We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...) patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions. (shrink)

The Enhanced Indispensability Argument appeals to the existence of Mathematical Explanations of Physical Phenomena to justify mathematical Platonism, following the principle of Inference to the Best Explanation. In this paper, I examine one example of a MEPP—the explanation of the 13-year and 17-year life cycle of magicicadas—and argue that this case cannot be used defend the EIA. I then generalize my analysis of the cicada case to other MEPPs, and show that these explanations rely on what I will (...) call ‘optimal representations’, which are representations that capture all that is relevant to explain a physical phenomenon at a specified level of description. In the end, because the role of mathematics in MEPPs is ultimately representational, they cannot be used to support mathematical Platonism. I finish the paper by addressing the claim, advanced by many EIA defendants, that quantification over mathematical objects results in explanations that have more theoretical virtues, especially that they are more general and modally stronger than alternative explanations. I will show that the EIA cannot be successfully defended by appealing to these notions. (shrink)

The optimality approach to modeling natural selection has been criticized by many biologists and philosophers of biology. For instance, Lewontin (1979) argues that the optimality approach is a shortcut that will be replaced by models incorporating genetic information, if and when such models become available. In contrast, I think that optimality models have a permanent role in evolutionary study. I base my argument for this claim on what I think it takes to best explain an event. In (...) certain contexts, optimality and game-theoretic models best explain some central types of evolutionary phenomena. ‡Thanks to Michael Friedman, Helen Longino, Michael Weisberg, and especially Elliott Sober for comments on earlier drafts of this paper. †To contact the author, please write to: Department of Philosophy, Stanford University, Stanford, CA 94305-2155; e-mail: [email protected]. (shrink)

Optimality models are widely used in different parts of biology. Two important questions that have been asked about such models are: are they explanatory and, if so, what type of explanations do they offer? My concern in this paper is with the approach of Rice (2012, 2015) and Irvine (2015), who claim that these models provide non-causal explanations. I argue that there are serious problems with this approach and with the accounts of explanation it is intended to (...) justify. The idea behind this undertaking is to draw attention to an important issue associated with the recent pluralist stance on explanation: the rampant proliferation of theories of explanation. This proliferation supports a pluralist perspective on explanation, and pluralism encourages such a proliferation. But, if we are not careful about how we arrive at and how we justify new accounts of explanation — i.e., if we do not try to avoid the sort of problems discussed in this paper — we may end up trivializing the concept of explanation. (shrink)

The value of optimality modeling has long been a source of contention amongst population biologists. Here I present a view of the optimality approach as at once playing a crucial explanatory role and yet also depending on external sources of confirmation. Optimality models are not alone in facing this tension between their explanatory value and their dependence on other approaches; I suspect that the scenario is quite common in science. This investigation of the optimality approach thus (...) serves as a case study, on the basis of which I suggest that there is a widely felt tension in science between explanatory independence and broad epistemic interdependence, and that this tension influences scientific methodology. (shrink)

The interventionist account of causal explanation, in the version presented by Jim Woodward, has been recently claimed capable of buttressing the widely felt—though poorly understood—hunch that high-level, relatively abstract explanations, of the sort provided by sciences like biology, psychology and economics, are in some cases explanatorily optimal. It is the aim of this paper to show that this is mistaken. Due to a lack of effective constraints on the causal variables at the heart of the interventionist causal-explanatory scheme, as (...) presently formulated it is either unable to prefer high-level explanations to low, or systematically overshoots, recommending explanations at so high of a level as to be virtually vacuous. (shrink)

Batterman and Rice ([2014]) argue that minimal models possess explanatory power that cannot be captured by what they call ‘common features’ approaches to explanation. Minimal models are explanatory, according to Batterman and Rice, not in virtue of accurately representing relevant features, but in virtue of answering three questions that provide a ‘story about why large classes of features are irrelevant to the explanandum phenomenon’ ([2014], p. 356). In this article, I argue, first, that a method (the renormalization group) they propose (...) to answer the three questions cannot answer them, at least not by itself. Second, I argue that answers to the three questions are unnecessary to account for the explanatoriness of their minimal models. Finally, I argue that a common features account, what I call the ‘generalized ontic conception of explanation’, can capture the explanatoriness of minimal models. (shrink)

Abstract: This is necessarily an evolutionary work in progress. Its purpose and goal is enabling real progress of science, mathematics, society, and civilization. Thus, to accomplish the mission, upgrading the current sociocultural paradigm is essential. That makes holistic ontology and optimal epistemics essential to ongoing work and to further development of new theory and metatheory. Hence, the current listings of key terms, definitions, and explanations presented here provide some core concepts and supporting theorems, metatheorems, equations, and examples. That enables (...) remedial revision of relevant domains of thought and discourse. The project is also intended to inspire collaborative extension and ongoing refinement of the work. The appendices provide additional perspective. ~ A preprint draft, v.2e, for review and comment only. (shrink)

Theories of explanation need to account for a puzzling feature of our explanatory practices: the fact that we prefer explanations that are relatively abstract but only moderately so. Contra Franklin-Hall ([2016]), I argue that the interventionist account of explanation provides a natural and elegant explanation of this fact. By striking the right balance between specificity and generality, moderately abstract explanations optimally subserve what interventionists regard as the goal of explanation, namely identifying possible interventions that would have changed the (...) explanandum. (shrink)

