To believe that every single scientist ought to be individually engaged in ethical thinking in order for science to be responsible at a collective level may be too demanding, if not plainly unrealistic. In fact, ethical labor is typically distributed across different kinds of scientists within the scientific community. Based on the empirical data collected within the Horizon 2020 ‘RRI-Practice’ project, we propose a classification of the members of the scientific community depending on their engagement in this collective activity. Our (...) classification offers, on the one hand, a model of how the ethical aspects of science are taken into consideration by scientists and, on the other, some indications on how to institutionalize ethics in science. (shrink)

Understanding the dynamics of information is crucial to many areas of research, both inside and outside of philosophy. Using computer simulations of three kinds of information, germs, genes, and memes, we show that the mechanism of information transfer often swamps network structure in terms of its effects on both the dynamics and the fitness of the information. This insight has both obvious and subtle implications for a number of questions in philosophy, including questions about the nature of information, whether there (...) is genetic information, and how to arrange scientific communities. (shrink)

Supposedly, stubbornness on the part of scientists—an unwillingness to change one’s position on a scientific issue even in the face of countervailing evidence—helps efficiently divide scientific labor. Maintaining disagreement is important because it keeps scientists pursuing a diversity of leads rather than all working on the most promising, and stubbornness helps preserve this disagreement. Planck’s observation that “Science progresses one funeral at a time” might therefore be an insight into epistemically beneficial stubbornness on the part of researchers. In conversation with (...) extant formal models, recent empirical research, and a novel agent-based model of my own I explore whether the epistemic goods which stubbornness can secure—disagreement and diversity—are attainable through less-costly methods. I make the case that they are, at least in part, and also use my modeling results to show that if stubbornness is scientifically valuable, it still involves a willingness to change one’s mind. (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)

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.”.

In this paper we examine the epistemic value of highly idealized agent-based models of social aspects of scientific inquiry. On the one hand, we argue that taking the results of such simulations as informative of actual scientific inquiry is unwarranted, at least for the class of models proposed in recent literature. Moreover, we argue that a weaker approach, which takes these models as providing only “how-possibly” explanations, does not help to improve their epistemic value. On the other hand, we suggest (...) that if ABMs of science underwent two types of robustness analysis, they could indeed have a clear epistemic function, namely by providing evidence for philosophical and historical hypotheses. In this sense, ABMs can obtain evidential and explanatory properties and thus be a useful tool for integrated history and philosophy of science. We illustrate our point with an example of a model—building on the work by Kevin Zollman—which we apply to a concrete historical case study. (shrink)

Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ elements of (...) truth as correspondence. Results, though often intuitive, prove more complex than in simpler models (Hegselmann & Krause 2002, 2006; Grim, Singer, Reade & Fisher 2015). The optimistic finding is that pressures toward internal coherence can exploit and expand on small elements of truth as correspondence is introduced into epistemic networks. Less optimistic results show that pressures for coherence can also work directly against the incorporation of truth, particularly when coherence is established first and new data is introduced later. (shrink)

Recent studies of scientific interaction based on agent-based models suggest that a crucial factor conducive to efficient inquiry is what Zollman has dubbed ‘transient diversity’. It signifies a process in which a community engages in parallel exploration of rivaling theories lasting sufficiently long for the community to identify the best theory and to converge on it. But what exactly generates transient diversity? And is transient diversity a decisive factor when it comes to the efficiency of inquiry? In this paper we (...) examine the impact of different conditions on the efficiency of inquiry, as well as the relation between diversity and efficiency. This includes certain diversity-generating mechanisms previously proposed in the literature, as well as some factors that have so far been neglected. This study is obtained via an argumentation-based ABM. Our results suggest that cautious decision-making does not always have a significant impact on the efficiency of inquiry while different evaluations underlying theory-choice and different social networks do. Moreover, we find a correlation between diversity and a successful performance of agents only under specific conditions, which indicates that transient diversity is sometimes not the primary factor responsible for efficiency. Altogether, when comparing our results to those obtained by structurally different ABMs based on Zollman’s work, the impact of specific factors on efficiency of inquiry, as well as the role of transient diversity in achieving efficiency, appear to be highly dependent on the underlying model. (shrink)

