Mohammad Rashedul Hasan

Emergence of Cooperation Using Commitments and Complex Network Dynamics

In this paper, our goal is to achieve the emergence of cooperation in self-interested agent societies operating on highly connected scale-free networks. The novelty of this work is that agents are able to control topological features during the network formation phase. We propose a commitment-based dynamic coalition formation approach that result in a single coalition where agents mutually cooperate. Agents play an iterated Prisoners Dilemma game with their immediate neighbors and offer commitments to their wealthiest neighbors in order to form coalitions. A commitment proposal, that includes a high breaching penalty, incentivizes opponent agents to form coalitions within which they mutually cooperate and thereby increase their payoff. We have analytically determined, and experimentally substantiated, how the value of the penalty should be set with respect to the minimum node degree and the payoff values such that convergence into optimal coalitions is possible. Using a computational model, we determine an appropriate partner selection strategy for the agents that results in a network facilitating the convergence into a single coalition and thereby maximizing average expected payoff.

A multiagent solution to overcome selfish routing in transportation networks

It is well-known that selfish routing, where individual agents make uncoordinated greedy routing decisions, does not produce a socially desirable outcome in transport and communication networks. In this paper, we address this general problem of the loss of social welfare that occurs due to uncoordinated behavior in networks and model it as a multiagent coordination problem. Specifically we study strategies to overcome selfish routing in traffic networks with multiple routes where a subset of vehicles are part of a social network that exchanges traffic related data. We investigate classic traffic flow paradoxes that are ubiquitous in various types of networks leading to severe congestion. We present a novel distributed traffic coordination algorithm that alleviates congestion by harnessing the real-time information available through the drivers online social network. We also propose a utility computation mechanism for route choice that generates near-optimal flows. Our extensive simulation results show that social network based multiagent traffic route coordination contributes to mitigate the effects of these paradoxes and significantly reduces congestion.

Establishing Cooperation in Highly-Connected Networks Using Altruistic Agents

This paper addresses the importance and challenges of establishing cooperation among self-interested agents in multiagent systems (MAS). We study MAS operating on highly-connected random and scale-free (SF) networks. However, we emphasize SF networks as these are prevalent in society and nature. Existing imitation-based approaches for cooperation have been shown to not fare very well in these highly-connected networks. Motivated by studies that show the advantage of altruistic privacy buddies in online social networks to provide better privacy guarantees in highly-connected networks, we present a stochastic influencer altruistic agent (StIAA) mechanism for cooperation. In StIAA, a small proportion of altruistic agents which irrespective of their payoff, always cooperate with their neighbors are introduced into a network of self-interested agents that try to maximize their payoff by imitating the wealthiest agents in their neighborhood. To determine optimality of their action choices, the self-interested agents imitate the cooperative action of their altruistic neighbors (should there be one) with a small exploration probability. We show, both analytically and experimentally, that StIAA leads to significantly higher cooperation in highly-connected networks than the existing imitation-based approaches. We also conduct a comprehensive study on the performance of StIAA and the results indicate that it is both robust and scalable.

Game-theoretic approach for user migration in Diaspora

Diaspora is a decentralized online social networking platform where user profiles are hosted in multiple Diaspora nodes (pods) and the social connections can exist across different pods. User profile migration is a promising feature that would enable users to seamlessly migrate their profile data between different pods. However, to the best of our knowledge, there has been no research done on how this data portability may affect the user distribution and the performance of the pods. In this paper, our goal is to design an approach that facilitates the users to choose appropriate pods that would ensure better service quality. We propose a decentralized game-theoretic approach that is based on users local neighborhood information and the quality of the pods. We have analytically determined, and experimentally substantiated, that through the proposed profile migration approach the users of Diaspora reach a stable and balanced distribution that improves their overall experience in respective pods.