NetEcon 2021

The 16th Workshop on the Economics of Networks, Systems and Computation

Program: July 23, 2021, 10AM-12PM EDT

Paper details:

by Yongkang Guo, Zhihuan Huang, Yuqing Kong and Qian Wang

Abstract: Community structure is an important feature of many networks. One of the most popular ways to capture community structure is using a quantitative measure, modularity, which can serve as both a standard benchmark comparing different community detection algorithms, and a optimization objective for detecting communities. Previous works on modularity mainly focus on the approximation method for modularity maximization to detect communities, or minor modifications to the definition. 
In this paper, we study modularity from an information-theoretical perspective and show that modularity and mutual information in networks are essentially the same. The main contribution is that we develop a family of generalized modularity measure, $f$-Modularity, which includes the original modularity as a special case. At a high level, we show the significance of community structure is equivalent to the amount of information contained in the network. On the one hand, $f$-Modularity has an information-theoretical interpretation and enjoys the desired properties of mutual information measure. On the other hand, quantifying community structure also provides an approach to estimate the mutual information between discrete random samples with a large value space but given only limited samples. We demonstrate the algorithm for optimizing $f$-Modularity in a relatively general case, and validate it through experimental results on simulated networks. We also apply $f$-Modularity to real-world market networks. Our results bridge two important fields, complex network and information theory, and also shed light on the design of measures on community structure in the future. 

by Christos Papadimitriou, Kiran Vodrahalli and Mihalis Yannakakis

Abstract: On-line firms deploy suites of software platforms, where each platform is designed to interact with users during a certain activity, such as browsing, chatting, socializing, emailing, driving, etc. The economic and incentive structure of this exchange, as well as its algorithmic nature, have not been explored to our knowledge. We model this interaction as a Stackelberg game between a Designer and one or more Agents. We model an Agent as a Markov chain whose states are activities; we assume that the Agent's utility is a linear function of the steady-state distribution of this chain. The Designer may design a platform for each of these activities/states; if a platform is adopted by the Agent, the transition probabilities of the Markov chain are affected, and so is the objective of the Agent. The Designer's utility is a linear function of the steady state probabilities of the accessible states (that is, the ones for which the platform has been adopted), minus the development cost of the platforms. The underlying optimization problem of the Agent --- that is, how to choose the states for which to adopt the platform --- is an MDP. If this MDP has a simple yet plausible structure (the transition probabilities from one state to another only depend on the target state and the recurrent probability of the current state) the Agent's problem can be solved by a greedy algorithm. The Designer's optimization problem (designing a custom suite for the Agent so as to optimize, through the Agent's optimum reaction, the Designer's revenue), is in general NP-hard to approximate within any finite ratio; however, in the special case, while still NP-hard, has an FPTAS. These results generalize, under mild additional assumptions, from a single Agent to a distribution of Agents with finite support, as well as to the setting where other Designers have already created platforms, and the Designer must find the best response to the strategies of the other Designers. We discuss other implications of our results and directions of future research. 

by Matheus Venturyne Xavier Ferreira, Daniel J. Moroz, David C. Parkes and Mitchell Stern

Abstract: In recent years, prominent blockchain systems such as Bitcoin and Ethereum have experienced explosive growth in transaction volume, leading to frequent surges in demand for limited block space, causing transaction fees to fluctuate by orders of magnitude. Under the standard first-price auction approach, users find it difficult to estimate how much they need to bid to get their transactions accepted (balancing the risk of delay with a preference to avoid paying more than is necessary). 
In light of these issues, new transaction fee mechanisms have been proposed, most notably EIP-1559, proposed by \citet{buterin2019eip1559}. A problem with EIP-1559 is that under market instability, it again reduces to a first-price auction. Here, we propose dynamic posted-price mechanisms, which are {\em ex post} Nash incentive compatible for myopic bidders and dominant strategy incentive compatible for myopic miners. We give sufficient conditions for which our mechanisms are stable and approximately welfare optimal in the probabilistic setting where each time step, bidders are drawn i.i.d. from a static (but unknown) distribution. Under this setting, we show instances where our dynamic mechanisms are stable, but EIP-1559 is unstable. Our main technical contribution is an iterative algorithm that, given oracle access to a Lipschitz continuous and concave function $f$, converges to a fixed point of $f$. 

by Meng Zhang, Ermin Wei and Randall Berry

Abstract: Federated learning enables machine learning algorithms to be trained over multiple decentralized edge devices without requiring the exchange of local datasets. Successfully deploying federated learning requires ensuring that agents (e.g., mobile devices) faithfully execute the intended algorithm, which has been largely overlooked in the literature. In this study, we first use risk bounds to analyze how the key feature of federated learning, unbalanced and non-i.i.d. data, affects agents' incentives to voluntarily participate and obediently follow traditional federated learning algorithms. Our analysis reveals that agents with less typical data distributions and relatively more samples are more inclined to opt out of or tamper with federated learning algorithms. We then design a Faithful Federated Learning (FFL) mechanism which approximates the Vickrey–Clarke–Groves (VCG) payments via an incremental computation. We show that it achieves (probably approximate) optimality, faithful implementation, voluntary participation, and budget balance. Further, the time complexity of computing all agents' payments in the number of agents is $\mathcal{O}(1)$. 

From 2020, the following papers which were presented virtually are carried forward: