of authorities. Another relevant epistemic factor when it comes to the epistemic decisions of scientists is the skepticism towards the scientific method. However, the question is whether these epistemic attitudes are (...) influenced by their sociopolitical counterparts, such as the researcher’s degree of conservatism. To empirically investigate the interplay between epistemic and sociopolitical attitudes of scientists, we conducted a survey with researchers (N = 655) across different disciplines. We propose scales for measuring epistemic tolerance and epistemic authoritarianism, as well as a scale for detecting the participants' readiness to question the scientific method. Furthermore, we investigate the relationship between epistemic tolerance and epistemic authoritarianism on the one hand, and career stage and sociopolitical views on the other hand. Interestingly, our study found only small correlations between the participants' degree of conservatism and their epistemic attitudes. This suggests that political views, against common argumentation, actually do not play an important role in one’s scientific decisions. Moreover, social scientists scored higher on the epistemic tolerance and lower on the epistemic authoritarianism scale than natural scientists. Finally, the results indicate that natural scientists question the scientific method less than social scientists. (shrink)

Computational modeling should play a central role in philosophy. In this introduction to our topical collection, we propose a small topology of computational modeling in philosophy in general, and show how the various contributions to our topical collection fit into this overall picture. On this basis, we describe some of the ways in which computational models from other disciplines have found their way into philosophy, and how the principles one found here still underlie current trends in the field. Moreover, we (...) argue that philosophers contribute to computational modeling not only by building their own models, but also by thinking about the various applications of the method in philosophy and the sciences. In this context, we note that models in philosophy are usually simple, while models in the sciences are often more complex and empirically grounded. Bridging certain methodological gaps that arise from this discrepancy may prove to be challenging and fruitful for the further development of computational modeling in philosophy and beyond. (shrink)

Data which constitute satisfactory evidence in other contexts are sometimes not treated as valid evidence in the context of scientic conrmation. I give a justicatory explanation of this fact, appealing to the incentives, biases, and social situatedness of scientists.

In this article we focus on the concept of concept in conceptual change. We argue that (1) theories of higher learning must often employ two different notions of concept that should not be conflated: psychological and scientific concepts. The usages for these two notions are partly distinct and thus straightforward identification between them is unwarranted. Hence, the strong analogy between scientific theory change and individual learning should be approached with caution. In addition, we argue that (2) research in psychology and (...) cognitive science provides a promising theoretical basis for developing explanatory mechanistic models of conceptual change. Moreover, we argue that (3) arguments against deeper integration between the fields of psychology and conceptual change are not convincing, and that recent theoretical developments in the cognitive sciences might prove indispensable in filling in the details in mechanisms of conceptual change. (shrink)

We use game theoretic models to take an in-depth look at the dynamics of discrimination and academic collaboration. We find that in collaboration networks, small minority groups may be more likely to end up being discriminated against while collaborating. We also find that discrimination can lead members of different social groups to mostly collaborate with in-group members, decreasing the effective diversity of the social network. Drawing on previous work, we discuss how decreases in the diversity of scientific collaborations might negatively (...) affect the progress of epistemic communities. (shrink)

This paper is a supplement to, and provides a proof of principle of, Kuhn vs. Popper on Criticism and Dogmatism in Science: A Resolution at the Group Level. It illustrates how calculations may be performed in order to determine how the balance between different functions in science—such as imaginative, critical, and dogmatic—should be struck, with respect to confirmation (or corroboration) functions and rules of scientific method.

Because inappropriate built-in or managed vaccination campaigns, regardless of the causes of vaccine hesitation, can have side effects, the most important being the opposition to vaccination, the understanding of vaccination hesitation can have an influence on specific public policies. In this article we identify a set of possible explanations for vaccine hesitation, which can be used to assess situations, identify problems and adopt appropriate solutions. We highlight the fact that the vaccination hesitation is not just about vaccination, the approach of (...) identifying explanatory hypotheses involves identifying all relevant frames of reference, all social areas that participate in establishing the appropriate interpretation context. The level of compliance with the rules could be an important explanatory hypothesis for the differences between different states in the share of vaccinated citizens. Other explanatory hypotheses: the structure of epistemic communities, community and individual cognitive resources on strategies facing the uncertainty and risk, distrust. We also assess the limits of using the deficit model. We keep in mind two cognitive asymmetries: inaction is perceived as less risky or responsible than action; the indecision is in a similar situation to the decision. Because the values that people adhere reflect their personal beliefs and convictions, and they guide the way they seek, receive, and interpret information, strategies to reduce vaccine hesitation should be based on verifying the compatibility of the intended outcome with individual values and to continue testing the various solutions to change the beliefs that contribute to the determination of vaccine hesitation or refusal. (shrink)

