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Many of the most interesting cognitive feats that humans perform require us to consider not just the things that actually occur but also alternative possibilities. We often do this explicitly (e.g., when imagining precisely how a first date could have gone better), but other times we do it spontaneously and implicitly (e.g., when thinking, "I have to catch this bus," implying bad alternatives if the bus is not caught). A growing body of research has identified a core set of neural processes involved in explicit, episodic counterfactual thinking. Little is known, however, about the processes supporting the spontaneous, possibly implicit representation of alternatives. To make progress on this question, we induced participants to spontaneously generate counterfactual alternatives by asking them to judge whether agents were forced to make a particular choice or chose freely—a judgment that implicitly depends on their alternative options. Using functional magnetic resonance imaging, we found 14 clusters that were preferentially engaged when participants were making force judgments (which elicit the spontaneous consideration of alternatives) compared to judgments of what actually occurred (which do not elicit alternatives). These clusters were widely distributed throughout the brain, including in the bilateral prefrontal cortex, bilateral inferior parietal lobule, bilateral middle and inferior temporal gyri, bilateral posterior cingulate, and bilateral caudate. In many of these regions, we additionally show that variability in the neural signal correlates with trial-by-trial variability in participants' force judgments. Our findings provide a first characterization of the neural substrates of the spontaneous representation of counterfactual alternatives. Public Significance Statement: Many of the most interesting cognitive feats that humans perform require us to consider not just the things that actually occur but also alternative possibilities. We often do this explicitly (e.g., when imagining precisely how a first date could have gone better), but other times we do it spontaneously and implicitly (e.g., when thinking, "I have to catch this bus," implying bad alternatives if the bus is not caught). Little is known about how the brain engages in this type of spontaneous and often implicit counterfactual thought. In this study, we use functional neuroimaging to identify a set of brain regions that are preferentially engaged when spontaneously considering counterfactual alternatives. [ABSTRACT FROM AUTHOR]

Writing a book can feel like a solitary endeavor. You labor for (in my case) years, sometimes talking about parts of the project with others, but mostly toiling alone to work out the consequences of commitments you made months and years prior. I'm grateful for the opportunity to engage with three brilliant interlocutors about these ideas, which for so long seemed to matter to no one besides myself (and maybe my publisher). [ABSTRACT FROM AUTHOR]

People often think about how things could have been better or worse. People make these upward and downward comparisons in different situations and with differing emotional consequences. We investigated whether the direction of counterfactual comparisons affects people’s judgements of counterfactual closeness. In four preregistered experiments (N = 2,142), participants saw vignettes where agents lost or won a luck-based game. In Experiments 1, 2, and 4, participants judged counterfactual closeness in two ways: if a counterfactual outcome almost happened, and if it easily could have happened. These judgments were affected by different factors, but did not substantially differ based on the direction of comparison. In Experiments 3 and 4, participants predicted agents’ emotions – whether losers would be sad, winners would be happy, and whether both would be surprised by the outcome. Emotion predictions showed similar patterns regardless of whether agents lost or won. Participants predicted stronger emotional reactions when the prior probability of the counterfactual outcome was high rather than low, though this effect was somewhat stronger when agents lost. Together, these findings join recent work in suggesting that Almost and Easily judgments tap into distinct forms of counterfactual closeness, and also suggest this distinction is robust to the direction of counterfactual reasoning. [ABSTRACT FROM AUTHOR]

The system‐property or 'cybernetic' theory of goals and goal‐directedness became popular in the twentieth century. It is a theory that has reductionist and behaviourist roots. There are reasons to think that the system‐property theory needs to be formulated in terms of counterfactuals. However, it proves to be difficult to formulate a counterfactual analysis of goal‐directedness that is counterexample‐free, non‐circular, and non‐trivial. These difficulties closely mirror those facing reductionists about dispositions, though the parallels between the two debates have been overlooked in the literature. After outlining those parallels, the paper considers what goal theorists might learn from the dispositions debate. In particular, the paper discusses the need for a realist, non‐reductionist account of goal‐directedness, and explores the idea that properties of goal‐directedness are themselves dispositions or 'powers' of a certain sort. [ABSTRACT FROM AUTHOR]

Richard Dawkins has suggested that the explananda of Darwinian explanations are the existence and character of entities that actively work at making a living. This paper discusses three issues related to this suggestion. First, it offers an account of what it is to actively work at making a living in terms of counterfactuals. Second, the paper articulates why it is that the existence and character of entities that actively work at making a living might require a different kind of explanation than those on offer in the sciences of physics and chemistry. Finally, it discusses why it is that it seems appropriate to use teleological locutions in the description of the behavior, structure, and metabolism of entities that actively work at making a living. [ABSTRACT FROM AUTHOR]