This paper is based on the comments I gave to Alfred Mele regarding his original Zygote Argument during my presentation at a small workshop on manipulation arguments in Budapest back in 2012. After those comments, Mele changed the conclusion of his original Zygote Argument (OZA) from a positive, explanatory conclusion to a negative, non-explanatory conclusion--and, correspondingly, redefined 'incompatibilism' so that it would no longer refer in his work to the view that determinism precludes (undermines, eliminates, destroys, etc.) free will, but (...) refers narrowly to the perhaps spurious association claim that appears in the conclusion of the revised Zygote Argument (Mele 2013+). Yet, over the last decade, Mele has made no comment in print about the changes he made, the invalidity objection that led to them, nor to the fact that his revised Zygote Argument was originally presented by me as a (non-optimal) reaction to my invalidity objection to his original Zygote Argument. To my knowledge, Mele has only mentioned this 2015 paper once, at it was not in the context of understanding manipulation arguments; it was, rather, in the context of defending his revised, non-explanatory definition of 'incompatibilism' at the opening of Aspects of Agency (p. 6, n. 4). There, Mele singles out *just this 2015 paper* as a place where they'll find what Mele degrades as a type of nonstandard/nontraditional definition of 'incompatibilism' that he rejects. This is notable because Mele provides no empirical evidence that his preferred definition is standard, and most (including Lehrer, who coined the term) reject the negative, non-explanatory, association-based definition that Mele came to endorse in the aftermath of my invalidity objection to OZA. So, if Mele had a genuine concern about philosophers who use the term 'incompatibilism', then Mele could have--and should have--named almost any leading figure in free will for this purpose (since most--including Pereboom, Vihvelin, Haji, etc.) reject the non-explanatory definition that Mele-2013+ now endorses. Yet, for untold reasons, Mele isolated the working definition of 'incompatibilism' used in one paper by one unemployed junior researcher to use as his example--even though Neil Levy had used this same definition in previously published work (eg. Levy 2011). Moreover, if Mele were genuinely interested in helping his readers understand the ambiguity of terms like 'compatibilism' and 'incompatibilism' it is a mystery that he referenced a paper in which nothing of substance hung on the definitions given to these terms -- especially considering that he might have instead pointed them to my OTHER 2015 paper, "A Critique of Vihvelin's Three-Fold Classification" in which the MAIN AIM of the paper is to expose the difficulties facing anyone who wants to define the terms 'compatibilism' and 'incompatibilism' in a way that everyone will accept (including a critique of the negative, non-explanatory "incompossibilist" definition that Mele adopted after my objection to OZA). By all appearances, Mele's main aim in citing specifically THIS paper and none of my other work was to give readers the false impression that my working definition of 'incompatibilism' played some role in my charge that his argument is invalid (which it doesn't, given that the term doesn't appear in either the premises or conclusion of that argument). Mele adds to this unseemly rhetorical campaign in his book *Manipulation Agents* where he mentions an invalidity objection in the intro (p. 84) and then discusses that objection in the final lines of Chapter 5 of the book (p. 120-121). The invalidity objection discussed here is completely pedantic, has never been seriously raised in the primary literature (and was previously addressed in multiple places my multiple philosophers, including me), and has nothing whatever to do with my invalidity objection. In other words, Mele was content to discuss an invalidity objection that no one has ever had to an argument that is clearly valid while completely ignoring the ONLY substantive invalidity objection to his argument in print. This is unfortunate, because it has led to serious confusion --including the patently false description of my invalidity objection to OZA as an objection to the revised version of the Zygote Argument that Mele adopted in *response* my objection (e.g., Capes mistakenly claims "Mickelson (2015) contends that arguments like this [i.e. like the revised Zygote Argument] are invalid. But that’s because she defines “incompatibilism” differently than I’ve defined it here" in his "Manipulation Arguments and Direct Arguments" chapter in the 2023 Wiley-Blackwell Companion to Free Will; I did not say this, and the definition of 'incompatibilism' is irrelevant to my objection). In "The Zygote Argument Is Invalid--Now What?", I simply use the invalidity of the simple 2-premise Zygote Argument as a foil to make a series of more philosophically significant points (e.g. regarding the dialectical structure of the free-will debate, the role of best-explanation reasoning, and to develop the first-ever "master manipulation argument"). Mele has yet to address my reasons for calling attention to the non-trivial invalidity of his OZA or to any of my answers to the "Now What?" question in this paper, even though this question and my proposed answers to this question are the paper's main point. For those interested in a more recent discussion of how the logical and metaphilosophical observations made in this "Now What?" paper have helped to move the free-will debate forward, I suggest looking at the discussions of the "paradigm shift" in the free-will debate that helps to explain why the free-will debate has become mired in stalemates and empty verbal disputes that are found, for example, in my contributions to the Wiley-Blackwell Companion to Free Will (Intro and Chapter 4, 2023) and my paper "Free Will, Self-Creation, and the Paradox of Moral Luck" (2019). (shrink)

Resumen -/- El objetivo de este trabajo consiste en analizar las relaciones entre los modelos de optimalidad y la selección natural. Defenderemos que esas relaciones pueden dividirse en dos tipos, en tanto hay dos tipos de explicaciones seleccionistas, que llamaremos “históricas” y “ahistóricas”. Las explicaciones históricas revelan como una población dada adquiere un rasgo que es adaptativo en ese ambiente e involucran muchas generaciones, variación, etc. Las explicaciones ahistóricas, explican por qué, en determinado momento, ciertos tipos de organismos tienen un (...) mayor éxito reproductivo que otros. Mostraremos que los modelos de optimalidad pueden jugar un rol en la determinación del explanandum de las explicaciones histórica seleccionistas, esto es, que ayudan a reconocer qué rasgos son adaptativos. Por otra parte, mostramos que los modelos de optimalidad nos permiten determinar a veces la parte del explanans de las explicaciones ahistóricas (particularmente, el concepto de fitness). -/- Abstract -/- The goal of this paper is to analyze the relations between optimality models and natural selection. We contend that these relationships can be divided into two kinds, as there are two kinds of natural selection explanations, which we call “historical” and “ahistorical”. Historical explanations reveal how a given population acquires a trait which is adaptive in its environment, and involves many generations, variations, etc. Ahistorical ones, explain why, at a given moment, certain kinds of organisms have a greater reproductive success than others. We show that optimality models can play a role in determining the explanandum of historical selectionist explanations, that is, they help us to recognize which traits are adaptive. And, on the other hand, that optimality models sometimes allow us to determine part of the explanans of ahistorical explanations (particularly, the concept of fitness). (shrink)

According to the Principle of Sufficient Reason (‘PSR’), every fact has an explanation for why it obtains. If the PSR is true, there must be a sufficient reason for why God chose to create our world. But a sufficient reason for God’s choice plausibly necessitates that choice. It thus seems that God could not have done otherwise, and that our world exists necessarily. We therefore appear forced to pick between the PSR, and the contingency of creation and divine choice. I (...) show that a third option remains open, and thus that it is possible to preserve the contingency of creation and divine choice even while endorsing the PSR. My solution depends on the coherence of a restricted modal realism. On this modal realism, there is more than one possible morally optimal created world, and for each such world there is an existing possibility in which God creates that world. (shrink)

