Stefan Sashev Petkov

When developing theories of understanding, philosophers have tried to provide a full package by offering criteria both for the epistemic success of understanding and for various related epistemic failures. One of those failures is misunderstanding. Currently, there are only two theories of misunderstanding. The first one, from Yu and Petkov, analyzes the unique epistemic conditions for misunderstanding. The other one, by Rice and Khalifa, studies the process of correcting misunderstanding. In this paper, I show that both theories have shortcomings. I argue that Rice and Khalifa’s theory is too broad and permissive. It effectively blurs existing distinctions between proto-understanding or understanding based on falsified theories. The reverse holds for Yu and Petkov’s theory. It is too narrow and thus fails to capture broad issues related to clearing misunderstanding. I conclude by suggesting that Yu and Petkov’s theory describes misunderstanding more plausibly, whilst Rice and Khalifa’s theory can capture the correction of misunderstanding.

This paper defends the view that narratives that bring understanding of the past need not be exhaustively analyzable as explanatory inferences, nor as causal narratives. Instead of treating historical narrative as explanations, I argue that understanding of history can be analyzed by the general epistemic criteria of understanding. I explore one such criterion, which is of chief importance for good historical narratives: potential inferential power. As a corollary, I dispute one of the distinctive features of narratives described by some philosophers: the non-aggregativity of narrative histories. Instead, I propose that historical narratives modestly aggregate and this aggregation depends on the success of the colligatory concepts they offer.

Current debates on the nature of explanatory understanding have converged on the idea that at least one of the core components of understanding is inferential. Philosophers have characterized the inferential dimension of understanding as consisting of several related cognitive abilities to grasp a given explanation and the nexus of complementing explanations to which it belongs. Whilst analyses of both the subjective epistemic abilities related to grasping and objective features of the inferential links within explanations have received much attention, both within theories of explanation and in the literature on understanding, the criteria for evaluating the specific structure and organization of explanatory clusters or nexuses have received much less attention. Nevertheless, two notable exceptions stand out—Khalifa’s characterization of an explanatory nexus and theories of explanatory unification. I take Khalifa’s ideas, together with the basic criteria of successful explanatory unification, as my starting point. To both, I make some corrections and additions, in order to arrive at a more robust notion of an explanatory nexus and ultimately show that its structural properties and the inter-explanatory relations it contains are relevant to the resulting understanding. I propose to represent such nexuses as directed graph trees and show that some of their properties can be related to the degree of understanding that such nested explanatory structures can offer. I will further illustrate these ideas by a case study on an eco-logical theory of predation.

This paper studies the epistemic failures to reach understanding in relation to scientific explanations. We make a distinction between genuine understanding and its negative phenomena—lack of understanding and misunderstanding. We define explanatory understanding as inclusive as possible, as the epistemic success that depends on abilities, skills, and correct explanations. This success, we add, is often supplemented by specific positive phenomenology which plays a part in forming epistemic inclinations—tendencies to receive an insight from familiar types of explanations. We define lack of understanding as the epistemic failure that results from a lack of an explanation or from an incorrect one. This can occur due to insufficient abilities and skills, or to fallacious explanatory information. Finally, we characterize misunderstanding by cases where one’s epistemic inclinations do not align with an otherwise correct explanation. We suggest that it leads to potential debates about the explanatory power of different explanatory strategies. We further illustrate this idea with a short meta-philosophical study on the current debates about distinctively mathematical explanations.

This paper discusses the polemical question of whether explanations that produce understanding must be true. It argues positively for the role of truth in reaching explanatory understanding, by presenting three lines of criticism of alternative accounts. The first is that by rejecting truth as a criterion for evaluating explanations, any non-factual account thereby effectively cuts ties with the central theories of explanations, which provide at least partial criteria for explanatory understanding. The second line of criticism is that some of the most well-known non-factual accounts implicitly operate over a notion of partial-truth, and as such, they do not provide a valid alternative. The final critical argument is that, in the place of truth evaluations, these accounts often offer a multiplicity of other criteria, and by changing a unitary criterion such as truth for a collection of other requirements, these non-factive theories introduce a level of ad hoc-ness, which diminishes their normative value.

