John Myles White

Bio: John Myles White is an academic researcher from Princeton University. The author has contributed to research in topics: Choice set & Intertemporal choice. The author has an hindex of 5, co-authored 6 publications receiving 423 citations. Previous affiliations of John Myles White include Microsoft.

Humans use directed and random exploration to solve the explore–exploit dilemma

Abstract: All adaptive organisms face the fundamental tradeoff between pursuing a known reward (exploitation) and sampling lesser-known options in search of something better (exploration). Theory suggests at least two strategies for solving this dilemma: a directed strategy in which choices are explicitly biased toward information seeking, and a random strategy in which decision noise leads to exploration by chance. In this work we investigated the extent to which humans use these two strategies. In our "Horizon task," participants made explore-exploit decisions in two contexts that differed in the number of choices that they would make in the future (the time horizon). Participants were allowed to make either a single choice in each game (horizon 1), or 6 sequential choices (horizon 6), giving them more opportunity to explore. By modeling the behavior in these two conditions, we were able to measure exploration-related changes in decision making and quantify the contributions of the two strategies to behavior. We found that participants were more information seeking and had higher decision noise with the longer horizon, suggesting that humans use both strategies to solve the exploration-exploitation dilemma. We thus conclude that both information seeking and choice variability can be controlled and put to use in the service of exploration.

Measuring Time Preferences

Abstract: National Institutes of Health (NIA R01AG021650 and P01AG005842) and the Pershing Square Fund for Research in the Foundations of Human Behavior.

Money Earlier or Later? Simple Heuristics Explain Intertemporal Choices Better Than Delay Discounting Does

Abstract: Heuristic models have been proposed for many domains involving choice. We conducted an out-of-sample, cross-validated comparison of heuristic models of intertemporal choice (which can account for many of the known intertemporal choice anomalies) and discounting models. Heuristic models outperformed traditional utility-discounting models, including models of exponential and hyperbolic discounting. The best-performing models predicted choices by using a weighted average of absolute differences and relative percentage differences of the attributes of the goods in a choice set. We concluded that heuristic models explain time-money trade-off choices in experiments better than do utility-discounting models.

Money Earlier or Later? Simple Heuristics Explain Intertemporal Choices Better than Delay Discounting

Abstract: Heuristic models have been proposed for many domains of choice We compare heuristic models of intertemporal choice, which can account for many of the known intertemporal choice anomalies, to discounting models We conduct an out-of-sample, cross-validated comparison of intertemporal choice models Heuristic models outperform traditional utility discounting models, including models of exponential and hyperbolic discounting The best performing models predict choices by using a weighted average of absolute differences and relative (percentage) differences of the attributes of the goods in a choice set We conclude that heuristic models explain time-money tradeoff choices in experiments better than utility discounting models

Measuring Time Preferences

Abstract: We review research that measures time preferences – i.e., preferences over intertemporal tradeoffs. We distinguish between studies using financial flows, which we call “money earlier or later” (MEL) decisions and studies that use time-dated consumption/effort. Under different structural models, we show how to translate what MEL experiments directly measure (required rates of return for financial flows) into a discount function. We summarize empirical regularities found in MEL studies and the predictive power of those studies. We explain why MEL choices are driven in part by some factors that are distinct from underlying time preferences.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Rates of time preference and consumer valuations of automobile safety and fuel efficiency. in: the automobile

Abstract: This paper examines the valuation of safety and fuel economy by private automobile owners. It attempts to determine the implicit value of life and the injury price trade off consumers exhibit on the market. It also examines how an individual values the differences in the fuel economy as it relates to individual cars; and estimates the implicit interest rate that individuals exhibit when valuing the long-run safety and fuel economy attributes of their automobiles. For each of the areas of inquiry, the paper's interest is twofold: what is the nature of the market trade-offs and is there a clear-cut evidence of market failure; and to what extent is it possible to rely on market-based policy interventions to promote government objectives with respect to automobile use.

Generalization guides human exploration in vast decision spaces

Abstract: From foraging for food to learning complex games, many aspects of human behaviour can be framed as a search problem with a vast space of possible actions. Under finite search horizons, optimal solutions are generally unobtainable. Yet, how do humans navigate vast problem spaces, which require intelligent exploration of unobserved actions? Using various bandit tasks with up to 121 arms, we study how humans search for rewards under limited search horizons, in which the spatial correlation of rewards (in both generated and natural environments) provides traction for generalization. Across various different probabilistic and heuristic models, we find evidence that Gaussian process function learning—combined with an optimistic upper confidence bound sampling strategy—provides a robust account of how people use generalization to guide search. Our modelling results and parameter estimates are recoverable and can be used to simulate human-like performance, providing insights about human behaviour in complex environments.