The present paper aims at presenting how the possible world framework can function for fictional worlds. It starts from presenting different perspectives on how fictional universes can be understood. It then grounds its reconstructive approach in modal logic theories, such as the ones postulated by Kripke and Lewis, attempting to find a possible world system which can be adapted and become custom-fit for fiction. The theoretical approach favorizes re-creations of the modal system for fiction, as presented by Marie-Laure Ryan or Ruth Ronen. The study of this particular topic is eclectic, putting together various perspectives on literature, fiction and modality. The theoretical input is accompanied by practical reverberations from the three novels presented in the title, which function as its scientific playground. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, which spanned much of 2020–2023 and beyond, daily case and death counts were recorded globally. In this study, we examined available mortality counts and associated case counts, with a focus on the estimation missing information related to age distributions. In this paper, we explored a model-based paradigm for generating age distributions of mortality counts in a spatio-temporal context. We pursued this aim by employing Bayesian spatio-temporal lagged dependence models for weekly mortality at the county level. We compared three US states at the county level: South Carolina (SC), Ohio, and New Jersey (NJ). Models were developed for mortality counts using Bayesian spatio-temporal constructs, incorporating both dependence on current and cumulative case counts and lagged dependence on previous deaths. Age dependence was predicted based on total deaths in proportion to population estimates. This latent age field was generated as counterfactuals and then compared to observed deaths within age groups. The optimal retrospective space–time models for weekly mortality counts were those with lagged dependence and a function of caseload. Added random effects were found to vary across states: Ohio favored a spatially correlated model, while SC and NJ favored a simpler formulation. The generation of age-specific latent fields was performed for SC only and compared to a 15-month, 13-county data set of observed >65 age population. It is possible to model spatio-temporal variations in mortality at the county level with lagged dependencies, spatial effects, and case dependencies. In addition, it is also possible to generate latent age-specific fields based on estimates of death risk (using population proportions or more sophisticated modeling approaches). More detailed data will be needed to make more calibrated comparisons for future epidemic monitoring. The proposed discrepancy tool could serve as a useful resource for public health planners in tailoring interventions during epidemic situations. [ABSTRACT FROM AUTHOR]

Justin Khoo [2022. The Meaning of If . Oxford: Oxford University Press.] presents a novel theory of conditionals designed to solve two prominent puzzles for conditionals, the so-called ‘bounding puzzles’. One component of Khoo's solution is to propose a new update rule for learning conditionals that allows for violations of classical conditionalization. In this paper, I will defend classical conditionalization. My proposal is that one can achieve a similar effect as Khoo's new update rule by putting to work the epistemic norms that govern assertion and belief. [ABSTRACT FROM AUTHOR]

This article uses the interventionist theory of causation, a counterfactual theory taken from philosophy of science, to strengthen causal analysis in process tracing research. Causal claims from process tracing are re-expressed in terms of so-called hypothetical interventions, and concrete evidential tests are proposed which are shown to corroborate process tracing claims. In particular, three steps are prescribed for an interventionist investigation, and each step in turn is shown to make the causal analysis more robust, amongst others by disambiguating causal claims and clarifying or strengthening the existing methodological advice on counterfactual analysis. The article's claims are then illustrated using a concrete example, Haggard and Kaufman's analysis of the Argentinian transition to democracy. It is shown that interventionism could have strengthened the authors' conclusions. The article concludes with a short Bayesian analysis of its key methodological proposals. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, an extraordinary phenomenon is observed: the GAN model is more sensitive to the amount of training data for RS image generation than for natural image generation (Fig. 1). In other words, the generation quality of RS images will change significantly with the number of training categories or samples per category. In this paper, we first analyze this phenomenon from two kinds of toy experiments and conclude that the amount of feature information contained in the GAN model decreases with reduced training data (Fig. 2). Then we establish a structural causal model (SCM) of the data generation process and interpret the generated data as the counterfactuals. Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information. This provides insights for enriching the feature information learned by the GAN model during training. Consequently, we propose two innovative adjustment schemes, namely uniformity regularization and entropy regularization, to increase the information learned by the GAN model at the distributional and sample levels, respectively. Extensive experiments on eight RS datasets and three natural datasets show the effectiveness and versatility of our methods. The source code is available at https://github.com/rootSue/Causal-RSGAN. [ABSTRACT FROM AUTHOR]

This is a reply to Frank Harvey's critique of my article last year in JSS, which was titled: 'Rethinking Gore-War: Counterfactuals and the 2003 Iraq War'. This is part of an exchange between Professor Harvey and me over counterfactual methodologies and the Iraq War. [ABSTRACT FROM AUTHOR]

This paper offers a constructivist critique of Frank Harvey's 'Gore-War' counterfactual, in which he argues that the hypothetical President Al Gore also would have gone to war with Iraq. Harvey overlooks how the George W. Bush administration shaped the structural context in which it acted in ways that made war increasingly likely. I trace two key phases in the road to war in which this dynamic occurred: 1. The half-year after 9/11 in which Bush established Iraq as the centerpiece of his response to terrorism. 2. The period from fall 2002 to early 2003 in which Bush pursued the strategy of 'coercive diplomacy' in a manner that all but predetermined the failure of inspections. Using historical evidence about the views of Gore, his likely advisors, and the Democratic policy establishment, I argue for the plausibility of the 'Gore-Peace' counterfactual in which President Gore shaped the context of decision-making on Iraq differently than Bush, prioritized Iraq less, and thereby avoided generating pressure or momentum for war. I conclude with reflections on this argument's implications for counterfactual methodology, historiography, and policy. [ABSTRACT FROM AUTHOR]

