179,951 research outputs found
Social Learning with Payoff Complementarities
We incorporate strategic complementarities into a multi-agent sequential choice model with observable actions and private information. In this framework agents are concerned with learning from predecessors, signalling to successors, and coordinating their actions with those of others. Coordination problems have hitherto been studied using static coordination games which do not allow for learning behavior. Social learning has been examined using games of sequential action under uncertainty, but in the absence of strategic complementarities (herding models). Our model captures the strategic behavior of static coordination games, the social learning aspect of herding models, and the signalling behavior missing from both of these classes of models in one unified framework. In sequential action problems with incomplete information, agents exhibit herd behavior if later decision makers assign too little importance to their private information, choosing instead to imitate their predecessors. In our setting we demonstrate that agents may exhibit either strong herd behavior (complete imitation) or weak herd behavior (overoptimism) and characterize the informational requirements for these distinct outcomes. We also characterize the informational requirements to ensure the possibility of coordination upon a risky but socially optimal action in a game with finite but unboundedly large numbers of players.
Evolutionary Sequential Trading
This paper analyzes an Easley and O'Hara (1992) type sequential trading model in an evolutionary setting. We assume that the memory of a market maker is limited, and that traders endogenously choose whether to acquire private information with a fixed cost. We show that the ratio of the informed traders is proportional to the width of the bid ask spread, and that the price converges to the strong-form efficient level exponentially.Market microstructure; Information asymmetry; Bayesian learning; Bid-ask spread; Evolutionary game theory
An Experimental Study of Information Revelation Policies in Sequential Auctions
Theoretical models of information asymmetry have identied a tradeoff between the desire to learn and the desire to prevent an opponent from learning private information. This paper reports a laboratory experiment that investigates if actual bidders account for this tradeoff, using a sequential procurement auction with private cost information and varying information revelation policies. Specically, the Complete Information Policy, where all submitted bids are revealed between auctions, is compared against the Incomplete Information Policy, where only the winning bid is revealed. The experimental results are largely consistent with the theoretical predictions. For example, bidders pool with other types to prevent an opponent from learning signicantly more often under a Complete Information Policy. Also as predicted, the procurer pays less when employing an Incomplete Information Policy only when the market is highly competitive. Bids are usually more aggressive than the risk neutral quantitative prediction, which is usually consistent with risk aversion.Complete and Incomplete Information Revelation Policies, Laboratory Study, Procurement Auction, Multistage Game
Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy
We explore the promises and challenges of employing sequential
decision-making algorithms - such as bandits, reinforcement learning, and
active learning - in law and public policy. While such algorithms have
well-characterized performance in the private sector (e.g., online
advertising), their potential in law and the public sector remains largely
unexplored, due in part to distinct methodological challenges of the policy
setting. Public law, for instance, can pose multiple objectives, necessitate
batched and delayed feedback, and require systems to learn rational, causal
decision-making policies, each of which presents novel questions at the
research frontier. We highlight several applications of sequential
decision-making algorithms in regulation and governance, and discuss areas for
needed research to render such methods policy-compliant, more widely
applicable, and effective in the public sector. We also note the potential
risks of such deployments and describe how sequential decision systems can also
facilitate the discovery of harms. We hope our work inspires more investigation
of sequential decision making in law and public policy, which provide unique
challenges for machine learning researchers with tremendous potential for
social benefit.Comment: Version 1 presented at Causal Inference Challenges in Sequential
Decision Making: Bridging Theory and Practice, a NeurIPS 2021 Worksho
Learning optimization models in the presence of unknown relations
In a sequential auction with multiple bidding agents, it is highly
challenging to determine the ordering of the items to sell in order to maximize
the revenue due to the fact that the autonomy and private information of the
agents heavily influence the outcome of the auction.
The main contribution of this paper is two-fold. First, we demonstrate how to
apply machine learning techniques to solve the optimal ordering problem in
sequential auctions. We learn regression models from historical auctions, which
are subsequently used to predict the expected value of orderings for new
auctions. Given the learned models, we propose two types of optimization
methods: a black-box best-first search approach, and a novel white-box approach
that maps learned models to integer linear programs (ILP) which can then be
solved by any ILP-solver. Although the studied auction design problem is hard,
our proposed optimization methods obtain good orderings with high revenues.
Our second main contribution is the insight that the internal structure of
regression models can be efficiently evaluated inside an ILP solver for
optimization purposes. To this end, we provide efficient encodings of
regression trees and linear regression models as ILP constraints. This new way
of using learned models for optimization is promising. As the experimental
results show, it significantly outperforms the black-box best-first search in
nearly all settings.Comment: 37 pages. Working pape
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