In today's online advertising markets, a crucial requirement for an advertiser is to control her total expenditure within a time horizon under some budget. Among various budget control methods, throttling has emerged as a popular choice, managing an advertiser's total expenditure by selecting only a subset of auctions to participate in. This paper provides a theoretical panorama of a single advertiser's dynamic budget throttling process in repeated second-price auctions. We first establish a lower bound on the regret and an upper bound on the asymptotic competitive ratio for any throttling algorithm, respectively, when the advertiser's values are stochastic and adversarial. Regarding the algorithmic side, we propose the OGD-CB algorithm, which guarantees a near-optimal expected regret with stochastic values. On the other hand, when values are adversarial, we prove that this algorithm also reaches the upper bound on the asymptotic competitive ratio. We further compare throttling with pacing, another widely adopted budget control method, in repeated second-price auctions. In the stochastic case, we demonstrate that pacing is generally superior to throttling for the advertiser, supporting the well-known result that pacing is asymptotically optimal in this scenario. However, in the adversarial case, we give an exciting result indicating that throttling is also an asymptotically optimal dynamic bidding strategy. Our results bridge the gaps in theoretical research of throttling in repeated auctions and comprehensively reveal the ability of this popular budget-smoothing strategy.

We study the framework of a dynamic decision-making scenario with resource constraints. In this framework, an agent, whose target is to maximize the total reward under the initial inventory, selects an action in each round upon observing a random request, leading to a reward and resource consumptions that are further associated with an unknown random external factor. While previous research has already established an $\widetilde{O}(\sqrt{T})$ worst-case regret for this problem, this work offers two results that go beyond the worst-case perspective: one for the worst-case gap between benchmarks and another for logarithmic regret rates. We first show that an $\Omega(\sqrt{T})$ distance between the commonly used fluid benchmark and the online optimum is unavoidable when the former has a degenerate optimal solution. On the algorithmic side, we merge the re-solving heuristic with distribution estimation skills and propose an algorithm that achieves an $\widetilde{O}(1)$ regret as long as the fluid LP has a unique and non-degenerate solution. Furthermore, we prove that our algorithm maintains a near-optimal $\widetilde{O}(\sqrt{T})$ regret even in the worst cases and extend these results to the setting where the request and external factor are continuous. Regarding information structure, our regret results are obtained under two feedback models, respectively, where the algorithm accesses the external factor at the end of each round and at the end of a round only when a non-null action is executed.

This paper considers the hidden-action model of the principal-agent problem, in which a principal incentivizes an agent to work on a project using a contract. We investigate whether contracts with bounded payments are learnable and approximately optimal. Our main results are two learning algorithms that can find a nearly optimal bounded contract using a polynomial number of queries, under two standard assumptions in the literature: a costlier action for the agent leads to a better outcome distribution for the principal, and the agent's cost/effort has diminishing returns. Our polynomial query complexity upper bound shows that standard assumptions are sufficient for achieving an exponential improvement upon the known lower bound for general instances. Unlike the existing algorithms which relied on discretizing the contract space, our algorithms directly learn the underlying outcome distributions. As for the approximate optimality of bounded contracts, we find that they could be far from optimal in terms of multiplicative or additive approximation, but satisfy a notion of mixed approximation.

In today's online advertising markets, it is common for advertisers to set long-term budgets. Correspondingly, advertising platforms adopt budget control methods to ensure that advertisers' payments lie within their budgets. Most budget control methods rely on the value distributions of advertisers. However, due to the complex advertising landscape and potential privacy concerns, the platform hardly learns advertisers' true priors. Thus, it is crucial to understand how budget control auction mechanisms perform under unassured priors.

We consider a decision aggregation problem with two experts who each make a binary recommendation after observing a private signal about an unknown binary world state. An agent, who does not know the joint information structure between signals and states, sees the experts' recommendations and aims to match the action with the true state. Under the scenario, we study whether supplemented additionally with second-order information (each expert's forecast on the other's recommendation) could enable a better aggregation.

