Vlasta Sikimić

Throughout scientific history, there have been cases of mainstream science dismissing novel ideas of less prominent researchers. Nowadays, many researchers with different social and academic backgrounds, origins and gender identities work together on topics of crucial importance. Still, it is questionable whether the privileged groups consider the views of underprivileged colleagues with sufficient attention. To profit from the diversity of thoughts, the scientific community first has to be open to minority viewpoints and epistemically include them in mainstream research. Moreover, the idea of inclusive science poses stronger requirements than the paradigm of open science. We argue that the concept of integration of different opinions is insufficient because the process of integration assumes adjusting oneself to the majority view and fitting into the dominant paradigm while contributing only with smaller amendments. Epistemic inclusion, on the other hand, means dynamically changing the research paradigm during the interaction with diverse methods and hypotheses. The process of inclusion preserves marginalized views and increases epistemic justice.

Modern physics encompasses theoretical and experimental research divided in subfields with specific features. For instance, high energy physics (HEP) attracts significant funding and has distinct organizational structures, i.e., large laboratories and cross-institutional collaborations. Expensive equipment and large experiments create a specific work atmosphere and human relations. While the gender misbalance is characteristic for STEM, early-career researchers are inherently dependent on their supervisors. This raises the question of how satisfied researchers with working in physics are and how different subgroups – female and early-career researchers – perceive their work environment. We empirically investigated job satisfaction and satisfaction with the academic system among physicists (N=123) working in large laboratories, universities, and independent institutes. The scale for measuring the satisfaction with the academic system in physics yielded three factors: experience of research autonomy, opportunities to use one's knowledge, and appreciation of the research by the general public. The results show that physicists are less satisfied with the academic system than with their work environment. Moreover, female scientists and junior researchers experience their jobs more negatively. The results emphasize the need for improving work and research conditions for underprivileged groups in physics. Interestingly, no significant effect was found between different types of academic institutions and general job satisfaction. Finally, physicists felt that their work has not been well understood by the public.

Though a common language of science allows for easier communication of the results among researchers, the use of lingua franca also comes with the cost of losing some of the diverse ideas and results arising from the plurality of languages. Following Quine’s famous thesis about the indeterminacy of translation, we elaborate on the inherent loss of diverse ideas when only one language of science is used. Non-native speakers sometimes experience epistemic injustice due to their language proficiency and consequently, their scientific insights get marginalized. Thus, it is important epistemically to include the results of all researchers independent of their native language. As a solution, we promote epistemic equity and inclusion both on the individual level and on the level of the scientific community. Epistemic equity means that researchers who suffer disadvantages because of their language skills get support from the rest of the scientific community that will compensate for their disadvantage and at the same time facilitate their epistemic inclusion. This can be achieved through the introduction of mitigating agents – the individuals and organizations that ought to serve as a communication bridge between individual researchers and the scientific community.

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model.

We used agent-based modelling to highlight the advantages and disadvantages of several management styles in biology, ranging from centralized to egalitarian ones. In egalitarian groups, all team members are connected with each other, while in centralized ones, they are only connected with the principal investigator. Our model incorporated time constraints, which negatively influenced weakly connected groups such as centralized ones. Moreover, our results show that egalitarian groups outperform others if the questions addressed are relatively simple or when the communication among agents is limited. Complex epistemic spaces are explored best by centralized groups. They outperform other team structures because the individual members can develop their own ideas with less interference of the opinions of others. The optimal ratio between time spent on experimentation and dissemination varies between different organizational structures. Furthermore, if the evidence is shared only after a relevant degree of certainty is reached, all investigated groups epistemically profit. We discovered that the introduction of seminars to the model changes the epistemic performance in favour of weakly connected teams. Finally, the abilities of the principal investigator do not seem to outperform cognitive diversity, as group performances were not strongly influenced by the increase of her abilities.

Private funding of life sciences has been extensively criticized as lacking objectivity (e.g., Bekelman et al. 2003). However, it is also important to point out that public funding of life sciences faces many objections. In order to improve the system of publicly funded life sciences and its ability to respond to global health challenges such as the COVID-19 pandemic, we should focus on several aspects. First of all, providing existential stability for researchers, in turn, could result in the decrease of academic misconduct. Secondly, COVID-19 vaccines come from all parts of the world and have to be distributed throughout the world, which is a strong argument in favor of the positive stimulation of global science conducted at less-known research institutions and throughout all countries. This brings us to the quest of working on epistemic decolonization and inclusion in contrast to the current elitist paradigm in science. Finally, publicly funded research, which is at the moment mainly focused on foundational questions, should also be extended to applied ones. Public funding of applied research would lead to its development independently of private financial interests.

