How we decide shapes what we choose: decision modes track consumer decisions that help decarbonize electricity generation

Theory and Decision - With concerns regarding climate change rising, companies and policy makers seek to understand the precursors to environmentally-friendly consumer choice. Decision modes are...     

Judged knowledge and ambiguity aversion

Competence has recently been proposed as an explanation for the degree of ambiguity aversion. Using general knowledge questions we presented subjects with simple lotteries in which they could bet on an event and against the same event. We show that the sum of certainty equivalents for both bets depends on the judged knowledge of the class of events. We also elicited the decision weights for events and complementary events. We found a similar effect of knowledge on the sum of decision weights.     

Ambiguity aversion in first-price sealed-bid auctions

Experiments on first-price sealed-bid auctions with independent private values have shown that submitted bids typically exceed Nash-equilibrium predictions for risk-neutral bidders. Existing bidding models explain this phenomenon by assuming that the bidders are risk-averse and capable of drawing complete and correct inferences about their winning probabilities. In this article, we use the Choquet expected utility (CEU) theory to demonstrate that the observed bidding behavior can also be attributed to ambiguity aversion which causes the bidders to underestimate their chances of winning the auction. Empirical support for CEU bidding models is given through an analysis of recent bidding data.     

Preference for gradual resolution of uncertainty

Analyses of preference for the timing of uncertainty resolution usually assumes all uncertainty to resolve in one point in time. More realistically, uncertainty should be modelled to resolve gradually over time. Kreps and Porteus (1978) have introduced an axiomatically based model of time preference which can explain preferences for gradual uncertainty resolution. This paper presents an experimental test of the Kreps-Porteus model. We derive implications of the model relating preferences for gradual and one-time resolving lotteries. Our data do not support the Kreps-Porteus model but show that some of the behaviour observed may be explained by similarity heuristics.     

Why does myopia decrease the willingness to invest? Is it myopic loss aversion or myopic loss probability aversion?

For loss averse investors, a sequence of risky investments looks less attractive if it is evaluated myopically—an effect called myopic loss aversion (MLA). The consequences of this effect have been confirmed in several experiments and its robustness is largely undisputed. The effect’s causes, however, have not been thoroughly examined with regard to one important aspect. Due to the construction of the lotteries that were used in the experiments, none of the studies is able to distinguish between MLA and an explanation based on (myopic) loss probability aversion (MLPA). This distinction is important, however, in discussion of the practical relevance and the generalizability of the phenomenon. We designed an experiment that is able to disentangle lottery attractiveness and loss probabilities. Our analysis reveals that mere loss probabilities are not as important in this dynamic context as previous findings in other domains suggest. The results favor the MLA over the MLPA explanation.     

Quantification of follow-up time in oncology clinical trials with a time-to-event endpoint: Asking the right questions

For the analysis of a time-to-event endpoint in a single-arm or randomized clinical trial it is generally perceived that interpretation of a given estimate of the survival function, or the comparison between two groups, hinges on some quantification of the amount of follow-up. Typically, a median of some loosely defined quantity is reported. However, whatever median is reported, is typically not answering the question(s) trialists actually have in terms of follow-up quantification. In this paper, inspired by the estimand framework, we formulate a comprehensive list of relevant scientific questions that trialists have when reporting time-to-event data. We illustrate how these questions should be answered, and that reference to an unclearly defined follow-up quantity is not needed at all. In drug development, key decisions are made based on randomized controlled trials, and we therefore also discuss relevant scientific questions not only when looking at a time-to-event endpoint in one group, but also for comparisons. We find that different thinking about some of the relevant scientific questions around follow-up is required depending on whether a proportional hazards assumption can be made or other patterns of survival functions are anticipated, for example, delayed separation, crossing survival functions, or the potential for cure. We conclude the paper with practical recommendations.