This thesis is concerned with game-theoretic models of oligopoly resource markets. They revolve around an open market, on which a number of firms sell a common resource. The market price-demand relationship means that the price (demand) that results from the firm’s production (pricing) decisions is a function of the decisions of all firms selling to that market. This means that firms must generally anticipate the actions of competing firms when determining their own strategies, which means that these models often need to be analysed using game theory. We focus on games in which the resource is exhaustible, with the exception of Chapter 5, in which the majority of the analysis is carried out in an inexhaustible resources setting. Exhaustibility introduces an additional complication into these games; that of allocating the extraction and sale of a limited resource pool over time. We consider several separate areas of extension, which we outline below. In Chapter 2, we consider a dynamic Stackelberg game. Stackelberg competition is an asymmetric form of competition in which one player (the leader) has the ability to pre-commit to and announce a strategy in advance. The ability to pre-commit to a strategy is almost always highly valuable, and in this case allows the leader to drive down the follower’s production by pre-committing to drive up their own. We follow the framework used in [62] to analyse Cournot competition to derive our results. In Chapter 3, we compare the two settings in which resource extraction models are usually formulated: Open-Loop, in which the players determine their strategies as functions of time and the initial resource levels of the players only; and Feedback-Loop, in which the players determine their strategies at each point in time as a function of the current resource levels at that time. Our focus is on the investigation of the relationship between the difference in the production or value of a firm under these two models, and the distribution of resources across the firms. In Chapter 4, we consider a common property resource game. These involve multiple firms which can extract from a common resource pool. We study a widely-used Open- viii Loop model, as formulated in [79]. We examine the result that analysis of the problem by standard methods results in two candidate equilibria, and argue that one of these equilibria can be ruled out by construction of a superior response. In Chapter 5, we analyse joint constraints on production, namely constraints which are met when the total production is above or below a certain level. It is a well- established result that these constraints can result in multiple equilibria. We provide several brief extensions to existing uniqueness results. We also demonstrate methods by which these results can be utilised to analyse games with piecewise-linear windfall taxes or congestion charges. Finally, we discuss the problems of extending these results to games with resource exhaustibility.

Chapter 1 : I attempt a detailed literature review on the passage from the probabilistic versions of the Condorcet Jury Theorem to models augmented by the concept of strategic agents, including both theoretical and relevant empirical work. In the first part, I explore the most influential relevant game theoretic models and their main predictions. In the second part, I review what voting experiments have to say about these predictions, with a brief mention of the experiments' key methodological aspects. In the final part, I provide with an attempt to map the recent strategic voting literature in terms of structure and scope. I close with a philosophical question on the exogeneity of a "correct" choice of a voting outcome, which is inherent in the current strategic voting literature. Chapter 2 : I develop a two stage game with individually costly political action and costless voting on a binary agenda where, in equilibrium, agents rationally cast honest votes in the voting stage. I show that a positive but sufficiently low individual cost of political action can lead to a loss in aggregate welfare for any electorate size. When the individual cost of political action is lower than the signalling gain, agents will engage in informative political action. In the voting stage, since everyone's signal is revealed, agents will unanimously vote for the same policy. Therefore, the result of the ballot will be exactly the same as the one without prior communication, but with the additional aggregate cost of political action. However, when agents have heterogeneous prior beliefs, society is large and the state of the world is sufficiently uncertain, a moderate individual cost of political action can induce informative collective action of only a subset of the members of society, which increases ex ante aggregate welfare relative to no political action. The size of the subset of agents engaging in collective action depends on the dispersion of prior opinions. Chapter 3 : This chapter shows theoretically that hearing expert opinions can be a double-edged sword for decision making committees. We study a majoritarian voting game of common interest where committee members receive not only private information, but also expert information that is more accurate than private information and