Tom Wysocki

Moral psychologists have recently turned their attention to the study of folk metaethical beliefs. We report the results of a cross-cultural study using Chinese, Polish and Ecuadorian participants that seeks to advance this line of investigation. Individuals in all three demographic groups were observed to attribute objectivity to ethical statements in very similar patterns. Differences in participants’ strength of opinion about an issue, the level of societal agreement or disagreement about an issue, and participants’ age were found to significantly affect their inclination to view the truth of an ethical statement as a matter of objective fact. Implications for theorizing about folk morality are discussed.

Deutsch 2010 (The Review of Philosophy and Psychology 1: 447–460) claims that hypothetical scenarios are evaluated using arguments, not intuitions, and therefore experiments on intuitions are philosophically inconsequential. Using the Gettier case as an example, he identifies three arguments that are supposed to point to the right response to the case. In the paper, I present the results of studies ran on Polish, Indian, Spanish, and American participants that suggest that there’s no deep difference between evaluating the Gettier case with intuitions and evaluating it with Deutsch’s arguments. Specifically, I argue that one would find these arguments persuasive if and only if one is already disposed to exhibit the relevant intuition.

Consider how we evaluate how normal an object is. On the dual-nature hypothesis, a normality evaluation depends on the object’s goodness and frequency. On the single-nature hypothesis, the evaluation depends solely on either frequency or goodness. To assess these hypotheses, I ran four experiments. Study 1 shows that normality evaluations vary with both the goodness and the frequency assessment of the object. Study 2 shows that manipulating the goodness and the frequency dimension changes the normality evaluation. Yet, neither experiment rules out that some people evaluate normality solely based on frequency, and the rest evaluate normality solely based on goodness. Whence two more experiments. Study 3 reveals that when scenarios are contrasted—presented one after another—only frequency matters. But, as study 4 shows, when scenarios are evaluated alone, both frequency and goodness influence normality evaluations in a single person, although the more a person is sensitive to one dimension, the less she’s sensitive to the other. The dual-nature hypothesis seems thus true of uncontrasted applications of the concept of normality, whereas the single-nature hypothesis seems true of contrasted applications.

Philosophy and statistics have studied two causal species, deterministic and probabilistic. There's a third species, however, hitherto unanalysed: underdeterministic causal phenomena, which are non-deterministic yet non-probabilistic. Here, I formulate a framework for modelling them. Consider a simple case. If I go out, I may stumble into you but also may miss you. If I don’t go out, we won't meet. I go out. We meet. My going out is a cause of our encounter even if there was no determinate probability of us meeting conditional on my going out. The cause is neither deterministic (it didn't necessitate the effect) nor probabilistic (the relevant conditional probabilities are undefined). Rather, it's underdeterministic: it raises the modal status of the effect from causally impossible to possible. Here, I won't offer a theory of such token causes but develop the prerequisite for any such theory: the underdeterministic framework. The framework is like the deterministic structural-equations framework but with one consequential difference---an equation can return multiple values. This change allows me to define causal possibility and necessity, and corresponding notions of interventionist might- and would-counterfactuals. I also define conditional independence, which obeys the graphoid axioms, and prove that underdeterministic models satisfy the causal Markov condition. The framework can causally model situations that other frameworks cannot: decision-making under bounded uncertainty, games with multiple equilibria, infinite fair lotteries, and any other non-deterministic situation where indeterminacies are essentially non-probabilistic, or where we have a reason not to use probabilities.

Science promises benefits, some true and some illusory. Consider a scientific agnostic who thinks that to reap the true benefits of a scientific theory he does not have to believe in its theoretical posits. Instead, it is enough if he believes that the theory successfully predicts the behavior of the observables, as ultimately only such predictions matter. Say, however, that given the results of her thorough research, a psychologist proposes a theory describing a psychological mechanism underlying a certain class of normative judgments. Moreover, the mechanism seems unfit for the task—once you see the details of the mechanism, you will realize that this is not the way they should be produced. Therefore, if the psychologist is right, it seems that these normative judgments should not inform one's normative theorizing or one's actions (Greene 2008, 2014, Kelly 2014). And say that the agnostic accepts the psychologist's theory, trusting that it makes correct predictions about, for example, fMRI images and subjects' reaction times, as they are observable. He also thinks that if the psychologist's description of the mechanism is correct, the judgments should not be trusted. Yet, since the mechanism posited by the theory is not observable, the agnostic is agnostic about it. He thus cannot be convinced that these judgments are produced in a flawed way and, consequently, has no reason to distrust them. Scientific agnosticism comes, therefore, at the cost of dismissing normative arguments that invoke unobservable posits of psychological models. The ability to make such arguments is a true (rather than illusory) benefit of science, despite the agnostic's promise that his philosophical theory leaves intact benefits that genuinely matter.

“If the tower is any taller than 320 ms, it may collapse,” Eiffel thinks out loud. Although understanding this counterfactual poses no trouble, the most successful interventionist semantics struggle to model it because the antecedent can come about in infinitely many ways. My aim is to provide a semantics that will make modeling such counterfactuals easy for philosophers, computer scientists, and cognitive scientists who work on causation and causal reasoning. I first propose three desiderata that will guide my theory: it should be general, yet conservative, yet useful. Next, I develop a formalization of events in the form of an algebra. I identify an event with all the ways in which it can be brought about and provide rules for determining the referent of an arbitrary event description. I apply this algebra to counterfactuals expressed using underdeterministic causal models-models that encode non-probabilistic causal indeterminacies. Specifically, I develop semaphore interventions, which represent how the target system may be modified from without in a coordinated fashion. This, in turn, allows me to bring about any event within a single model. Finally, I explain the advantages of this semantics over other interventionist competitors.