Bert Baumgaertner

The method of reflective equilibrium is typically characterized as a process of two kinds of adjustments: hold fixed one’s current set of commitments/intuitions and adjust rules/principles to account for them, then hold fixed those rules while making adjustments to one’s set of commitments. Repeat until no further adjustments are required. Such characterizations ignore the role of precedent, i.e., information about the commitments and rules of others and how those might serve as guides in one’s own process of deliberation. In this paper we develop a model that narrows in on a local part of a reflective equilibrium process in which a group of peer agents start with identical lists of commitments and work towards finding the rule that captures that set. If successful, we say they reach rest stop interpersonal convergence. The trouble is that, without precedent, reaching this convergence rests on implausible deterministic assumptions. We analyze how far the inclusion of a kind of soft precedent goes towards aiding the group in reaching said convergence when those assumptions are relaxed.

We build a model of the reflective equilibrium method to better understand under what conditions a community of agents would achieve a shared equilibrium. We find that, despite guaranteeing that agents individually reach equilibrium and numerous constraints on how agents deliberate, it is surprisingly difficult for a community to converge on a small number of equilibria. Consequently, the literature on reflective equilibrium has underestimated the challenge of coordinating intrapersonal convergence and interpersonal convergence.

Some common explanations of issue polarization and echo chambers rely on social or cognitive mechanisms of exclusion. Accordingly, suggested interventions like “be more open-minded” target these mechanisms: avoid epistemic bubbles and don’t discount contrary information. Contrary to such explanations, we show how a much weaker mechanism—the preference for belief—can produce issue polarization in epistemic communities with little to no mechanisms of exclusion. We present a network model that demonstrates how a dynamic interaction between the preference for belief and common structures of epistemic communities can turn very small unequal distributions of initial beliefs into full-blown polarization. This points to a different class of explanations, one that emphasizes the importance of the initial spread of information. We also show how our model complements extant explanations by including a version of biased assimilation and motivated reasoning—cognitive mechanisms of exclusion. We find that mechanisms of exclusion can exacerbate issue polarization, but may not be the ultimate root of it. Hence, the recommended interventions suggested by extant literature is expected to be limited and the problem of issue polarization to be even more intractable.

John Stuart Mill advocated for increased interactions between individuals of dissenting opinions for the reason that it would improve society. Whether Mill and similar arguments that advocate for opinion diversity are valid depends on background assumptions about the psychology and sociality of individuals. The field of opinion dynamics is a burgeoning testing ground for how different combinations of sociological and psychological facts contribute to phenomena that affect opinion diversity, such as polarization. This paper applies some recent results from the opinion dynamics literature to assess the impacts of the Millian suggestion. The goal is to understand how the scope of the validity of Mill-style arguments depends on plausible assumptions that can be formalized using agent-based models, a common modeling approach in opinion dynamics. The most salient insight is that homophily (increased interactions between like-minded individuals) does not sufficiently explain decreased opinion diversity. Hence, decreasing homophily by increasing interactions between individuals of dissenting opinions is not the simple solution that a Millian-style argument may advocate.

Vaccination complacency occurs when perceived risks of vaccine-preventable diseases are sufficiently low so that vaccination is no longer perceived as a necessary precaution. Disease outbreaks can once again increase perceptions of risk, thereby decrease vaccine complacency, and in turn decrease vaccine hesitancy. It is not well understood, however, how change in perceived risk translates into change in vaccine hesitancy. We advance the concept of vaccine propensity, which relates a change in willingness to vaccinate with a change in perceived risk of infection—holding fixed other considerations such as vaccine confidence and convenience.

Opinions are rarely binary; they can be held with different degrees of conviction, and this expanded attitude spectrum can affect the influence one opinion has on others. Our goal is to understand how different aspects of influence lead to recognizable spatio-temporal patterns of opinions and their strengths. To do this, we introduce a stochastic spatial agent-based model of opinion dynamics that includes a spectrum of opinion strengths and various possible rules for how the opinion strength of one individual affects the influence that this individual has on others. Through simulations, we find that even a small amount of amplification of opinion strength through interaction with like-minded neighbors can tip the scales in favor of polarization and deadlock.

