People often engage in “offline simulation”, considering what would happen if they performed certain actions in the future, or had performed different actions in the past. Prior research shows that these simulations are biased towards actions a per- son considers to be good—i.e., likely to pay off. We ask whether, and why, this bias might be adaptive. Through com- putational experiments we compare five agents who differ only in the way they engage in offline simulation, across a variety of different environment types. Broadly speaking, our exper- iments reveal that simulating actions one already regards as good does in fact confer an advantage in downstream decision making, although this general pattern interacts with features of the environment in important ways. We contrast this bias with alternatives such as simulating actions whose outcomes are instead uncertain.

The aim of this paper is to argue that models in cognitivescience based on probabilistic computation should not be re-stricted to those procedures that almost surely (with probabil-ity 1) terminate. There are several reasons to consider non-terminating procedures as candidate components of cognitivemodels. One theoretical reason is that there is a perfect cor-respondence between the enumerable semi-measures and allprobabilistic programs, as we demonstrate here (generalizinga better-known fact about computable measures and almost-surely halting programs). One practical reason is that the linebetween almost sure termination and non-termination is elu-sive, as well as arbitrary. We argue that this matters not onlyfor theorists, but also potentially for a learner faced with thetask of inducing programs from experience.

We offer an account of the role of normality—both statisti-cal and prescriptive—in judgments of actual causation. Us-ing only standard tools from the literature on causal cognition,we argue that the phenomenon can be explained simply on theassumption that people stochastically sample (counterfactual)scenarios in a way that reflects normality. We show that a for-malization of this idea, giving rise to a novel measure of causalstrength, can account for some of the most puzzling qualitativepatterns uncovered in recent experimental work

While much of semantic theorizing is based on intuitions about logical phenomena associated with linguistic constructions—phenomena such as consistency and entailment—it is rare to see axiomatic treatments of linguistic fragments. Given a fragment interpreted in some class of formally specified models, it is often possible to ask for a characterization of the reasoning patterns validated by the class of models. Axiomatizations provide such a characterization, often in a perspicuous and efficient manner. In this paper, we highlight some of the benefits of providing axiomatizations for the purpose of semantic theorizing. We illustrate some of these benefits using three examples from the study of modality.

Recent ideas about epistemic modals and indicative conditionals in formal semantics have significant overlap with ideas in modal logic and dynamic epistemic logic. The purpose of this paper is to show how greater interaction between formal semantics and dynamic epistemic logic in this area can be of mutual benefit. In one direction, we show how concepts and tools from modal logic and dynamic epistemic logic can be used to give a simple, complete axiomatization of Yalcin's [16] semantic consequence relation for a language with epistemic modals and indicative conditionals. In the other direction, the formal semantics for indicative conditionals due to Kolodny and MacFarlane [9] gives rise to a new dynamic operator that is very natural from the point of view of dynamic epistemic logic, allowing succinct expression of dependence (as in dependence logic) or supervenience statements. We prove decidability for the logic with epistemic modals and Kolodny and MacFarlane's indicative conditional via a full and faithful computable translation from their logic to the modal logic K45.

In this paper, we explore semantics for comparative epistemic modals that avoid the entailment problems shown by Yalcin (2006, 2009, 2010) to result from Kratzer’s (1991) semantics. In contrast to the alternative semantics presented by Yalcin and Lassiter (2010, 2011) based on finitely additive measures, we introduce semantics based on qualitatively additive measures, as well as semantics based on purely qualitative orderings, including orderings on propositions derived from orderings on worlds in the tradition of Kratzer (1991, 2012). All of these semantics avoid the entailment problems that result from Kratzer’s semantics. Our discussion focuses on methodological issues concerning the choice between different semantics.

In this paper, we explore semantics for comparative epistemic modals that avoid the entailment problems shown by Yalcin (2006, 2009, 2010) to result from Kratzer’s (1991) semantics. In contrast to the alternative semantics presented by Yalcin and Lassiter (2010, 2011) based on finitely additive measures, we introduce semantics based on qualitatively additive measures, as well as semantics based on purely qualitative orderings, including orderings on propositions derived from orderings on worlds in the tradition of Kratzer (1991, 2012). All of these semantics avoid the entailment problems that result from Kratzer’s semantics. Our discussion focuses on methodological issues concerning the choice between different semantics. 

What do we learn from a causal explanation? Upon being told that The fire occurred because a lit match was dropped, welearn that both of these events occurred, and that there is a causal relationship between them. However, causal explanationsof the kind E because C typically disclose much more than what is explicitly stated. Here, we offer a communication-theoretic account of causal explanations and show specifically that explanations can provide information about the extentto which a cited cause is normal or abnormal, and about the causal structure of the situation.In Experiment 1, we demonstrate that people infer the normality of a cause from an explanation when they know theunderlying causal structure. In Experiment 2, we show that people infer the causal structure from an explanation ifthey know the normality of the cited cause. We find these patterns both for scenarios that manipulate the statistical andprescriptive normality of events.Finally, we consider how the communicative function of explanations, as highlighted in this series of experiments, mayhelp to elucidate the distinctive roles that normality and causal structure play in causal explanation. Link to pre-print:https://psyarxiv.com/x5mqc

This paper studies connections between two alternatives to the standard probability calculus for representing and reasoning about uncertainty: imprecise probability andcomparative probability. The goal is to identify complete logics for reasoning about uncertainty in a comparative probabilistic language whose semantics is given in terms of imprecise probability. Comparative probability operators are interpreted as quantifying over a set of probability measures. Modal and dynamic operators are added for reasoning about epistemic possibility and updating sets of probability measures.