NetEcon '18- Proceedings of the 13th Workshop on Economics of Networks, Systems and Computation

Full Citation in the ACM Digital Library

Are Data Caps Reasonable?

We discuss how data caps may be evaluated under the FCC's 2015 Open Internet Order. We find that heavy-users caps on mobile broadband service are likely to satisfy the Order's rules, that profit-maximizing caps on mobile broadband service may or may not satisfy the rules, and that caps on fixed broadband service are unlikely to satisfy the rules.

Watch Ads, Earn Data: Economics of Mobile Data Rewards

A Winners-Take-All Incentive Mechanism for Crowd-Powered Systems

This paper studies incentive mechanisms for crowd-powered systems, including applications such as collection of personal data for big-data analytics and crowdsourcing. In big-data analytics using personal data, an individual may control the quality of reported data via a privacy-preserving mechanism that randomizes the answer. In crowdsourcing, the quality of the reported answer depends on the amount of effort spent by a worker or a team. In these applications, incentive mechanisms are critical for eliciting data/answers with target quality. This paper focuses the following two fundamental questions: what is the minimum payment required to incentivize an individual to submit a data/answer with quality level ∈? and what incentive mechanisms can achieve the minimum payment?

Let ∈i denote the quality of the data/answer reported by individual i. In this paper, we first derive a lower bound on the minimum amount of payment required for guaranteeing quality level ∈i. Inspired by the lower bound, we propose an incentive mechanism, named Winners-Take-All (WINTALL). WINTALL first decides a winning answer based on the reported data, cost functions of individuals, and some prior distribution; and then pays to individuals whose reported data match the winning answer. Under some assumptions, we show that the expected payment of WINTALL matches the lower bound. In the application of private discrete distribution estimation, we show that WINTALL simply rewards individuals whose reported answers match the most popular answer from the reported ones (the prior distribution is not needed in this case).

Relating Metric Distortion and Fairness of Social Choice Rules

Disaggregating User Evaluations Using the Shapley Value

We consider a market where final products or services are compositions of a number of basic services. Users are asked to evaluate the quality of the composed product after purchase. The quality of the basic service influences the performance of the composed services but cannot be observed directly. The question we pose is whether it is possible to use user evaluations on composed services to assess the quality of basic services. We discuss how to combine aggregation of evaluations across users and disaggregation of information on composed services to derive valuations for the single components. As a solution we propose to use the (weighted) average as aggregation device in connection with the Shapley value as disaggregation method, since this combination fulfills natural requirements in our context. In addition, we address some occurring computational issues: We give an approximate solution concept using only a limited number of evaluations which guarantees nearly optimal results with reduced running time. Lastly, we show that a slightly modified Shapley value and the weighted average are still applicable if the evaluation profiles are incomplete.

Optimal pricing for a peer-to-peer sharing platform under network externalities

In this paper, we analyse how a peer-to-peer sharing platform should price its service (when imagined as an excludable public good) to maximize profit, when each user's participation adds value to the platform service by creating a positive externality to other participants. To characterize network externalities as a function of the number of participants, we consider different bounded and unbounded user utility models. The bounded utility model fits many infrastructure sharing applications with bounded network value, in which complete coverage has a finite user valuation (e.g., WiFi or hotspot). The unbounded utility model fits the large scale data sharing and explosion in social media, where it is expected that the network value follows Metcalfe's or Zipf's law. For both models, we analyze the optimal pricing schemes to select heterogeneous users in the platform under complete and incomplete information of users' service valuations. We propose the concept of price of information (PoI) to characterize the profit loss due to lack of information, and present provable PoI bounds for different utility models. We show that the PoI = 2 for the bounded utility model, meaning that just half of profit is lost, whereas the PoI ≥ 2 for the unbounded utility model and increases as for a less concave utility function. We also show that the complicated differentiated pricing scheme which is optimal under incomplete user information, can be replaced by a single uniform price scheme that is asymptotic optimal. Finally, we extend our pricing schemes to a two-sided market by including a new group of 'pure' service users contributing no externalities, and show that the platform may charge zero price to the original group of users in order to attract the pure user group.

