Among the many functions of models, explanation is central to the functioning and aims of science. However, the discussions surrounding modeling and explanation in philosophy have largely remained separate from each other. This chapter seeks to bridge the gap by focusing on the puzzle of model-based explanation, asking how different philosophical accounts answer the following question: if idealizations and fictions introduce falsehoods into models, how can idealized and fictional models provide true explanations? The chapter provides (...) a selective and critical overview of the available strategies for solving this puzzle, mainly focusing on idealized models and how they explain. (shrink)

Read argues that modeling cultural idea systems serves to make explicit the cultural rules through which "cultural idea systems" frame behaviors that are culturally meaningful. Because cultural rules are typically "invisible" to us, one of the anthropologists' tasks is to elicit these rules, make them explicit and then use them to build explanations for patterns in cultural phenomena. The main example of Read's approach to cultural idea systems is the formal modeling of kinship terminologies. I reconstruct Read's modeling strategy as (...) comprising the following steps:From the way in which culture-bearers compute kin relations a data model is construed that makes explicit the cultural theory embedded in a kinship .. (shrink)

We apply three state‐of‐the‐art models for syllogistic reasoning to data from experiments where participants received feedback for their conclusions in order to demonstrate the use of model parameters to derive new hypotheses and present possible explanations for the feedback effect.

The question of whether the idealized models of theoretical economics are explanatory has been the subject of intense philosophical debate. It is sometimes presupposed that either a model provides the actual explanation or it does not provide an explanation at all. Yet, two sets of issues are relevant to the evaluation of model-based explanation: what conditions should a model satisfy in order to count as explanatory and does the model satisfy those conditions. (...) My aim in this paper is to unpack this distinction and show that separating the first set of issues from the second is crucial to an accurate diagnosis of the distinctive challenges that economic models pose. Along the way I sketch a view of model-based explanation in economics that focuses on the role that non-empirical and empirical strategies play in increasing confidence in the adequacy of a given model-based explanation. (shrink)

Highly idealized models may serve various epistemic functions, notably explanation, in virtue of representing the world. Inferentialism provides a prima facie compelling characterization of what constitutes the representation relation. In this paper, I argue that what I call factive inferentialism does not provide a satisfactory solution to the puzzle of model-based—factive—explanation. In particular, I show that making explanatory counterfactual inferences is not a sufficient guide for accurate representation, factivity, or realism. I conclude by calling for a (...) more explicit specification of model-world mismatches and properties imputation. (shrink)

Detailed examinations of scientific practice have revealed that the use of idealized models in the sciences is pervasive. These models play a central role in not only the investigation and prediction of phenomena, but in their received scientific explanations as well. This has led philosophers of science to begin revising the traditional philosophical accounts of scientific explanation in order to make sense of this practice. These new model-based accounts of scientific explanation, however, raise a number of (...) key questions: Can the fictions and falsehoods inherent in the modeling practice do real explanatory work? Do some highly abstract and mathematical models exhibit a noncausal form of scientific explanation? How can one distinguish an exploratory "how-possibly" model explanation from a genuine "how-actually" model explanation? Do modelers face tradeoffs such that a model that is optimized for yielding explanatory insight, for example, might fail to be the most predictively accurate, and vice versa? This chapter explores the various answers that have been given to these questions. (shrink)

Why would philosophers interested in the points or functions of our conceptual practices bother with genealogical explanations if they can focus directly on paradigmatic examples of the practices we now have?? To answer this question, I compare the method of pragmatic genealogy advocated by Edward Craig, Bernard Williams, and Miranda Fricker—a method whose singular combination of fictionalising and historicising has met with suspicion—with the simpler method of paradigm-based explanation. Fricker herself has recently moved towards paradigm-based explanation, (...) arguing that it is a more perspicuous way of reaping the same explanatory pay-off as pragmatic genealogy while dispensing with its fictionalising and historicising. My aim is to determine when and why the reverse movement from paradigm-based explanation to pragmatic genealogy remains warranted. I argue that the fictionalising and historicising of pragmatic genealogy is well motivated, and I outline three ways in which the method earns its keep: by successfully handling historically inflected practices which paradigm-based explanation cannot handle; by revealing and arguing for connections to generic needs we might otherwise miss; and by providing comprehensive views of practices that place and relate the respects in which they serve both generic and local needs. (shrink)