We show that previous results from epistemic network models showing the benefits of decreased connectivity in epistemic networks are not robust across changes in parameter values. Our findings motivate discussion about whether and how such models can inform real-world epistemic communities. As we argue, only robust results from epistemic network models should be used to generate advice for the real-world, and, in particular, decreasing connectivity is a robustly poor recommendation.

We represent consensus formation processes based on iterated opinion pooling as a dynamic approach to common knowledge of posteriors :1236–1239, 1976; Geanakoplos and Polemarchakis in J Econ Theory 28:192–200, 1982). We thus provide a concrete and plausible Bayesian rationalization of consensus through iterated pooling. The link clarifies the conditions under which iterated pooling can be rationalized from a Bayesian perspective, and offers an understanding of iterated pooling in terms of higher-order beliefs.

In the past few years, social epistemologists have developed several formal models of the social organisation of science. While their robustness and representational adequacy has been analysed at length, the function of these models has begun to be discussed in more general terms only recently. In this article, I will interpret many of the current formal models of the scientific community as representing the latest development of what I will call the ‘Kuhnian project’. These models share with Kuhn a number (...) of questions about the relation between individuals and communities. At the same time, they also inherit some of Kuhn’s problematic characterisations of the scientific community. In particular, current models of the social organisation of science represent the scientific community as essentially value-free. This may put into question both their representational adequacy and their normative ambitions. In the end, it will be shown that the discussion on the formal models of the scientific community may contribute in fruitful ways to the ongoing debates on value judgements in science. (shrink)

The organization of cutting-edge HEP laboratories has evolved in the intersection of academia, state agencies, and industry. Exponentially ever-larger and more complex knowledge-intensive operations, the laboratories have often faced the challenges of, and required organizational solutions similar to, those identified by a cluster of diverse theories falling under the larger heading of organization theory. The cluster has either shaped or accounted for the organization of industry and state administration. The theories also apply to HEP laboratories, as they have gradually and (...) uniquely hybridized their principles and solutions. Yet scholarship has virtually ignored this linkage and has almost exclusively focused on the laboratories’ presumably unique egalitarian organizational aspects. Guided by the principles developed in the organization theory cluster, we identify the basic organizational features of HEP laboratories in relation to their pursuit of narrow and broad epistemic goals. We also provide a set of criteria and methods for assessing the efficiency of the identified organizational features in achieving such goals. (shrink)

In Word and Object, W.V. Quine dismisses connotations that result from the work of explicating expressions as “don’t-cares.” This paper traces the history of this phrase to an algorithm that Quine developed in the 1950s, which became important in early computer engineering. Computer programmers eventually came to realize that it was in their best interests to abandon the “don’t-care” attitude. Similarly, I argue that naturalists who properly appreciate the communal nature of their inquiries have reason to adopt a more careful (...) approach when they propose and evaluate explications. (shrink)

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)

Epistemologists can be divided into two camps: those who think that nothing short of certainty or (subjective) probability 1 can warrant assertion and those who disagree with this claim. This paper addressed this issue by inquiring into the problem of setting the probability threshold required for assertion in such a way that that the social epistemic good is maximized, where the latter is taken to be the veritistic value in the sense of Goldman (Knowledge in a social world, 1999). We (...) provide a Bayesian model of a test case involving a community of inquirers in a social network engaged in group deliberation regarding the truth or falsity of a proposition $p.$ p . Results obtained by means of computer simulation indicate that the certainty rule is optimal in the limit of inquiry and communication but that a lower threshold is preferable in less idealized cases. (shrink)