The best empirical research in political science clearly defines substantive parameters of interest, presents a set of assumptions that guarantees their identification, and uses an appropriate estimator. We argue for the importance of explicitly integrating rigorous theory into this process and focus on the advantages of doing so. By integrating a theoretical structure into one's empirical strategy, researchers can quantify the effects of competing mechanisms, consider the ex-ante effects of new policies, extrapolate findings to new environments, estimate model-specific theoretical parameters, evaluate the fit of a theoretical model, and test competing models that aim to explain the same phenomena. As a guide to such a methodology, we provide an overview of structural estimation, including formal definitions, implementation suggestions, examples, and comparisons to other methods. [ABSTRACT FROM AUTHOR]

People judge causation and attribute responsibility by simulating counterfactual alternatives. The counterfactual simulation model (CSM) captures people's causal judgments about physical events and responsibility judgments about social events. In the physical domain, the CSM predicts people's judgments about dynamic collision events, about omissive causes, and about physical support. People spontaneously engage in counterfactual simulation when making causal judgments, as evidenced by their eye-movements. In the social domain, the CSM predicts people's responsibility judgments in helping and hindering scenarios. How do people make causal judgments and assign responsibility? In this review article, I argue that counterfactual simulations are key. To simulate counterfactuals, we need three ingredients: a generative mental model of the world, the ability to perform interventions on that model, and the capacity to simulate the consequences of these interventions. The counterfactual simulation model (CSM) uses these ingredients to capture people's intuitive understanding of the physical and social world. In the physical domain, the CSM predicts people's causal judgments about dynamic collision events, complex situations that involve multiple causes, omissions as causes, and causes that sustain physical stability. In the social domain, the CSM predicts responsibility judgments in helping and hindering scenarios. [ABSTRACT FROM AUTHOR]

Everything that happens has a multitude of causes, but people make causal judgments effortlessly. How do people select one particular cause (e.g., the lightning bolt that set the forest ablaze) out of the set of factors that contributed to the event (the oxygen in the air, the dry weather ...)? Cognitive scientists have suggested that people make causal judgments about an event by simulating alternative ways things could have happened. We argue that this counterfactual theory explains many features of human causal intuitions, given two simple assumptions. First, people tend to imagine counterfactual possibilities that are both a priori likely and similar to what actually happened. Second, people judge that a factor C caused effect E if C and E are highly correlated across these counterfactual possibilities. In a reanalysis of existing empirical data, and a set of new experiments, we find that this theory uniquely accounts for people's causal intuitions. [ABSTRACT FROM AUTHOR]

Recent years have seen increasing interest in interventionist analyses of metaphysical explanation. One area where interventionism traditionally shines, is in providing an account of explanatory depth; the sense in which explanation comes in degrees. However, the literature on metaphysical explanation has left the notion depth almost entirely unexplored. In this paper I shall attempt to rectify this oversight by motivating an interventionist analysis of metaphysical explanatory depth (MED), in terms of the range of interventions under which a metaphysically explanatory generalization remains invariant. After elucidating the notion through a toy-example, I demonstrate the important work which MED can perform in characterizing debate within contemporary metaphysics. Focusing upon rival approaches to explaining the identity and distinctness of concrete objects, I argue that the progress achieved in this debate can be characterized in terms of increasing explanatory depth. Having made an initial case for the utility of MED, I then turn this analysis to the metaphysics of explanation itself. By adopting an interventionist framework with respect to MED, I will show that we can assess the depth of competing theories of explanation. This application has two interesting results: first, it suggests that an interventionist analysis of explanation provides deeper explanations of the connection between explanans and explanandum than rival accounts; and second, it suggests that explanations provided by interventionism become deeper still, if one accepts that this methodology ranges over metaphysical, as well as causal, instances. [ABSTRACT FROM AUTHOR]

As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework that addresses when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with the many examples that have been presented, leading some to suggest that the notion of harm should be abandoned and “replaced by more well-behaved notions”. As harm is generally something that is caused, most of these definitions have involved causality at some level. Yet surprisingly, none of them makes use of causal models and the definitions of actual causality that they can express. In this paper, which is an expanded version of the conference paper Beckers et al. (Adv Neural Inform Process Syst 35:2365–2376, 2022), we formally define a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality. The key features of our definition are that it is based on contrastive causation and uses a default utility to which the utility of actual outcomes is compared. We show that our definition is able to handle the examples from the literature, and illustrate its importance for reasoning about situations involving autonomous systems. [ABSTRACT FROM AUTHOR]

In the field of eXplainable Artificial Intelligence (XAI), the generation of counterfactuals is a promising method for human-interpretable explanations. A counterfactual explanation describes a causal situation in the form: “If X had not occurred, Y would not have occurred”. In this work, we study the generation of visual counterfactuals in the latent space for deep learning image classification models. We explore how to adapt the training environment to facilitate the generation of counterfactuals, combining ideas coming from different fields such as multitasking or generative learning, with the aim of developing more interpretable models. We study well-known counterfactual methods and how to apply them in the latent space. Furthermore, we propose a new way of generating counterfactuals working in the latent space and compare it with the other studied approaches, achieving competitive results. [ABSTRACT FROM AUTHOR]

This is the second part of a two-part series on the logic of hyperlogic, a formal system for regimenting metalogical claims in the object language (even within embedded environments). Part A provided a minimal logic for hyperlogic that is sound and complete over the class of all models. In this part, we extend these completeness results to stronger logics that are sound and complete over restricted classes of models. We also investigate the logic of hyperlogic when the language is enriched with hyperintensional operators such as counterfactual conditionals and belief operators. [ABSTRACT FROM AUTHOR]