In online ad markets, a rising number of advertisers are employing bidding agencies to participate in ad auctions. These agencies are specialized in designing online algorithms and bidding on behalf of their clients. Typically, an agency usually has information on multiple advertisers, so she can potentially coordinate bids to help her clients achieve higher utilities than those under independent bidding. In this paper, we study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding. We show that these algorithms achieve maximal social welfare and discuss bidders' incentives to misreport their budgets, in symmetric cases. Our proofs combine the techniques of online learning and equilibrium analysis, overcoming the difficulty of competing with a multi-dimensional benchmark. The performance of our algorithms is further evaluated by experiments on both synthetic and real data. To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.

We consider a hierarchical structure of a permissioned blockchain with three types of participant: providers, collectors, and governors. Providers forward transactions to collectors; collectors upload received transactions to governors after verifying and labeling them; and governors validate a portion of the labeled transactions they receive, pack valid transactions into a block, and append the block to the ledger. This model has various fields of application including data collection from the Internet-of-Things and second-hand markets. Our main contribution is to propose a reputation-based protocol to help governors evaluate the reliability of collectors. Specifically, given a transaction, each governor runs a softmax-based function to calculate a probability for each collector that sent and labeled this transaction. The probabilities, calculated using collectors’ reputations as inputs, represent the likelihood of the lead governor selecting the labeled transaction from collectors to consider for further validation. After the lead governor verifies a transaction, all collectors’ reputations are updated in line with the agreement of their labeling and the validity of the transaction as found by the lead governor. We show, both theoretically and empirically, that our protocol can significantly reduce governors’ verification workloads while maintaining firm liveness and high incentives.

Nowadays, sharding is deemed a promising way to save traditional blockchain protocols from their low scalability. However, such a technique also brings several potential risks and a huge communication burden. An improper design may give rise to an inconsistent state among different committees. Further, the communication burden arising from cross-shard transactions, unfortunately, reduces the system's performance. In this paper, we first summarize five essential issues that all sharding blockchain designers face. For each issue, we discuss its key challenge and propose our suggested solutions. In order to break the performance bottlenecks, we design a committee structure and propose a reputation mechanism for selecting leaders. The term reputation in our design reflects each node's honest computation resources. In addition, we present a recovery procedure in case the leader is malicious. Theoretically, we prove that the system is robust under our design. Further simulation results also support this. In addition, the results show that selecting leaders by reputation can dramatically improve the system's performance.

Permissioned blockchains take more reliability on participants than permissionless ones. In this poster, we focus on a hierarchical scenario of permissioned blockchains, which includes three types of participants: providers, collectors, and governors. Such a scenario has many applications in the field of IoT data collection, horizontal strategic alliances, etc. Our object is to reduce the cost of the governor's transaction verification. For this purpose, we propose a reputation protocol to help the governor measure the reliability of collectors. Based on the measurement of collectors' reputations, governors can pack high-quality transactions from reliable collectors into blocks, and thus the cost of verifying transactions can be decreased effectively. Through theoretical analysis, our protocol dramatically reduces the verification loss of governors.

Traditional public distributed ledgers have not been able to scale-out well and work efficiently. Sharding is deemed as a promising way to solve this problem. By partitioning all nodes into small committees and letting them work in parallel, we can significantly lower the amount of communication and computation, reduce the overhead on each node’s storage, as well as enhance the throughput of the distributed ledger. Existing sharding-based protocols still suffer from several serious drawbacks. The first thing is that all non-faulty nodes must connect well with each other, which demands a huge number of communication channels in the network. Moreover, previous protocols have faced great loss in efficiency in the case where the honesty of each committee’s leader is in question. At the same time, no explicit incentive is provided for nodes to actively participate in the protocol.We present CycLedger, a scalable and secure parallel protocol for distributed ledger via sharding. Our protocol selects a leader and a partial set for each committee, who are in charge of maintaining intra-shard consensus and communicating with other committees, to reduce the amortized complexity of communication, computation, and storage on all nodes. We introduce a novel semi-commitment scheme between committees and a recovery procedure to prevent the system from crashing even when leaders of committees are malicious. To add incentive for the network, we use the concept of reputation, which measures each node’s trusty computing power. As nodes with a higher reputation receive more rewards, there is an encouragement for nodes with strong computing ability to work honestly to gain reputation. In this way, we strike out a new path to establish scalability, security, and incentive for the sharding-based distributed ledger.