In a seminal book, Alvin I. Goldman outlines a theory for how to evaluate social practices with respect to their , i.e., their tendency to promote the acquisition of true beliefs (and impede the acquisition of false beliefs) in society. In the same work, Goldman raises a number of serious worries for his account. Two of them concern the possibility of determining the veritistic value of a practice in a concrete case because (1) we often don't know what beliefs are (...) actually true, and (2) even if we did, the task of determining the veritistic value would be computationally extremely difficult. Neither problem is specific to Goldman's theory and both can be expected to arise for just about any account of veritistic value. It is argued here that the first problem does not pose a serious threat to large classes of interesting practices. The bulk of the paper is devoted to the computational problem, which, it is submitted, can be addressed in promising terms by means of computer simulation. In an attempt to add vividness to this proposal, an up-and-running simulation environment (Laputa) is presented and put to some preliminary tests. (shrink)

Contemporary societies are often “polarized”, in the sense that sub-groups within these societies hold stably opposing beliefs, even when there is a fact of the matter. Extant models of polarization do not capture the idea that some beliefs are true and others false. Here we present a model, based on the network epistemology framework of Bala and Goyal, 784–811 1998), in which polarization emerges even though agents gather evidence about their beliefs, and true belief yields a pay-off advantage. As we (...) discuss, these results are especially relevant to polarization in scientific communities, for these reasons. The key mechanism that generates polarization involves treating evidence generated by other agents as uncertain when their beliefs are relatively different from one’s own. (shrink)

Scientists are generally subject to social pressures, including pressures to conform with others in their communities, that affect achievement of their epistemic goals. Here we analyze a network epistemology model in which agents, all else being equal, prefer to take actions that conform with those of their neighbors. This preference for conformity interacts with the agents’ beliefs about which of two (or more) possible actions yields the better result. We find a range of possible outcomes, including stable polarization in belief (...) and action. The model results are sensitive to network structure. In general, though, conformity has a negative effect on a community’s ability to reach accurate consensus about the world. (shrink)

In the latter half of the twentieth century, philosophers of science have argued (implicitly and explicitly) that epistemically rational individuals might compose epistemically irrational groups and that, conversely, epistemically rational groups might be composed of epistemically irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they together imply that methodological prescriptions for scientific communities and those for individual scientists might be logically independent of one another. We develop a formal model of scientific inquiry, define four (...) criteria for individual and group epistemic rationality, and then prove that the four definitions diverge, in the sense that individuals will be judged rational when groups are not and vice versa. We conclude by explaining implications of the inconsistency thesis for (i) descriptive history and sociology of science and (ii) normative prescriptions for scientific communities. (shrink)

Modeling and computer simulations, we claim, should be considered core philosophical methods. More precisely, we will defend two theses. First, philosophers should use simulations for many of the same reasons we currently use thought experiments. In fact, simulations are superior to thought experiments in achieving some philosophical goals. Second, devising and coding computational models instill good philosophical habits of mind. Throughout the paper, we respond to the often implicit objection that computer modeling is “not philosophical.”.

Several current debates in the epistemology of testimony are implicitly motivated by concerns about the reliability of rules for changing one’s beliefs in light of others’ claims. Call such rules testimonial norms (tns). To date, epistemologists have neither (i) characterized those features of communities that influence the reliability of tns, nor (ii) evaluated the reliability of tns as those features vary. These are the aims of this paper. I focus on scientific communities, where the transmission of highly specialized information is (...) both ubiquitous and critically important. Employing a formal model of scientific inquiry, I argue that miscommunication and the “communicative structure” of science strongly influence the reliability of tns, where reliability is made precise in three ways. (shrink)

At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, size (...) of groups, and communication channels among researchers. In this paper we argue that the current research faces an empirical challenge. The challenge is to connect simulation models with data. We present possible scenarios about how the challenge may unfold. (shrink)