Scientists appeal to models when explaining phenomena. Such explanations are often dubbed model explanations or model-based explanations. But what are the precise conditions for ME? Are ME special explanations? In our paper, we first rebut two definitions of ME and specify a more promising one. Based on this analysis, we single out a related conception that is concerned with explanations that are induced from working with a model. We call them ‘model-induced explanations’. Second, we (...) study three paradigmatic cases of alleged ME. We argue that all of them are MIE, upon closer examination. Third, we argue that this undermines the building consensus that model explanations are special explanations that, e.g., challenge the factivity of explanation. Instead, it suggests that what is special about models in science is the epistemology behind how models induce explanations. (shrink)

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative mechanistic (...) perspective and concentrates the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration. (shrink)

Automated Software Testing (AST) using Model Checking is in this article epistemologically analysed in order to argue in favour of a model-based reasoning paradigm in computer science. Preliminarily, it is shown how both deductive and inductive reasoning are insufficient to determine whether a given piece of software is correct with respect to specified behavioural properties. Models algorithmically checked in Model Checking to select executions to be observed in Software Testing are acknowledged as analogical models which establish (...) isomorphic relations with the target system’s data set. Analogical models developed in AST are presented as abductive models providing hypothetical explanations to observed executions. The model assumption—algorithmic check—software testing process is understood as the abduction—deduction—induction process defining the selective abduction and turned to isolate a set of model-based hypotheses concerning the target system behaviours. A manipulative abduction process is finally recognized in the practice of adapting, abstracting and refining models that do not provide successful predictions. (shrink)

This article argues that evolutionary models based on selection validate, under appropriate conditions, the relevance of optimality as an explanatory mechanism in rational choice theory. The reason is that these frameworks share the mechanism that drives the results, namely, optimization, even if they situate it at different levels. The consequences of our argument are twofold. First, it resolves the tension between those predictions of rational choice theory that are accurate and the evidence showing that individuals seldom optimize. Second, it (...) relativizes the explanatory import of rationality without diminishing the role of optimization as a mechanism. (shrink)

What I call theoretical abduction (sentential and model-based)certainly illustrates much of what is important in abductive reasoning, especially the objective of selecting and creating a set of hypotheses that are able to dispense good (preferred) explanations of data, but fails to account for many cases of explanation occurring in science or in everyday reasoning when the exploitation of the environment is crucial. The concept of manipulative abduction is devoted to capture the role of action in many interesting (...) situations: action provides otherwise unavailable information that enables the agent to solve problems by starting and performing a suitable abductive process of generation or selection of hypotheses. Many external things, usually inert from the epistemological point of view, can be transformed into what I call epistemic mediators, which are illustrated in the last part of the paper, together with an analysis of the related notions of ``perceptual and inceptual rehearsal'' and of ``external representation''. (shrink)

Philosophers have yet to provide a systematic analysis of the relationship between historical science and model-based science. In this paper I argue that prototypical model-based sciences exhibit features understood to be central to historical science. Philosophers of science have argued that historical scientists are distinctly concerned with inference to the best explanation, that explanations in historical science tend to increase in complexity over time, and that the explanations take the form of narratives. Using general circulation (...) models in climate science as a reference, I illustrate how simulation models in model-based science share these features exhibited by historical science. That model based sciences share these features raises important philosophical questions about how we should understand prototypical types of scientific enquiry, including the relationship between experimental science, historical science, and model-based science. I conclude by exploring several options for how to accommodate the noted similarities within a more general taxonomy of the sciences. (shrink)

There are a number of reasons to question the established molecular-only (or materialist) model of life. These include a number of extraordinary behaviors and more generally the unfolding inability to identify a DNA (or genetic) basis for many innate, and presumed heritable, conditions. Perhaps the simplest way to question materialism, though, is by looking at prodigal (human) behaviors. There you can find some incredible abilities and inclinations which strongly challenge the plausibility of materialist explanations. Herein three such phenomena will (...) be considered: a childhood behavioral syndrome termed the Einstein syndrome; prodigious savants; and then the observed historical rise in our IQ’s, termed the Flynn effect. In isolation, each of these mysteries offers plenty of puzzlement, but in the context of the lack of a genetic explanation for the variations in intelligence, it will be argued that together they represent a major conundrum for the modern understanding of humans. Additionally, and in possible support for dualism, some reincarnation-based explanations will be presented. Furthermore, the particular challenges and complications posed by the Flynn effect (as well as instincts) will be considered. (shrink)