The article presents an agent-based model of scientific interaction aimed at examining how different degrees of connectedness of scientists impact their efficiency in knowledge acquisition. The model is built on the basis of Zollman’s ABM by changing some of its idealizing assumptions that concern the representation of the central notions underlying the model: epistemic success of the rivalling scientific theories, scientific interaction and the assessment in view of which scientists choose theories to work on. Our results suggest that whether and (...) to what extent the degree of connectedness of a scientific community impacts its efficiency is a highly context-dependent matter since different conditions deem strikingly different results. More generally, we argue that simplicity of ABMs may come at a price: the requirement to run extensive robustness analysis before we can specify the adequate target phenomenon of the model.1 1Introduction2Zollman's 2010 Model3Static versus Dynamic Epistemic Success 3.1Introducing the notion of dynamic epistemic success3.2Implementation and results for the basic setup4Critical Interaction 4.1Introducing critique4.2Implementation and results5Inertia of Inquiry 5.1Introducing rational inertia5.2Implementation and results6Threshold Below Which Theories Are Equally Promising 6.1An inquiry that is even more difficult6.2Implementation and results7Discussion8Conclusion. (shrink)

Typically, public discussions of questions of social import exhibit two important properties: they are influenced by conformity bias, and the influence of conformity is expressed via social networks. We examine how social learning on networks proceeds under the influence of conformity bias. In our model, heterogeneous agents express public opinions where those expressions are driven by the competing priorities of accuracy and of conformity to one’s peers. Agents learn, by Bayesian conditionalization, from private evidence from nature, and from the public (...) declarations of other agents. Our key findings are that networks that produce configurations of social relationships that sustain a diversity of opinions empower honest communication and reliable acquisition of true beliefs, and that the networks that do this best turn out to be those which are both less centralized and less connected. (shrink)

There is a commonly made distinction between two types of scientists: risk-taking, trailblazing mavericks and detail-oriented followers. A number of recent papers have discussed the question what a desirable mixture of mavericks and followers looks like. Answering this question is most useful if a scientific community can be steered toward such a desirable mixture. One attractive route is through credit incentives: manipulating rewards so that reward-seeking scientists are likely to form the desired mixture of their own accord. Here I argue (...) that this idea is less straightforward than it may seem. Interpreting mavericks as scientists who prioritize rewards over speed and risk, I show in a deliberatively simple model that there is a fixed mixture which is not particularly likely to be desirable and which credit incentives cannot alter. I consider a way around this result, but this has some major drawbacks. I conclude that credit incentives are not as promising a way to create a desirable mixture of mavericks and followers as one might have thought. (shrink)

This paper examines lessons obtained by means of simulations in the form of agent-based models about the norms that are to guide disagreeing scientists. I focus on two types of epistemic and methodological norms: norms that guide one’s attitude towards one’s own theory, and norms that guide one’s attitude towards the opponent’s theory. Concerning I look into ABMs that have been designed to examine the context of peer disagreement. Here I challenge the conclusion that the given ABMs provide a support (...) for the so-called Steadfast Norm, according to which one is epistemically justified in remaining steadfast in their beliefs in face of disagreeing peers. I argue that the proposed models at best provide evidence for a weaker norm, which concerns methodological steadfastness. Concerning I look into ABMs aimed at examining epistemic effects of scientific interaction. Here I argue that the models provide diverging suggestions and that the link between each ABM and the type of represented inquiry is still missing. Moreover, I examine alternative strategies of arguing in favor of the benefits of scientific interaction, relevant for contemporary discussions on scientific pluralism. (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)

I show that the social stratification of academic science can arise as a result of academics’ preference for reading work of high epistemic value. This is consistent with a view on which academic superstars are highly competent academics, but also with a view on which superstars arise primarily due to luck. I argue that stratification is beneficial if most superstars are competent, but not if most superstars are lucky. I also argue that it is impossible to tell whether most superstars (...) are in fact competent or lucky, or which group a given superstar belongs to, and hence whether stratification is overall beneficial. (shrink)

This article aims to describe the last 10 years of the collaborative scientific endeavors on polarization in particular and collective problem-solving in general by our multidisciplinary research team. We describe the team’s disciplinary composition—social psychology, political science, social philosophy/epistemology, and complex systems science— highlighting the shared and unique skill sets of our group members and how each discipline contributes to studying polarization and collective problem-solving. With an eye to the literature on team dynamics, we describe team logistics and processes that (...) we believe make our multidisciplinary team persistent and productive. We emphasize challenges and difficulties caused by disciplinary differences in terms of terminology, units/levels of analysis, methodology, and theoretical assumptions. We then explain how work disambiguating the concepts of polarization and developing an integrative theoretical and methodological framework with complex systems perspectives has helped us overcome these challenges. We summarize the major findings that our research has produced over the past decade, and describe our current research and future directions. Last, we discuss lessons we have learned, including difficulties in a “three models” project and how we addressed them, with suggestions for effective multidisciplinary team research. (shrink)