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 often need to have beliefs about things on which we are not experts. Luckily, we often have access to expert judgements on such topics. But how should we form our beliefs on the basis of expert opinion when experts conflict in their judgments? This is the core of the novice/2-expert problem in social epistemology. A closely related question is important in the context of policy making: how should a policy maker use expert judgments when making policy in domains in (...) which she is not herself an expert? This question is more complex, given the messy and strategic nature of politics. In this paper we argue that the prediction with expert advice framework from machine learning provides helpful tools for addressing these problems. We outline conditions under which we should expert PWEA to be helpful and those under which we should not expect these methods to perform well. (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.”.

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)

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)

The US populace appears to be increasingly polarized on partisan lines. Political fissures bifurcate the country even on empirical matters like vaccine safety and anthropogenic climate change. There now exists an ever-expanding interdisciplinary research program in which theorists attempt to explain increases in political polarization and myriad other phenomena collected under the “post-truth” heading by appeal to social-epistemic structures, like echo chambers and epistemic bubbles, that affect the flow and uptake of information in various communities. In this paper, I critically (...) analyze C. Thi Nguyen’s important and popular analysis of echo chambers and epistemic bubbles. As I demonstrate, the explanatory mechanisms on which Nguyen focuses are, arguably, overly cognitive and obscure significant effects of social-epistemic structures on our affective lives. The broader lesson to draw from my discussion is the following: commonly used expressions intended to refer to social-epistemic problems, like “political polarization”, possess no univocal definition across theorists, and various ways of making the terms precise are differentially successful in characterizing verifiable phenomena. Theorizing about social-epistemic structures should be responsive to relevant empirical work on various phenomena that we have good reason to believe constitute real and substantive problems that result from the flow and uptake of information. (shrink)

Is it possible to disagree with someone without considering them cognitively flawed? The answer seems to be a resounding yes: disagreeing with someone doesn't entail thinking less of them. You can disagree with someone and not think that they are unreasonable. Deep disagreements, however, may challenge this assumption. A disagreement is deep when it involves many interrelated issues, including the proper way to resolve the disagreement, resulting in its persistence. The parties to a deep disagreement can hold neutral or even (...) positive judgements of each other's epistemic character, as parties’ judging each other's epistemic character negatively (i.e., epistemic disdain) is not a defining feature of deep disagreements. When analysing real-life cases, however, we find that epistemic disdain is typical of deep disagreements. In this article, I analyse why this is the case. Given that epistemic disdain undermines cognitive peerhood, the prospects of deep disagreements between epistemic peers seem bleak. Finally, it is discussed how the phenomenon of epistemic disdain, as it relates to deep disagreements, may increase affective polarisation. (shrink)

Formal models of scientific inquiry, aimed at capturing socio-epistemic aspects underlying the process of scientific research, have become an important method in formal social epistemology and philosophy of science. In this introduction to the special issue we provide a historical overview of the development of formal models of this kind and analyze their methodological contributions to discussions in philosophy of science. In particular, we show that their significance consists in different forms of ‘methodological iteration’ whereby the models initiate new lines (...) of inquiry, isolate and clarify problems with existing knowledge claims, and stimulate further research. (shrink)

When public opinion gets polarized, the population’s beliefs can experience two different changes: they can become more extreme in their contents or they can be held with greater confidence. These two possibilities point to two different understandings of the rupture that characterizes political polarization: extremism and radicalism. In this article, I show that from the close examination of the best available evidence regarding how we get polarized, it follows that the pernicious type of political polarization has more to do with (...) radicalism than with extremism. Reinforcing the confidence in the core beliefs of the group we identify with makes our beliefs immune to the reasons coming from the other political side. Finally, I also suggest that the rise of political polarization is not necessarily the result of an irrational process. (shrink)

A perennial problem in social epistemology is the problem of expert testimony, specifically expert testimony regarding scientific issues: for example, while it is important for me to know information pertaining to anthropogenic climate change, vaccine safety, Covid-19, etc., I may lack the scientific background required to determine whether the information I come across is, in fact, true. Without being able to evaluate the science itself, then, I need to find trustworthy expert testifiers to listen to. A major project in social (...) epistemology has thus become determining what the markers of trustworthiness are that laypersons can appeal to in order to identify and acquire information from expert testifiers. At the same time, the ways in which we acquire scientific information has changed significantly, with much of it nowadays being acquired in online environments. While much has been said about the potential pitfalls of seeking information online, little has been said about how the nature of seeking information online should make us think about the problem of expert testimony. Indeed, it seems to be an underlying assumption that good markers of trustworthiness apply equally well when seeking information from expert testifiers in online and offline environments alike, and that the new challenges and opportunities presented by online environments merely affects the methods by which we can acquire evidence of said trustworthiness. Here I argue that in making this assumption one risks failing to account for how unique features of the ways in which we acquire information online affect how we evaluate the trustworthiness of experts. Specifically, I argue for two main claims: first, that the nature of information-seeking online is such that the extent to which information is susceptible to manipulation is a dominant marker of trustworthiness; second, as a result, one will be more likely to seek out a particular kind of expert testifier in online environments, what I call a cooperative as opposed to preemptive expert. The result is that criteria for expert trustworthiness may look significantly different when acquiring information online as opposed to offline. (shrink)

Some common explanations of issue polarization and echo chambers rely on social or cognitive mechanisms of exclusion. Accordingly, suggested interventions like “be more open-minded” target these mechanisms: avoid epistemic bubbles and don’t discount contrary information. Contrary to such explanations, we show how a much weaker mechanism—the preference for belief—can produce issue polarization in epistemic communities with little to no mechanisms of exclusion. We present a network model that demonstrates how a dynamic interaction between the preference for belief and common structures (...) of epistemic communities can turn very small unequal distributions of initial beliefs into full-blown polarization. This points to a different class of explanations, one that emphasizes the importance of the initial spread of information. We also show how our model complements extant explanations by including a version of biased assimilation and motivated reasoning—cognitive mechanisms of exclusion. We find that mechanisms of exclusion can exacerbate issue polarization, but may not be the ultimate root of it. Hence, the recommended interventions suggested by extant literature is expected to be limited and the problem of issue polarization to be even more intractable. (shrink)