Mechanistic explanations satisfy widely held norms of explanation: the ability to manipulate and answer counterfactual questions about the explanandum phenomenon. A currently debated issue is whether any nonmechanistic explanations can satisfy these explanatory norms. Weiskopf argues that the models of object recognition and categorization, JIM, SUSTAIN, and ALCOVE, are not mechanistic yet satisfy these norms of explanation. In this article I argue that these models are mechanism sketches. My argument applies recent research using model-based functional magnetic (...) resonance imaging, a novel neuroimaging method whose significance for current debates on psychological models and mechanistic explanation has yet to be explored. (shrink)

This article presents a fundamental advance in the theory of mental models as an explanation of reasoning about facts, possibilities, and probabilities. It postulates that the meanings of compound assertions, such as conditionals (if) and disjunctions (or), unlike those in logic, refer to conjunctions of epistemic possibilities that hold in default of information to the contrary. Various factors such as general knowledge can modulate these interpretations. New information can always override sentential inferences; that is, reasoning in daily life is (...) defeasible (or nonmonotonic). The theory is a dual process one: It distinguishes between intuitive inferences (based on system 1) and deliberative inferences (based on system 2). The article describes a computer implementation of the theory, including its two systems of reasoning, and it shows how the program simulates crucial predictions that evidence corroborates. It concludes with a discussion of how the theory contrasts with those based on logic or on probabilities. (shrink)

Agent-based models (ABMs) are one type of simulation model used in the context of the COVID-19 pandemic. In contrast to equation-based models, ABMs are algorithms that use individual agents and attribute changing characteristics to each one, multiple times during multiple iterations over time. This paper focuses on three philosophical aspects of ABMs as models of causal mechanisms, as generators of emergent phenomena, and as providers of explanation. Based on my discussion, I conclude that while ABMs (...) cannot help much with causal inference, they can be viewed as etio-prognostic explanations of illness occurrence and outcome. (shrink)

In the early days of natural language semantics, Donald Davidson issued a challenge to those, like Richard Montague, who would do semantics in a model-theoretic framework that gives a central role to a model-relative notion of truth. Davidson argued that no theory of this kind can claim to be an account of real truth conditions unless it first makes clear how the relativized notion relates to our ordinary non-relativized notion of truth. In the 1990s, Davidson’s challenge was developed (...) by Etchemendy into an argument against the model-theoretic account of logical consequence, one that also threatens the attempt to capture natural language entailment relations in modeltheoretic terms—one of the central desiderata of semantics. Nevertheless, the modeltheoretic framework has flourished within natural language semantics. But it has flourished without any consensus among semanticists as to how to answer Davidson’s challenge. The aim of this essay is to develop an answer. I argue that model-theoretic semantics is best understood as model-based science: a semantics for a natural language is a scientific model of truth conditions. This makes good sense of the way model-theoretic tools are used in natural language semantics. And it allows us to answer Davidson’s challenge by showing how a theory that employs a relativized notion of truth manages to tell us about ordinary truth conditions. Moreover, I argue that it helps us see how semantics can provide genuine explanations for natural language entailment and other truth-conditional phenomena. (shrink)

Johnson -Laird has argued that spatial reasoning is based on the construction and manipulation of mental models in memory. The present article addresses the question of whether reasoning about time relations is constrained by the same factors as reasoning about spatial relations. An experiment is reported that explored the similarities and the differences in the performance of subjects in comparable spatial and temporal reasoning tasks. The results indicated that, in both the temporal and the spatial content domains, the data (...) were in agreement with the view that subjects solved problems by constructing models in memory rather than with a logical rule conceptualisation of reasoning. An analysis of the premise-reading times on the basis of premise-linking order provided support for an on-line process of mental models construction, and offered an explanation for the finding that spatial problems that did require an inference of transitivity were easier than problems that did not. No essential differences in processing and performance were observed across the two content domains, although in the time domain the correctness data were in agreement with both the mental models theory and the logical rules view. The results are discussed with respect to the mental models theory and the structural characteristics of the problems. (shrink)

