Psychological studies show that the beliefs of two agents in a hypothesis can diverge even if both agents receive the same evidence. This phenomenon of belief polarisation is often explained by invoking biased assimilation of evidence, where the agents’ prior views about the hypothesis affect the way they process the evidence. We suggest, using a Bayesian model, that even if such influence is excluded, belief polarisation can still arise by another mechanism. This alternative mechanism involves differential weighting of the evidence (...) arising when agents have different initial views about the reliability of their sources of evidence. We provide a systematic exploration of the conditions for belief polarisation in Bayesian models which incorporate opinions about source reliability, and we discuss some implications of our findings for the psychological literature. (shrink)

Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...) shows the need for quantitative, probabilistic models of mechanisms, too. In this paper we argue that the formal framework of causal graph theory is well-suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information. On the ba-sis of an example from contemporary biological practice, namely feedback regulation of fatty acid biosynthesis in Brassica napus, we show that causal graph theoretical models can account for feedback as well as for the multi-level character of mechanisms. However, we do not claim that causal graph theoretical representations of mechanisms are advantageous in all respects and should replace common qualitative models. Rather, we endorse the more balanced view that causal graph theoretical models of mechanisms are useful for some purposes, while being insufficient for others. (shrink)

The principle of total evidence says that all relevant information should be considered when making an inference about a hypothesis. In this article, we argue that the criminalist’s paradox from the literature on the methodology of forensic science constitutes a counterexample against the principle of total evidence. The paradox arises, for example, when a forensic scientist uses the results from other forensic procedures to inform their own analysis. In such cases, their results can become more reliable, but at the same (...) time also dependent on those other forensic results and therefore less useful for decisions in court cases. Consequently, such influences are generally avoided in forensic science. We argue that structurally similar problems plague also other scientific disciplines, anticipate an objection to the counterexample, and propose two new versions of the principle of total evidence that do not fall prey to the criminalist’s paradox. (shrink)

In his Critique of Pure Reason Immanuel Kant presents four anti- nomies. In his attempt to solve the first of these antinomies he examines and analyzes"thesis" and "antithesis" more thoroughly and employs the terms `part', `whole' and `boundary' in his argumentation for their validity. According to Kant, the whole problem surrounding the antinomy was caused by applying the concept of the world to nature and then using both terms interchangeably. While interesting, this solution is still not that much more than (...) a well thought out idea if it does not also include an adequate formal explication. Since the aforementioned terms all have counterparts in modern mereotopology, a discipline that has seen significant progress in recent times, we will apply these concepts to Kant's analysis in an attempt to evaluate Kant's solution in light of modern analytic philosophy. (shrink)

In (Gebharter 2014) I suggested a framework for modeling the hierarchical organization of mechanisms. In this short addendum I want to highlight some connections of my approach to the statistics and machine learning literature and some of its limitations not mentioned in the paper.

Causal Bayes nets (CBNs) provide one of the most powerful tools for modelling coarse-grained type-level causal structure. As in other fields (e.g., thermodynamics) the question arises how such coarse-grained characterisations are related to the characterisation of their underlying structure (in this case: token-level causal relations). Answering this question meets what is called a “coherence-requirement” in the reduction debate: How are different accounts of one and the same system (or kind of system) related to each other. We argue that CBNs as (...) tools for type-level causal inference are abstract enough to roughly fit any current token-level theory of causation as long as certain modelling assumptions are satisfied, but accounts of actual causation, i.e. accounts that attempt to infer token-causation based on CBNs, for the very same reason, face certain limitations. (shrink)