Decisions are often temporally separated from their outcomes. Using theories of structural alignment and temporal construal, we examined how temporal distance and the associated shift in decision processes moderate susceptibility to context effects. Specifically, in two experiments (one hypothetical, one with real outcomes), we demonstrated that people attend more to nonalignable differences when the outcome of the decision is in the distant future than when it is in the near future. This shift in decision processes was found in preference and choice data, as well as coded written protocols. We further show that this temporal shift cannot be explained by differential involvement with the decision or by the feasibility and desirability of the attributes. Our findings establish temporal distance as an important moderator of structural alignment effects and also extend the implications of temporal construal theory beyond the nature of the attributes to the structural relationships among attributes.
One of the goals of psychological research on subjective probability judgment is to develop prescriptive procedures that can improve such judgments. In this paper, our aim is to reduce partition dependence, a judgmental bias that arises from the particular way in which a state space is partitioned for the purposes of making probability judgments. We explore a property of subjective probabilities called interior additivity (IA). Our story begins with a psychological model of subjective probability judgment that exhibits IA. The model is a linear combination of underlying support for the event in question and a term that reflects a prior belief that all elements in the state space partition are equally likely. The model is consistent with known properties of subjective probabilities, such as binary complementarity, subadditivity, and partition dependence, and has several additional properties related to IA. We present experimental evidence to support our model. The model further suggests a simple prescriptive method based on IA that decision and risk analysts can use to reduce partition dependence, and we present preliminary empirical evidence demonstrating the effectiveness of the method.decision analysis, subjective probability assessment, support theory, partition dependence, ignorance prior, debiasing
Consumers or firms contemplating purchasing a new product or adopting a new technology are often plagued by uncertainty: Will the benefits outweigh the costs? Should we buy now or wait and gather more information? In this paper, we study a dynamic programming model of this technology adoption problem. In each period, the consumer decides whether to adopt the technology, reject it, or wait and gather additional information by observing a signal about the technology's benefit. The technology's actual benefit may be constant or changing stochastically over time. The dynamic programming state variable is a probability distribution that describes the consumer's beliefs about the benefits of the technology. We allow general probability distributions on benefits and general signal processes and assume that the consumer updates her beliefs over time using Bayes' rule. We are interested in structural properties of this model. We show that improving the technology's benefit need not make the consumer better off and that first-order stochastic dominance improvements in the consumer's distribution on benefits need not increase the consumer's value function. Nevertheless, the model possesses a great deal of structure. For example, we obtain monotonic value functions and policies if we order distributions using likelihood-ratio dominance rather than first-order stochastic dominance. We also examine convexity properties and provide many comparative statics results.
In this paper we study the impact of uncertainty about future innovations in quality and costs on consumers' technology adoption decisions. We model the uncertainty in the technology's quality and costs as a Markov process and consider three models of the adoption decision. The first model assumes that consumers do a simple net present value (NPV) analysis that compares the NPV of adopting to that of not adopting, without considering the possibility of waiting. The second model is a stochastic dynamic program that considers the possibility of waiting and views the adoption decision as a one-time event, i.e., the consumer will only make a single purchase, the only question is when. The third model allows repeat purchases so the consumer may "upgrade" by purchasing new versions of the technology whenever it suits her. We study structural properties of these models, e.g., the following: What changes in qualities and costs will make the consumer better off? What changes will encourage adoption? We will see that the simple NPV and single-purchase model have many intuitive properties: with the right notion of improvements and reasonable assumptions about the technology changes, we find that improvements in the technology make the consumer better off and encourage adoption. Here improvements are defined using a partial order on quality and cost pairs. The results are more complicated in the repeat-purchase model. Under the same conditions on technology changes, technology improvements will make the consumer better off. However, except for special cases of transitions, these improvements may make the consumer better off and discourage adoption.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.