In this paper I present a semantic approach to the analysis of the function of the landscape metaphor within evolutionary biology. The concept of adaptive landscape has drawn a considerable attention in recent philosophy of biology. Most writers have treated the concept in one of the following ways: as a heuristic tool, as an intrinsic part of robustly defined mathematical models, or as a definable set of analogies on which models are based and tested. All these views lead to the conclusion that the value of the landscape metaphor depends only on the success of the models, which the metaphor underlies, to adequately represent evolutionary dynamics. I have tried to show that this conclusion, and respectively, the views which imply it, do not account for important episodes from the history of the landscape metaphor. These views rather stem from the general views, in philosophy of science, about the role of metaphors in scientific theories. The semantic analysis which I propose reveals that the concept of adaptive reliefs’ primary function during the evolutionary synthesis has been to serve as a general unifying conceptual framework which has made possible the theoretical reconciliation of heterogeneous empirical phenomena. From this perspective, the landscape metaphor is a linguistic theoretical tool which should not be abandoned (and in fact has not been abandoned) after the falsification of the models which have been built and interpreted by means of the metaphor.

My aim here is to show that approximate truth as a paraconsistent notion can be successfully incorporated into the analysis of scientific unification, thus advancing towards a more realistic representation of theory development that takes into account the controversies that often loom alongside the progress of research programmes. I support my analysis with a case study of the recent debate in ecology centred around the existence of the paradox of enrichment and the controversy between ecological models of predation that employ prey-dependent and ratio-dependent functional responses. These models were initially proposed as equally good representations of the basic aspects of predator–prey dynamics. However, both models generated inconsistent observational consequences and, therefore, introduced a contradiction within predator–prey theory. I argue that by accepting these models as approximately true representations of predator–prey dynamics we can convey how the available observational data have been successfully systematized in a consistent way under them. This first step in resolving the controversy relied on building a series of contrastive arguments based on both models’ derivations about population dynamics and the available empirical data. The heightening of this contrast between the models, in turn, was also essential in defining a limiting function which can be used to integrate both models and reach a new unified expression of predator–prey dynamics.

There are several important criticisms against the unificationist model of scientific explanation: Unification is a broad and heterogeneous notion and it is hard to see how a model of explanation based exclusively on unification can make a distinction between genuine explanatory unification from cases of ordering or classification. Unification alone cannot solve the asymmetry and irrelevance problems. Unification and explanation pull in different directions and should be decoupled, because for good scientific explanation extra ad explanandum information is often required. I am presenting a possible solution to those problems, by focusing on an often overlooked but important element of how theoretic unification is achieved—the conceptual frameworks of theories. The core conceptual assumptions behind theories are decisive for discriminating between explanatory and non-explanatory unification. The conceptual framework is also flexible enough to balance the tension between informativeness and maximum systematization in constructing explanatory inferences. A short case study of orthogenetic and Darwinian explanations in paleontology is presented as an illustration of how my addition to the unificationist model is applicable to a historical debate between rival explanations

The debate between the dynamical and the statistical interpretations of natural selection is centred on the question of whether all explanations that employ the concepts of natural selection and drift are reducible to causal explanations. The proponents of the statistical interpretation answer negatively, but insist on the fact that selection/drift arguments are explanatory. However, they remain unclear on where the explanatory power comes from. The proponents of the dynamical interpretation answer positively and try to reduce selection/drift arguments to some of the most prominent accounts of causal explanation. In turn, they face the criticism raised by statisticalists that current accounts of causation have to be violated in some of their core conditions or otherwise used in a very loose manner in order to account for selection/drift explanations. We propose a reconciliation of both interpretations by conveying evolutionary explanations within the unificationist model of scientific explanation. Therefore, we argue that the explanatory power in natural selection arguments is a result of successful unification of individual- and population-level facts. A short case study based on research on sympatric speciation will be presented as an example of how population- and individual-level facts are unified to explain the morphological mosaic of bill shape in island scrub jays.