Making decisions about future environmental alternatives such as aging dams can be complex, technical, and challenging for the public. This study uses a split sample, labeled choice experiment to examine how information delivery method—combinations of text, images, and video—affects willingness to pay (WTP) for alternative scenarios for an aging dam. The results indicate that the still image treatment leads to higher WTP across all dam modification alternatives compared to keeping the dam in its current condition. In contrast, the video treatment leads in lower WTP for most alternatives and results in different preference ranking for dam management, i.e., higher WTP to maintain the dam over removing it. Our study suggests that preferences are sensitive to how information is delivered and reinforces the need for credible and legitimate visualizations that can correctly capture the projected future alternatives.

We propose a framework in order to econometrically estimate case-based learning and apply it to empirical data from twelve 2 × 2 mixed strategy equilibria experiments. Case-based learning allows agents to explicitly incorporate information available to the experimental subjects in a simple, compact, and arguably natural way. We compare the estimates of case-based learning to other learning models (reinforcement learning and self-tuned experience weighted attraction learning) while using in-sample and out-of-sample measures. We find evidence that case-based learning explains these data better than the other models based on both in-sample and out-of-sample measures. Additionally, the case-based specification estimates how factors determine the salience of past experiences for the agents. We find that, in constant sum games, opposing players’ behavior is more important than recency and, in non-constant sum games, the reverse is true.