Abstract

Civic Crowdfunding is emerging as a popular means to mobilize funding from citizens for public projects. A popular mechanism deployed on civic crowdfunding platforms is a provision point mechanism, wherein, the total contributions must reach a predetermined threshold in order for the project to be provisioned (undertaken). Such a mechanism may have multiple equilibria; unfortunately, in many of these equilibria, the project is not funded even if it is highly valued among the agents.Recent work has proposed mechanisms with refund bonuses where the project gets funded in equilibrium if its net value is higher than a threshold among the agents who are aware of the crowdfunding effort. In this paper, we formalize the notion of social desirability of a public project and propose mechanisms which use the idea of referrals to expand the pool of participants and achieve an equilibrium in which the project gets funded if its net value exceeds a threshold among the entire agent population. We call this new class of mechanisms Referral-Embedded Provision Point Mechanisms (REPPM). We specifically propose two variants of REPPM and both these mechanisms have the remarkable property that, at equilibrium, referral bonuses are offered but there is no need for actual payment of these bonuses. We establish that any given agent’s equilibrium strategy is to refer other agents and to contribute in proportion to the agent’s true value for the project. By referring others to contribute, an agent can, in fact, reduce his equilibrium contribution. In our view, the proposed mechanisms will lead to an increase in the number of projects that are funded on civic crowdfunding platforms.

Abstract

Motivated by web based marketplaces where the number of bidders in an auction is a small subset of potential bidders, we consider auctions where the auctioneer (seller) wishes to increase her revenue and/or social welfare by expanding the pool of participants. To this end, the seller crowdsources this task by offering a referral bonus to the participants. With the introduction of referrals, a participant can now bid and/or refer other agents to bid. We call our auctions crowdsourced referral auctions since the seller exploits the knowledge that agents have about other potential participants in the crowd. We introduce the notion of price of locality to quantify the loss in social welfare due to restricted (local) access of the seller to potential bidders. We introduce the notion of Crowdsourced Referral Auction Mechanisms (CRAMs), propose two novel versions of CRAMs and study the induced referral game in the canonical context of an auction for selling a single indivisible item. We compare their revenue performance and game theoretic properties and show that both of them outperform the baseline auction without referrals

Abstract

Crowdfunding is emerging as a popular means to generate funding from citizens for public projects. This is popularly known as civic crowdfunding. In this paper, we focus on crowdfunding public projects with provision point: these are projects in which contributions must reach a predetermined threshold in order for the project to be provisioned. On web based civic crowdfunding platforms, the success of crowdfunding public projects has been somewhat mixed. In this paper, our objective is to design a mechanism that improves the success of crowdfunding public projects. In particular, we propose a class of mechanisms for crowdfunding platforms with sequentially arriving agents. This class of mechanisms induces an extensive form game for agents arriving on the platform and we show that the game has a non-empty set of sub-game perfect equilibria at which the project is fully funded. We call this new class of mechanisms Provision Point Mechanism with Securities (PPS). The novelty of PPS lies in the use of a prediction market to incentivize agents to contribute in proportion to their true value for the project and to contribute as soon as they arrive at the crowdfunding platform. Different variations of PPS are possible depending on the underlying prediction market. In this paper, we use a cost function (or equivalently, scoring rule) based prediction market; in fact, we specify the requirements that a cost function should satisfy to be used in PPS. We study and compare two specific instances of PPS: (1) Logarithmic Market Scoring Rule based and (2) Quadratic Scoring Rule based. We also discuss the considerations that should guide the choice of the cost function when deploying our mechanism on crowdfunding platforms

Abstract

We consider a general crowdsourcing setting with strategic workers whose qualities are unknown and design a multiarmed bandit (MAB) mechanism, CrowdUCB, which is deterministic, regret minimizing, and offers immediate payments to the workers. The problem involves sequentially selecting workers to process tasks in order to maximize the social welfare while learning the qualities of the strategic workers (strategic about their costs). Existing MAB mechanisms are either: (a) deterministic which potentially cause significant loss in social welfare, or (b) randomized which typically lead to high variance in payments. CrowdUCB completely addresses the above problems with the following features: (i) offers deterministic payments, (ii) achieves logarithmic regret in social welfare, (iii) renders allocations more effective by allocating blocks of tasks to a worker instead of a single task, and (iv) offers payment to a worker immediately upon completion of an assigned block of tasks. CrowdUCB is a mechanism with learning that learns the qualities of the workers while eliciting their true costs, irrespective of whether or not the workers know their own qualities. We show that CrowdUCB is ex-post individually rational (EPIR) and ex-post incentive compatible (EPIC) when the workers do not know their own qualities and when they update their beliefs in sync with the requester. When the workers know their own qualities, CrowdUCB is EPIR and ε−EPIC where ε is sub-linear in terms of the number of tasks.

Abstract

Dynamic resource assignment is a common problem in multi-agent systems. We consider scenarios in which dynamic agents have preferences about assignments and the resources that can be assigned using online auctions. We study the trade-off between the following online auction properties: (i) truthfulness, (ii) expressiveness, (iii) efficiency, and (iv) average case performance. We theoretically and empirically compare four different online auctions: (i) Arrival Priority Serial Dictatorship, (ii) Split Dynamic VCG, (iii) e-Action, and (iv) Online Ranked Competition Auction. The latter is a novel design based on the competitive secretary problem. We show that, in addition to truthfulness and algorithmic efficiency, the degree of competition also plays an important role in selecting the best algorithm for a given context

Abstract

This paper presents a framework for Robust Infrastructure over Shared Computing resource (RISC), which can offer Organizations with Small-scale Computing infrastructures (OSCs) a way to share their unused resources in an ad-hoc manner for suitable monetary incentives. Such a framework provides dual benefits to an OSC: it enables sharing of unused resource during periods of low computing load while allowing execution of any long-term computation on public or anonymized data at a very low cost during periods of high load. The ad-hoc and heterogeneous nature of the shared infrastructure make the resource management problem in RISC non-trivial—a resource manager needs to: (i) maximize profit while determining incentives for resource owners and prices for resource users in an integrated manner; and (ii) emulate large-scale cloud-like robustness and capabilities out of unreliable, small-scale and intermittently available resources at a low cost. This leads to a constrained market situation where offered prices and incentives should lead to a desired level of SLA and reliability for the consumers. Existing approaches of incentive based scheduling for market-like grids assume an open market, based only on demand response; and thus are inapplicable for the constrained market situation in shared resources infrastructure. Specifically, RISC framework has two main components: (i) a firstof-a-kind Dynamic Pricing and Incentivization (DPI) strategy that computes the incentives and the prices while maximizing profit for RISC, using an epoch-by-epoch pricing feedback loop; and (ii) a DPI dependent Reliability, Cost and SLAaware (RCS) scheduler that takes the resource reservation requests as input and assigns replicas of these requests to one or more shared resources for guaranteeing performance SLAs and reliability, while minimizing the cost of resource reservations. Moreover, to handle the communication overhead of computing over geographically distributed resources, the scheduler strives to reduce the network cost of resource allocation. Results from extensive trace-driven experimentation show that our approach can indeed provide appropriate incentives for resource providers, and robust cost-efficient infrastructure solution for resource users.

Abstract

Crowdsourcing marketplaces link large populations of workers to an even larger number of tasks. Thus, it is necessary to have mechanisms for matching workers with interesting and suitable tasks. Earlier work has addressed the problem of finding optimal workers for a given set of tasks. However, workers also have preferences and will stay with a platform only if it gives them interesting tasks. We therefore analyze several matching mechanisms that take into account workers’ preferences as well. We propose that the workers pay premiums to get preferred matches and auction-based models where preferences are expressed through variations of the payment for a task. We analyze the properties of two matching different mechanisms: Split Dynamic VCG (SDV) and e-Auction. We compare both the mechanisms with Arrival Priority Serial Dictatorship (APSD) empirically for efficiency.

Abstract

For the task assignment problem in an expert crowdsourcing platform, we propose that the dynamically arriving workers report their preferences for the tasks as ordinal preferences to the platform. We model then the task assignment problem as a dynamic two sided matching problem. In this paper we study the dynamic two sided matching when the men (the workers) side of the market is arriving dynamically and the women (the requesters) side is available since beginning. We assume strict preferences of the agents. Using a deferred acceptance algorithm as a building block, we first develop f AP ODA, a class of strategy-proof online mechanisms. We design f AP ODA and f T hODA in this class. As no mechanism can achieve stability in our settings, we propose a weaker notion of stability, namely, progressive stability. We introduce an online mechanism f RODA that achieve the progressive stability. For achieving good rank-efficiency, we design an online matching mechanism f BOMA. We study all the four mechanisms empirically for stability and rank-efficiency.