The aim of this study is to justify the belief that there are biological normative mechanisms that fulfill non-trivial causal roles in the explanations (as formulated by researchers) of actions and behaviors present in specific systems. One example of such mechanisms is the predictive mechanisms described and explained by predictive processing (hereinafter PP), which (1) guide actions and (2) shape causal transitions between states that have specific content and fulfillment conditions (e.g. mental states). Therefore, I am guided by a (...) specific theoretical goal associated with the need to indicate those conditions that should be met by the non-trivial theory of normative mechanisms and the specific models proposed by PP supporters. In this work, I use classical philosophical methods, such as conceptual analysis and critical reflection. I also analyze selected studies in the field of cognitive science, cognitive psychology, neurology, information theory and biology in terms of the methodology, argumentation and language used, in accordance with their theoretical importance for the issues discussed in this study. In this sense, the research presented here is interdisciplinary. My research framework is informed by the mechanistic model of explanation, which defines the necessary and sufficient conditions for explaining a given phenomenon. The research methods I chose are therefore related to the problems that I intend to solve. In the introductory chapter, “The concept of predictive processing”, I discuss the nature of PP as well as its main assumptions and theses. I also highlight the key concepts and distinctions for this research framework. Many authors argue that PP is a contemporary version of Kantianism and is exposed to objections similar to those made against the approach of Immanuel Kant. I discuss this thesis and show that it is only in a very general sense that the PP framework is neo-Kantian. Here we are not dealing with transcendental deduction nor with the application of transcendental arguments. I argue that PP is based on reverse engineering and abduction inferences. In the second part of this chapter, I respond to the objection formulated by Dan Zahavi, who directly accuses this research framework of anti-realistic consequences. I demonstrate that the position of internalism, present in the so-called conservative PP, does not imply anti-realism, and that, due to the explanatory role played in it by structural representations directed at real patterns, it is justified to claim that PP is realistic. In this way, I show that PP is a non-trivial research framework, having its subject, specific methods and its own epistemic status. Finally, I discuss positions classified as the socalled radical PP. In the chapter “Predictive processing as a Bayesian explanatory model” I justify the thesis according to which PP offers Bayesian modeling. Many researchers claim that the brain is an implemented statistical probabilistic network that is an approximation of the Bayesian 7 rule. In practice, this means that all cognitive processes are to apply Bayes' rule and can be described in terms of probability distributions. Such a solution arouses objections among many researchers and is the subject of wide criticism. The purpose of this chapter is to justify the thesis that Bayesian PP is a non-trivial research framework. For this purpose, I argue that it explains certain phenomena not only at the computational level described by David Marr, but also at the level of algorithms and implementation. Later in this chapter I demonstrate that PP is normative modeling. Proponents of the use of Bayesian models in psychology or decision theory argue that they are normative because they allow the formulation of formal rules of action that show what needs to be done to make a given action optimal. Critics of this approach emphasize that such thinking about the normativity of Bayesian modeling is unjustified and that science should shift from prescriptive to descriptive positions. In a polemic with Shira Elqayam and Jonathan Evans (2011), I show that the division they propose into prescriptivism and Bayesian descriptivism is apparent, because, as I argue, there are two forms of prescriptivism, i.e. the weak and the strong. I argue that the weak version is epistemic and can lead to anti-realism, while the strong version is ontic and allows one to justify realism in relation to Bayesian models. I argue that a weak version of prescriptivism is valid for PP. It allows us to adopt anti-realism in relation to PP. In practice, this means that you can explain phenomena using Bayes' rule. This does not, however, imply that they are Bayesian in nature. However, the full justification of realism in relation to the Bayesian PP presupposes the adoption of strong prescriptivism. This position assumes that phenomena are explained by Bayesian rule because they are Bayesian as such. If they are Bayesian in nature, then they should be explained using Bayesian modeling. This thesis will be substantiated in the chapters “Normative functions and mechanisms in the context of predictive processing” and “Normative mechanisms and actions in predictive processing”. In the chapter “The Free Energy Principle in predictive processing”, I discuss the Free Energy Principle (hereinafter FEP) formulated by Karl Friston and some of its implications. According to this principle, all biological systems (defined in terms of Markov blankets) minimize the free energy of their internal states in order to maintain homeostasis. Some researchers believe that PP is a special case of applying this principle to cognition, and that predictive mechanisms are homeostatic mechanisms that minimize free energy. The discussion of FEP is important due to the fact that some authors consider it to be important for explanatory purposes and normative. If this is the case, then FEP turns out to be crucial in explaining normative predictive mechanisms and, in general, any normative biological mechanisms. To define the explanatory possibilities of this principle, I refer to the discussion of its supporters on the issue they define as the problem of continuity and discontinuity between life and mind. A critical analysis of this discussion and the additional arguments I have formulated have allowed me to revise the explanatory ambitions of FEP. I also reject the belief that this principle is necessary to explain the nature of predictive mechanisms. I argue that the principle formulated and defended by Friston is an important research heuristic for PP analysis. 8 In the chapter “Normative functions and mechanisms in predictive processing”, I start my analyzes by formulating an answer to the question about the normative nature of homeostatic mechanisms. I demonstrate that predictive mechanisms are not homeostatic. I defend the view that a full explanation of normative mechanisms presupposes an explanation of normative functions. I discuss the most important proposals for understanding the normativity of a function, both from a systemic and teleosemantic perspective. I conclude that the non-trivial concept of a function must meet two requirements which I define as explanatory and normative. I show that none of the theories I have invoked satisfactorily meets both of these requirements. Instead, I propose a model of normativity based on Bickhard's account, but supplemented by a mechanistic perspective. I argue that a function is normative when: (1) it allows one to explain the dysfunction of a given mechanism; (2) it contributes to the maintenance of the organism's stability by shaping and limiting possible relations, processes and behaviors of a given system; and when (3) (according to the representational and predictive functions) it enables explaining the attribution of logical values of certain representations / predictions. In such an approach, a mechanism is normative when it performs certain normative functions and when it is constitutive for a specific action or behavior, despite the fact that for some reason it cannot realize it either currently or in the long-term. Such an understanding of the normativity of mechanisms presupposes the acceptance of the epistemic hypothesis. I argue that this hypothesis is not cognitively satisfactory, and therefore the ontic hypothesis should be justified, which is directly related to adopting the position of ontic prescriptivism. For this reason, referring to the mechanistic theory of scientific explanations, I formulate an ontical interpretation of the concept of a normative mechanism. According to this approach, a mechanism or a function is normative when they perform such and such causal roles in explaining certain actions and behaviors. With regard to the normative properties of predictive mechanisms and functions, this means that they are the causes of specific actions an organism carries out in the environment. In this way, I justify the necessity of accepting the ontic hypothesis and rejecting the epistemic hypothesis. The fifth chapter, “Normative mechanisms and actions in predictive processing”, is devoted to the dark room problem and the related exploration-exploitation trade-off. A dark room is the state that an agent could be in if it minimized the sum of all potential prediction errors. I demonstrate that, in accordance with the basic assumption of PP about the need for continuous and long-term minimization of prediction errors, such a state should be desirable for the agent. Is it really so? Many authors believe it is not. I argue that the test of the value of PP is the possibility of a non-trivial solution of this problem, which can be reduced to the choice between active and uncertainty-increasing exploration and safe and easily predictable exploitation. I show that the solution proposed by PP supporters present in the literature does not enable a fully satisfactory explanation of this dilemma. Then I defend the position according to which the full explanation of the normative mechanisms, and, subsequently, the solution to the dilemma of exploration and exploitation, involves reference to the existence of constraints present in the environment. The constraints 9 include elements of the environment that make a given mechanism not only causal but also normative. They are therefore key to explaining the predictive mechanisms. They do not only play the role of the context in which the mechanism is implemented, but, above all, are its constitutive component. I argue that the full explanation of the role of constraints in normative predictive mechanisms presupposes the integration of individual models of specific cognitive phenomena, because only the mechanistic integration of PP with other models allows for a non-trivial explanation of the nature of normative predictive mechanisms that would have a strong explanatory value. The explanatory monism present in many approaches to PP makes it impossible to solve the problem of the dark room. Later in this chapter, I argue that the Bayesian PP is normative not because it enables the formulation of such and such rules of action, but because the predictive mechanisms themselves are normative. They are normative because they condition the choice of such and such actions by agents. In this way, I justify the hypothesis that normative mechanisms make it possible to explain the phenomenon of agent motivation, which is crucial for solving the dark room problem. In the last part of the chapter, I formulate the hypothesis of distributed normativity, which assumes that the normative nature of certain mechanisms, functions or objects is determined by the relations into which these mechanisms, functions or objects enter. This means that what is normative (in the primary sense) is the relational structure that constitutes the normativity of specific items included in it. I suggest that this hypothesis opens up many areas of research and makes it possible to rethink many problems. In the “Conclusion”, I summarize the results of my research and indicate further research perspectives. (shrink)