Embodied cognition theories propose that language comprehension triggers a sensorimotor system in the brain. However, most previous research has paid much attention to concrete and factual sentences, and little emphasis has been put on the research of abstract and counterfactual sentences. The primary challenges for embodied theories lie in elucidating the meanings of abstract and counterfactual sentences. The most prevalent explanation is that abstract and counterfactual sentences are grounded in the activation of a sensorimotor system, in exactly the same way as concrete and factual ones. The present research employed a dual-task experimental paradigm to investigate whether the embodied meaning is activated in comprehending action-related abstract Chinese counterfactual sentences through the presence or absence of action-sentence compatibility effect (ACE). Participants were instructed to read and listen to the action-related abstract Chinese factual or counterfactual sentences describing an abstract transfer word towards or away from them, and then move their fingers towards or away from them to press the buttons in the same direction as the motion cue of the transfer verb. The action-sentence compatibility effect was observed in both abstract factual and counterfactual sentences, in line with the embodied cognition theories, which indicated that the embodied meanings were activated in both action-related abstract factuals and counterfactuals. [ABSTRACT FROM AUTHOR]

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Recent years have witnessed the rise of accurate but obscure classification models that hide the logic of their internal decision processes. Explaining the decision taken by a black-box classifier on a specific input instance is therefore of striking interest. We propose a local rule-based model-agnostic explanation method providing stable and actionable explanations. An explanation consists of a factual logic rule, stating the reasons for the black-box decision, and a set of actionable counterfactual logic rules, proactively suggesting the changes in the instance that lead to a different outcome. Explanations are computed from a decision tree that mimics the behavior of the black-box locally to the instance to explain. The decision tree is obtained through a bagging-like approach that favors stability and fidelity: first, an ensemble of decision trees is learned from neighborhoods of the instance under investigation; then, the ensemble is merged into a single decision tree. Neighbor instances are synthetically generated through a genetic algorithm whose fitness function is driven by the black-box behavior. Experiments show that the proposed method advances the state-of-the-art towards a comprehensive approach that successfully covers stability and actionability of factual and counterfactual explanations. [ABSTRACT FROM AUTHOR]

The debate about the characteristics and advantages of quantitative and qualitative methods is decades old. In their seminal monograph, A Tale of Two Cultures (2012, ATTC), Gary Goertz and James Mahoney argue that methods and research design practices for causal inference can be distinguished as two cultures that systematically differ from each other along 25 specific characteristics. ATTC's stated goal is a description of empirical patterns in quantitative and qualitative research. Yet, it does not include a systematic empirical evaluation as to whether the 25 are relevant and valid descriptors of applied research. In this paper, we derive five observable implications from ATTC and test the implications against a stratified random sample of 90 qualitative and 90 quantitative articles published in six journals between 1990–2012. Our analysis provides little support for the two-cultures hypothesis. Quantitative methods are largely implemented as described in ATTC, whereas qualitative methods are much more diverse than ATTC suggests. While some practices do indeed conform to the qualitative culture, many others are implemented in a manner that ATTC characterizes as constitutive of the quantitative culture. We find very little evidence for ATTC's anchoring of qualitative research with set-theoretic approaches to empirical social science research. The set-theoretic template only applies to a fraction of the qualitative research that we reviewed, with the majority of qualitative work incorporating different method choices. [ABSTRACT FROM AUTHOR]

Previous theories have established the mental model activation of processing different types of conditionals, stating that counterfactual conditionals expressing events that contradict known facts (e.g., "If it had rained, then they would not go to the park.") are considered to trigger two mental models: (1) a hypothetical but factually wrong model (e.g., "rain" and "did not go to the park") and (2) a corresponding real-world model (e.g., "did not rain" and "went to the park"). This study aimed to investigate whether pragmatic factors differentially influence readers' comprehension and distinction between counterfactual and hypothetical conditional sentences in Mandarin Chinese. Participants were required to read and judge the comprehensibility of Chinese hypothetical and counterfactual conditionals, which were different in temporal indicators (past vs. future temporal indicators) in the antecedent. Different polarities (with vs. without negators) and different moving directions (different directional verbs: lai2 [come] vs. qu4 [go]) in the consequent were also manipulated. Linear mixed-effects models (LMEM) revealed that hypothetical conditionals (with future temporal indicators) were more comprehensible than counterfactual conditionals (with past temporal indicators). The semantic similarities within the subordinate clause revealed future temporal indicators had higher lexical–semantic co-occurrence than past indicators, suggesting that temporal indicators impact comprehension partly through lexical semantics in the premise, and hypothetical conditionals are more easily processed. However, the semantic similarity analysis of the main and the subordinate clauses showed no effect of temporal indicators, suggesting that lexical–semantic co-occurrence across clauses may not substantially contribute to the distinction between hypothetical conditionals and counterfactual conditionals. In conclusion, this study offers insights into the comprehension of Chinese conditional sentences by shedding light on the pragmatic factors influencing the activation of different mental models. [ABSTRACT FROM AUTHOR]