In light of the increasing refusal of some parents to vaccinate children, public health strategies have focused on increasing knowledge and awareness based on a “knowledge-deficit” approach. However, decisions about vaccination are based on more than mere knowledge of risks, costs, and benefits. Individual decision making about vaccinating involves many other factors including those related to emotion, culture, religion, and socio-political context. In this paper, we use a nationally representative internet survey in the U.S. to investigate socio-political characteristics to assess attitudes about vaccination. In particular, we consider how political ideology and trust affect opinions about vaccinations for flu, pertussis, and measles. Our findings demonstrate that ideology has a direct effect on vaccine attitudes. In particular, conservative respondents are less likely to express pro-vaccination beliefs than other individuals. Furthermore, ideology also has an indirect effect on immunization propensity. The ideology variable predicts an indicator capturing trust in government medical experts, which in turn helps to explain individual-level variation with regards to attitudes about vaccine choice.

Spatially situated opinions that can be held with different degrees of conviction lead to spatiotemporal patterns such as clustering (homophily), polarization, and deadlock. Our goal is to understand how sensitive these patterns are to changes in the local nature of interactions. We introduce two different mixing mechanisms, spatial relocation and nonlocal interaction (“telephoning”), to an earlier fully spatial model (no mixing). Interestingly, the mechanisms that create deadlock in the fully spatial model have the opposite effect when there is a sufficient amount of mixing. With telephoning, not only is polarization and deadlock broken up, but consensus is hastened. The effects of mixing by relocation are even more pronounced. Further insight into these dynamics is obtained for selected parameter regimes via comparison to the mean-field differential equations.

One feature of vague predicates is that, as far as appearances go, they lack sharp application boundaries. I argue that we would not be able to locate boundaries even if vague predicates had sharp boundaries. I do so by developing an idealized cognitive model of a categorization faculty which has mobile and dynamic sortals (`classes', `concepts' or `categories') and formally prove that the degree of precision with which boundaries of such sortals can be located is inversely constrained by their flexibility. Given the literature, it is plausible that we are appropriately like the model. Hence, an inability to locate sharp boundaries is not necessarily because there are none; boundaries could be sharp and it is plausible that we would nevertheless be unable to locate them.

I approach the study of echo chambers from the perspective of veritistic social epistemology. A trichotomous belief model is developed featuring a mechanism by which agents will have a tendency to form agreement in the community. The model is implemented as an agent-based model in NetLogo and then used to investigate a social practice called Impartiality, which is a plausible means for resisting or dismantling echo chambers. The implementation exposes additional factors that need close consideration in an evaluation of Impartiality. In particular, resisting or dismantling echo chambers requires the selection of sufficiently low levels of doxastic entrenchment, but this comes with other tradeoffs.

Mechanistic accounts of explanation have recently found popularity within philosophy of science. Presently, we introduce the idea of an extended mechanistic explanation, which makes explicit room for the role of environment in explanation. After delineating Craver and Bechtel’s account, we argue this suggestion is not sufficiently robust when we take seriously the mechanistic environment and modeling practices involved in studying contemporary complex biological systems. Our goal is to extend the already profitable mechanistic picture by pointing out the importance of the mechanistic environment. It is our belief that extended mechanistic explanations, or mechanisms that take into consideration the temporal sequencing of the interplay between the mechanism and the environment, allow for mechanistic explanations regarding a broader group of scientific phenomena.

Many of our categorization experiences are non-transitive. For some objects a, b and c, a and b can appear indistinguishable, and likewise b and c, but a and c can appear distinguishable. Many categories also appear to be smooth; transitions between cases are not experienced as sharp, but rather as continuous. These two features of our categorization experiences tend to be addressed separately. Moreover, many views model smoothness by making use of infinite degrees. This paper presents a methodological strategy that shows how solving the transitivity problem first can thereby introduce explanatory resources that feed into an account of smoothness without having to make use of infinite degrees