Some adaptationist explanations are regarded as maximally solid and others fanciful just-so stories. Just-so stories are explanations based on very little evidence. Lack of evidence leads to circular-sounding reasoning: “this trait was shaped by selection in unseen ancestral populations and this selection must have occurred because the trait is present.” Well-supported adaptationist explanations include evidence that is not only abundant but selected from comparative, populational, and optimality perspectives, the three adaptationist subdisciplines. Each subdiscipline obtains its broad (...) relevance in evolutionary biology via assumptions that can only be tested with the methods of the other subdisciplines. However, even in the best-supported explanations, assumptions regarding variation, heritability, and fitness in unseen ancestral populations are always present. These assumptions are accepted given how well they would explain the data if they were true. This means that some degree of “circularity” is present in all evolutionary explanations. Evolutionary explanation corresponds not to a deductive structure, as biologists usually assert, but instead to ones such as abduction or induction. With these structures in mind, we show the way to a healthier view of “circularity” in evolutionary biology, and why integration across the comparative, populational, and optimality approaches is necessary. (shrink)

Contemporary theoretical physics is divided into diversified theories of different phenomena. These are characteristically frameworks for giving mathematical descriptions of perceptions, in the absence of understandable explanations or a unified worldview. This is not an optimal state of affairs, for people by nature desire to understand; this sets a major obstacle for an optimal progress rate of physics, for understandable explanations are more prolific of new predictions and applications than non-understandable explanations. Disunification in physics can be remedied (...) only by discovering an understandable ontology and applying it as the basis of unifying explanations of phenomena that were earlier explained by different theories. Due to disunified physics and the nature of traditional analysis in philosophy, there is no consensus about central concepts such as time, possibility and truth. It is suggested that philosophical analysis would be more prolific of understandable and applicable concepts, were it complemented by a method of unification. (shrink)

This study clarifies the need for a renewed account of the body in physiotherapy to fill sizable gaps between physiotherapeutical theory and practice. Physiotherapists are trained to approach bodily functioning from an objectivist perspective; however, their therapeutic interactions with patients are not limited to the provision of natural-scientific explanations. Physiotherapists’ practice corresponds well to theorisation of the body as the bearer of original bodily intentionality, as outlined by Merleau-Ponty and elaborated upon by enactivists. We clarify how physiotherapeutical practice corroborates (...) Merleau-Ponty’s critical arguments against objectivist interpretations of the body; particularly, his analyses demonstrate that norms of optimal corporeal functioning are highly individual and variable in time and thus do not directly depend on generic physiological structures. In practice, objectively measurable physical deviations rarely correspond to specific subjective difficulties and, similarly, patients’ reflective insights into their own motor deficiencies do not necessarily produce meaningful motor improvements. Physiotherapeutical procedures can be understood neither as mechanical manipulations of patients’ machine-like bodies by experts nor as a process of such manipulation by way of instructing patients’ explicit conscious awareness. Rather, physiotherapeutical practice and theory can benefit from the philosophical interpretation of motor disorders as modifications of bodily intentionality. Consequently, motor performances addressed in physiotherapy are interpreted as relational features of a living organism coupled with its environment, and motor disorders are approached as failures to optimally manage the motor requirements of a given situation owing to a relative loss of the capacity to structure one’s relation with their environment through motor action. Building on this, we argue that the process of physiotherapy is most effective when understood as a bodily interaction to guide patients towards discovering better ways of grasping a situation as meaningful through bodily postures and movements. (shrink)