Counterfactual explanations modify the feature values of an instance in order to alter its prediction from an undesired to a desired label. As such, they are highly useful for providing trustworthy interpretations of decision-making in domains where complex and opaque machine learning algorithms are utilized. To guarantee their quality and promote user trust, they need to satisfy the faithfulness desideratum, when supported by the data distribution. We hereby propose a counterfactual generation algorithm for mixed-feature spaces that prioritizes faithfulness through k-justification, a novel counterfactual property introduced in this paper. The proposed algorithm employs a graph representation of the search space and provides counterfactuals by solving an integer program. In addition, the algorithm is classifier-agnostic and is not dependent on the order in which the feature space is explored. In our empirical evaluation, we demonstrate that it guarantees k-justification while showing comparable performance to state-of-the-art methods in feasibility, sparsity, and proximity. [ABSTRACT FROM AUTHOR]

Possible Worlds for Fiction. A Reconstructive Approach, With a Practical Outlook into Ian McEwan’s Atonement, On Chesil Beach and The Child in Time. The present paper aims at presenting how the possible world framework can function for fictional worlds. It starts from presenting different perspectives on how fictional universes can be understood. It then grounds its reconstructive approach in modal logic theories, such as the ones postulated by Kripke and Lewis, attempting to find a possible world system which can be adapted and become custom-fit for fiction. The theoretical approach favorizes re-creations of the modal system for fiction, as presented by Marie-Laure Ryan or Ruth Ronen. The study of this particular topic is eclectic, putting together various perspectives on literature, fiction and modality. The theoretical input is accompanied by practical reverberations from the three novels presented in the title, which function as its scientific playground. REZUMAT. Lumile posibile și domeniul ficțional. O abordare reconstructivă cu aplicații practice în romanele lui Ian McEwan Ispășire, Pe plaja Chesil și Copilul furat. Prezenta lucrare își propune să prezinte modul în care sistemul lumilor posibile poate fi aplicat și lumilor ficționale. Demersul de cercetare pornește de la prezentarea unor perspective diverse asupra modului în care lumile ficționale pot fi înțelese. Apoi fixează abordarea reconstructivă în teoriile logicii modale, așa cum apar ele prezentate de Kripke sau Lewis, încercând să găsească un cadru modal care să poată fi adaptat și să fie relevant pentru studiul ficțiunii. Abordarea propusă favorizează re-creații ale sistemului modal pentru domeniul ficțional, cum ar fi teoriile lui Marie-Laure Ryan sau ale lui Ruth Ronen. Viziunea teoretică este una eclectică, analizând diverse perspective asupra literaturii, ficțiunii și modalității. Perspectivele teoretice sunt însoțite de exemplificări practice din cele trei romane expuse în titlu, acestea funcționând ca un ideal teritoriu practic. Cuvinte-cheie: lumi posibile, semantica lumilor posibile, ficțiune, lumi ficționale, ficțiune postmodernă, entități ficționale, logică modală Article history: Received 11 June 2024; Revised 25 October 2024; Accepted 15 September 2024; Available online 10 December 2024; Available print 30 December 2024.

This paper illuminates a typically obscured ground for rectificatory obligations: harms justified as 'lesser evils.' Lesser-evil harms are not the result of overall morally prohibited acts but of acts permissibly carried out to prevent significantly greater harm. The paper argues that harms caused as unintended side effects of acting on lesser-evil justifications, notably in military rescue operations, may give rise to claims to compensation, even if (1) the military acts that caused the harms in question were justified on lesser-evil grounds and (2) the victims in question are no worse off as a result; they may even owe their survival to the act of rescue. The paper defends three claims. First, being better off as a result of a harmful rescue than one would otherwise have been does not preclude claims to be compensated for harms suffered as a side effect. Second, identifying the relevant counterfactual for purposes of compensatory justice is sometimes a prescriptive, rather than a descriptive, matter. Rather than relying on empirical speculations about what would have happened had a harm not occurred, we must, in certain cases, consider what agents ought to have done. Finally, duties of compensation need not fall on those who caused the to-be-compensated harms. That infringing rights is permissible in certain cases does not imply that no compensation is owed, but merely that it is not necessarily rights-infringers on whom duties of compensation fall. [ABSTRACT FROM AUTHOR]

This paper discusses a puzzle raised by Sharon Berry, published in Analysis. The question in the background of this puzzle is how we should deal with seemingly plausible possibility judgements that commit us to counterfactual truths that find no basis in reality. Three answers to this question and their corresponding solutions to the puzzle will be discussed. The last answer provides a way to make sense of the claim that it is in some cases indeterminate whether a proposition is possible and offers an account of indeterminate modal status. [ABSTRACT FROM AUTHOR]

The theist needs a conception of the distinction between doing and allowing because much of the literature focused on the problem of evil attempts to justify (via theodicy) or defend (via defence) God's allowing evil to occur. I present a counterfactual account of the doing/allowing distinction in the divine context and argue that, even if there are compelling objections to counterfactual accounts of the distinction in the human context, they do not work against such an account in the divine context. The counterfactual analysis to follow will allow the theist to plausibly claim that God does not ever bring about evil, which is crucial to some defences against the problem of evil. I conclude by defending my account against possible objections. [ABSTRACT FROM AUTHOR]