A new layer of complexity, constituted of networks of information token recurrence, has been identified in socio-technical systems such as the Wikipedia online community and the Zooniverse citizen science platform. The identification of this complexity reveals that our current understanding of the actual structure of those systems, and consequently the structure of the entire World Wide Web, is incomplete, which raises novel questions for data science research but also from the perspective of social epistemology. Here we establish the principled foundations (...) and practical advantages of analyzing information diffusion within and across Web systems with Transcendental Information Cascades, and outline resulting directions for future study in the area of socio-technical systems. We also suggest that Transcendental Information Cascades may be applicable to any kind of time-evolving system that can be observed using digital technologies, and that the structures found in such systems comprise properties common to all naturally occurring complex systems. (shrink)

This model explores consensus among agents in a population in terms of two properties. The first is a probability of belief change. This value indicates how likely agents are to change their mind in interactions. The other is the size of the agents audience: the proportion of the population the agent has access to at any given time. In all instances, the agents converge on a single belief, although the agents are arational. I argue that this generates a skeptical hypothesis: (...) any instance of purportedly rational consensus might just as well be a case of arational belief convergence. I also consider what the model tells us about increasing the likelihood that one agent’s belief is adopted by the rest. Agents are most likely to have their beliefs adopted by the entire population when their value for PBC is low relative to the rest of the population and their audience sizes are roughly three-quarters of the largest possible audience. I further explore the consequences of dogmatists to the population; individuals who refuse to change their mind end up polarizing the population. I conclude with reflections on the supposedly special character of rationality in belief-spread. (shrink)

Sometimes retracted or refuted scientific information is used and propagated long after it is understood to be misleading. Likewise, retracted news items may spread and persist, despite being publi...

Those who comment on modern scientific institutions are often quick to praise institutional structures that leave scientists to their own devices. These comments reveal an underlying presumption that scientists do best when left alone—when they operate in what we call the ‘scientific state of nature’. Through computer simulation, we challenge this presumption by illustrating an inefficiency that arises in the scientific state of nature. This inefficiency suggests that one cannot simply presume that science is most efficient when institutional control is (...) absent. In some situations, actively encouraging unpopular, risky science would improve scientific outcomes. 1 Introduction2 Scientists and Bandits3 Choosing an ϵ4 Structure of Communication5 Discussion. (shrink)

This paper argues for a novel account of deceitful scientific communication, as “wishful speaking”. This concept is of relevance both to philosophy of science and to discussions of the ethics of lying and misleading. Section 1 outlines a case-study of “ghost-managed” research. Section 2 introduces the concept of “wishful speaking” and shows how it relates to other forms of misleading communication. Sections 3–5 consider some complications raised by the example of pharmaceutical research; concerning the ethics of silence; how research strategies—as (...) well as the communication of results—may be misleading; and questions of multiple authorship. The conclusion suggests some more general conclusions. (shrink)

In the literature on expert trust, it is often assumed that track records are the gold standard for evaluating expertise, and the difficulty of expert identification arises from either the lack of access to track records, or the inability to assess them. I show, using a computational model, that even in an idealized environment where agents have a God’s eye view on track records, they may fail to identify experts. Under plausible conditions, selecting testimony based on track records ends up (...) reducing overall accuracy, and preventing the community from identifying the real experts. (shrink)

In recent years the social nature of scientific inquiry has generated considerable interest. We examine the effect of an epistemically impure agent on a community of honest truth seekers. Extending a formal model of network epistemology pioneered by Zollman, we conclude that an intransigently biased agent prevents the community from ever converging to the truth. We explore two solutions to this problem, including a novel procedure for endogenous network formation in which agents choose whom to trust. We contend that our (...) model nicely captures aspects of current problems in medical research and gesture at some morals for medical epistemology more generally. (shrink)

Stephen John has recently suggested that the ethics of communication yields important insights as to how values should be incorporated into science. In particular, he examines cases of “wishful speaking” in which a scientific actor endorses unreliable conclusions in order to obtain the consequences of the listener treating the results as credible. He concludes that what is wrong in these cases is that the speaker surreptitiously relies on values not accepted by the hearer, violating what he terms “the value-apt ideal”. (...) I expand on this view by integrating into it Miranda Fricker’s account of testimonial injustice. I find testimonial injustice can arise in a manner unanticipated by Fricker, specifically that a credibility excess given to a speaker typically reduces the ability of others in the epistemic community to transmit knowledge, a phenomenon I term “collateral epistemic injustice”. I argue that this possibility entails that receivers have an ethical obligation to assign credibility judiciously. A further consequence of this view is that the value-apt ideal is insufficient. Because audience members have an obligation to assign credibility judiciously, a speaker cannot merely rely on shared values, they must also be open about the extent to which their conclusions depend on those values. Thus, both wishful speaking and obscuring the value-dependent nature of a conclusion makes one an unreliable source of information. Accordingly, other community members have an ethical obligation to ignore a speaker that frequently engages in either. (shrink)

