In this thesis, I show how non-choice data (response times and gaze data) can be used in economics, specifically in the individual preferences domain, strategic settings, and value learning. Particularly, the first two chapters demonstrate how response times (RTs) can be used to infer individual preferences or and thus can be considered and/or manipulated in strategic settings. In the last chapter, I use gaze data to identify attention effects in reinforcement learning.
In the first chapter of the dissertation, “Revealed Indifference: Using Response Times to Infer Preferences”, response time data are used to estimate individual utility functions. Revealed preference is the dominant approach for inferring preferences, but it relies on discrete, stochastic choices. The choice process also produces response times which are continuous and can often be observed in the absence of informative choice outcomes. Moreover, there is a consistent relationship between RTs and strength-of-preference, namely that people make slower decisions as they approach indifference. This relationship arises from optimal solutions to sequential information sampling problems. We investigate several ways in which this relationship can be used to infer preferences when choice outcomes are uninformative or unavailable. We show that RTs from a single binary-choice problem are enough to usefully rank people according to their risk preferences.
The second chapter, titled “On The Strategic Use of Response Times”, further investigates the role of response times in strategic settings. We designed a laboratory experiment with a the bargaining game has two periods, where a seller with zero marginal costs makes two price offers to a buyer with a value randomly drawn from a uniform distribution, and profits are discounted if a deal is made in the second period. We found that the RTs were negatively correlated with the buyers’ values: low value buyers were faster to say “no” in the first round. In the second part of the experiment, participants were able to correctly infer the values and earn higher profits. In the final part of the experiment, subjects in the role of buyers were more likely to pick an offer that was made in response to a faster RT, as if they were manipulating their RTs to get better offers.
The last chapter, “Attention Effects in Model-Based and Model-Free Reinforcement Learning” uses eye-tracking to study learning strategies known as model-free and model-based value learning; the former is mere reinforcement of previously rewarded actions and the latter is a forward-looking strategy that involves evaluation of action-state transition probabilities. Prior work has used neural data to argue that both model-based and model-free learners implement a value comparison process at trial onset, but model-based learners assign more weight to forward-looking computations. Using eye-tracking, we report evidence for a different interpretation of prior results: model-based subjects make their choices prior to trial onset. In contrast, model-free subjects tend to ignore model-based aspects of the task and instead seem to treat the decision problem as a simple comparison process.