REPUTATION COMPUTATION IN SOCIAL NETWORKS AND ITS APPLICATIONS

This thesis focuses on a quantification of reputation and presents models which compute reputation within networked environments. Reputation manifests past behaviors of users and helps others to predict behaviors of users and therefore reduce risks in future interactions. There are two approaches in computing reputation on networks- namely, the macro-level approach and the micro-level approach. A macro-level assumes that there exists a computing entity outside of a given network who can observe the entire network including degree distributions and relationships among nodes. In a micro-level approach, the entity is one of the nodes in a network and therefore can only observe the information local to itself, such as its own neighbors behaviors. In particular, we study reputation computation algorithms in online distributed environments such as social networks and develop reputation computation algorithms to address limitations of existing models. We analyze and discuss some properties of reputation values of a large number of agents including power-law distribution and their diffusion property. Computing reputation of another within a network requires knowledge of degrees of its neighbors. We develop an algorithm for estimating degrees of each neighbor. The algorithm considers observations associated with neighbors as a Bernoulli trial and repeatedly estimate degrees of neighbors as a new observation occurs. We experimentally show that the algorithm can compute the degrees of neighbors more accurately than a simple counting of observations. Finally, we design a bayesian reputation game where reputation is used as payoffs. The game theoretic view of reputation computation reflects another level of reality in which all agents are rational in sharing reputation information of others. An interesting behavior of agents within such a game theoretic environment is that cooperation- i.e., sharing true reputation information- emerges without an explicit punishment mechanism nor a direct reward mechanisms

Game Theory-Based Cooperation for Underwater Acoustic Sensor Networks: Taxonomy, Review, Research Challenges and Directions.

Exploring and monitoring the underwater world using underwater sensors is drawing a lot of attention these days. In this field cooperation between acoustic sensor nodes has been a critical problem due to the challenging features such as acoustic channel failure (sound signal), long propagation delay of acoustic signal, limited bandwidth and loss of connectivity. There are several proposed methods to improve cooperation between the nodes by incorporating information/game theory in the node's cooperation. However, there is a need to classify the existing works and demonstrate their performance in addressing the cooperation issue. In this paper, we have conducted a review to investigate various factors affecting cooperation in underwater acoustic sensor networks. We study various cooperation techniques used for underwater acoustic sensor networks from different perspectives, with a concentration on communication reliability, energy consumption, and security and present a taxonomy for underwater cooperation. Moreover, we further review how the game theory can be applied to make the nodes cooperate with each other. We further analyze different cooperative game methods, where their performance on different metrics is compared. Finally, open issues and future research direction in underwater acoustic sensor networks are highlighted

Averting Depletion in a Two-Player Common Pool Resource Game: Being Seen, the Expectation of Future Encounters, and Biophilia Play a Role in Cooperation

Using a two-player common pool resource game, we investigated the influence of multiple factors on cooperation: (1) probability of future rounds, (2) visibility of other participants, (3) biophilia, (4) future discounting, and (5) life history. In each round, participants simultaneously and independently (without conferring) decided how much of the common pool to consume. Participants (n = 116) were informed that the shared resource would be fully replenished in the next round—but only if—both players together consumed ≤ 50% of the common pool in the current round. Additionally, participants were told the probability (0–100%) of further rounds of play with the same player (this probability was not real; it was purely to manipulate the player’s expectations). To assess the effect of the probability of future rounds, we developed a mathematical model to predict the threshold that would permit a Nash Equilibrium of Conditional Cooperation (CC). To manipulate visibility, half the pairs were tested in the same room (seen condition) and half in separate rooms (unseen condition). To measure biophilia, the “Nature Relatedness” (NR) scale was used. To measure future discounting, the “consideration of future consequences” (CFC) scale was used. To measure life history, the participant’s UK postcode was obtained (indicating possible residence in a deprived neighbourhood). Participants in our study were not paid. In our results, there was a significant effect showing more cooperation in the visible than not visible condition, but no significant effects of NR, CFC, nor postcodes (but NR was significant in interaction with visibility). For predictability of future rounds, we found a number of significant effects using different tests. A notable result was that there was significantly more CC when the probability of future rounds was ≥ 69% (congruent with one of our model predictions)