Following Marr’s famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in cognitive processes (...) that make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general. (shrink)

Consider a set of shuffled observations drawn from a fixed probability distribution over some instance domain. What enables learning of inductive generalizations which proceed from such a set of observations? The scenario is worthwhile because it epistemically characterizes most of machine learning. This kind of learning from observations is also inverse and ill-posed. What reduces the non-uniqueness of its result and, thus, its problematic epistemic justification, which stems from a one-to-many relation between the observations and many learnable generalizations? The paper (...) argues that this role belongs to any complexity regularization which satisfies Norton’s Material Theory of Induction (MTI) by localizing the inductive risk to facts in the given domain. A prime example of the localization is the Lottery Ticket Hypothesis (LTH) about overparameterized neural networks. The explanation of MTI’s role in complexity regularization of neural networks is provided by analyzing the stability of Empirical Risk Minimization (ERM), an inductive rule that controls the learning process and leads to an inductive generalization on the given set of observations. In cases where ERM might become asymptotically unstable, making the justification of the generalization by uniform convergence unavailable, LTH and MTI can be used to define a local stability. A priori, overparameterized neural networks are such cases and the combination of LTH and MTI can block ERM’s trivialization caused by equalizing the strengths of its inductive support for risk minimization. We bring closer the investigation of generalization in artificial neural networks and the study of inductive inference and show the division of labor between MTI and the optimality justifications (developed by Gerhard Schurz) in machine learning. (shrink)

Bayesian cognitive science is a research programme that relies on modelling resources from Bayesian statistics for studying and understanding mind, brain, and behaviour. Conceiving of mental capacities as computing solutions to inductive problems, Bayesian cognitive scientists develop probabilistic models of mental capacities and evaluate their adequacy based on behavioural and neural data generated by humans (or other cognitive agents) performing a pertinent task. The overarching goal is to identify the mathematical principles, algorithmic procedures, and causal mechanisms that enable cognitive agents (...) to take uncertainty into account and weigh it appropriately in producing adaptive behaviour. -/- The appeal of Bayesian cognitive science derives from its transparency, normativity, and unifying power. Resources from Bayesian statistics allow cognitive scientists to characterise transparently, in terms of random variables and probabilistic dependencies between them, the inductive problems that many mental capacities are presumed to solve. Given this characterisation, researchers can define an upper bound on one’s performance on that problem and use it as a benchmark for interpreting experimental results, formulating hypotheses about why and when deviations from optimal performance should be expected, and seeking explanations of mental capacities within one encompassing theoretical framework. -/- Although critics question the testability, explanatory power, and plausibility of Bayesian models of mental capacities, Bayesian cognitive science has proved itself to be an incredibly fruitful research programme. Fuelled by empirical and theoretical results from psychology, neuroscience, philosophy, computer science, artificial intelligence, and evolution, Bayesian cognitive science has: advanced our understanding of why cognitive systems should keep track of uncertainty and use it to facilitate adaptive behaviour; clarified how the brain might encode information about the uncertainty of its stimuli and perform computations with probability distributions; and crystallised the insight that cognitive agents’ management of uncertainty might be grounded in predictive processes aimed at avoiding surprising exchanges with the environment. (shrink)

This paper concerns Warren Quinn’s famous “The Puzzle of the Self-Torturer.” I argue that even if we accept his assumption that practical rationality is purely instrumental such that what he ought to do is simply a function of how the relevant options compare to each other in terms of satisfying his actual preferences that doesn’t mean that every explanation as to why he shouldn’t advance to the next level must appeal to the idea that so advancing would be suboptimal in (...) terms of the satisfaction of his actual preferences. Rather, we can admit that his advancing would always be optimal, but argue that advancing isn’t always what he ought to do given that advancing sometimes fails to meet some necessary condition for being what he ought to do. For instance, something can be what he ought to do only if it’s an option for him. What’s more, something can be what he ought to do only if it’s something that he can do without responding inappropriately to his reasons—or, so, I argue. Thus, the solution to the puzzle is, I argue, to realize that, in certain circumstances, advancing is not what the self-torturer ought to do given that he can do so only by responding inappropriately to his reasons. (shrink)

Suboptimality of decision making needs no explanation. High level accounts of suboptimality in diverse tasks cannot add up to a mechanistic theory of perceptual decision making. Mental processes operate on the contents of information brought by the experimenter and the participant to the task, not on the amount of information in the stimuli without regard to physical and social context.

Standpoint logic is a recently proposed formalism in the context of knowledge integration, which advocates a multi-perspective approach permitting reasoning with a selection of diverse and possibly conflicting standpoints rather than forcing their unification. In this paper, we introduce nested sequent calculi for propositional standpoint logics---proof systems that manipulate trees whose nodes are multisets of formulae---and show how to automate standpoint reasoning by means of non-deterministic proof-search algorithms. To obtain worst-case complexity-optimal proof-search, we introduce a novel technique in the context (...) of nested sequents, referred to as "coloring," which consists of taking a formula as input, guessing a certain coloring of its subformulae, and then running proof-search in a nested sequent calculus on the colored input. Our technique lets us decide the validity of standpoint formulae in CoNP since proof-search only produces a partial proof relative to each permitted coloring of the input. We show how all partial proofs can be fused together to construct a complete proof when the input is valid, and how certain partial proofs can be transformed into a counter-model when the input is invalid. These "certificates" (i.e. proofs and counter-models) serve as explanations of the (in)validity of the input. (shrink)

