Voting, Deliberation and Truth

There are various ways to reach a group decision on a factual yes-no question. One way is to vote and decide what the majority votes for. This procedure receives some epistemological support from the Condorcet Jury Theorem. Alternatively, the group members may prefer to deliberate and will eventually reach a decision that everybody endorses - a consensus. While the latter procedure has the advantage that it makes everybody happy (as everybody endorses the consensus), it has the disadvantage that it is difficult to implement, especially for larger groups. Besides, the resulting consensus may be far away from the truth. And so we ask: Is deliberation truth-conducive in the sense that majority voting is? To address this question, we construct a highly idealized model of a particular deliberation process, inspired by the movie Twelve Angry Men, and show that the answer is "yes". Deliberation procedures can be truth-conducive just as the voting procedure is. We then explore, again on the basis of our model and using agent-based simulations, under which conditions it is better epistemically to deliberate than to vote. Our analysis shows that there are contexts in which deliberation is epistemically preferable and we will provide reasons for why this is so

Voting, Deliberation and Truth

There are various ways to reach a group decision on a factual yes-no question. One way is to vote and decide what the majority votes for. This procedure receives some epistemological support from the Condorcet Jury Theorem. Alternatively, the group members may prefer to deliberate and will eventually reach a decision that everybody endorses - a consensus. While the latter procedure has the advantage that it makes everybody happy (as everybody endorses the consensus), it has the disadvantage that it is difficult to implement, especially for larger groups. Besides, the resulting consensus may be far away from the truth. And so we ask: Is deliberation truth-conducive in the sense that majority voting is? To address this question, we construct a highly idealized model of a particular deliberation process, inspired by the movie Twelve Angry Men, and show that the answer is "yes". Deliberation procedures can be truth-conducive just as the voting procedure is. We then explore, again on the basis of our model and using agent-based simulations, under which conditions it is better epistemically to deliberate than to vote. Our analysis shows that there are contexts in which deliberation is epistemically preferable and we will provide reasons for why this is so

Anchoring in Deliberations

Deliberation is a standard procedure to make decisions in not too large groups. It has the advantage that the group members can learn from each other and that, at the end, often a consensus emerges that everybody endorses. But a deliberation procedure also has a number of disadvantages. E.g., what consensus is reached usually depends on the order in which the different group members speak. More specifically, the group member who speaks first often has an unproportionally high impact on the final decision: She anchors the deliberation process. While the anchoring effect undoubtably appears in real deliberating groups, we ask whether it also appears in groups whose members are truth-seeking and rational in the sense that they take the information provided by their fellow group members properly into account by updating their beliefs according to plausible rules. To answer this question and to make some progress towards explaining the anchoring effect, a formal model is constructed and analyzed. Using this model, we study the anchoring effect in homogenous groups (i.e. groups whose members consider each other as equally reliable), for which we provide analytical results, and in inhomogeneous groups, for which we provide simulation results

Empirische Deliberationsforschung - eine systematische Übersicht

Zusammenfassung: Die empirische Deliberationsforschung hat nach zögerlichem Start in den 1990er Jahren einen wahren Boom erlebt: das philosophische Konstrukt des vernünftigen Dialogs wurde nicht nur auf sein Vorkommen in der politischen und zivilgesellschaftlichen Sphäre hin untersucht, sondern zunehmend auch in Modelle politischen Entscheidungshandelns eingebaut. Folgender Literaturbericht fragt systematisch nach den Funktionsweisen deliberativen Handelns, seiner institutionellen, kulturellen und akteursspezifischen Voraussetzungen sowie den Ergebnissen, die aus deliberativ hochwertigen Prozessen erfolgen. Die mittlerweile vielfältigen empirischen Studien zeigen, dass insbesondere unter günstigen institutionellen Bedingungen Akteure in Politik und Zivilgesellschaft vernünftig miteinander diskutieren können, wobei sich dann auch normativ wünschbare Ergebnisse (wie höhere epistemische Qualität oder breiter abgestützte Kompromisse) einstellen. Gleichwohl bleiben nach einer Dekade intensiver Forschung einige zentrale Fragen offen, insbesondere die Frage nach der stringenten Trennung von deliberativem (und verständigungsorientiertem) und strategischem Handel

Anchoring in Deliberations

Deliberation is a standard procedure for making decisions in not too large groups. It has the advantage that group members can learn from each other and that, at the end, often a consensus emerges that everybody endorses. Unfortunately, however, implementing a deliberation procedure also has a number of disadvantages due to the cognitive limitations of the individual group members. What is more, the very process of deliberation introduces an additional bias which we investigate in this article. We demonstrate that even in a group of (boundedly) rational agents the resulting consensus (if there is one) depends on the order in which the group members speak. More specifically, the group member who speaks first has an unproportionally high impact on the final decision, which we interpret as a new instance of the well-known anchoring effect.To show this, we construct and analyze an agent-based model -- inspired by the disagreement debate in social epistemology -- and obtain analytical results for homogenous groups (i.e. for groups whose members consider each other as epistemic peers) as well as simulation results for inhomogeneous groups

