In 1999 a research program was started with the goal of finding the main reasons that influence people's decisions to insure themselves against losses caused by flood. In a field study a questionnaire that measured perception of the flood risk and motives for insuring or not insuring against the consequences of this disaster was used. The data were collected on a group of 66 households that were hit by flood in 1997. As expected, the results showed that the insurance decisions were related to a few basic psychological factors of flood-risk perception. The most important finding was that people who reported greater fear while thinking about flood bought insurance more often after the flood had occurred than people who did not report fear. Another factor of flood-risk perception, knowledge about flood, was not related to any aspect of insurance decisions. The practical implications of these findings are discussed.

We re-examine the subjects’ behavior in the ultimatum bargaining experiments studied by Roth et al. (1991, American Economic Review). In our model, un-modeled factors including rationality determine players’ initial aspirations, and a modified version of the case-based learning process governs subsequent adaptations. The calibration results of this paper show that ‘satisficing’ can explain the actual subjects’ behavior surprisingly well. More precisely, it is shown that 77.7% to 96.7% of the observed behavior is consistent with our model prediction. We also argue that the closer the initial aspiration levels are to the perfect equilibrium payoff, the lower the mean or modal offer tends to be in the actual experiments.

This paper develops a behavioral model of farmer decision making to analyze the incentives for adoption of a technology that provides information about spatial variability in nutrient availability and enables variable rate application of fertilizers. It examines the implications of uncertainty about the accuracy of the technology on input application and adoption decisions. Risk aversion and uncertainty can lead to the imposition of a risk premium on input use, which create incentives to over or under apply fertilizers. This influences not only the incentives to adopt the technology but also the impact of adoption on pollution. High spatial variability mitigates the disincentives for adoption even if fixed costs are high and farm size is small but the extent to which this occurs depends on the degree of risk aversion and uncertainty. These results have implications for the design and effectiveness of cost–share subsidies to induce adoption and reduce over application of fertilizers.

This study examined the relations between risk cognitions and risk behavior. Adolescents’ perceptions of the risks associated with driving after drinking (DAD) and their perceptions of the prevalence of this behavior among their peers were assessed, and these perceptions were used to predict their DAD behavior. Results provided evidence of a type of cognitive social influence: the more common adolescents thought the behavior was, the less risk, both personal and general, they attributed to it. As expected, however, this relation was significant only for those who were high in a tendency to engage in social comparison. Perceptions of risk, in turn, were prospectively related to risk behavior for all participants. Specifically, low perceived risk, especially personal risk, was associated with an increase in DAD behavior. Implications of the results for DAD interventions are discussed.

In a multistage investment task the decision maker attempts to increase the capital by committing some part of it over several trials in a risky investment. Despite the richness of experimental results from this task, no decision strategy exists which is able to predict investment behavior. In this paper we formulate three strategies for predicting decisions in the investment task. In the success-expectation strategy (SES), the main factor determining the choice is the expected subjective probability of a success in the coming trial. In the win–loss ratio strategy (WLRS), the main factor is the relation between the possible win and the possible loss in a specific investment situation. In the linear function of capital strategy (LFCS), the available capital is the principal determinant of choice. We tested these strategies with the help of computer simulation, using data from previous experiments. With each strategy we predicted each subject's choices and compared these predictions with the observed decisions. The predictions of WLRS correlated significantly and positively with the observed data for 24 of the 39 subjects (62%), those of LFCS correlated with 14 subjects (36%), and those of SES correlated with nine subjects (23%). The findings reveal previously unsuspected patterns in deploying capital, as well as individual differences in investment behavior. Additional factors influencing strategy use and choices are discussed.

Two views of game theory are discussed: (1) game theory as a description of the behavior of rational individuals who recognize each other's reationality and reasoning abilities, and (2) game theory as an internally consistent recommendation to individuals on how to act in interactive situations. It is shown that the same mathematical tool, namely modal logic, can be used to explicitly model both views.

In processing expert judgements, it is common to assign experts’ opinions different degrees of plausibility or probability. This results in the necessity of constructing some kind of hierarchical model. Hierarchical models are widely used in Bayesian inference. Several models have been proposed by the adherents of imprecise statistical reasoning. This paper describes a new hierarchical uncertainty model of a more general form: first- and second-order uncertainty models are both imprecise. We demonstrate that depending on what kind of evidence the modeller has at hand and the reliability quantifying the quality of expert judgement one comes to different rules of combination. It is shown that the rules of combination known in imprecise statistical reasoning can be inferred from the approach proposed in the paper. We describe how the model can be used to generate first-order coherent interval-valued previsions. The paper is concluded by addressing the issue of computing the probability of an event ‘one imprecise value is greater than another’. This case is prominent in light of decision making based on imprecise previsions.