Supporting cooperation and coordination in open multi-agent systems

Cooperation and coordination between agents are fundamental processes for increasing aggregate and individual benefit in open Multi-Agent Systems (MAS). The increased ubiquity, size, and complexity of open MAS in the modern world has prompted significant research interest in the mechanisms that underlie cooperative and coordinated behaviour. In open MAS, in which agents join and leave freely, we can assume the following properties: (i) there are no centralised authorities, (ii) agent authority is uniform, (iii) agents may be heterogeneously owned and designed, and may consequently have con icting intentions and inconsistent capabilities, and (iv) agents are constrained in interactions by a complex connecting network topology. Developing mechanisms to support cooperative and coordinated behaviour that remain effective under these assumptions remains an open research problem. Two of the major mechanisms by which cooperative and coordinated behaviour can be achieved are (i) trust and reputation, and (ii) norms and conventions. Trust and reputation, which support cooperative and coordinated behaviour through notions of reciprocity, are effective in protecting agents from malicious or selfish individuals, but their capabilities can be affected by a lack of information about potential partners and the impact of the underlying network structure. Regarding conventions and norms, there are still a wide variety of open research problems, including: (i) manipulating which convention or norm a population adopts, (ii) how to exploit knowledge of the underlying network structure to improve mechanism efficacy, and (iii) how conventions might be manipulated in the middle and latter stages of their lifecycle, when they have become established and stable. In this thesis, we address these issues and propose a number of techniques and theoretical advancements that help ensure the robustness and efficiency of these mechanisms in the context of open MAS, and demonstrate new techniques for manipulating convention emergence in large, distributed populations. Specfically, we (i) show that gossiping of reputation information can mitigate the detrimental effects of incomplete information on trust and reputation and reduce the impact of network structure, (ii) propose a new model of conventions that accounts for limitations in existing theories, (iii) show how to manipulate convention emergence using small groups of agents inserted by interested parties, (iv) demonstrate how to learn which locations in a network have the greatest capacity to in uence which convention a population adopts, and (v) show how conventions can be manipulated in the middle and latter stages of the convention lifecycle

Essays on bargaining and repeated games

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 194-202).The thesis consists of four essays on bargaining and repeated games. The first essay studies whether allowing players to sign binding contracts governing future play leads to reputation effects in repeated games with long-run players. Given any prior over behavioral types, a modified prior is constructed with the same total weight on behavioral types and a larger support under which almost all efficient, feasible, and individually rational payoffs are attainable in perfect Bayesian equilibrium. Thus, whether reputation effects emerge in repeated games with contracts depends on details of the prior distribution over behavioral types other than its support. The second essay studies reputational bargaining under the assumption of first-order knowledge of rationality. The share of the surplus that a player can guarantee herself is determined, as is the bargaining posture that she must announce in order to guarantee herself this much. It is shown that this maxmin share of the surplus is large relative to the player's initial reputation, and that the corresponding bargaining posture simply demands this share plus compensation for any delay in reaching agreement. The third essay studies the maximum level of cooperation that can be sustained in sequential equilibrium in repeated games with network monitoring. The foundational result is that the maximum level of cooperation can be sustained in grim trigger strategies. Comparative statics on the maximum level of cooperation are shown to be highly tractable. For the case of fixed monitoring networks, a new notion of network centrality is introduced, which characterizes which players have greater capacities for cooperation and which networks can support more cooperation. The fourth essay studies the price-setting problem of a monopoly that in each time period has the option of failing to deliver its good after receiving payment. Optimal equilibrium pricing and profits are characterized. For durable goods, a lower bound on optimal profit for any discount factor is provided. The bound converges to the optimal static monopoly profit as the discount factor converges to one, in contrast to the Coase conjecture.by Alexander G. Wolitzky.Ph.D

Intelligent Security Provisioning and Trust Management for Future Wireless Communications