Deliberation and the Wisdom of Crowds

Does pre-voting group deliberation increase majority competence? To address this question, we develop a probabilistic model of opinion formation and deliberation. Two new jury theorems, one pre-deliberation and one post-deliberation, suggest that deliberation is beneficial. Successful deliberation mitigates three voting failures: (1) overcounting widespread evidence, (2) neglecting evidential inequality, and (3) neglecting evidential complementarity. Simulations and theoretic arguments confirm this. But there are five systematic exceptions where deliberation reduces majority competence, always by increasing failure (1). Our analysis recommends deliberation that is 'participatory', 'even', but possibly 'unequal', i.e., that involves substantive sharing, privileges no evidences, but possibly privileges some persons

The Wisdom of the Small Crowd: Myside Bias and Group Discussion

The my-side bias is a well-documented cognitive bias in the evaluation of arguments, in which reasoners in a discussion tend to overvalue arguments that confirm their prior beliefs, while undervaluing arguments that attack their prior beliefs. The first part of this paper develops and justifies a Bayesian model of myside bias at the level of individual reasoning. In the second part, this Bayesian model is implemented in an agent-based model of group discussion among myside-biased agents. The agent-based model is then used to perform a number of experiments with the objective to study whether the myside bias hinders or enhances the ability of groups to collectively track the truth, that is, to reach the correct answer to a given binary issue. An analysis of the results suggests the following: First, whether the truth-tracking ability of groups is helped or hindered by myside bias crucially depends on how the strength of myside bias is differentially distributed across subgroups of discussants holding different beliefs. Second, small groups are more likely to track the truth than larger groups, suggesting that increasing group size has a detrimental effect on collective truth-tracking through discussion

Optimizacija zaključivanja u nauci : pristup zasnovan na podacima

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 Perovic & Sikimic (under revision) 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 datadriven 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 interpretationZakljucivanje 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 svojim analizama, u trecem istraživanju Perovic & Sikimic (under revision) su razmatrali i upore ivali induktivne šematizme u eksperimentalnoj fizici i biologiji sa pouzdanošcu mere citiranosti u ovim oblastima. Zakljucili su da, iako su citati relativno pouzdana mera efikasnosti u fizici visokih energija, to nije slucaj u najvecem delu istraživanja u oblasti eksperimentalne biologije. Povrh toga, doprinosi autorke koji su prvi put objavljeni u ovoj disertaciji jesu: (a) empirijski kalibrirani model naucne komunikacije unutar istraživackih grupa u biologiji, (b) analiza neocekivanih argumentativnih struktura u otkricima nekih patogena i (c) uvodna diskusija u pogledu prednosti i ogranicenja pristupa zasnovanih na podacima u socijalnoj epistemologiji nauke. Korišcenjem kompjuterskih simulacija na empirijski kalibriranim modelima, pokazujemo da je raslojavanje i podela na manje istraživacke timove epistemicki korisno za istraživace u eksperimentalnoj biologiji. Tako e, pokazujemo da je analiza argumenata u biologiji dobra osnova za dalje analize zasnovane na podacima u ovoj oblasti. Na kraju, zakljucujemo da je pristup zasnovan na podacima informativan i koristan za kreiranje naucne politike, ali da zahteva pažljiva razmatranja u pogledu prikupljanja podataka, njihovog sortiranja i interpretiranj

Optimization of scientific reasoning : а data-driven approach

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 svojim analizama, u trecem istraživanju Perovic & Sikimic (under revision) su razmatrali i upore ivali induktivne šematizme u eksperimentalnoj fizici i biologiji sa pouzdanošcu mere citiranosti u ovim oblastima. Zakljucili su da, iako su citati relativno pouzdana mera efikasnosti u fizici visokih energija, to nije slucaj u najvecem delu istraživanja u oblasti eksperimentalne biologije. Povrh toga, doprinosi autorke koji su prvi put objavljeni u ovoj disertaciji jesu: (a) empirijski kalibrirani model naucne komunikacije unutar istraživackih grupa u biologiji, (b) analiza neocekivanih argumentativnih struktura u otkricima nekih patogena i (c) uvodna diskusija u pogledu prednosti i ogranicenja pristupa zasnovanih na podacima u socijalnoj epistemologiji nauke. Korišcenjem kompjuterskih simulacija na empirijski kalibriranim modelima, pokazujemo da je raslojavanje i podela na manje istraživacke timove epistemicki korisno za istraživace u eksperimentalnoj biologiji. Tako e, pokazujemo da je analiza argumenata u biologiji dobra osnova za dalje analize zasnovane na podacima u ovoj oblasti. Na kraju, zakljucujemo da je pristup zasnovan na podacima informativan i koristan za kreiranje naucne politike, ali da zahteva pažljiva razmatranja u pogledu prikupljanja podataka, njihovog sortiranja i interpretiranjaScientific 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 Perovic & Sikimic (under revision) 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 datadriven 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 interpretatio