observed by all members. We identify three types of equilibria of interest, namely i) the symmetric mixed strategy equilibrium where each member randomizes between following the private and public signals should they disagree; ii) the asymmetric pure strategy equilibrium where a certain number of members always follow the public signal while the others always follow the private signal; and iii) a class of equilibria where a supermajority and hence the committee decision always follow the expert signal. We find that in the first two equilibria, the expert signal is collectively taken into account in such a way that it enhances the efficiency (accuracy) of the committee decision, and a fortiori the CJT holds. However, in the third type of equilibria, private information is not reflected in the committee decision and the efficiency of committee decision is identical to that of public information, which may well be lower than the efficiency the committee could achieve without expert information. In other words, the introduction of expert information might reduce efficiency in equilibrium. Chapter 4 : In this chapter we present experimental results on the theory of the previous chapter. In the laboratory, too many subjects voted according to expert information compared to the predictions from the efficient equilibria. The majority decisions followed the expert signal most of the time, which is consistent with the class of obedient equilibria mentioned in the previous chapter. Another interesting finding is the marked heterogeneity in voting behaviour. We argue that the voters' behaviour in our data can be best described as that in an obedient equilibrium where a supermajority (and hence the decision) always follow the expert signal so that no voter is pivotal. A large efficiency loss manifests due to the presence of expert information when the committee size was large. We suggest that it may be desirable for expert information to be revealed only to a subset of committee members. Finally, in the Appendix we describe a new alternative method for producing the signal matrix of the game. Chapter 5 : There is a significant gap between the theoretical predictions and the empirical evidence about the efficiency of policies in reducing crime rates. This chapter argues that one important reason for this is that the current literature of economics of crime overlooks an important hysteresis effect in criminal behaviour. One important consequence of hysteresis is that the effect on an outcome variable from positive exogenous variations in the determining variables has a different magnitude from negative variations. We present a simple model that characterises hysteresis in both the micro and macro levels. When the probability of punishment decreases, some law abiding agents will find it more beneficial to enter a criminal career. If the probability of punishment returns to its original level, a subset of these agents will continue with their career in crime. We show that, when crime choice exhibits weak hysteresis at the individual level, crime rate in a society consisted from a continuum of agents that follows any non-uniform distribution will exhibit strong hysteresis. Only when punishment is extremely severe the effect of hysteresis ceases to exist. The theoretical predictions corroborate the argument that policy makers should be more inclined to set pre-emptive policies rather than mitigating measures.

<p>Scheduling a set of jobs over a collection of machines to optimize a certain quality-of-service measure is one of the most important research topics in both computer science theory and practice. In this thesis, we design algorithms that optimize {\em flow-time} (or delay) of jobs for scheduling problems that arise in a wide range of applications. We consider the classical model of unrelated machine scheduling and resolve several long standing open problems; we introduce new models that capture the novel algorithmic challenges in scheduling jobs in data centers or large clusters; we study the effect of selfish behavior in distributed and decentralized environments; we design algorithms that strive to balance the energy consumption and performance. </p><p>The technically interesting aspect of our work is the surprising connections we establish between approximation and online algorithms, economics, game theory, and queuing theory. It is the interplay of ideas from these different areas that lies at the heart of most of the algorithms presented in this thesis.</p><p>The main contributions of the thesis can be placed in one of the following categories.</p><p>1. Classical Unrelated Machine Scheduling: We give the first polygorithmic approximation algorithms for minimizing the average flow-time and minimizing the maximum flow-time in the offline setting. In the online and non-clairvoyant setting, we design the first non-clairvoyant algorithm for minimizing the weighted flow-time in the resource augmentation model. Our work introduces iterated rounding technique for the offline flow-time optimization, and gives the first framework to analyze non-clairvoyant algorithms for unrelated machines.