Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems

Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task's requester, and may be adjusted based on the quality of the completed work, for example, through the use of "bonus" payments. In this paper, we study the requester's problem of dynamically adjusting quality-contingent payments for tasks. We consider a multi-round version of the well-known principal-agent model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work. We treat this problem as a multi-armed bandit problem, with each "arm" representing a potential contract. To cope with the large (and in fact, infinite) number of arms, we propose a new algorithm, AgnosticZooming, which discretizes the contract space into a finite number of regions, effectively treating each region as a single arm. This discretization is adaptively refined, so that more promising regions of the contract space are eventually discretized more finely. We analyze this algorithm, showing that it achieves regret sublinear in the time horizon and substantially improves over non-adaptive discretization (which is the only competing approach in the literature). Our results advance the state of art on several different topics: the theory of crowdsourcing markets, principal-agent problems, multi-armed bandits, and dynamic pricing.Comment: This is the full version of a paper in the ACM Conference on Economics and Computation (ACM-EC), 201

Low-Cost Learning via Active Data Procurement

We design mechanisms for online procurement of data held by strategic agents for machine learning tasks. The challenge is to use past data to actively price future data and give learning guarantees even when an agent's cost for revealing her data may depend arbitrarily on the data itself. We achieve this goal by showing how to convert a large class of no-regret algorithms into online posted-price and learning mechanisms. Our results in a sense parallel classic sample complexity guarantees, but with the key resource being money rather than quantity of data: With a budget constraint $B$, we give robust risk (predictive error) bounds on the order of $1/\sqrt{B}$. Because we use an active approach, we can often guarantee to do significantly better by leveraging correlations between costs and data. Our algorithms and analysis go through a model of no-regret learning with $T$ arriving pairs (cost, data) and a budget constraint of $B$. Our regret bounds for this model are on the order of $T/\sqrt{B}$ and we give lower bounds on the same order.Comment: Full version of EC 2015 paper. Color recommended for figures but nonessential. 36 pages, of which 12 appendi

Competitive Information Disclosure with Multiple Receivers

This paper analyzes a model of competition in Bayesian persuasion in which two symmetric senders vie for the patronage of multiple receivers by disclosing information about the qualities (i.e., binary state -- high or low) of their respective proposals. Each sender is allowed to commit to a signaling policy where he sends a private (possibly correlated) signal to every receiver. The sender's utility is a monotone set function of receivers who make a patron to this sender. We characterize the equilibrium structure and show that the equilibrium is not unique (even for simple utility functions). We then focus on the price of stability (PoS) in the game of two senders -- the ratio between the best of senders' welfare (i.e., the sum of two senders' utilities) in one of its equilibria and that of an optimal outcome. When senders' utility function is anonymous submodular or anonymous supermodular, we analyze the relation between PoS with the ex ante qualities $\lambda$ (i.e., the probability of high quality) and submodularity or supermodularity of utility functions. In particular, in both families of utility function, we show that $\text{PoS} = 1$ when the ex ante quality $\lambda$ is weakly smaller than $1/2$, that is, there exists equilibrium that can achieve welfare in the optimal outcome. On the other side, we also prove that $\text{PoS} > 1$ when the ex ante quality $\lambda$ is larger than $1/2$, that is, there exists no equilibrium that can achieve the welfare in the optimal outcome. We also derive the upper bound of $\text{PoS}$ as a function of $\lambda$ and the properties of the value function. Our analysis indicates that the upper bound becomes worse as the ex ante quality $\lambda$ increases or the utility function becomes more supermodular (resp.\ submodular)

Performative Prediction with Bandit Feedback: Learning through Reparameterization

Performative prediction, as introduced by Perdomo et al. (2020), is a framework for studying social prediction in which the data distribution itself changes in response to the deployment of a model. Existing work on optimizing accuracy in this setting hinges on two assumptions that are easily violated in practice: that the performative risk is convex over the deployed model, and that the mapping from the model to the data distribution is known to the model designer in advance. In this paper, we initiate the study of tractable performative prediction problems that do not require these assumptions. To tackle this more challenging setting, we develop a two-level zeroth-order optimization algorithm, where one level aims to compute the distribution map, and the other level reparameterizes the performative prediction objective as a function of the induced data distribution. Under mild conditions, this reparameterization allows us to transform the non-convex objective into a convex one and achieve provable regret guarantees. In particular, we provide a regret bound that is sublinear in the total number of performative samples taken and only polynomial in the dimension of the model parameter

