Dunja Šešelja

Abstract argumentation and explanation applied to scientific debates

Abstract argumentation has been shown to be a powerful tool within many fields such as artificial intelligence, logic and legal reasoning. In this paper we enhance Dung’s well-known abstract argumentation framework with explanatory capabilities. We show that an explanatory argumentation framework (EAF) obtained in this way is a useful tool for the modeling of scientific debates. On the one hand, EAFs allow for the representation of explanatory and justificatory arguments constituting rivaling scientific views. On the other hand, different procedures for selecting arguments, corresponding to different methodological and epistemic requirements of theory evaluation, can be formulated in view of our framework.

Towards the Proof-theoretic Unification of Dung’s Argumentation Framework

The article presents a unifying adaptive logic framework for abstract argumentation. It consists of a core system for abstract argumentation and various adaptive logics based on it. These logics represent in an accurate sense all standard extensions defined within Dung’s abstract argumentation system with respect to sceptical and credulous acceptance. The models of our logics correspond exactly to specific extensions of given argument systems. Additionally, the dynamics of adaptive proofs mirror the argumentative reasoning of a rational agent. In particular, the presented logics allow for external dynamics, i.e. they are able to deal with the arrival of new arguments and are therefore apt to model open-ended argumentations by providing provisional conclusions.

Epistemic justification in the context of pursuit: a coherentist approach

The aim of this paper is to offer an account of epistemic justification suitable for the context of theory pursuit, that is, for the context in which new scientific ideas, possibly incompatible with the already established theories, emerge and are pursued by scientists. We will frame our account paradigmatically on the basis of one of the influential systems of epistemic justification: Laurence Bonjour’s coherence theory of justification. The idea underlying our approach is to develop a set of criteria which indicate that the pursued system is promising of contributing to the epistemic goal of robustness of scientific knowledge and of developing into a candidate for acceptance. In order to realize this we will (a) adjust the scope of Bonjour’s standards—consistency, inferential density, and explanatory power, and (b) complement them by the requirement of a programmatic character. In this way we allow for the evaluation of the “potential coherence” of the given epistemic system.

Robustness and Idealizations in Agent-Based Models of Scientific Interaction

The article 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 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 1. Introduction2. Zollmans 2010 Model3. Static versus Dynamic Epistemic Success 3.1. Introducing the notion of dynamic epistemic success3.2. Implementation and results for the basic setup4. Critical Interaction 4.1. Introducing critique4.2. Implementation and results5. Inertia of Inquiry 5.1. Introducing rational inertia5.2. Implementation and results6. Threshold Below Which Theories Are Equally Promising 6.1. An inquiry that is even more difficult6.2. Implementation and results7. Discussion8. Conclusion Introduction Zollmans 2010 Model Static versus Dynamic Epistemic Success 3.1. Introducing the notion of dynamic epistemic success3.2. Implementation and results for the basic setup Introducing the notion of dynamic epistemic success Implementation and results for the basic setup Critical Interaction Introducing critique Implementation and results Inertia of Inquiry Introducing rational inertia Implementation and results Threshold Below Which Theories Are Equally Promising An inquiry that is even more difficult Implementation and results Discussion Conclusion

An Argumentative Agent-Based Model of Scientific Inquiry

In this paper we present an agent-based model (ABM) of scientific inquiry aimed at investigating how different social networks impact the efficiency of scientists in acquiring knowledge. As such, the ABM is a computational tool for tackling issues in the domain of scientific methodology and science policy. In contrast to existing ABMs of science, our model aims to represent the argumentative dynamics that underlies scientific practice. To this end we employ abstract argumentation theory as the core design feature of the model.

Withstanding Tensions: Scientific Disagreement and Epistemic Tolerance

Many philosophers of science consider scientific disagreement to be a major promoter of scientific progress. However, we lack an account of the epistemically and heuristically appropriate response scientists should have towards opposing positions in peer disagreements. Even though some scientific pluralists have advocated a notion of tolerance, the implications of this notion for one’s epistemic stance and, more generally, for the scientific practice have been insufficiently explicated in the literature. In this paper we explicate a characteristic tension in which disagreeing scientists are situated and on this basis we propose a notion of epistemic tolerance.

Heuristic Reevaluation of the Bacterial Hypothesis of Peptic Ulcer Disease in the 1950s

AbstractThroughout the first half of the twentieth century the research on peptic ulcer disease (PUD) focused on two rivaling hypothesis: the “acidity” and the “bacterial” one. According to the received view, the latter was dismissed during the 1950s only to be revived with Warren’s and Marshall’s ndiscovery of Helicobacter pylori in the 1980s. In this paper we investigate why the bacterial hypothesis was largely abandoned in the 1950s, and whether there were good epistemic reasons for its dismissal. Of special interest for our research question is Palmer’s 1954 large-scale study, which challenged the bacterial hypothesis with serious ncounter-evidence, and which by many scholars is considered as the shifting point in the research on PUD. However, we show that: (1) The perceived refutatory impact of Palmer’s study was disproportionate to its methodological rigor. This undermines its perceived status as a crucial experiment against the bacterial hypothesis. (2) In view of this and other considerations we argue that the bacterial hypothesis was worthy of pursuit in the 1950s.

Concerning Peter Vickers's Recent Treatment of ‘Paraconsistencitis’

Inconsistencies have long been considered one of the major challenges for the explication of scientific reasoning and rationality. Consistency has traditionally been taken to be a necessary requirement for accepted scientific theories (e.g. Popper [1935] 1959, but also more recently Douglas 2009). In the view of classical logic (CL) anything can be derived from an inconsistent set of premises by the rule Ex contradictione quodlibet (ECQ). 1 This motivates the requirement that scientists should reason from consistent sets of premises. Philosophers of science (especially in the post-Kuhnian era) have challenged this traditional stance by pointing out cases of inconsistencies in scientific theories, and by arguing for the importance of tolerating them in specific circumstances. As a result, many have suggested that methodological and heuristic requirements need to be weakened in order to allow for inconsistencies in, for instance, young, underdeveloped theories (Feyerabend 1975; Lakatos 1978; Nickles 2002), and more generally that:

What Is the Epistemic Function of Highly Idealized Agent-Based Models of Scientific Inquiry?

In this paper we examine the epistemic value of highly idealized agent-based models (ABMs) 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.

Examining Network Effects in an Argumentative Agent-Based Model of Scientific Inquiry

In this paper we present an agent-based model (ABM) of scientific inquiry aimed at investigating how different social networks impact the efficiency of scientists in acquiring knowledge. The model is an improved variant of the ABM introduced in [3], which is based on abstract argumentation frameworks. The current model employs a more refined notion of social networks and a more realistic representation of knowledge acquisition than the previous variant. Moreover, it includes two criteria of success: a monist and a pluralist one, reflecting different desiderata of scientific inquiry. Our findings suggest that, given a reasonable ratio between research time and time spent on communication, increasing the degree of connectedness of the social network tends to improve the efficiency of scientists.