Roman Frigg and Adam Toon, both, defend a fictionalist view of scientific modeling. One fundamental thesis of their view is that scientists are participating in games of make-believe when they study models in order to learn about the models themselves and about target systems represented by the models. In this paper, the epistemology of these two fictionalist views is critically discussed. I will argue that both views can give an explanation of how scientists learn about models they are studying. (...) However, how the use of models can foster an understanding of target systems is not sufficiently accounted for by Frigg and Toon. (shrink)

The paper offers an account of the structure of information provided by models that relevantly deviate from reality. It is argued that accounts of scientific modeling according to which a model’s epistemic and pragmatic relevance stems from the alleged fact that models give access to possibilities fail. First, it seems that there are models that do not give access to possibilities, for what they describe is impossible. Secondly, it appears that having access to a possibility is epistemically and pragmatically (...) idle. Based on these observations, an alternative is developed. (shrink)

The revival of action based explanations as well as their formal structuring have been two of the most important topics within explanatory sociology since the 1980s. The two newly developed approaches, being structural individualism and analytical sociology based on mechanism models, will be outlined in this article. The article is dedicated to a comparison of the aims and the formal structure of both approaches. It is shown that explanations within analytical sociology tend to be more realistic but also (...) more complex. They do not differentiate between micro and macro levels in analytical terms and use micro mechanisms instead of an analytically strong action theory that makes it difficult to systemize mechanism models. On the other hand, structural individualistic explanations that use a general action law from which social interdependencies are to be interpreted as an opportunity structure can formulate a default-option from which models can be expanded and also worked out to mechanism types. (shrink)

Designing, building, and experimenting with physical simulation models are central problem‐solving practices in the engineering sciences. Model‐based simulation is an epistemic activity that includes exploration, generation and testing of hypotheses, explanation, and inference. This paper argues that to interpret and understand how these simulation models function in creating knowledge and technologies requires construing problem solving as accomplished by a researcher–artifact system. It draws on and further develops the framework of “distributed cognition” to interpret data collected in ethnographic (...) and cognitive‐historical studies of two biomedical engineering research laboratories, and articulates the notion of distributed model‐based cognition to answer the question posed in the title. (shrink)

In this paper we discuss a view of the Machine Learning technique called Explanation-Based Learning (EBL) or Explanation-Based Generalization (EBG) as a process for the interpretation of vague concepts in logic-based models of law.The open-textured nature of legal terms is a well-known open problem in the building of knowledge-based legal systems. EBG is a technique which creates generalizations of given examples on the basis of background domain knowledge. We relate these two topics by considering (...) EBG''s domain knowledge as corresponding to statute law rules, and EBG''s training example as corresponding to a precedent case. (shrink)

The legitimacy of going back to the classical view of God’s action in the world based on the list of causes and understanding of chance in the works of Aristotle and Aquinas – in the context of contemporary science – seems to depend on whether there is a space for causal analysis within the current models of scientific explanation. This article offers a brief account of the path leading to negation and rediscovery of the importance of causality in (...) scientific explanation and reintroduces the semicausal position of the prominent philosopher of science, Mario Bunge, who treats causation as one of several categories of determination. The diversity of the categories he lists finds analogy in the commonly accepted pluralist approach to the search of the model which adequately describes the practice of scientific research. What is more, the same diversity of the categories of determination opens the way back to the classical Aristotle’s fourfold account of causation and his understanding of chance. This fact allows us, in turn, to defend the contemporary version of the classical notion of divine action against the accusation of methodical error in the form of imposing the notion of the ancient categories of causality on the results of contemporary scientific research, which notion, as some maintain, has little in common with the models of explanation currently accepted in natural sciences. (shrink)

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen (...) as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models. (shrink)

