Matteo Michelini

Scientific disagreements sometimes persist even if scientists fully share results of their research. In this paper we develop an agent-based model to study the impact of diverging diagnostic values scientists may assign to the evidence, given their different background assumptions, on the emergence of polarization in the scientific community. Scientists are represented as Bayesian updaters for whom the diagnosticity of evidence is given by the Bayes factor. Our results suggest that an initial disagreement on the diagnostic value of evidence can, but does not necessarily, lead to polarization, depending on the sample size of the performed studies and the confidence interval within which scientists share their opinions. In particular, the more data scientists share,the more likely it is that the community will end up polarized.

Current models of scientific inquiry assume scientists to all share the same evaluative standards. However, science is often characterised by multiple ones, that is by evaluative diversity. We investigate how scientific success is affected by evaluative diversity through computer-based simulations. Our results show that communities with diverse standards profit immensely from scientists sharing all the approaches they explored, regardless of whether they considered them valuable. Moreover, we find that even a moderate degree of evaluative diversity can, under certain conditions, lead scientists to reach more satisfying results than those they would reach in homogeneous communities.

Current models of scientific inquiry assume that scientists all share the same evaluative standards. However, scientists often rely on different yet legitimate ones, a feature we call evaluative diversity. We investigate how scientific success is affected by diversity in evaluative standards through computer-based simulations. Our results show that communities with diverse standards benefit substantially from scientists sharing all the approaches they explored, regardless of whether they considered them valuable. Moreover, we find that even a moderate degree of evaluative diversity can, under certain conditions, lead scientists to reach more satisfying results than those they would reach in homogeneous communities.

The best strategy for getting away with lying is to lie small by only deviating from the truth as much as is necessary to achieve the intended deception. Why then do some demagogues lie big? One set of views has it that the only difference between small and big lies concerns the size of their contents. They claim that the purpose of big lies is the formation of false beliefs in their literal contents via counterfactual reasoning, conspiracy theories, or the illusory truth effect. The negative part of this paper questions these accounts. The positive part proposes a different explanation for why demagogues use big lies and argues that big lies may serve three distinct purposes for demagogues: they reinforce their supporters’ deeply held beliefs, test the loyalty of their close followers, or publicly demonstrate the demagogue’s power. For a big lie to serve these purposes, genuine belief in the lie is not required – in fact, few are likely to believe it. What matters is that the demagogue’s supporters publicly endorse the lie. We contend that they do so, either because they interpret them as motivational statements or use them to express or justify their shared emotions or convictions.

Current models of scientific inquiry assume scientists to all share the same evaluative standards. However, science is often characterised by multiple ones, that is by evaluative diversity. We investigate how scientific success is affected by evaluative diversity through computer-based simulations. Our results show that communities with diverse standards profit immensely from scientists sharing all the approaches they explored, regardless of whether they considered them valuable. Moreover, we find that even a moderate degree of evaluative diversity can, under certain conditions, lead scientists to reach more satisfying results than those they would reach in homogeneous communities.

Communities frequently experience belief polarization, even in the face of overwhelming scientific evidence supporting one side of the debate. Current explanations for this phenomenon, which we define as bad polarization, attribute its emergence to the influence of social incentives on belief formation. However, these explanations presuppose the existence of a fragmented community in which opposing groups develop different beliefs. Here, we provide a model of belief formation in which social incentives bring about bad polarization even in structurally cohesive communities. We assume agents to distribute a finite amount of social support among their like-minded neighbors and to sample evidence partially in order to form the belief that grants them the highest possible support. Accordingly, we show that bad polarization emerges more frequently when communities are highly connected, and that bad believers, individuals holding unsupported beliefs, are consistently a minority. Bad polarization is driven by the competition for social support: bad believers form a minority because this allows them to gain a higher amount of support than if they adhered to the majority view.

Communication plays a pivotal role in social phenomena such as belief polar- ization, scientific inquiry, and collective problem-solving. Agent-Based Models (ABMs) are computational tools that simulate the emergence of macro-level phenomena from micro-level interactions among agents. This paper focuses on Argumentative Agent-Based Models (AABMs), a specialized subset of ABMs that study argumentative communication, where agents provide reasons to sup- port or counter opinions. We present a systematic overview of AABMs, detailing their design, methodologies, and applications across disciplines. Key research questions include understanding the dynamics of consensus versus polarization, the conditions for epistemic reliability in collective decision-making, and the mechanisms that foster efficient collaboration within diverse groups through ar- gumentative exchanges. By synthesizing contributions from computer science, social science, and philosophy, this paper serves as both an entry point for new- comers and a comprehensive resource for researchers advancing the study of AABMs.

In this paper, we present an agent-based model for studying the impact of 'myside bias' on the argumentative dynamics in scientific communities. Recent insights in cognitive science suggest that scientific reasoning is influenced by `myside bias'. This bias manifests as a tendency to prioritize the search and generation of arguments that support one's views rather than arguments that undermine them. Additionally, individuals tend to apply more critical scrutiny to opposing stances than to their own. Although myside bias may pull individual scientists away from the truth, its effects on communities of reasoners remain unclear. The aim of our model is two-fold: first, to study the argumentative dynamics generated by myside bias, and second, to explore which mechanisms may act as a mitigating factor against its pernicious effects. Our results indicate that biased communities are epistemically less successful than non-biased ones, and that they also tend to be less polarized than non-biased ones. Moreover, we find that two socio-epistemic mechanisms help communities to mitigate the effect of the bias: the presence of a common filter on weak arguments, which can be interpreted as shared beliefs, and an equal distribution of agents for each alternative at the beginning.