Diffusion in Random Networks: Impact of Degree Distribution

Word of Mouth (WoM) is known as a powerful marketing force, as numerous empirical studies reveal that consumers' purchasing decisions are based on the advice of those in their social networks rather than on direct advertising. A recent international survey by Nielsen reports that 92% of consumers around the world count on recommendations from friends and family more than all other forms of advertising. With technological advances in online communications that enable consumers to easily share their experience with their "friends," the effect and importance of WoM has only grown. In this new era, firms not only harness the power of WoM, but also improve its efficacy by targeting consumers based on the wealth of information available about their online activities. In particular, firms can utilize information on connections among consumers (1) to predict the diffusion trajectory (for both time and cost), and (2) to devise effective seeding strategies to impact the trajectory. In this work, we provide a theoretical framework to study the diffusion process for a general class of network models and drive insights on the impact of heterogeneity in the degree of connections on the cost and speed of diffusion as well as on optimal seeding strategies.

To this end, we study a diffusion process of a new product that spreads through the contacts that adopters make with their neighbors. In particular, we assume that an adopter makes contact with each of her neighbors according to an independent Poisson process with rate γ. We assume that the network underlying the connections is a random network with a given degree distribution. This general class of network models has been extensively used in the study of social networks, and it serves as the network model when the firm's knowledge about the pattern of connections is limited to the degree of each agent, rather that having access to the identity of every neighbor of an agent.

In our setting, the firm incurs a fixed cost c for each contact by an adopter. Further, we assume that the firm has a budget for seeding. In particular, prior to the adoption process, he can directly contact a fraction q > 0 of agents, who become adopters. The firm decides who to seed with the goal of minimizing the total cost or the total time to reach his target proportion of adopters. Targeting agents based on social network information is a common practice. It has been studied in network economics literature in monopolistic settings as well as in competitive settings, mainly under the assumption that the firm has complete information about the network structure. However, in our setting the firm can only target based on the degree of an agent, since he does not have access to more detailed information. Seeding agents based on their degrees seems to be more practical (as it requires the firm to acquire much less information), and empirically it has been shown to be effective.

Summary of Our Contributions: In the above setting,

(1) We compute the cost and time to reach any adoption proportion q < s < 1, for any general bounded degree distribution in the limit as the number of agents grows. To the best of our knowledge, this is the first exact characterization of the diffusion process for such a general class of degree distributions. The other exact characterizations are for the special cases of a complete network, which is equivalent to the Bass model, and a one-dimensional grid (i.e., a cycle).

(2) Using our exact characterization, we study the impact of degree distribution on the cost and time to reach any adoption proportion q < s < 1 and demonstrate a trade-off between contact cost and speed. In particular, we show that lower variability in the degree distribution results in lower cost. Fixing the average degree k ∈ N, the most cost-efficient network (to reach any adoption fraction s) is the k-regular network. The impact of degree distribution on timing is more involved, as it depends on the target adoption level (i.e., s) as well as higher moments of the degree distribution. However, our numerical analysis suggests that unless the target level is very high (e.g., s = 0.9), higher variance improves the speed.

(3) We also study the problem of optimal seeding that the firm faces for a network with a given degree distribution. Somewhat contrary to the general wisdom, we show that the optimal strategy does not necessarily entail seeding high degree agents. In fact, we prove that for the objective of minimizing cost (to achieve any target level of adoption), the optimal strategy is to maximally seed low degree agents. For the objective of minimizing time, the optimal strategy depends on the target level s and on the seeding budget q. We present examples illustrating that the optimal strategy can be to seed a mixture of high and low degree agents.

(4) In the absence of seeding, diffusion has a very slow start simply because there are not enough adopters to make contacts. We also study diffusion in such a setting by assuming that the diffusion starts with a single (randomly selected) agent. We characterize the cost and time it takes to reach α log(n) adopters, where n is the number of agents in the network, and α > 0 is a constant. We call this phase of diffusion the early adoption regime. We provide comparative statics with respect to the degree distribution in the early adoption regime, and we show that the cost is independent of degree distribution. Further, the time to diffuse to α log(n) adopters only depends on the first two moments of the degree distribution. Fixing the average degree, the time decreases as variance increases.1

Social Learning from Online Reviews with Product Choice

Economics of UAV-provided Mobile Services

Signaling in Online Retail: Efficacy of Public Signals