Agent-based modelling has become a well-established method in social epistemology and philosophy of science but the question of what kind of explanations these models provide remains largely open. This paper is dedicated to this issue. It starts by distinguishing between real-world phenomena, real-world possibilities, and logical possibilities as different kinds of targets which agent-based models can represent. I argue that models representing the former two kinds provide how-actually explanations or causal how-possibly explanations. In contrast, models that represent logical (...) possibilities provide epistemically opaque how-possibly explanations. While highly idealised ABMs in the form in which they are initially proposed typically fall into the last category, the epistemic opaqueness of explanations they provide can be reduced by validation procedures. To this purpose, an examination of results of simulations in terms of classes of models can be particularly helpful. I illustrate this point by discussing a class of ABMs of scientific interaction and the claim that a high degree of interaction can impede scientific inquiry. (shrink)

Jonathan Walkan challenges cognitive science's dominant model of mental representation and proposes a novel, well-devised alternative. The traditional view in the cognitive sciences uses a linguistic model of mental representation. That logic-based model of cognition informs and constrains both the classical tradition of artificial intelligence and modeling in the connectionist tradition. It falls short, however, when confronted by the frame problem---the lack of a principled way to determine which features of a representation must be updated when (...) new information becomes available. So far, proposed alternatives, including the imagistic model, have not resolved the problem. Waskan proposes the Intrinsic Cognitive Models hypothesis, according to which representational states can be concpetualized as the cognitive equivalent of scale models.Waskan argues further that the proposal that humans harbor and manipulate cognitive counterparts to scale models offers the only viable explanation for what most clearly differentiates humans from other creatures: the capacity to engage in truth-preserving manipulation of representations. The ICM hypothesis, he claims, can be distinguished from sentence-based accounts of truth preservation in a way that is fully compatible with what is known about the brain. (shrink)

erative clustering. First, we show formally that the common heuristic agglomerative clustering algorithms – Ward’s method, single-link, complete-link, and a variant of group-average – are each equivalent to a hierarchical model-based method. This interpretation gives a theoretical explanation of the empirical behavior of these algorithms, as well as a principled approach to resolving practical issues, such as number of clusters or the choice of method. Second, we show how a model-based viewpoint can suggest variations on (...) these basic agglomerative algorithms. We introduce adjusted complete-link, Mahalanobis-link, and line-link as variants, and demonstrate their utility. (shrink)

This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of (...) biology and cognitive science. Clearly, defences of non-causal explanation are far from new (e.g. Batterman, Br J Philos Sci 53:21–38, 2002a; The devil in the details: asymptotic reasoning in explanation, reduction, and emergence, 2002b; Pincock, Noûs 41:253–275, 2007; Mathematics and scientific representation 2012; Rice, Noûs. doi:10.1111/nous.12042, 2013; Biol Philos 27:685–703, 2012), so the targets here are focused on a particular type of robust phenomenon and how strong invariance to interventions can block a range of causal explanations. By focusing on a common form of model construction, the paper also ties functional or computational style explanations found in cognitive science and biology more firmly with explanatory practices across model-based science in general. (shrink)

Microgames are rapidly gaining increased attention and are highly being considered because of the technology-based media that enhances students’ learning interests and educational activities. Therefore, this study aims to develop a new construct through confirmatory factor analysis, to comprehensively understand the factors influencing the use of microgames in mathematics class. Participants of the study were the secondary school teachers in West Java, Indonesia, which had a 1-year training in microgames development. We applied a quantitative approach to collect the data (...) via online questionnaires through google form. Structural Equation Modelling with AMOS software was used to analyze the proposed model. Empirical results confirmed the perceived easy to use and subjective norm influence relationship with teachers’ microgame usage behaviors and intentions. In this condition, SN was found to have the initial significant influence on behavioral intention, as attitude, BI, and facilitating conditions also correlated with the actual use of microgames. Furthermore, the largest influential factor was BI, with the results subsequently showing that TPACK had no significant influence on the actual use of microgames. This report is expected to help bridge the gap across several previous studies, as well as contribute to the explanation and prediction of the factors influencing the teachers’ mathematical utilization of the study’s program. Besides this, it also helps to increase the use of microgames in teaching and learning activities. (shrink)

