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A common goal of a prediction market maker is to upper bound the worst-case loss since it makes sense for a market maker not to lose an arbitrarily large amount of money. Nevertheless, another objective of profit for a market maker has also been considered in some works. None of these previous works on market makers that ensure profit exploits a connection to no-regret online learning. The goal of regret minimization for learners corresponds to the goal of minimizing the worst-case loss for market makers. With the insights from online learning about designing no-regret algorithms under a “friendlier” loss environment (e.g., specifically, low deviation or variation), which corresponds to some “patterns” of trade sequences in market making, we aim to achieve market making that furthermore guarantees profits, i.e., negative regrets, under appropriate patterns of trade sequences, which may require conditions other than those suggested by just low deviation or variation. Besides, we also want to model strategic behavior and thus “truthfulness” in prediction market making.

Our series of previous work during 2014-2018 positively answers one of the questions in multiagent online learning: in “which classes of games” would “which kinds of no-regret learning algorithms” strengthen the convergence results? Our previous results confirmed that a large class of learning algorithms can make the joint strategy profile converge to approximate Wardrop/Nash equilibria, a stricter class than the class of coarse correlated equilibria, and furthermore bounded the price of anarchy. The design of algorithms (particularly, the part for the partial information model) and the analysis techniques for convergences can be good references for follow-up work in this type of multiagent online learning problem that combines machine learning and game theory. Extending research in this direction is one of the research problems in this proposal: it is about generalizing or expanding the classes of specific no-regret learning algorithms that can be used in more general or different classes of games for players to reach equilibria. The other research direction is to pursue a group objective function, instead of each participant using certain no-regret learning algorithms to achieve system equilibria, concerned with cumulative losses as individual cost functions, imagine that our multiagent system is a team that needs to optimize a system objective together, for example, the social cost optimization. But each participant after taking action in every time step can only receive individual partial information regarding her/his own action. “Equilibrium” and “system objective optimum” is different. We first consider congestion games. Participants now all care for a common system objective, not an individual cost anymore: we will need to design distributed learning algorithms, different from those leading the system to equilibria, to make the system converge to a system objective optimum, and can only do this using partial feedback information under the bandit model.

We propose to extend the data broker system concept and perform empirical experiments and lab experiments using human subjects. Our empirical and lab experiments will test users’ strategies and behavior with respect to their bidding as well as their willingness to share their personal data. For theoretic extension toward more practical online advertising systems, we propose to study online advertising market mechanism design in a repeated setting where users, mediators, and advertisers could be no-regret learners, and ask: how should a mechanism do to (approximately) maximize the gain over time?