The question of when to stop an unsuccessful experiment can be difficult to answer from an individual perspective. To help to guide these decisions, we turn to the social epistemology of science and investigate knowledge inquisition within a group. We focused on the expensive and lengthy experiments in high energy physics, which were suitable for citation-based analysis because of the relatively quick and reliable consensus about the importance of results in the field. In particular, we tested whether the time spent on a scientific project correlates with the project output. Our results are based on data from the high energy physics laboratory Fermilab. They point out that there is an epistemic saturation point in experimenting, after which the likelihood of obtaining major results drops. With time the number of less significant publications does increase, but highly cited ones do not get published. Since many projects continue to run after the epistemic saturation point, it becomes clearer that decisions made about continuing them are not always rational.

In the present article, we introduce a multi-type display calculus for dynamic epistemic logic, which we refer to as Dynamic Calculus. The display approach is suitable to modularly chart the space of dynamic epistemic logics on weaker-than-classical propositional base. The presence of types endows the language of the Dynamic Calculus with additional expressivity, allows for a smooth proof-theoretic treatment, and paves the way towards a general methodology for the design of proof systems for the generality of dynamic logics, and certainly beyond dynamic epistemic logic. We prove that the Dynamic Calculus adequately captures Baltag–Moss–Solecki's dynamic epistemic logic, and enjoys Belnap-style cut elimination.

Scientific reasoning represents complex argumentation patterns that eventually lead to scientific discoveries. Social epistemology of science provides a perspective on the scientific community as a whole and on its collective knowledge acquisition. Different techniques have been employed with the goal of maximization of scientific knowledge on the group level. These techniques include formal models and computer simulations of scientific reasoning and interaction. Still, these models have tested mainly abstract hypothetical scenarios. The present thesis instead presents data-driven approaches in social epistemology of science. A data-driven approach requires data collection and curation for its further usage, which can include creating empirically calibrated models and simulations of scientific inquiry, performing statistical analyses, or employing data- mining techniques and other procedures. We present and analyze in detail three co-authored research projects on which the thesis’ author was engaged during her PhD. The first project sought to identify optimal team composition in high energy physics laboratories using data-mining techniques. The results of this project are published in (Perović et al. 2016), and indicate that projects with smaller numbers of teams and team members outperform bigger ones. In the second project, we attempted to determine whether there is an epistemic saturation point in experimentation in high energy physics. The initial results from this project are published in (Sikimić et al. 2018). In the thesis, we expand on this topic by using computer simulations to test for biases that could induce scientists to invest in projects beyond their epistemic saturation point. Finally, in previous examples of data-driven analyses, citations are used as a measure of epistemic efficiency of projects in high energy physics. In order to additionally justify and analyze the usage of this parameter in their data-driven research, in the third project Perović & Sikimić (2019) analyzed and compared inductive patterns in experimental physics and biology with the reliability of citation records in these fields. They conclude that while citations are a relatively reliable measure of efficiency in high energy physics research, the same does not hold for the majority of research in experimental biology. Additionally, contributions of the author that are for the first time published in this theses are: (a) an empirically calibrated model of scientific interaction of research groups in biology, (b) a case study of irregular argumentation patterns in some pathogen discoveries, and (c) an introductory discussion of the benefits and limitations of data- driven approaches to the social epistemology of science. Using computer simulations of an empirically calibrated model, we demonstrate that having several levels of hierarchy and division into smaller research sub-teams is epistemically beneficial for researchers in experimental biology. We also show that argumentation analysis in biology represents a good starting point for further data-driven analyses in the field. Finally, we conclude that a data-driven approach is informative and useful for science policy, but requires careful considerations about data collection, curation, and interpretation.