We propose a cognitive-dissonance model of norm compliance to identify conditions for selfishly biased information acquisition. The model distinguishes between: (i) objective norm compliers, for whom the right action is a function of the state of the world; (ii) subjective norm compliers, for whom it is a function of their belief. The former seek as much information as possible; the latter acquire only information that lowers, in expected terms, normative demands. The source of ‘moral wiggle room’ is not (...) belief manipulation, but the coarseness of normative prescriptions under conditions of uncertainty. In a novel experimental setup, we find evidence for such strategic information uptake. Our results suggest that attempts to change behavior by subjecting individuals to norms can lead to biased information acquisition instead of compliance. (shrink)

One puzzle concerning highly idealized models is whether they explain. Some suggest they provide so-called ‘how-possibly explanations’. However, this raises an important question about the nature of how-possibly explanations, namely what distinguishes them from ‘normal’, or how-actually, explanations? I provide an account of how-possibly explanations that clarifies their nature in the context of solving the puzzle of model-based explanation. I argue that the modal notions of actuality and possibility provide the relevant dividing lines between how-possibly and how-actually (...) explanations. Whereas how-possibly explanations establish claims of possible explanations, how-actually explanations establish claims of actual ones. Models, in turn, simply provide evidence for these claims. (shrink)

Life scientists increasingly rely upon abstraction-based modeling and reasoning strategies for understanding biological phenomena. We introduce the notion of constraint-based reasoning as a fruitful tool for conceptualizing some of these developments. One important role of mathematical abstractions is to impose formal constraints on a search space for possible hypotheses and thereby guide the search for plausible causal models. Formal constraints are, however, not only tools for biological explanations but can be explanatory by virtue of clarifying general dependency-relations and (...) patterning between functions and structures. We describe such situations as constraint-based explanations and argue that these differ from mechanistic strategies in important respects. While mechanistic explanations emphasize change-relating causal features, constraint-based explanations emphasize formal dependencies and generic organizational features that are relatively independent of lower-level changes in causal details. Our distinction between mechanistic and constraint-based explanations is pragmatically motivated by the wish to understand scientific practice. We contend that delineating the affordances and assumptions of different explanatory questions and strategies helps to clarify tensions between diverging scientific practices and the innovative potentials in their combination. Moreover, we show how constraint-based explanation integrates several features shared by otherwise different philosophical accounts of abstract explanatory strategies in biology. (shrink)

The focus in the literature on scientific explanation has shifted in recent years towards model-based approaches. In recent work, Alisa Bokulich has argued that idealization has a central role to play in explanation. Bokulich claims that certain highly-idealized, structural models can be explanatory, even though they are not considered explanatory by causal, mechanistic, or covering law accounts of explanation. This paper focuses on Bokulich’s account in order to make the more general claim that there are (...) problems with maintaining that a structural criterion can capture the way that highly-idealized models explain. This paper examines Bokulich’s claim that the structural model explanation of quantum wavefunction scarring, featuring semiclassical mechanics, is deeper than the explanation provided by the local quantum model. The challenge for Bokulich is to show that the semiclassical model answers a wider range of w-questions, as this is her method of assessing structural information. I look at two reasonable approaches employing w-questions, and I argue that neither approach is ultimately satisfactory. Because structural similarity has preferences for more fundamental models, I argue that the local quantum model provides explanations that at least as deep as the semiclassical ones. The criterion either wrongly identifies all models as explanatory, or prefers models from fundamental theory. Either way, it cannot capture the way that highly-idealized models explain. (shrink)

Social scientists associate agent-based simulation (ABS) models with three ideas about explanation: they provide generative explanations, they are models of mechanisms, and they implement methodological individualism. In light of a philosophical account of explanation, we show that these ideas are not necessarily related and offer an account of the explanatory import of ABS models. We also argue that their bottom-up research strategy should be distinguished from methodological individualism.

