Epistemic Effects of Scientific Interaction: Approaching the Question with an Argumentative Agent-Based Model

The question whether increased interaction among scientists is beneficial or harmful for their efficiency in acquiring knowledge has in recent years been tackled by means of agent-based models (ABMs) (e.g. Zollman 2007, 2010; Grim 2009; Grim et al. 2013). Nevertheless, the relevance of some of these results for actual scientific practice has been questioned in view of specific parameter choices used in the simulations (Rosenstock et al. 2016). In this paper we present a novel ABM that aims at tackling the same question, while representing scientific interaction in terms of argumentative exchange. In this way we examine the robustness of previously obtained results under different modeling choices

Exploring Scientific Inquiry via Agent-Based Modeling

In this paper I examine the epistemic function of agent-based models (ABMs) of scientific inquiry, proposed in the recent philosophical literature. In view of Boero and Squazzoni's (2005) classification of ABMs into case-based models, typifications and theoretical abstractions, I argue that proposed ABMs of scientific inquiry largely belong to the third category. While this means that their function is primarily exploratory, I suggest that they are epistemically valuable not only as a temporary stage in the development of ABMs of science, but by providing insights into theoretical aspects of scientific rationality. I illustrate my point with two examples of highly idealized ABMs of science, which perform two exploratory functions: Zollman's (2010) ABM which provides a proof-of-possibility in the realm of theoretical discussions on scientific rationality, and ArgABM (Borg et al., 2017b, 2018a,b), which provides insights into potential mechanisms underlying the efficiency of scientific inquiry

Exploring Scientific Inquiry via Agent-Based Modeling

In this paper I examine the epistemic function of agent-based models (ABMs) of scientific inquiry, proposed in the recent philosophical literature. In view of Boero and Squazzoni's (2005) classification of ABMs into case-based models, typifications and theoretical abstractions, I argue that proposed ABMs of scientific inquiry largely belong to the third category. While this means that their function is primarily exploratory, I suggest that they are epistemically valuable not only as a temporary stage in the development of ABMs of science, but by providing insights into theoretical aspects of scientific rationality. I illustrate my point with two examples of highly idealized ABMs of science, which perform two exploratory functions: Zollman's (2010) ABM which provides a proof-of-possibility in the realm of theoretical discussions on scientific rationality, and ArgABM (Borg et al., 2017b, 2018a,b), which provides insights into potential mechanisms underlying the efficiency of scientific inquiry

Exploring Scientific Inquiry via Agent-Based Modeling

In this paper I examine the epistemic function of agent-based models (ABMs) of scientific inquiry, proposed in the recent philosophical literature. In view of Boero and Squazzoni's (2005) classification of ABMs into case-based models, typifications and theoretical abstractions, I argue that proposed ABMs of scientific inquiry largely belong to the third category. While this means that their function is primarily exploratory, I suggest that they are epistemically valuable not only as a temporary stage in the development of ABMs of science, but by providing insights into theoretical aspects of scientific rationality. I illustrate my point with two examples of highly idealized ABMs of science, which perform two exploratory functions: Zollman's (2010) ABM which provides a proof-of-possibility in the realm of theoretical discussions on scientific rationality, and ArgABM (Borg et al., 2017b, 2018a,b), which provides insights into potential mechanisms underlying the efficiency of scientific inquiry

Theory-Choice, Transient Diversity and the Efficiency of Scientific Inquiry

Recent studies of scientific interaction based on agent-based models (ABMs) suggest that a crucial factor conducive to efficient inquiry is what Zollman, 2010 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 (such as different social networks and cautious decision-making), as well as some factors that have so far been neglected (such as evaluations underlying theory-choice performed by scientists). This study is obtained via an argumentation-based ABM (Borg et al., 2017, 2018). 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

Robustness and Idealizations in Agent-Based Models of Scientific Interaction

The paper presents an agent-based model (ABM) 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 (2010) ABM by changing some of its idealizing assumptions that concern the representation of the central notions underlying the model: epistemic success of the rivaling scientific theories, scientific interaction and the assessment in view of which scientists choose theories to work on. Our results suggest that whether and to which 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

Formal models of the scientific community and the value-ladenness of science

In the past few years, several formal models of the social organisation of science have been developed. 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. This paper is a contribution to the general philosophical debate on the formal models of the social organisation of science. Its aim is to understand which view of science these models end up supporting as a consequence of their philosophical presuppositions, which are also in need to be explicated. It will be argued that, because of some of their philosophical underpinnings, current formal models of the scientific community do not explain the internal role that moral and societal values play in scientific research. It will also be discussed whether formal models of the social organization of science can actually capture the value-ladenness of science. At the same time, it will be shown that the discussion on the formal models of the scientific community may contribute in fruitful ways to the ongoing discussions about value judgements in science

Argument mining: A machine learning perspective

Argument mining has recently become a hot topic, attracting the interests of several and diverse research communities, ranging from artificial intelligence, to computational linguistics, natural language processing, social and philosophical sciences. In this paper, we attempt to describe the problems and challenges of argument mining from a machine learning angle. In particular, we advocate that machine learning techniques so far have been under-exploited, and that a more proper standardization of the problem, also with regards to the underlying argument model, could provide a crucial element to develop better systems

Robustness and Idealizations in Agent-Based Models of Scientific Interaction

The paper presents an agent-based model (ABM) 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 (2010) ABM by changing some of its idealizing assumptions that concern the representation of the central notions underlying the model: epistemic success of the rivaling scientific theories, scientific interaction and the assessment in view of which scientists choose theories to work on. Our results suggest that whether and to which 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