Computational aspects of optimal strategic network diffusion

Waniek, M., Elbassioni, K., Pinheiro, F. L., Hidalgo, C. A., & Alshamsi, A. (2020). Computational aspects of optimal strategic network diffusion. Theoretical Computer Science, 814, 153-168. https://doi.org/10.1016/j.tcs.2020.01.027Diffusion on complex networks is often modeled as a stochastic process. Yet, recent work on strategic diffusion emphasizes the decision power of agents [1] and treats diffusion as a strategic problem. Here we study the computational aspects of strategic diffusion, i.e., finding the optimal sequence of nodes to activate a network in the minimum time. We prove that finding an optimal solution to this problem is NP-complete in a general case. To overcome this computational difficulty, we present an algorithm to compute an optimal solution based on a dynamic programming technique. We also show that the problem is fixed parameter-tractable when parametrized by the product of the treewidth and maximum degree. We analyze the possibility of developing an efficient approximation algorithm and show that two heuristic algorithms proposed so far cannot have better than a logarithmic approximation guarantee. Finally, we prove that the problem does not admit better than a logarithmic approximation, unless P=NP.authorsversionpublishe

Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning

A fundamental question in any peer-to-peer ridesharing system is how to, both effectively and efficiently, dispatch user's ride requests to the right driver in real time. Traditional rule-based solutions usually work on a simplified problem setting, which requires a sophisticated hand-crafted weight design for either centralized authority control or decentralized multi-agent scheduling systems. Although recent approaches have used reinforcement learning to provide centralized combinatorial optimization algorithms with informative weight values, their single-agent setting can hardly model the complex interactions between drivers and orders. In this paper, we address the order dispatching problem using multi-agent reinforcement learning (MARL), which follows the distributed nature of the peer-to-peer ridesharing problem and possesses the ability to capture the stochastic demand-supply dynamics in large-scale ridesharing scenarios. Being more reliable than centralized approaches, our proposed MARL solutions could also support fully distributed execution through recent advances in the Internet of Vehicles (IoV) and the Vehicle-to-Network (V2N). Furthermore, we adopt the mean field approximation to simplify the local interactions by taking an average action among neighborhoods. The mean field approximation is capable of globally capturing dynamic demand-supply variations by propagating many local interactions between agents and the environment. Our extensive experiments have shown the significant improvements of MARL order dispatching algorithms over several strong baselines on the gross merchandise volume (GMV), and order response rate measures. Besides, the simulated experiments with real data have also justified that our solution can alleviate the supply-demand gap during the rush hours, thus possessing the capability of reducing traffic congestion.Comment: 11 pages, 9 figure

Multiagent Self-organization for a Taxi Dispatch System

The taxi dispatch problem involves assigning taxis to callers waiting at different locations. A dispatch system currently in use by a major taxi company divides the city (in which the system operates) into regional dispatch areas. Each area has fixed designated adjacent areas hand-coded by human experts. When a local area does not have vacant cabs, the system chooses an adjacent area to search. However, such fixed, hand-coded adjacency of areas is not always a good indicator because it does not take into consideration frequent changes in traffic patterns and road structure. This causes dispatch officials to override the system by manually enforcing movement on taxis. In this dissertation, I apply a multiagent self-organization technique to this problem, dynamically modifying the adjacency of dispatch areas. We compare performance with actual data from, and a simulation of, an operational dispatch system. The proposed technique decreases the total waiting time by up to 25 % in comparison with the real system and increases taxi utilization by 20 % in comparison with results of the simulation without self-organization. Interestingly, we also discover that human intervention (by either the taxi-dispatch officials or the taxi drivers) to manually overcome the limitations of the existing dispatch system can be counterproductive when used with a self-organizing system

Bilateral relatedness: knowledge diffusion and the evolution of bilateral trade

Abstract During the last two decades, two important contributions have reshaped our understanding of international trade. First, countries trade more with those with whom they share history, language, and culture, suggesting that trade is limited by information frictions. Second, countries are more likely to start exporting products that are related to their current exports, suggesting that shared capabilities and knowledge diffusion constrain export diversification. Here, we join both of these streams of literature by developing three measures of bilateral relatedness and using them to ask whether the destinations to which a country will increase its exports of a product are predicted by these forms of relatedness. The first form is product relatedness, and asks whether a country already exports many similar products to a destination. The second is importer relatedness, and asks whether the country exports the same product to the neighbors of the target destination. The third is exporter relatedness, and asks whether a country’s neighbors are already exporting the same product to the destination. We use bilateral trade data from 2000 to 2015, and a variety of controls in multiple gravity specifications, to show that countries are more likely to increase their exports of a product to a destination when they have more product relatedness, importer relatedness, and exporter relatedness. Then, we use several sample splits to explore whether the effects of these forms of relatedness are stronger for products of higher complexity, technological sophistication, and differentiation. We find that, in the case of product relatedness, the effects are stronger for differentiated, complex, and technologically sophisticated products. Also, we find the effects of common language and shared colonial past to increase with differentiation, complexity, and technological sophistication, while the effects of shared borders decrease with these three variables. These results suggest that product relatedness and common language capture dimensions of knowledge relatedness that are more important for the exchange of more sophisticated and differentiated products. These findings extend the ideas of relatedness to bilateral trade and show that the evolution of bilateral trade networks are shaped by relatedness among products, exporters, and importers

Shooting Low or High : Do Countries Benefit from Entering Unrelated Activities?

It is well known that countries tend to diversify their exports by entering products that are related to their current exports. Yet this average behavior is not representative of every diversification path. In this paper, we introduce a method to identify periods when countries enter relatively more unrelated products. We analyze the economic diversification paths of 93 countries between 1970 and 2010 and find that countries enter unrelated products in only about 7.2% of all observations. Then, we show that countries enter more unrelated products when they are at an intermediate level of economic development, and when they have higher levels of human capital. Finally, we ask whether countries entering more unrelated products grow faster than those entering only related products. The data shows that countries that enter more unrelated activities experience an increase in short-term economic growth of 0.5% per annum compared to those with similar levels of income, human capital, capital stock per worker, and economic complexity