Route vs. segment: an experiment on real-time travel information in congestible networks

We report the results of an experimental study of route choice in congestible networks with a common origin and common destination. In one condition, in each round of play network users independently committed themselves at the origin to a three-segment route; in the other condition, they chose route segments sequentially at each network junction upon receiving en route information. At the end of each round, players received ex-post complete information about the distribution of the route choices. Although the complexity of the network defies analysis by common users, traffic patterns in both conditions converged rapidly to the equilibrium solution. We account for the observed results by a Markov adaptive learning model postulating regret minimization and inertia. We find that subjects' learning behavior was similar across conditions, except that they exhibited more inertia in the condition with en route information

Dynamic pricing decisions and seller-buyer interactions under capacity constraints

Focusing on sellers’ pricing decisions and the ensuing seller-buyer interactions, we report an experiment on dynamic pricing with scarcity in the form of capacity constraints. Rational expectations equilibrium solutions are constructed and then tested experimentally with subjects assigned the roles of sellers and buyers. We investigate behavior in two between-subject conditions with high and moderate levels of capacity. Our laboratory market exhibits strategic sophistication: the price offers of sellers and the buyers’ aggregate responses largely approximate equilibrium predictions. We also observe systematic deviations from equilibrium benchmarks on both sides of the market. Specifically, in our experiment the sellers are boundedly strategic: their prices often exhibit strategic adjustments to profit from buyers with limited strategic sophistication, but they are also often biased towards equilibrium pricing even when that would not be ex-post optimal

When a few undermine the whole: a class of social dilemmas in ridesharing

We investigate a class of social dilemmas that arise when a heterogeneous group of agents potentially benefit from a joint enterprise such as ridesharing. Participation in the enterprise incurs positive externalities to other participants; social welfare is maximized with full participation. However, if some agents find it a dominant strategy to opt out, then the potential benefit from the enterprise will decrease, leading to more members opting out. This iterated disincentivizing effect could result in massive welfare losses. We construct a game-theoretical model to implement these social dilemmas and report experimental evidence for their existence and welfare impact.NSF Grant SES-141892

Voting rules in sequential search by committees: theory and experiments

We propose a committee extension of the individual sequential search model called the “secretary problem,” where collective decisions on when to stop the search are reached via a pre-specified voting rule. We offer a game-theoretic analysis of our model, and then report two experiments on three-person committees with either uncorrelated or perfectly correlated preferences under three different voting rules, followed by a third experiment on single decision makers. Relative to equilibrium predictions, committees with uncorrelated preferences over-searched under minority and majority voting rules, but otherwise under-searched or approximated equilibrium play. Individually, committee members were often less strategic when their preferences were uncorrelated than when they were perfectly correlated. Collectively, committees’ decisions were more strategic than single decision makers’ only under the unanimity rule, though still not significantly better in terms of the decision makers’ welfare. Finally, across our experiments that involved committee search, the unanimity rule always optimized committee welfare

A network ridesharing experiment with sequential choice of transportation mode

Within the last decade, there has been a dramatic bloom in ridesharing businesses along with the emergence of new enabling technologies. A central issue in ridesharing, which is also important in the general domain of cost-sharing in economics and computer science, is that the sharing of cost implies positive externalities and hence coordination problems for the network users. We investigate these problems experimentally in the present study. In particular, we focus on how sequential observability of transportation mode choices can be a powerful facilitator of coordination in ridesharing. Our study abstracts the essential issues of coordination in ridesharing into a directed network game with experimentally testable predictions. In line with the theoretical analysis, our experimental evidence shows that even a limited extent of sequential choice observability might lead to efficient coordination. However, convergence to efficiency is slower with more limited observability, resulting in a significant increase in travel cost

Human Decision-Making in Multi-Agent Systems

In order to avoid suboptimal collective behaviors and resolve social dilemmas, researchers have tried to understand how humans make decisions when interacting with other humans or smart machines and carried out theoretical and experimental studies aimed at influencing decision-making dynamics in large populations. We identify the key challenges and open issues in the related research, list a few popular models with the corresponding results, and point out future research directions