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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."

"Signals" are a conceptual apparatus in many scientific disciplines. Biologists inquire about the evolution of signals, economists talk about the signaling function of purchases and prices, and philosophers discuss the conditions under which signals acquire meaning. However, little attention has been paid to what is a signal. This paper is an attempt to fill this gap with a definition of signal that avoids reference to form or purpose. Along the way we introduce novel notions of "information revealing" and "information concealing" moves in games. In the end, our account offers an alternative to teleological accounts of communication.

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 datamining 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 (Perovic 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 (Sikimic 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 5 6 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 Pero, Zakljucivanje u nauci ogleda se u složenim argumentativnim strukturama koje u krajnjoj instanci dovode do naucnih otkrica. Socijalna epistemologija nauke posmatra nauku iz perspektive celokupne naucne zajednice i bavi se kolektivnim sticanjem znanja. Razlicite tehnike su se primenjivale u cilju maksimizacije naucnog znanja na nivou grupe. Ove tehnike ukljucuju formalne modele i kompijuterske simulacije naucnog zakljucivanja i interakcije. Ipak, ovi modeli su uglavnom testirali hipoteticke scenarije. Sa druge strane, ova disertacija predstavlja pristupe u socijalnoj epistemologiji nauke koji se zasnivaju na podacima. Pristup zasnovan na podacima podrazumeva prikupljanje podataka i njihovo sistematizovanje za dalju upotrebu. Ova upotreba podrazumeva empirijski kalibrirane modele i simulacije naucnog procesa, statisticke analize, algoritme za obradu velikog broja podataka itd. U tekstu predstavljamo i detaljno analiziramo tri koautorska istraživanja u kojima je autorka disertacije ucestvovala tokom doktorskih studija. Prvo istraživanje imalo je za cilj da odredi optimalnu strukturu timova u laboratorijama fizike visokih energija koristeci algoritme za obradu velikog broja podataka. Rezultati ovog istraživanja su objavljeni u (Perovic et al. 2016) i ukazuju na to da su projekti u koje je ukljucen manji broj timova i istraživaca efikasniji od vecih. U drugom istraživanju smo pokušali da utvrdimo da li postoji tacka epistemickog zasicenja, kada su u pitanju eksperimenti u fizici visokih energija. Inicijalni rezultati ovog istraživanja objavljeni su u (Sikimic et al. 2018). U disertaciji produbljujemo ovu temu korišcenjem kompjuterskih simulacija da 7 8 bismo testirali mehanizme pristrasnosti koji navode naucnike da ulažu u projekte iznad tacke epistemickog zasicenja. Konacno, u prethodnim primerima analiza zasnovanih na podacima, citiranost je korišcena kao mera epistemicke efikasnosti pojekata u fizici visokih energija. Da bi dodatno opravdali upotrebu ovog parametra u s

Fraud and misleading research represent serious impediments to scientific progress. We must uncover the causes of fraud in order to understand how science functions and in order to develop strategies for combating epistemically detrimental behavior. This paper investigates how the incentive to commit fraud is enhanced by the structure of the scientific reward system. Science is an "accumulation process:" success begets resources which begets more success. Through a simplified mathematical model, I argue that this cyclic relationship enhances the appeal of fraud and makes combating it extremely difficult.

Flipping a coin to decide what to do is a common feature of everyday life. Mixed strategies, as these are called, have a thorny status in normative decision theories. This paper explores various ways to justify choosing one's actions at random. I conclude that it is hard to make sense of this behavior without dealing with some difficult consequences.

Traditionally, epistemologists have distinguished between epistemic and pragmatic goals. In so doing, they presume that much of game theory is irrelevant to epistemic enterprises. I will show that this is a mistake. Even if we restrict attention to purely epistemic motivations, members of epistemic groups will face a multitude of strategic choices. I illustrate several contexts where individuals who are concerned solely with the discovery of truth will nonetheless face difficult game theoretic problems. Examples of purely epistemic coordination problems and social dilemmas will be presented. These show that there is a far deeper connection between economics and epistemology than previous appreciated.

The concept of fitness is central to evolutionary biology. Models of evolutionary change typically use some quantity called “fitness” which measures an organism’s reproductive success. But what exactly does it mean that fitness is such a measure? In what follows, we look at the interplay between abstract evolutionary models and quantitative measures of fitness and develop a measurement-theoretic perspective on fitness in order to explore what makes certain measures of fitness significant.