Although there has been a lot of investigation into the influence of the network structure of scientific communities on the one hand and into testimonial norms on the other, a discussion of TNs that take the network structure into account has been lacking. In this paper, I introduce two TNs which are sensitive to the local network structure. According to these norms, scientists should give less weight to the results of well-connected colleagues, as compared to less connected ones. I employ (...) an Agent Based Model to test the reliability of the two novel TNs against different versions of conventional, structure-insensitive TNs in networks of varying size and structure. The results of the simulations show that the novel TNs are more reliable. This suggests that it would be beneficial for scientific communities if their members followed such norms. For individual scientists, I show that there are both reasons for and reasons against adopting them. (shrink)

ABSTRACT The rise of social media has correlated with an increase in political polarization, which many perceive as a threat to public discourse and democratic governance. This paper presents a framework, drawing on social epistemology and the economic theory of public goods, to explain how social media can contribute to polarization, making us collectively poorer, even while it provides a preferable media experience for individual consumers. Collective knowledge and consensus is best served by having richly connected networks that are epistemically (...) integrated: individuals with diverse levels of expertise should be relatively well connected to each other. In epistemically segregated networks, by contrast, we have reason to predict collective epistemic failures. Expert knowledge will be isolated from the majority, leading average opinion to be less informed than is socially optimal, and entrenching disagreements. Because social media enables users to very easily adopt homophilous network connections – connections to those with similar opinions, education levels, and social backgrounds – it is likely to have increased epistemic segregation compared to older media platforms. The paper explains the theoretical foundations of these predictions, and sketches regulatory measures – such as taxes – that might be employed to preserve the public good of a well integrated social media network. (shrink)

Our beliefs and opinions are shaped by others, making our social networks crucial in determining what we believe to be true. Sometimes this is for the good because our peers help us form a more accurate opinion. Sometimes it is for the worse because we are led astray. In this context, we address via agent-based computer simulations the extent to which patterns of connectivity within our social networks affect the likelihood that initially undecided agents in a network converge on a (...) true opinion following group deliberation. The model incorporates a fine-grained and realistic representation of belief and trust, and it allows agents to consult outside information sources. We study a wide range of network structures and provide a detailed statistical analysis concerning the exact contribution of various network metrics to collective competence. Our results highlight and explain the collective risks involved in an overly networked or partitioned society. Specifically, we find that 96% of the variation in collective competence across networks can be attributed to differences in amount of connectivity and clustering, which are negatively correlated with collective competence. A study of bandwagon or “group think” effects indicates that both connectivity and clustering increase the probability that the network, wholly or partly, locks into a false opinion. Our work is interestingly related to Gerhard Schurz’s work on meta-induction and can be seen as broadly addressing a practical limitation of his approach. (shrink)

Topics in Cognitive Science, Volume 14, Issue 3, Page 602-620, July 2022.

Group agents can act, and they can have knowledge. How should we understand the species of collective action which aims at knowledge? In this paper, I present an account of group inquiry. This account faces two challenges: to make sense of how large-scale distributed activities might be a kind of group action, and to make sense of the kind of division of labour involved in collective inquiry. In the first part of the paper, I argue that existing accounts of group (...) action face problems dealing with large-scale group actions, and propose a minimal alternative account. In the second part of the paper, I draw on an analogy between inquiry and conversation, arguing that work by Robert Stalnaker and Craige Roberts helps us to think about the division of labour. In the final part of the paper I put the accounts of group action and inquiry together, and consider how to think about group knowledge, deep ignorance, and the different kinds of division of labour. (shrink)