Introduction: Characterizing the Questions of causal reasoning This chapter describes the mechanism approach to the study of causal reasoning. We will first offer a characterization of the central issues in human causal reasoning, and will discuss how the mechanism approach addresses these issues. In the course of this presentation, we will frequently compare the mechanism approach with alternative accounts based on analyses of covariation, or what is often termed the regularity view. The aims of this chapter are the following: to explain why covariation and mechanism are different, to discuss why such a distinction is actually a useful tool for our understanding of causal reasoning, and to explicate the complementary nature of the two views. Before presenting these two approaches, it is necessary first to offer a description of the domain or problem itself : namely, what are these alternative approaches to? Although there are a number of different ways of characterizing the study of

this paper were circulated to other researchers to elicit additional studies. The findings are provided below in three sections: making forecasts, estimating uncertainty, and gaining acceptance. Making the Forecasts

issues are described here: (1) prior commitment, (2) scenarios, and (3) presentation strategies. Prior Commitment Prior commitment should be used to gain acceptance of forecasts. This advice is simple. The clients are asked (1) what forecasts they expect? and (2) what decisions will be made given various possible forecasts? If the decisions do not vary when the forecasts vary, then there is no need to spend the money on forecasting. Formal questionnaires can be administered to the clients, asking them to record their forecasts and confidence intervals. Also, the questionnaire could sketch out possible forecasts and ask the clients what decisions they would make. As an alternative to questionnaires, the process might be conducted in structured meetings. Prior commitment to the forecasting process is also important. If the stakeholders feel that the process is rational and fair, then they are more likely to accept the forecasts. 2 Forecasts are most likely to be use

Building on research that examines the nature of similarity processes, we suggest that (i) assessments of the similarity between objects shifts systematically depending on the basis used to evaluate similarity, which is influenced by contextual factors; (ii) judgments and choices can be affected by the perceived similarity relations between items in a set; and (iii) such shifts in similarity can therefore play a key role in inferences impacting valuation and choice. In three studies, we show that changes in temporal framing (near future vs. distant future) impact evaluation and choice due to shifts in the basis of perceived similarity, leading to effects that would otherwise be unanticipated. In particular, we find that manipulating temporal context impacts evaluation and choice via its influence on which attributes form the basis of perceived similarity between choice alternatives, in addition to any direct influence it might have on preferences.