Constructing Preferences in the Physical World: A Distributed-Cognition Perspective on Preferences and Risky Choices

Psychological research has firmly established that risk preferences are transient states shaped by past experiences, current knowledge, and feelings as well as the characteristics of the decision environment. We begin this article with a brief review of evidence supporting this conception as well as different psychological theories explaining how preferences are constructed. Next, we introduce the distributed perspective on human cognition and show how it may offer a promising framework for unifying seemingly incompatible accounts. We conclude by suggesting new directions for better capturing the essence of preference construction in laboratory research

When decision support systems fail: insights for strategic information systems from Formula

Decision support systems (DSS) are sophisticated tools that increasingly take advantage of big data and are used to design and implement individual - and organization - level strategic decisions . Yet, when organizations excessively rely on their potential the outcome may be decision - making failure, particularly when such tools are applied under high pressure and turbulent conditions. Partial understanding and unidimensional interpretation can prevent learning from failure. Building on a practice perspective, we study an iconic case of strategic failure in Formula 1 racing. Our approach, which integrates the decision maker as well as the organizational and material context , identifies three interrelated sources of strategic failure that are worth investigation for decision - makers using DSS and big data: (1) t he situated nature and affordances of decision - making ; (2) t he distributed nature of cognition in decision - making; and (3) the performativity of the DSS. We outline specific research questions and their implications for firm performance and competitive advantage. Finally, we advance an agenda that can help close timely gaps in strategic IS research

Structural mapping in statistical word problems: A relational reasoning approach to Bayesian inference

Presenting natural frequencies facilitates Bayesian inferences relative to using percentages. Nevertheless, many people, including highly educated and skilled reasoners, still fail to provide Bayesian responses to these computationally simple problems. We show that the complexity of relational reasoning (e.g., the structural mapping between the presented and requested relations) can help explain the remaining difficulties. With a non-Bayesian inference that required identical arithmetic but afforded a more direct structural mapping, performance was universally high. Furthermore, reducing the relational demands of the task through questions that directed reasoners to use the presented statistics, as compared with questions that prompted the representation of a second, similar sample, also significantly improved reasoning. Distinct error patterns were also observed between these presented- and similar-sample scenarios, which suggested differences in relational-reasoning strategies. On the other hand, while higher numeracy was associated with better Bayesian reasoning, higher-numerate reasoners were not immune to the relational complexity of the task. Together, these findings validate the relational-reasoning view of Bayesian problem solving and highlight the importance of considering not only the presented task structure, but also the complexity of the structural alignment between the presented and requested relations

What if you are not Bayesian? The consequences for decisions involving risk

YesMany studies have examined the extent to which individuals’ probability judgments depart from Bayes’ theorem when revising probability estimates in the light of new information. Generally, these studies have not considered the implications of such departures for decisions involving risk. We identify when such departures will occur in two common types of decisions. We then report on two experiments where people were asked to revise their own prior probabilities of a forthcoming economic recession in the light of new information. When the reliability of the new information was independent of the state of nature, people tended to overreact to it if their prior probability was low and underreact if it was high. When it was not independent, they tended to display conservatism. We identify the circumstances where discrepancies in decisions arising from a failure to use Bayes’ theorem were most likely to occur in the decision context we examined. We found that these discrepancies were relatively rare and, typically, were not serious