Projects

As the number of unexceptional participants in a market increases, players to shift towards price-taking behavior. We explore the extent to which this shift in strategy is driven by (lack of) attention to the distribution of other players.

According to the efficient coding hypothesis, the precision with which we observe a number or value scales with the amount of time we spend sampling information about it. We show that if consumers are aware of this, then consumers will differ in how much attention they pay to the price of an item, depending on their price elasticity of demand for that product. We propose a way in which this finding may be used to improve welfare.

We studied the role of attention in aversive risky choices where all outcomes were unpleasant. We used two eye-tracking experiments in which participants made binary choices between two lotteries in two conditions: (a) a gain condition where outcomes for lotteries were weakly positive, and (b) a loss condition where outcomes were weakly negative. Contrary to the predictions of the standard aDDM, we found that attentional choice biases in the loss condition were identical to those found in the gain condition, suggesting that attention nudges choices towards the attended option even in losses. To explain these results, we propose a variation of the Attentional Drift-Diffusion-Model (called the Hybrid aDDM) that incorporates (a) both a value-dependent and a value-independent effect of attention on the choice process and (b) reference-dependent value signals. We show that the observed attentional choice biases and other behavioral signatures in the loss condition can only be explained by the Hybrid aDDM with a reference-point rule that sets the reference-point at or below the minimum possible outcome in a given context.

We show that sequential sampling models apply to risky choices from experience. Our paradigm also allows us to test whether samples are weighted equally, or whether they depend on the scale of values or the time at which they are sampled during the decision process. We find evidence of a small primacy and large recency bias in sequential sampling. We also prove a link between the decision boundaries of the Drift-Diffusion-Model and a Modified Probit model which demonstrates a relationship between individuals' preferences for variance in information structures and their willingness to trade of speed for accuracy. Exploratory analysis is still in progress.