Although logical consistency is desirable in scientific research, standard statistical hypothesis tests are typically logically inconsistent. To address this issue, previous work introduced agnostic hypothesis tests and proved that they can be logically consistent while retaining statistical optimality properties. This article characterizes the credal modalities in agnostic hypothesis tests and uses the hexagon of oppositions to explain the logical relations between these modalities. Geometric solids that are composed of hexagons of oppositions illustrate the conditions for these modalities to be (...) logically consistent. Prisms composed of hexagons of oppositions show how the credal modalities obtained from two agnostic tests vary according to their threshold values. Nested hexagons of oppositions summarize logical relations between the credal modalities in these tests and prove new relations. (shrink)

The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together (Gould and Lewontin, Proc Relig Soc Lond 205:581–598, 1979; Orzack and Sober, Am Nat 143(3):361–380, 1994). I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim (...) that selection is the only important influence on the evolutionary outcome in question and is thus linked to adaptationism. However, biologists seldom intend this strong use of optimality models. One common alternative that I term the weak use simply involves the claim that an optimality model accurately represents the role of selection in bringing about the outcome. This and other weaker uses of optimality models insulate the optimality approach from criticisms of adaptationism, and they account for the prominence of optimality modeling (broadly construed) in population biology. The centrality of these uses of optimality models ensures a continuing role for the optimality approach, regardless of the fate of adaptationism. (shrink)

This paper applies ideas and tools from social choice theory (such as Arrow's theorem and related results) to linguistics. Specifically, the paper investigates the problem of constraint aggregation in optimality theory from a social-choice-theoretic perspective.

This article examines two questions about scientists’ search for knowledge. First, which search strategies generate discoveries effectively? Second, is it advantageous to diversify search strategies? We argue pace Weisberg and Muldoon, “Epistemic Landscapes and the Division of Cognitive Labor”, that, on the first question, a search strategy that deliberately seeks novel research approaches need not be optimal. On the second question, we argue they have not shown epistemic reasons exist for the division of cognitive labor, identifying the errors that led (...) to their conclusions. Furthermore, we generalize the epistemic landscape model, showing that one should be skeptical about the benefits of social learning in epistemically complex environments. (shrink)

In this paper I examine the role of optimality reasoning in Aristotle’s natural science. By “optimality reasoning” I mean reasoning that appeals to some conception of “what is best” in order to explain why things are the way they are. We are first introduced to this pattern of reasoning in the famous passage at Phaedo 97b8-98a2, where (Plato’s) Socrates invokes “what is best” as a cause (aitia) of things in nature. This passage can be seen as the intellectual (...) ancestor of Aristotle’s own principle, expressed by the famous dictum “nature does nothing in vain but always what is best for the substance from among the possibilities concerning each kind of animal” (Progression of Animals II, 704b12-18). The paper is focused around exploring three questions that arise in connection with Aristotle’s use of this optimality principle: (1) How do we understand the concept of “the best” at work in the principle? (2) How does Aristotle conceive of “the range of possibilities”? And, finally, (3) what role does optimality reasoning play in Aristotle’s natural science? Is it a special form of demonstration in which the optimality principle functions as one of its premises, or is it a heuristic device that helps uncover those causally relevant features of a natural substances that ultimately serve as middle terms in demonstrations? In the final section I return to the comparison between Plato and Aristotle and argue that, while both see the natural world as the product of an optimizing agent and while both see this assumption as licensing a pattern of reasoning that appeals to a certain conception of “the best”, they disagree fundamentally over what the optimization agent is and how it operates. Thus, despite their general agreement, it would be a mistake to think that Aristotle simply took over Plato’s use of optimality reasoning without significant modifications. (shrink)

Assuming that votes are independent, the epistemically optimal procedure in a binary collective choice problem is known to be a weighted supermajority rule with weights given by personal log-likelihood-ratios. It is shown here that an analogous result holds in a much more general model. Firstly, the result follows from a more basic principle than expected-utility maximisation, namely from an axiom (Epistemic Monotonicity) which requires neither utilities nor prior probabilities of the ‘correctness’ of alternatives. Secondly, a person’s input need not be (...) a vote for an alternative, it may be any type of input, for instance a subjective degree of belief or probability of the correctness of one of the alternatives. The case of a profile of subjective degrees of belief is particularly appealing, since here no parameters such as competence parameters need to be known. (shrink)

There is a widely shared belief that the higher-level sciences can provide better explanations than lower-level sciences. But there is little agreement about exactly why this is so. It is often suggested that higher-level explanations are better because they omit details. I will argue instead that the preference for higher-level explanations is just a special case of our general preference for informative, logically strong, beliefs. I argue that our preference for informative beliefs entirely accounts for why higher-level (...) explanations are sometimes better—and sometimes worse—than lower-level explanations. The result is a step in the direction of the unity of science hypothesis. 1Introduction2Background: Is Omitting Details an Explanatory Virtue? 2.1Anti-reductionist arguments2.2Reductionist argument2.3Logical strength3Bases, Links and Logical Strength4Functionalism and Fodor’s Argument5Two Generalizations6Should the Base Really Be Maximally Strong?7Anti-reductionist Arguments Regarding the Base8Should the Antecedent of the Link Really Be Maximally Weak? (shrink)

I discuss the methodological passage in the begin- ning of Ethica Eudemia I.6 (1216b26-35), which has received attention in connection with Aristotle’s notion of dialectic and his methodology in Ethics. My central focus is not to discuss whether Aristotle is prescribing and using what has been called the method of endoxa. I will focus on how this passage coheres with the remaining parts of the same chapter, which also are advancing methodological remarks. My claim is that the meth- od of (...) Ethica Eudemia I.6 is in agreement with many features of Aristotle’s theory of explanation as presented in the Posterior Analytics: Aristotle’s main concern is a warning against misuses of explanatory arguments. (shrink)