A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of prediction only. The component of predictive analytics concerned with interpreting the internals of the models and explaining their predictions for individual cases to stakeholders has largely been neglected. Additionally, works that attempt to employ data-driven prescriptive analytics to automatically generate evidence-based remedial advice for at-risk learners are in their infancy. eXplainable AI is a field that has recently emerged providing cutting-edge tools which support transparent predictive analytics and techniques for generating tailored advice for at-risk students. This study proposes a novel framework that unifies both transparent machine learning as well as techniques for enabling prescriptive analytics, while integrating the latest advances in large language models for communicating the insights to learners. This work demonstrates a predictive modelling framework for identifying learners at risk of qualification non-completion based on a real-world dataset comprising ∼ 7000 learners with their outcomes, covering 2018 - 2022. The study further demonstrates how predictive modelling can be augmented with prescriptive analytics on two case studies to generate human-readable prescriptive feedback for those who are at risk using ChatGPT. [ABSTRACT FROM AUTHOR]

This paper is concerned with uses of certain morphemes, most notably the past, to represent meanings of distance from reality in modal expressions. This class of morphology has been identified with the names subjunctive, fake tense, fake past, modal past and is referred to here as X-marking, after von Fintel and Iatridou (Linguist Philos, 2020). X-marking has been most studied in the context of English conditionals however, it is well-known that the morphology is observed in many non-English languages and can appear in various other types of constructions, including counterfactual desire expressions. I motivate two desiderata for theories of X-marking in pursuit of an analysis that unifies the phenomenon across expression types and languages. I then develop a novel, formally explicit analysis of X-marking which I show to satisfy these desiderata while providing greater empirical coverage of well-known cases compared to existing accounts. The proposed analysis makes use of modal presupposition projection together with pragmatic inference via Maximize Presupposition to provide a unified treatment of X-marking in English conditionals and counterfactual desires expressions of English featuring wish. I show how previous proposals for X-marking cannot satisfy these desiderata, making them insufficient for a unified account. Lastly, I introduce a hypothesis that all varieties of morphology that can be used as X-marking cross-linguistically-including past, imperfective, plural and habitual-are vacuous in both their X-marked and ordinary uses. [ABSTRACT FROM AUTHOR]

The present paper investigates if not for - if it weren't/wasn't for constructions in American English based on the analysis of more than 2,100 constructions extracted from The Corpus of Contemporary American English (COCA). In analyzing these constructions, we focus on significant interdependencies between the slots of the protasis (i.e. animacy of the referent of the NP) and apodosis (i.e. semantics of the verb lemma) and how these cross-clausal associations interact with other linguistic variables, such as the polarity of the apodosis. We apply a new multivariate extension of collostructional analysis that combines distinctive and (co-varying) collexeme analysis via a hierarchical configural frequency analysis. This allows the analyst to identify associations not just of one slot to a construction or one slot to one other slot in one construction, but to include other features to identify (i) which constructions are preferred by which fillers in, now, one or more slots of one construction and (ii) which constructions are preferred by which (combinations) of one or more fillers. [ABSTRACT FROM AUTHOR]

Bayesian networks are commonly used for learning with uncertainty and incorporating expert knowledge. However, they are hard to interpret, especially when the network structure is complex. Methods used to explain Bayesian networks operate under certain assumptions about what constitutes the best explanation, without actually verifying these assumptions. One such common assumption is that a shorter length of the causal chain of one variable to another enhances its explanatory strength. Counterfactual explanations gained popularity in artificial intelligence over the last years. It is well-known that it is possible to generate counterfactuals from causal Bayesian networks, but there is no indication which of them are useful for explanatory purposes. In this paper, we examine how to apply findings from psychology to search for counterfactuals that are perceived as more useful explanations for the end user. For this purpose, we have conducted a questionnaire to test whether counterfactuals that change an actionable cause are considered more useful than counterfactuals that change a direct cause. The results of the questionnaire indicate that actionable counterfactuals are preferred regardless of being the direct cause or having a longer causal chain. [ABSTRACT FROM AUTHOR]

This paper explores the expression of mistaken beliefs – as in for example the boy mistakenly believes that the turtle is dead (whereas in fact it is alive) – in the Indigenous languages of Australia. It is shown that some mode of expressing this meaning is attested in around 40% of the languages in a selection of 149 language varieties. In over 90% of the languages showing some mode of expressing the target meaning, it is – or can be – achieved through grammatical morphemes or constructions more or less dedicated to the expression of mistaken beliefs. These include particles, enclitics and various types of complement construction involving verbs of thinking, that frequently also convey meanings of saying, doing and hearing, rarely that specify the thought as mistaken. In just four or five languages, however, the meaning is attested only as a pragmatic implicature of a general statement of belief. To the extent possible given the limitations of the sources, the paper examines the range of meanings and uses of the morphemes/constructions expressing mistaken beliefs. [ABSTRACT FROM AUTHOR]

Previous research demonstrates that exposure to police lethal force through the media impacts individuals' support for police reform, but the mechanism driving this support is underexplored. The current study examines how news media counterfactuals (i.e., creating alternative outcomes for events that have already occurred) influence individuals' processing of police lethal force incidents, and how these perceptions, in turn, impact support for police reform. In a 2 (victim race: Black vs. White) × 3 (counterfactual target: victim, officer, or no counterfactual control) factorial design, 925 participants read a news article summarizing a lethal force incident, assigned blame for both the officer and victim, and reported their endorsement for various police reforms. Results indicated counterfactual target impacted blame attributions, and these blame attributions predicted endorsement of police reform while controlling for Attitudes Towards Police Legitimacy (APLS). These findings illustrate how media counterfactuals can influence blame, and importantly, influence perceptions of various police reforms. [ABSTRACT FROM AUTHOR]