The fifth-generation (5G)-and-beyond networks will provide broadband access to a massive number of heterogeneous devices with complex interconnections to support a wide variety of vertical Internet-of-Things (IoT) applications. Any potential security risk in such complex systems could lead to catastrophic consequences and even system failure of critical infrastructures, particularly for applications relying on tight collaborations among distributed devices and facilities. While security is the cornerstone for such applications, trust among entities and information privacy are becoming increasingly important. To effectively support future IoT systems in vertical industry applications, security, trust and privacy should be dealt with integratively due to their close interactions. However, conventional technologies always treat these aspects separately, leading to tremendous security loopholes and low efficiency. Existing solutions often feature various distinctive weaknesses, including drastically increased latencies, communication and computation overheads, as well as privacy leakage, which are extremely undesirable for delay-sensitive, resource-constrained, and privacy-aware communications. To overcome these issues, this thesis aims at creating new multi-dimensional intelligent security provisioning and trust management approaches by leveraging the most recent advancements in artificial intelligence (AI). The performance of the existing physical-layer authentication could be severely affected by the imperfect estimate and the variation of physical link attributes, especially when only a single attribute is employed. To overcome this challenge, two multi-dimensional adaptive schemes are proposed as intelligent processes to learn and track the all available physical attributes, hence to improve the reliability and robustness of authentication by fusing multiple attributes. To mitigate the effects of false authentication, an adaptive trust management-based soft authentication and progressive authorization scheme is proposed by establishing trust between transceivers. The devices are authorized by their trust values, which are dynamically evaluated in real-time based on the varying attributes, resulting in soft security and progressive authorization. By jointly considering security and privacy-preservation, a distributed accountable recommendation-based access scheme is proposed for blockchain-enabled IoT systems. Authorized devices are introduced as referrers for collaborative authentication, and the anonymous credential algorithm helps to protect privacy. Wrong recommendations will decrease the referrers’ reputations, named as accountability. Finally, to secure resource-constrained communications, a lightweight continuous authentication scheme is developed to identify devices via their pre-arranged pseudo-random access sequences. A device will be authenticated as legitimate if its access sequences are identical to the pre-agreed unique order between the transceiver pair, without incurring long latency and high overhead. Applications enabled by 5G-and-beyond networks are expected to play critical roles in the coming connected society. By exploring new AI techniques, this thesis jointly considers the requirements and challenges of security, trust, and privacy provisioning, and develops multi-dimensional intelligent continuous processes for ever-growing demands of the quality of service in diverse applications. These novel approaches provide highly efficient, reliable, model-independent, situation-aware, and continuous protection for legitimate communications, especially in the complex time-varying environment under unpredictable network dynamics. Furthermore, the proposed soft security enables flexible designs for heterogeneous IoT devices, and the collaborative schemes provide efficient solutions for massively distributed entities, which are of paramount importance to diverse industrial applications due to their ongoing convergence with 5G-and-beyond networks

Investor behaviour, financial markets and the international economy

This dissertation focuses on analysing investor behaviour and price processes in asset markets. It consists of four self-contained essays in the areas of market microstructure, risk attitude of boundedly rational investors, and international finance. Chapter 2 provides a review of the existing literature on the informational aspects of price processes. A common feature of these models is that prices reflect information that is dispersed among many traders. Dynamic models can explain crashes and illustrate a rationale for technical/chart analysis. The second emphasis of this survey is on herding models. In Chapter 3, I have developed a multi-period trading-game that analyses the impact of information leakage. I find that a trader who receives a signal about a future public announcement can exploit this information twice. First, when he receives his signal, and second, at the time of the public announcement. Furthermore, I show that the investor trades very aggressively on the rumour in order to manipulate the price. This enhances his informational advantage after the correct information is made public. He also trades for speculative reasons, i.e. he buys stocks that he plans to sell after the public announcement. Chapter 4 provides a theoretical rationale for experimental results such as loss aversion and diminishing sensitivity. A decision maker is considered to be boundedly rational if he can not find his new optimal consumption bundle with certainty when he is faced with a new income level. This makes him more risk averse at his current reference income level. It also makes him less risk averse for a range of incomes below his reference income level. Chapter 5 considers a two country economy similar to that in Obstfeld and Rogoff (1995). We find that conclusions about whether monetary shocks lead to exchange rate overshooting and spillovers on foreign production and consumption depend crucially on the form of price stickiness.' Sticky retail prices not only allow for a profitable ‘Beggar Thy Neighbour Policy’ but also lead to exchange rate overshooting. This is not the case under sticky wholesale prices and sticky wages

A Novel Methodology for designing Policies in Mobile Crowdsensing Systems

Mobile crowdsensing is a people-centric sensing system based on users' contributions and incentive mechanisms aim at stimulating them. In our work, we have rethought the design of incentive mechanisms through a game-theoretic methodology. Thus, we have introduced a multi-layer social sensing framework, where humans as social sensors interact on multiple social layers and various services. We have proposed to weigh these dynamic interactions by including the concept of homophily and we have modelled the evolutionary dynamics of sensing behaviours by defining a mathematical framework based on multiplex EGT, quantifying the impact of homophily, network heterogeneity and various social dilemmas. We have detected the configurations of social dilemmas and network structures that lead to the emergence and sustainability of human cooperation. Moreover, we have defined and evaluated local and global Nash equilibrium points by including the concepts of homophily and heterogeneity. We have analytically defined and measured novel statistical measures of social honesty, QoI and users' behavioural reputation scores based on the evolutionary dynamics. We have defined the Decision Support System and a novel incentive mechanism by operating on the policies in terms of users' reputation scores, that also incorporate users' behaviours other than quality and quantity of contributions. Experimentally, we have considered the Waze dataset on vehicular traffic monitoring application and derived the disbursement of incentives comparing our method with baselines. Results demonstrate that our methodology, which also includes the local (microscopic) spatio-temporal distribution of behaviours, is able to better discriminate users' behaviours. This multi-scale characterisation of users represents a novel research direction and paves the way for novel policies on mobile crowdsensing systems