This paper investigates conceptions of explanation, teleology, and analogy in the works of Immanuel Kant (1724-1804) and Georges Cuvier (1769-1832). Richards (2000, 2002) and Zammito (2006, 2012, 2018) have argued that Kant’s philosophy provided an obstacle for the project of establishing biology as a proper science around 1800. By contrast, Russell (1916), Outram (1986), and Huneman (2006, 2008) have argued, similar to suggestions from Lenoir (1989), that Kant’s philosophy influenced the influential naturalist Georges Cuvier. In this article, I wish to (...) expand on and further the work of Russell, Outram, and Huneman by adopting a novel perspective on Cuvier and considering (a) the similar conceptions of proper science and explanation of Kant and Cuvier, and (b) the similar conceptions of the role of teleology and analogy in the works of Kant and Cuvier. The similarities between Kant and Cuvier show, contrary to the interpretation of Richards and Zammito, that some of Kant’s philosophical ideas, whether they derived from him or not, were fruitfully applied by some life scientists who wished to transform life sciences into proper sciences around 1800. However, I also show that Cuvier, in contrast to Kant, had a workable strategy for transforming the life sciences into proper sciences, and that he departed from Kant’s philosophy of science in crucial respects. (shrink)

A standard way to challenge convergence-based accounts of inductive success is to claim that they are too weak to constrain inductive inferences in the short run. We respond to such a challenge by answering some questions raised by Juhl (1994). When it comes to predicting limiting relative frequencies in the framework of Reichenbach, we show that speed-optimal convergence—a long-run success condition—induces dynamic coherence in the short run.

Scientific explanations must bear the proper relationship to the world: they must depict what, out in the world, is responsible for the explanandum. But explanations must also bear the proper relationship to their audience: they must be able to create human understanding. With few exceptions, philosophical accounts of explanation either ignore entirely the relationship between explanations and their audience or else demote this consideration to an ancillary role. In contrast, I argue that considering an explanation’s communicative role (...) is crucial to any satisfactory account of explanation. (shrink)

It is not a particularly hard thing to want or seek explanations. In fact, explanations seem to be a large and natural part of our cognitive lives. Children ask why and how questions very early in development and seem genuinely to want some sort of answer, despite our often being poorly equipped to provide them at the appropriate level of sophistication and detail. We seek and receive explanations in every sphere of our adult lives, whether it be (...) to understand why a friendship has foundered, why a car will not start, or why ice expands when it freezes. Moreover, correctly or incorrectly, most of the time we think we know when we have or have not received a good explanation. There is a sense both that a given, successful explanation satisfies a cognitive need, and that a questionable or dubious explanation does not. There are also compelling intuitions about what make good explanations in terms of their form, that is, a sense of when they are structured correctly. (shrink)

Explanation is asymmetric: if A explains B, then B does not explain A. Tradition- ally, the asymmetry of explanation was thought to favor causal accounts of explanation over their rivals, such as those that take explanations to be inferences. In this paper, we develop a new inferential approach to explanation that outperforms causal approaches in accounting for the asymmetry of explanation.

This paper presents and argues for an account of objectual understanding that aims to do justice to the full range of cases of scientific understanding, including cases in which one does not have an explanation of the understood phenomenon. According to the proposed account, one understands a phenomenon just in case one grasps a sufficiently accurate and comprehensive model of the ways in which it or its features are situated within a network of dependence relations; one’s degree of understanding is (...) proportional to the comprehensiveness and accuracy of such a model. I compare this account with accounts of scientific understanding that explicate understanding in terms of having an explanation of the understood phenomenon. I discuss three distinct types of cases in which scientific understanding does not amount to possessing an explanation of any kind, and argue that the proposed model-based account can accommodate these cases while still retaining a strong link between understanding and explanation. (shrink)

Philippe Huneman has recently questioned the widespread application of mechanistic models of scientific explanation based on the existence of structural explanations, i.e. explanations that account for the phenomenon to be explained in virtue of the mathematical properties of the system where the phenomenon obtains, rather than in terms of the mechanisms that causally produce the phenomenon. Structural explanations are very diverse, including cases like explanations in terms of bowtie structures, in terms of the topological properties of (...) the system, or in terms of equilibrium. The role of mathematics in bowtie structured systems and in topologically constrained systems has recently been examined in different papers. However, the specific role that mathematical properties play in equilibrium explanations requires further examination, as different authors defend different interpretations, some of them closer to the new-mechanistic approach than to the structural model advocated by Huneman. In this paper, we cover this gap by investigating the explanatory role that mathematics play in Blaser and Kirschner’s nested equilibrium model of the stability of persistent long-term human-microbe associations. We argue that their model is explanatory because: i) it provides a mathematical structure in the form of a set of differential equations that together satisfy an ESS; ii) that the nested nature of the ESSs makes the explanation of host-microbe persistent associations robust to any perturbation; iii) that this is so because the properties of the ESS directly mirror the properties of the biological system in a non-causal way. The combination of these three theses make equilibrium explanations look more similar to structural explanations than to causal-mechanistic explanation. (shrink)

To be optimistic, it is standardly assumed, is to have positive expectations. I here argue that this definition is correct but captures only one variety of optimism – here called factual optimism. It leaves out two other important varieties of optimism. The first – focal optimism – corresponds to the idea of seeing the glass half full. The second – axiological optimism – consists in the view that good is stronger than bad. Those three varieties of optimism are irreducible to (...) each other and do not belong to a common kind. I define each of these, characterize their respective correctness conditions, and contrast hope with optimism. (shrink)

[This paper is available as open access from the publisher.] Normative theories aim to explain why things have the normative features they have. This paper argues that, contrary to some plausible existing views, one important kind of normative explanations which first-order normative theories aim to formulate and defend can fail to transmit downward along chains of metaphysical determination of normative facts by non-normative facts. Normative explanation is plausibly subject to a kind of a justification condition whose satisfaction may fail (...) to transmit along the relevant kind of metaphysical hierarchy. A broader aim of the paper is to contribute to systematic theorizing about normative explanation: whereas there has been a great deal of work on scientific explanation, there has been little by way of systematic exploration of what sort of explanations normative theories aim to formulate and defend. (shrink)

Normative explanations of why things are wrong, good, or unfair are ubiquitous in ordinary practice and normative theory. This paper argues that normative explanation is subject to a justification condition: a correct complete explanation of why a normative fact holds must identify features that would go at least some way towards justifying certain actions or attitudes. I first explain and motivate the condition I propose. I then support it by arguing that it fits well with various theories of normative (...) reasons, makes good sense of certain legitimate moves in ordinary normative explanatory discourse, and helps to make sense of our judgments about explanatory priority in certain cases of normative explanation. This last argument also helps to highlight respects in which normative explanation won’t be worryingly discontinuous with explanations in other domains even though these other explanations aren’t subject to the justification condition. Thus the paper aims not only to do some constructive theorizing about the relatively neglected topic of normative explanation but also to cast light on the broader question of how normative explanation may be similar to and different from explanations in other domains. (shrink)