Evaluation of Oral Antiretroviral Drugs in Mice With Metabolic and Neurologic Complications

Antiretroviral (ART) drugs has previously been associated with lipodystrophic syndrome, metabolic consequences, and neuropsychiatric complications. ART drugs include three main classes of protease inhibitors (PIs), nucleoside analog reverse transcriptase inhibitors (NRTIs), and non-nucleoside reverse transcriptase inhibitors (NNRTIs). Our previous work demonstrated that a high risk of hyperlipidemia was observed in HIV-1-infected patients who received ART drugs in Taiwan. Patients receiving ART drugs containing either Abacavir/Lamivudine (Aba/Lam; NRTI/NRTI), Lamivudine/Zidovudine (Lam/Zido; NRTI/NRTI), or Lopinavir/Ritonavir (Lop/Rit; PI) have the highest risk of hyperlipidemia. The aim of this study was to investigate the effects of Aba/Lam (NRTI/NRTI), Lam/Zido (NRTI/NRTI), and Lop/Rit (PI) on metabolic and neurologic functions in mice. Groups of C57BL/6 mice were administered Aba/Lam, Lam/Zido, or Lop/Rit, orally, once daily for a period of 4 weeks. The mice were then extensively tested for metabolic and neurologic parameters. In addition, the effect of Aba/Lam, Lam/Zido, and Lop/Rit on lipid metabolism was assessed in HepG2 hepatocytes and during the 3T3-L1 preadipocyte differentiation. Administration with Aba/Lam caused cognitive and motor impairments in mice, as well as their metabolic imbalances, including alterations in leptin serum levels. Administration with Lop/Rit also caused cognitive and motor impairments in mice, as well as their metabolic imbalances, including alterations in serum levels of total cholesterol, and HDL-c. Treatment of mice with Aba/Lam and Lop/Rit enhanced the lipid accumulation in the liver, and the decrease in AMP-activated protein kinase (AMPK) phosphorylation and/or its downstream target acetyl-CoA carboxylase (ACC) protein expression. In HepG2 hepatocytes, Aba/Lam, Lam/Zido, and Lop/Rit also enhanced the lipid accumulation and decreased phosphorylated AMPK and ACC proteins. In 3T3-L1 pre-adipocyte differentiation, Aba/Lam and Lop/Rit reduced adipogenesis by decreasing expression of transcription factor CEBPb, implicating the lipodystrophic syndrome. Our results demonstrate that daily oral administration of Aba/Lam and Lop/Rit may produce cognitive, motor, and metabolic impairments in mice, regardless of HIV-1 infection

The association of PBX1 polymorphisms with overweight/obesity and metabolic alterations in the Korean population

Pre-B-cell leukemia transcription factor 1 (PBX1), which is located on chromosome 1q23, was recently reported to be associated with type 2 diabetes mellitus. We examined whether single nucleotide polymorphisms (SNPs) of the PBX1 gene are associated with overweight/obesity in a Korean population. We genotyped 66 SNPs in the PBX1 gene and investigated their association with clinical phenotypes found in 214 overweight/obese subjects and 160 control subjects using the Affymetrix Targeted Genotyping chip array. Seven SNPs (g.+75186C>T, g.+78350C>A, g.+80646C>T, g.+138004C>T, g.+185219G>A, g.+191272A>C, and g.+265317T>A) were associated with the risk of obesity in three models (codominant, dominant, and recessive) (P=0.007-0.05). Haplotype 1 (CAC) and 3 (TAC) of block 3 and haplotype 2 (GGAAT) of block 10 were also strongly associated with the risk of obesity. In the control group, subjects that had homozygote for the major allele for both g.+185219G>A and g.+191272A>C showed lower high density lipoprotein-cholesterol (HDL-C) level compared to those possessing the minor allele, suggesting that the association between the homozygote for the major allele for both g.+185219G>A and g.+191272A>C and HDL-C is attributable to the increased risk of obesity. This study suggests that the PBX1 gene is a possible risk factor in overweight/obese patients