In this article, I examine the Possible Worlds Theory and its application to contemporary fictional universes, specifically focusing on eutopias and dystopias. I trace the historical development of the Theory from the 1970s. The Theory, emerging from formal logic and analytic philosophy, presents an alternative to binary thinking by considering all as fiction. I critique David Lewis's approach, which eliminates hierarchies between real and fictional worlds, and instead propose to analyze alternative fictions, particularly counterfactuals, fantasy, and science fiction, from their relation to the real world and their moral dimensions. The study contrasts utopias and dystopias, suggesting the use of "eutopia" and "cacotopia" for better and worse states than the current world. I explore whether these alternative fictions challenge or reinforce the boundary between reality and fiction. Through the analysis of works like Laurent Binet's Civilizations and Liu Cixin's The Three-Body Problem, I investigate the role of play in these narratives and their implications for understanding contemporary culture's obsession with alternative worlds. [ABSTRACT FROM AUTHOR]

Abstract Bayesian networks are commonly used for learning with uncertainty and incorporating expert knowledge. However, they are hard to interpret, especially when the network structure is complex. Methods used to explain Bayesian networks operate under certain assumptions about what constitutes the best explanation, without actually verifying these assumptions. One such common assumption is that a shorter length of the causal chain of one variable to another enhances its explanatory strength. Counterfactual explanations gained popularity in artificial intelligence over the last years. It is well-known that it is possible to generate counterfactuals from causal Bayesian networks, but there is no indication which of them are useful for explanatory purposes. In this paper, we examine how to apply findings from psychology to search for counterfactuals that are perceived as more useful explanations for the end user. For this purpose, we have conducted a questionnaire to test whether counterfactuals that change an actionable cause are considered more useful than counterfactuals that change a direct cause. The results of the questionnaire indicate that actionable counterfactuals are preferred regardless of being the direct cause or having a longer causal chain.

Abstract Background In the causal mediation analysis framework, several parametric regression-based approaches have been introduced in past years for decomposing the total effect of an exposure on a binary outcome into a direct effect and an indirect effect through a target mediator. In this context, a well-known strategy involves specifying a logistic model for the outcome and invoking the rare outcome assumption (ROA) to simplify estimation. Recently, exact estimators for natural direct and indirect effects have been introduced to circumvent the challenges prompted by the ROA. As for the approximate approaches relying on the ROA, these exact approaches cannot be used as is on case-control data where the sampling mechanism depends on the outcome. Methods Considering a continuous or a binary mediator, we empirically compare the approximate and exact approaches using simulated data under various case-control scenarios. An illustration of these approaches on case-control data is provided, where the natural mediation effects of long-term use of oral contraceptives on ovarian cancer, with lifetime number of ovulatory cycles as the mediator, are estimated. Results In the simulations, we found few differences between the performances of the approximate and exact approaches when the outcome was rare, both marginally and conditionally on variables. However, the performance of the approximate approaches degraded as the prevalence of the outcome increased in at least one stratum of variables. Differences in behavior were also observed among the approximate approaches. In the data analysis, all studied approaches were in agreement with respect to the natural direct and indirect effects estimates. Conclusions In the case where a violation of the ROA applies or is expected, approximate mediation approaches should be avoided or used with caution, and exact estimators favored.

Knowingly re-circulated misinformation online is a widespread phenomenon that is increasingly met with suspicion or even condemnation in spite of the sharer's intent. The article recasts misinformation sharing into cultural play practice questioning this sensibility. Drawing on Huizinga's concept of play and the Lacanian notion of belief-through-other, it claims that fascination with fakes owes not simply to misrepresentation of reality but the symbolic truth it conveys in staging its distortion. Satirical conspiracy theories such as SwitzerlandIsFake solicit commentary on media effects in simulating reality without mistaking them for facts. This and similar media sub-genres exploit context collapse prone online environments and thereby challenge clear-cut distinctions between misinformation and satire, truth-claims and mere entertainment of illusions. A closer comparative analysis of SwitzerlandIsFake with cases of irony used as a coverup for outrageous or conspiratorial rhetoric prominent on 4chan imageboard sites lends an insight into the demise of play communities who increasingly adopt faithful attitude to conspiratorial or ideological beliefs. Conversely, the sacredness of play that bonds communities and thereby anticipates trust calls for a shift in perspective from moral condemnation to acculturation regarding deeper causes of the post-truth condition, such as general distrust and cynicism, which cannot be eradicated by debunking. [ABSTRACT FROM AUTHOR]

This study aims to analyse the impact of the Movement towards Socialism (MAS) regime on GDP, income inequality and corruption levels. The empirical findings show that GDP has increased and corruption decreased with the MAS party in power. Income inequality, however, has tended to increase, even though average values for the Gini index during the MAS years have been lower. The difference between counterfactual and actual observations (i.e. treatment effects) was regressed against three key shocks experienced by the Bolivian economy and the findings demonstrate that all these shocks are correlated with the three outcome variables. [ABSTRACT FROM AUTHOR]