A compelling idea holds that reality has a layered structure. We often disagree about what inhabits the bottom layer, but we agree that higher up we find chemical, biological, geological, psychological, sociological, economic, /etc./, entities: molecules, human beings, diamonds, mental states, cities, interest rates, and so on. How is this intuitive talk of a layered structure of entities to be understood? Traditionally, philosophers have proposed to understand layered structure in terms of either reduction or supervenience. But these traditional views face (...) well-known problems. A plausible alternative is that layered structure is to be explicated by appeal to explanations of a certain sort, termed / grounding explanations/. Grounding explanations tell us what obtains in virtue of what. Unfortunately, the use of grounding explanations to articulate the layered conception faces a problem, which I call /the collapse/. The collapse turns on the question of how to ground the facts stated by the explanations themselves. In this paper I make a suggestion about how to ground explanations that avoids the collapse. Briefly, the suggestion is that the fact stated by a grounding explanation is grounded in its /explanans/. (shrink)

Humeanism about laws of nature — the view that the laws reduce to the Humean mosaic — is a popular view, but currently existing versions face powerful objections. The non-supervenience objection, the non-fundamentality objection and the explanatory circularity objection have all been thought to cause problems for the Humean. However, these objections share a guiding thought — they are all based on the idea that there is a certain kind of divergence between the practice of science and the metaphysical picture (...) suggested by Humeanism. -/- I suggest that the Humean can respond to these objections not by rejecting this divergence, but by arguing that is appropriate. In particular the Humean can, in the spirit of Loewer (2012), distinguish between scientific and metaphysical explanation — this is motivated by differing aims of explanation in science and metaphysics. And they can further leverage this into distinctions between scientific and metaphysical fundamentality and scientific and metaphysical possibility. We can use these distinctions to respond to the objections that the Humean faces. (shrink)

This paper examines explanations that turn on non-local geometrical facts about the space of possible configurations a system can occupy. I argue that it makes sense to contrast such explanations from "geometry of motion" with causal explanations. I also explore how my analysis of these explanations cuts across the distinction between kinematics and dynamics.

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...) the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly. (shrink)

Genetic explanations of religious belief, such as Freud’s analysis of theism as ‘a neurotic relic’, pose a problem for theists: how far do such explanations establish the irrationality of religious belief? I argue that genetic analyses of belief suffer from a number of limitations. Showing that some reason-irrelevant factor or factors were sufficient to produce conviction on some occasion would not establish that they were necessary in every case of religious conviction. Showing that reason-irrelevant factors were both necessary (...) and sufficient to produce conviction would not establish that reason-relevant factors were entirely absent. And showing that reason-relevant factors played no role in the adoption of the belief would not establish that they were absent in its continued retention. I conclude that we will profitably investigate the plausibility of religious beliefs by attending to the reasons that can be given for or against them rather than by speculating about their causes. (shrink)

The theories of pre-existence and epigenesis are typically taken to be opposing theories of generation in the seventeenth and eighteenth centuries. One can be a pre-existence theorist only if one does not espouse epigenesis and vice versa. It has also been recognized, however, that the line between pre-existence and epigenesis in the nineteenth century, at least, is considerably less sharp and clear than it was in earlier centuries. The debate (1759-1777) between Albrecht von Haller and Caspar Friedrich Wolff on their (...) theories of generation is usually taken to be a debate between a pre-existence theorist and an epigeneticist, and the supposed fact that these two theories of generation are mutually exclusive explains (so the story goes) the divide between Haller and Wolff. However, it’s not clear that Haller endorsed an especially robust form of pre-existence, and nor is it clear that Wolff’s theory of generation -- once he considered it carefully -- is clear epigenetic. Rather, Haller’s theory of generation is marked by traces of epigensis and Wolff’s theory has elements of pre-existence theory. This is not to say that their theories of generation are basically the same, but the debate between the two ought not to be framed in terms of pre-existence versus epigenesis. Their points of difference must be explained in some other way. In this way, their controversy bears characteristics of similar disputes over generation in the nineteenth century more than it resembles those in the seventeenth century. In this paper, I argue that (a) Haller’s and Wolff’s theories both blend elements from pre-existence and epigenesis; (b) but there are still deeply-rooted differences between the two generation theories; (c) one source of these differences is that Haller and Wolff have divergent conceptions of what an adequate explanation is; (d) we can see that they have different conceptions of what constitutes an adequate explanation by paying heed to their evaluations of Descartes’ epistemology and methodology in his theory of generation (such as it is); and (e) this shows that one of the main differences between Haller’s generation theory and that of Wolff is the degree to which each thinks we need to (and indeed can) explain the nature of the causes -- both efficient and final -- at play during the formation of organic beings. (shrink)

It is often thought that metaphysical grounding underwrites a distinctive sort of metaphysical explanation. However, it would be a mistake to think that all metaphysical explanations are underwritten by metaphysical grounding. In service of this claim, I offer a novel kind of metaphysical explanation called metaphysical explanation by constraint, examples of which have been neglected in the literature. I argue that metaphysical explanations by constraint are not well understood as grounding explanations.

We consider several natural fragments of the alternating-time temporal logics ATL* and ATL with restrictions on the nesting between temporal operators and strategic quantifiers. We develop optimal decision procedures for satisfiability in these fragments, showing that they have much lower complexities than the full languages. In particular, we prove that the satisfiability problem for state formulae in the full `strategically flat' fragment of ATL* is PSPACE-complete, whereas the satisfiability problems in the flat fragments of ATL and ATL$^{+}$ are $\Sigma^P_3$-complete. We (...) note that the nesting hierarchies for fragments of ATL* collapse in terms of expressiveness above nesting depth 1, hence our results cover all such fragments with lower complexities. (shrink)

Some properties are causally relevant for a certain effect, others are not. In this paper we describe a problem for our understanding of this notion and then offer a solution in terms of the notion of a program explanation.