50 research outputs found
Incentivizing the Dynamic Workforce: Learning Contracts in the Gig-Economy
In principal-agent models, a principal offers a contract to an agent to
perform a certain task. The agent exerts a level of effort that maximizes her
utility. The principal is oblivious to the agent's chosen level of effort, and
conditions her wage only on possible outcomes. In this work, we consider a
model in which the principal is unaware of the agent's utility and action
space. She sequentially offers contracts to identical agents, and observes the
resulting outcomes. We present an algorithm for learning the optimal contract
under mild assumptions. We bound the number of samples needed for the principal
obtain a contract that is within of her optimal net profit for every
Contextual Bandits with Cross-learning
In the classical contextual bandits problem, in each round , a learner
observes some context , chooses some action to perform, and receives
some reward . We consider the variant of this problem where in
addition to receiving the reward , the learner also learns the
values of for all other contexts ; i.e., the rewards that
would have been achieved by performing that action under different contexts.
This variant arises in several strategic settings, such as learning how to bid
in non-truthful repeated auctions (in this setting the context is the decision
maker's private valuation for each auction). We call this problem the
contextual bandits problem with cross-learning. The best algorithms for the
classical contextual bandits problem achieve regret
against all stationary policies, where is the number of contexts, the
number of actions, and the number of rounds. We demonstrate algorithms for
the contextual bandits problem with cross-learning that remove the dependence
on and achieve regret (when contexts are stochastic with
known distribution), (when contexts are stochastic
with unknown distribution), and (when contexts are
adversarial but rewards are stochastic).Comment: 48 pages, 5 figure
Forming Probably Stable Communities with Limited Interactions
A community needs to be partitioned into disjoint groups; each community
member has an underlying preference over the groups that they would want to be
a member of. We are interested in finding a stable community structure: one
where no subset of members wants to deviate from the current structure. We
model this setting as a hedonic game, where players are connected by an
underlying interaction network, and can only consider joining groups that are
connected subgraphs of the underlying graph. We analyze the relation between
network structure, and one's capability to infer statistically stable (also
known as PAC stable) player partitions from data. We show that when the
interaction network is a forest, one can efficiently infer PAC stable coalition
structures. Furthermore, when the underlying interaction graph is not a forest,
efficient PAC stabilizability is no longer achievable. Thus, our results
completely characterize when one can leverage the underlying graph structure in
order to compute PAC stable outcomes for hedonic games. Finally, given an
unknown underlying interaction network, we show that it is NP-hard to decide
whether there exists a forest consistent with data samples from the network.Comment: 11 pages, full version of accepted AAAI-19 pape
Brief Announcement: Bayesian Auctions with Efficient Queries
Generating good revenue is one of the most important problems in Bayesian auction design, and many (approximately) optimal dominant-strategy incentive compatible (DSIC) Bayesian mechanisms have been constructed for various auction settings. However, most existing studies do not consider the complexity for the seller to carry out the mechanism. It is assumed that the seller knows "each single bit" of the distributions and is able to optimize perfectly based on the entire distributions. Unfortunately this is a strong assumption and may not hold in reality: for example, when the value distributions have exponentially large supports or do not have succinct representations.
In this work we consider, for the first time, the query complexity of Bayesian mechanisms. We only allow the seller to have limited oracle accesses to the players\u27 value distributions, via quantile queries and value queries. For a large class of auction settings, we prove logarithmic lower-bounds for the query complexity for any DSIC Bayesian mechanism to be of any constant approximation to the optimal revenue. For single-item auctions and multi-item auctions with unit-demand or additive valuation functions, we prove tight upper-bounds via efficient query schemes, without requiring the distributions to be regular or have monotone hazard rate. Thus, in those auction settings the seller needs to access much less than the full distributions in order to achieve approximately optimal revenue