The purpose of the present paper is to demonstrate the inconsistencies between ethical theory and legal practice of animal treatment. Specifically, we discuss contemporary legal solutions, based on three case studies – Serbian, German and UK positive law, and point out the inconsistencies in them. Moreover, we show that the main cause of these inconsistencies is anthropocentric view of moral relevance. Finally, when it comes to the different treatment of animals living in the wild and domestic animals, we show that the current theoretical explanations are unsatisfactory.

The present article provides an analysis of the existing proof systems for dynamic epistemic logic from the viewpoint of proof-theoretic semantics. Dynamic epistemic logic is one of the best known members of a family of logical systems that have been successfully applied to diverse scientific disciplines, but the proof-theoretic treatment of which presents many difficulties. After an illustration of the proof-theoretic semantic principles most relevant to the treatment of logical connectives, we turn to illustrating the main features of display calculi, a proof-theoretic paradigm that has been successfully employed to give a proof-theoretic semantic account of modal and substructural logics. Then, we review some of the most significant proposals of proof systems for dynamic epistemic logics, and we critically reflect on them in the light of the previously introduced proof-theoretic semantic principles. The contributions of the present article include a generalization of Belnap's cut-elimination metatheorem for display calculi, and a revised version of the display-style calculus D.EAK. We verify that the revised version satisfies the previously mentioned proof-theoretic semantic principles, and show that it enjoys cut-elimination as a consequence of the generalized metatheorem.

The epistemic attitudes of scientists, such as epistemic tolerance and authoritarianism, play important roles in the discourse about rivaling theories. Epistemic tolerance stands for the mental attitude of an epistemic agent, e.g., a scientist, who is open to opposing views, while epistemic authoritarianism represents the tendency to uncritically accept views of authorities. Another relevant epistemic factor when it comes to the epistemic decisions of scientists is the skepticism towards the scientific method. However, the question is whether these epistemic attitudes are influenced by their sociopolitical counterparts, such as the researcher’s degree of conservatism. To empirically investigate the interplay between epistemic and sociopolitical attitudes of scientists, we conducted a survey with researchers (N = 655) across different disciplines. We propose scales for measuring epistemic tolerance and epistemic authoritarianism, as well as a scale for detecting the participants' readiness to question the scientific method. Furthermore, we investigate the relationship between epistemic tolerance and epistemic authoritarianism on the one hand, and career stage and sociopolitical views on the other hand. Interestingly, our study found only small correlations between the participants' degree of conservatism and their epistemic attitudes. This suggests that political views, against common argumentation, actually do not play an important role in one’s scientific decisions. Moreover, social scientists scored higher on the epistemic tolerance and lower on the epistemic authoritarianism scale than natural scientists. Finally, the results indicate that natural scientists question the scientific method less than social scientists.

We employ data envelopment analysis on a series of experiments performed in Fermilab, one of the major high-energy physics laboratories in the world, in order to test their efficiency (as measured by publication and citation rates) in terms of variations of team size, number of teams per experiment, and completion time. We present the results and analyze them, focusing in particular on inherent connections between quantitative team composition and diversity, and discuss them in relation to other factors contributing to scientific production in a wider sense. Our results concur with the results of other studies across the sciences showing that smaller research teams are more productive, and with the conjecture on curvilinear dependence of team size and efficiency.

This thesis provides an analysis of the existing proof systems for dynamic epistemic logic from the viewpoint of proof-theoretic semantics. After an illustration of the basic principles of proof-theoretic semantics, we review some of the most significant proposals of proof systems for dynamic epistemic logics, and we critically reject on them in the light of proof-theoretic semantic principles. The main original contributions of the present thesis are: (a) a revised version of the display-style calculus D.EAK, which we argue to be more adequate from the proof-theoretic semantic viewpoint; the main feature of this revision is that a smoother proof (so-called Belnap-style) of cut-elimination holds for it, which is problematic for the original version of D.EAK. (b) The intro duction of a novel, multi-type display calculus for dynamic epistemic logic, which we refer to as Dynamic Calculus. The presence of types endows the language of the Dynamic Calculus with additional expressivity, and makes it possible to design rules with an even smoother behavior. We argue that this calculus paves the way towards a general methodology for the design of proof systems for the generality of dynamic logics, and certainly for proof systems beyond dynamic epistemic logic. We prove that the Dynamic Calculus adequately captures Baltag-Moss-Solecki's dynamic epistemic logic, and enjoys Belnap-style cut elimination.