Absolute and relative stability of loss aversion across contexts

Individuals’ decisions under risk tend to be in line with the notion that “losses loom larger than gains”. This loss aversion in decision making is commonly understood as a stable in- dividual preference that is manifested across different contexts. The presumed stability and generality, which underlies the prominence of loss aversion in the literature at large, has been recently questioned by studies reporting how loss aversion can disappear, and even reverse, as a function of the choice context. The present study investigated whether loss aversion reflects a trait-like attitude of avoiding losses or rather individuals’ adaptability to different contexts. We report three experiments investigating the within-subject context sensitivity of loss aversion in a two-alternative forced-choice task. Our results show that the choice context can shift people’s loss aversion, though somewhat inconsistently. Moreover, individual estimates of loss aversion are shown to have a considerable degree of stability. Altogether, these results indicate that even though the absolute value of loss aversion can be affected by external factors such as the choice context, estimates of people’s loss aversion still capture the relative dispositions towards gains and losses across individuals

Assessing framing of uncertainties in water management practice

Dealing with uncertainties in water management is an important issue and is one which will only increase in light of global changes, particularly climate change. So far, uncertainties in water management have mostly been assessed from a scientific point of view, and in quantitative terms. In this paper, we focus on the perspectives from water management practice, adopting a qualitative approach. We consider it important to know how uncertainties are framed in water management practice in order to develop practice relevant strategies for dealing with uncertainties. Framing refers to how people make sense of the world. With the aim of identifying what are important parameters for the framing of uncertainties in water management practice, in this paper we analyze uncertainty situations described by decision-makers in water management. The analysis builds on a series of ¿Uncertainty Dialogues¿ carried out within the NeWater project with water managers in the Rhine, Elbe and Guadiana basins in 2006. During these dialogues, representatives of these river basins were asked what uncertainties they encountered in their professional work life and how they confronted them. Analysing these dialogues we identified several important parameters of how uncertainties get framed. Our assumption is that making framing of uncertainty explicit for water managers will allow for better dealing with the respective uncertainty situations. Keywords Framing - Uncertainty - Water management practic

Bayesian inference for the information gain model

One of the most popular paradigms to use for studying human reasoning involves the Wason card selection task. In this task, the participant is presented with four cards and a conditional rule (e.g., “If there is an A on one side of the card, there is always a 2 on the other side”). Participants are asked which cards should be turned to verify whether or not the rule holds. In this simple task, participants consistently provide answers that are incorrect according to formal logic. To account for these errors, several models have been proposed, one of the most prominent being the information gain model (Oaksford & Chater, Psychological Review, 101, 608–631, 1994). This model is based on the assumption that people independently select cards based on the expected information gain of turning a particular card. In this article, we present two estimation methods to fit the information gain model: a maximum likelihood procedure (programmed in R) and a Bayesian procedure (programmed in WinBUGS). We compare the two procedures and illustrate the flexibility of the Bayesian hierarchical procedure by applying it to data from a meta-analysis of the Wason task (Oaksford & Chater, Psychological Review, 101, 608–631, 1994). We also show that the goodness of fit of the information gain model can be assessed by inspecting the posterior predictives of the model. These Bayesian procedures make it easy to apply the information gain model to empirical data. Supplemental materials may be downloaded along with this article from www.springerlink.com

Stakeholder Workshops Informing System Modeling—Analyzing the Urban Food–Water–Energy Nexus in Amman, Jordan

Large cities worldwide are increasingly suffering from a nexus of food, water, and energy supply challenges. This complex nexus can be analyzed with modern physico-economic system models. Only when practical knowledge from those affected, experts, and decision makers is incorporated alongside various other data sources, however, are the analyses suitable for policy advice. Here, we present a concept for “Sustainability Nexus Workshops” suitable for extracting and preparing relevant practical knowledge for nexus modeling and apply it to the case of Amman, Jordan. The experiences of the workshop participants show that, although water scarcity is the predominant resource problem in Jordan, there is a close connection between food, water, and energy as well as between resource supply and urbanization. To prevent the foreseeable significant degradation of water supply security, actions are needed across all nexus dimensions. The stakeholders demonstrate an awareness of this and suggest a variety of technical measures, policy solutions, and individual behavioral changes—often in combination. Improving the supply of food, water, and energy requires political and institutional reforms. In developing these, it must be borne in mind that the prevalent informal structures and illegal activities are both strategies for coping with nexus challenges and causes of them

When fast logic meets slow belief: Evidence for a parallel-processing model of belief bias.

Two experiments pitted the default-interventionist account of belief bias against a parallel-processing model. According to the former, belief bias occurs because a fast, belief-based evaluation of the conclusion pre-empts a working-memory demanding logical analysis. In contrast, according to the latter both belief-based and logic-based responding occur in parallel. Participants were given deductive reasoning problems of variable complexity and instructed to decide whether the conclusion was valid on half the trials or to decide whether the conclusion was believable on the other half. When belief and logic conflict, the default-interventionist view predicts that it should take less time to respond on the basis of belief than logic, and that the believability of a conclusion should interfere with judgments of validity, but not the reverse. The parallel-processing view predicts that beliefs should interfere with logic judgments only if the processing required to evaluate the logical structure exceeds that required to evaluate the knowledge necessary to make a belief-based judgment, and vice versa otherwise. Consistent with this latter view, for the simplest reasoning problems (modus ponens), judgments of belief resulted in lower accuracy than judgments of validity, and believability interfered more with judgments of validity than the converse. For problems of moderate complexity (modus tollens and single-model syllogisms), the interference was symmetrical, in that validity interfered with belief judgments to the same degree that believability interfered with validity judgments. For the most complex (three-term multiple-model syllogisms), conclusion believability interfered more with judgments of validity than vice versa, in spite of the significant interference from conclusion validity on judgments of belief