In his latest book Mechanical Choices, Michael Moore provides an explication and defence of the idea that responsibility comes in degrees. His account takes as its point of departure the view that free action and free will consist in the holding of certain counterfactuals. In this paper, I argue that Moore's view faces several familiar counterexamples, all of which serve to motivate Harry Frankfurt's classic insight that whether and to what extent one is responsible for one's action has more to do with what actually caused that action than with what one could or couldn't have done instead. I then go on to sketch an alternative approach to degrees of responsibility that takes seriously this insight. I'll argue that Moore ought to be sympathetic to this approach, inasmuch as it combines two familiar Moorean ideas: the idea that causal contribution comes in degrees, and the idea that acting freely is compatible with, and indeed entails, the fact that one's action was caused by prior states of affairs. [ABSTRACT FROM AUTHOR]

Background: In the causal mediation analysis framework, several parametric regression-based approaches have been introduced in past years for decomposing the total effect of an exposure on a binary outcome into a direct effect and an indirect effect through a target mediator. In this context, a well-known strategy involves specifying a logistic model for the outcome and invoking the rare outcome assumption (ROA) to simplify estimation. Recently, exact estimators for natural direct and indirect effects have been introduced to circumvent the challenges prompted by the ROA. As for the approximate approaches relying on the ROA, these exact approaches cannot be used as is on case-control data where the sampling mechanism depends on the outcome. Methods: Considering a continuous or a binary mediator, we empirically compare the approximate and exact approaches using simulated data under various case-control scenarios. An illustration of these approaches on case-control data is provided, where the natural mediation effects of long-term use of oral contraceptives on ovarian cancer, with lifetime number of ovulatory cycles as the mediator, are estimated. Results: In the simulations, we found few differences between the performances of the approximate and exact approaches when the outcome was rare, both marginally and conditionally on variables. However, the performance of the approximate approaches degraded as the prevalence of the outcome increased in at least one stratum of variables. Differences in behavior were also observed among the approximate approaches. In the data analysis, all studied approaches were in agreement with respect to the natural direct and indirect effects estimates. Conclusions: In the case where a violation of the ROA applies or is expected, approximate mediation approaches should be avoided or used with caution, and exact estimators favored. [ABSTRACT FROM AUTHOR]

Machine learning (ML) applications have automated numerous real-life tasks, improving both private and public life. However, the black-box nature of many state-of-the-art models poses the challenge of model verification; how can one be sure that the algorithm bases its decisions on the proper criteria, or that it does not discriminate against certain minority groups? In this paper we propose a way to generate diverse counterfactual explanations from multilinear models, a broad class which includes Random Forests, as well as Bayesian Networks. [ABSTRACT FROM AUTHOR]

Counterfactuals are central in causal human reasoning and the scientific discovery process. The uplift, also called conditional average treatment effect, measures the causal effect of some action, or treatment, on the outcome of an individual. This paper discusses how it is possible to derive bounds on the probability of counterfactual statements based on uplift terms. First, we derive some original bounds on the probability of counterfactuals and we show that tightness of such bounds depends on the information of the feature set on the uplift term. Then, we propose a point estimator based on the assumption of conditional independence between the counterfactual outcomes. The quality of the bounds and the point estimators are assessed on synthetic data and a large real-world customer data set provided by a telecom company, showing significant improvement over the state of the art. [ABSTRACT FROM AUTHOR]

Can we feel that an unrealized outcome nearly happened if it was never possible in the first place? People often consider counterfactual events that did not happen, and some counterfactuals seem so close to reality that people say they "almost" or "easily could have" happened. Across four preregistered experiments (total N = 1,228), we investigated how judgments of counterfactual closeness depend on possibility, and whether this varies across two kinds of close counterfactuals. In judging whether outcomes almost happened, participants were more strongly impacted by possibility than by incremental manipulations of probability. In contrast, when judging whether outcomes easily could have happened, participants treated the distinction between impossible and possible like any other variation in probability. Both kinds of judgments were also impacted by propensity, though these effects were comparatively small. Together, these findings reveal novel differences between the two kinds of close counterfactuals and suggest that while possibility is privileged when judging what almost happened, probability is the focus when judging what easily could have happened. [ABSTRACT FROM AUTHOR]

How do people come to opposite causal judgments about societal problems, such as whether a public health policy reduced COVID‐19 cases? The current research tests an understudied cognitive mechanism in which people may agree about what actually happened (e.g., that a public health policy was implemented and COVID‐19 cases declined), but can be made to disagree about the counterfactual, or what would have happened otherwise (e.g., whether COVID‐19 cases would have declined naturally without intervention) via comparison cases. Across two preregistered studies (total N = 480), participants reasoned about the implementation of a public policy that was followed by an immediate decline in novel virus cases. Study 1 shows that people's judgments about the causal impact of the policy could be pushed in opposite directions by emphasizing comparison cases that imply different counterfactual outcomes. Study 2 finds that people recognize they can use such information to influence others. Specifically, in service of persuading others to support or reject a public health policy, people systematically showed comparison cases implying the counterfactual outcome that aligned with their position. These findings were robust across samples of U.S. college students and politically and socioeconomically diverse U.S. adults. Together, these studies suggest that implied counterfactuals are a powerful tool that individuals can use to manufacture others' causal judgments and warrant further investigation as a mechanism contributing to belief polarization. [ABSTRACT FROM AUTHOR]