Recent studies of emotion mindreading reveal that for three emotions, fear, disgust, and anger, deficits in face-based recognition are paired with deficits in the production of the same emotion. What type of mindreading process would explain this pattern of paired deficits? The simulation approach and the theorizing approach are examined to determine their compatibility with the existing evidence. We conclude that the simulation approach offers the best explanation of the data. What computational steps might be used, however, in (...) simulation-style emotion detection? Four alternative models are explored: a generate-and-test model, a reverse simulation model, a variant of the reverse simulation model that employs an “as if” loop, and an unmediated resonance model. (shrink)

We propose new definitions of explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion article. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent’s initial uncertainty. We show that the definition handles well a number of problematic examples (...) from the literature. (shrink)

Recent works in Explainable AI mostly address the transparency issue of black-box models or create explanations for any kind of models (i.e., they are model-agnostic), while leaving explanations of interpretable models largely underexplored. In this paper, we fill this gap by focusing on explanations for a specific interpretable model, namely pattern-based logistic regression (PLR) for binary text classification. We do so because, albeit interpretable, PLR is challenging when it comes to explanations. In particular, we found that a (...) standard way to extract explanations from this model does not consider relations among the features, making the explanations hardly plausible to humans. Hence, we propose AXPLR, a novel explanation method using (forms of) computational argumentation to generate explanations (for outputs computed by PLR) which unearth model agreements and disagreements among the features. Specifically, we use computational argumentation as follows: we see features (patterns) in PLR as arguments in a form of quantified bipolar argumentation frameworks (QBAFs) and extract attacks and supports between arguments based on specificity of the arguments; we understand logistic regression as a gradual semantics for these QBAFs, used to determine the arguments’ dialectic strength; and we study standard properties of gradual semantics for QBAFs in the context of our argumentative re-interpretation of PLR, sanctioning its suitability for explanatory purposes. We then show how to extract intuitive explanations (for outputs computed by PLR) from the constructed QBAFs. Finally, we conduct an empirical evaluation and two experiments in the context of human-AI collaboration to demonstrate the advantages of our resulting AXPLR method. (shrink)

Aristotle's view of the interdependency of explanation and definition rests on a metaphysical thesis: essences are what determine the nature of kinds in such a way as to make their causal structure completely intelligible to us and to locate them in their own distinctive niche in a nexus of genera and species. We can rationally base our understanding of the first principles of science on our understanding of this causally based pattern of kinds. The world, so understood, contains (...) its own intelligible structure, which Aristotle identifies with the Active Intellect. (shrink)

I distinguish two versions of kin selection theory—a purely genetic version and a version that also appeals to cultural forms of cooperation —and present an argument in favor of using the former when it comes to accounting for the evolution of cooperation in non-human organisms. Specifically, I first show that both GKST and WKST are equally mathematically coherent—they can both be derived from the Price equation—but not necessarily equally empirically plausible, as they are based on different assumptions about the (...) inheritance system underlying the cooperative phenotype. Given this, I then, second, present a model selection theoretic argument in favor of GKST over WKST. This argument is based on the fact that, in non-human cases, the former theory is likely to be as empirically successful as WKST, while containing fewer degrees of freedom. I end by defending both the intrinsic importance of this argument and its relevance to the discussion surrounding the “gene’s eye view of evolution.”. (shrink)

We propose new definitions of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion article. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent's initial uncertainty. We show that the definition handles well a number of problematic (...) examples from the literature. (shrink)

This essay develops an inferential account of model explanation, based on Mauricio Suárez’s inferential conception of scientific representation and Alisa Bokulich’s counterfactual account of model explanation. It is suggested that the fact that a scientific model can explain is essentially linked to how a modeler uses an established model to make various inferences about the target system on the basis of results derived from the model. The inference practice is understood as a (...) two-step activity, with the first step involving making counterfactual statements about the model itself and the second step involving making hypothetical statements transferring over claims derived from the model onto the target. To illustrate how this two-step activity proceeds, an agent-based simulation model is discussed. (shrink)

