The trajectory of counterfactual simulation in development

Young children often struggle to answer the question “what would have happened?” particularly in cases where the adult-like “correct” answer has the same outcome as the event that actually occurred. Previous work has assumed that children fail because they cannot engage in accurate counterfactual simulations. Children have trouble considering what to change and what to keep fixed when comparing counterfactual alternatives to reality. However, most developmental studies on counterfactual reasoning have relied on binary yes/no responses to counterfactual questions about complex narratives and so have only been able to document when these failures occur but not why and how. Here, we investigate counterfactual reasoning in a domain in which specific counterfactual possibilities are very concrete: simple collision interactions. In Experiment 1, we show that 5- to 10-year-old children (recruited from schools and museums in Connecticut) succeed in making predictions but struggle to answer binary counterfactual questions. In Experiment 2, we use a multiple-choice method to allow children to select a specific counterfactual possibility. We find evidence that 4- to 6-year-old children (recruited online from across the United States) do conduct counterfactual simulations, but the counterfactual possibilities younger children consider differ from adult-like reasoning in systematic ways. Experiment 3 provides further evidence that young children engage in simulation rather than using a simpler visual matching strategy. Together, these experiments show that the developmental changes in counterfactual reasoning are not simply a matter of whether children engage in counterfactual simulation but also how they do so.

A counterfactual simulation model of causation by omission

When do people say that an event that didn't happen was a cause? We extend the counterfactual simulation model (CSM) of causal judgment and test it in a series of three experiments that look at people's causal judgments about omissions in dynamic physical interactions. The problem of omissive causation highlights a series of sub-problems that need to be addressed in order to give an adequate causal explanation of why something happened: what are the relevant variables, what are their possible values, how are putative causal relationships evaluated, and how is the causal responsibility for an outcome attributed to multiple causes? The CSM predicts that people make causal judgments about omissions by mentally simulating what would have happened in relevant counterfactual situations. People use their intuitive understanding of physics to run these mental simulations. While prior work has argued that normative expectations affect judgments of omissive causation, we suggest a concrete mechanism of how this happens: expectations affect what counterfactuals people consider, and the more certain people are that the counterfactual outcome would have been different from what actually happened, the more causal they judge the omission to be. Our experiments show that both the structure of the physical situation as well as expectations about what will happen affect people's judgments.

A counterfactual simulation model of causal judgment

How do people make causal judgments? We introduce the counterfactual simulation model (CSM) which predicts causal judgments by comparing what actually happened with what would have happened in relevant counterfactual situations. The CSM postulates different aspects of causation that capture the extent to which a cause made a difference to whether and how the outcome occurred, and whether the cause was sufficient and robust. We test the CSM in three experiments in which participants make causal judgments about dynamic collision events. Experiment 1 establishes a very close quantitative mapping between causal judgments and counterfactual simulations. Experiment 2 demonstrates that counterfactuals are necessary for explaining causal judgments. Participants' judgments differed dramatically between pairs of situations in which what actually happened was identical, but where what would have happened differed. Experiment 3 features two candidate causes and shows that participants' judgments are sensitive to different aspects of causation. The CSM provides a better fit to participants' judgments than a heuristic model which uses features based on what actually happened. We discuss how the CSM can be used to model the semantics of different causal verbs, how it captures related concepts such as physical support, and how its predictions extend beyond the physical domain.

Unconventional Monetary Policy in Japan: Empirical Evidence from Estimated Shadow Rate DSGE Model

The Zero Lower Bound (ZLB) on short nominal interest rates has imposed serious constraint on stimulating and stabilizing economy of major central banks. Analysis of monetary policy by Dynamic Stochastic General Equilibrium (DSGE) models under the existence of ZLB has also been an important issue from both practical and academic views for central banks and macroeconomists. However, the nonlinearity of ZLB constraint makes linear solution and estimation techniques of DSGE models unreliable and impractical. In many recent empirical works, it has been proved that the shadow rate can be used as an accurate proxy to represent the stance of unconventional monetary policy in the ZLB environment. We use shadow rate to estimate a medium-scale DSGE model based on the theoretical foundation proposed by Wu and Zhang (2016). A shadow rate New Keynesian model (No. w22856). National Bureau of Economic Research, and conduct counterfactual simulation exercises to quantify the macroeconomic effects of unconventional monetary policy implemented by Bank of Japan (BoJ). Compared with the estimation results of pre-ZLB sub-sample (1980Q1–1998Q4), the structural parameters estimated from full-sample (1980Q1–2016Q3) with the shadow rate still have very reasonable values that are consistent with most related medium-scale DSGE literature. The statistical properties of model dynamics implied by two groups of estimation are also very close. Counterfactual simulation shows that without the unconventional monetary policy, macroeconomic variables would have worse performance than their actual realizations.

Eye-Tracking Causality

How do people make causal judgments? What role, if any, does counterfactual simulation play? Counterfactual theories of causal judgments predict that people compare what actually happened with what would have happened if the candidate cause had been absent. Process theories predict that people focus only on what actually happened, to assess the mechanism linking candidate cause and outcome. We tracked participants’ eye movements while they judged whether one billiard ball caused another one to go through a gate or prevented it from going through. Both participants’ looking patterns and their judgments demonstrated that counterfactual simulation played a critical role. Participants simulated where the target ball would have gone if the candidate cause had been removed from the scene. The more certain participants were that the outcome would have been different, the stronger the causal judgments. These results provide the first direct evidence for spontaneous counterfactual simulation in an important domain of high-level cognition.