Undoubtedly the Penrose-Hameroff Orch OR model may be considered as a good theory for describing information processing mechanisms and holistic phenomena in the human brain, but it doesn’t give us satisfactory explanation of human perception. In this work a new approach explaining our perception is introduced, which is in good agreement with Orch OR model and other mainstream science theories such as string theory, loop quantum gravity and holographic principle. It is shown that human perception cannot be (...) explained in the terms of elementary particles and we should introduce new indivisible holistic objects with geometry based on smooth infinitesimal analysis - elastic membranes. The example of such a membrane is our Universe which is an indivisible whole. It is shown that our perception may be considered as the result of elastic oscillations of two dimensional (2D) elastic membranes with closed topology embedded in our bodies. Only one elastic membrane responsible for its perceptions will correspond to the selected organism, but there may be other membranes, even at the cell level. In other words, reality may be considered as the process of time evolution of holistic energetically very weak macro objects - elastic membranes with the geometry based on smooth infinitesimal analysis. An embedded membrane in this multidimensional world will look different for the external and internal observers: from the outside it will look like a material object with smooth infinitesimal geometry, while from the inside our Universe-like space-time fabric. When interacting with elementary particles and other membranes, a membrane will transform their energy into its elastic energy (a new form of energy) - the energy of stretching of the infinitesimal segments. The theory postulates that these elastic deformations will not be observable from the point of view of the internal observer. Heisenberg’s uncertainty principle will work in this physics only from the point of view of the internal observer. For the external observer each embedded elastic membrane may be stretched and even a very small region will become observable. For example, living organisms play the role of internal observers of the Universe, and at the same time they serve as external observers for 2D membranes embedded into our Universe. We can observe our 2D self-membranes through our perceptions, which are encoded in elastic oscillations of the elastic membrane. According to the theory, elastic membranes occupy energetically favorable positions around microtubules involved into Orch OR. The theory not only gives us a really multidimensional holistic picture of reality, but it also provides us with a new method for understanding such phenomena as perception, self-awareness and will. (shrink)

In this paper, I try to develop an informal model for the analysis and evaluation of argumentative strategies based on active authorities. The explanations necessary to understand the idea of the model and what is modelled are given through the development of the paper. I first give an example of argumentative activities. After that, the main assumption of the model is given. In the third part, the relevant aspects of the argumentative activity are analysed, while the (...) principles of the evaluation are explained in the fourth part. The informal model is presented in the fifth part. In the final remarks, the suggestions for further work are given, as well as a comparison with some related recent works in argumentation theory. (shrink)

We propose new definitions of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion article. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent's initial uncertainty. We show that the definition handles well a number of problematic (...) examples from the literature. 1. Introduction2. Causal models and the definition of actual causality: a review2.1Causal models2.2Syntax and semantics2.3The definition of cause3. Explanation: the basic definition4. Partial explanations and explanatory power5. The general definition6. DiscussionAppendix: the formal definition of causality. (shrink)

This work contrasts the formalist approach to defining explanation in science, exemplified in the Deductive-Nomenological Model of Carl G. Hempel, with the contextualist approach of Thomas Kuhn. It is argued that both of these attempts to define the explanatory processes of science are inadequate. A connection is made between the view of rationality upon which each view is based and the way that it defines explanation. It is argued that a process of thought, which scientific (...) class='Hi'>explanation represents, is considered rational by these thinkers only if it meets the criteria of constraint under a rule, distance from subjective considerations and instrumental connection of method and goal. Given this strong definition of rationality, explanation is defined by both Kuhn and Hempel as thought within the constraint of a theory or a theoretical paradigm. Under this construal, explanation is separated as a process from theory invention which both thinkers hold to involve primarily non-cognitive criteria of choice. It is argued in this work that such a view contradicts the actual practice of science where theory invention seems to be primarily a process of rational explaining. ;A detailed historical case of an attempt to explain organic morphogenesis is given. It centers on the theory of generation of Pierre Louis Moreau de Maupertuis, an 18th Century scientific thinker. This case is meant to illustrate the kinds of cognitive interest and strategies of invention that go into dealing with an explanatory problem in science. ;Finally some general programmatic conclusions are made relating the foregoing arguments and the case, to the need to explore the nature of scientific explanation in the domain of theory invention where the reasoning processes seem to involve analogical reasoning and critical metaphysical thought. (shrink)

McCullough et al.'s target article is a psychological version of the reputation models pioneered by biologist Robert Trivers (1971) and economist Robert Frank (1988). The authors, like Trivers and Frank, offer an implausible explanation of the fact that revenge is common even when there are no possible reputational effects. I sketch a more plausible model based on recent research.