</p><p>2. Polytope Scheduling Problem: To capture the multidimensional nature of the scheduling problems that arise in practice, we introduce Polytope Scheduling Problem (\psp). The \psp problem generalizes almost all classical scheduling models, and also captures hitherto unstudied scheduling problems such as routing multi-commodity flows, routing multicast (video-on-demand) trees, and multi-dimensional resource allocation. We design several competitive algorithms for the \psp problem and its variants for the objectives of minimizing the flow-time and completion time. Our work establishes many interesting connections between scheduling and market equilibrium concepts, fairness and non-clairvoyant scheduling, and queuing theoretic notion of stability and resource augmentation analysis.</p><p>3. Energy Efficient Scheduling: We give the first non-clairvoyant algorithm for minimizing the total flow-time + energy in the online and resource augmentation model for the most general setting of unrelated machines.</p><p>4. Selfish Scheduling: We study the effect of selfish behavior in scheduling and routing problems. We define a fairness index for scheduling policies called {\em bounded stretch}, and show that for the objective of minimizing the average (weighted) completion time, policies with small stretch lead to equilibrium outcomes with small price of anarchy. Our work gives the first linear/ convex programming duality based framework to bound the price of anarchy for general equilibrium concepts such as coarse correlated equilibrium.</p> / Dissertation

There is a major trend that manufacturers sell their services to customers instead of selling their products. These services can be provided through leasing, warranty, or maintenance outsourcing. In this dissertation, we have studied leasing and maintenance outsourcing services from different aspects of reliability-based maintenance, game-theoretic decision making, and inventory and supply chain management. We have studied how different interactions and relationships between the manufacturer and customer in service contracting affect the decisions they make and the profits they gain. The methods used to tackle the related decision-making processes are stochastic modeling, non-convex optimization, game-theoretical framework, and simulation. For equipment leasing, two non-cooperative game-theoretic models and a cooperative model have been developed to describe the relationships between the manufacturer (lessor) and customer (lessee). Through the lease contracts, the lessor decides on the maintenance policy of the leased equipment, and the lessee decides on the lease period and usage rate. In the non-cooperative simultaneous move scenario, the lessee and the lessor act simultaneously and independently to make their decisions. In the leader-follower non- cooperative contract, the lessor is the leader who specifies the maintenance policy first, and the lessee, as the follower, decides on the lease period and usage rate accordingly. We have next determined the total maximum profit and shown that the Nash and Stackelberg equilibria are different from the total maximum solution. As a result, the players can increase their total profit by cooperation. We have implemented the cooperative solution as an equilibrium through a nonlinear transfer-payment contract. Our results illustrate that cooperation can be regarded as a value-added strategy in establishing such lease contracts. Besides, our numerical results show that although cooperation always increases the total profit of the players, the magnitude of increase is case specific. When the lease price is low or the revenue is high, the profits in the non-cooperative contracts will be close to the cooperative alternative, while the cooperation may increase the total profit significantly in other cases. For maintenance outsourcing, we have studied different bargaining scenarios in determining the contract terms. First, we have considered the Nash bargaining solution to compute the bargaining profit of players. Next, we have considered the case where players pose threat against each other in order to increase their own bargaining position. We have determined the optimal threat strategy for each player. Our result shows that although such threatening decreases the efficiency of the contract, it can dramatically increase the profit of the player with a higher bargaining position. We have finally provided a solution to the problem of how the service agent and customer can cooperate and negotiate on the price. We have determined the discounted price as a result of negotiation. Indeed, the discounted price induces the customer to choose the total maximum maintenance policy. Our numerical examples illustrate the feasibility of using such a price-discount contract in maintenance service outsourcing. Moreover, one can see that both the customer and agent can benefit from this price-discount contract.

Firms lose millions of dollars every year to cyber-attacks and the risk to these companies is growing exponentially. The threat to monetary and intellectual property has made Information Technology (IT) security management a critical challenge to firms. Security devices, including Intrusion Detections Systems (IDS), are commonly used to help protect these firms from malicious users by identifying the presence of malicious network traffic. However, the actual value of these devices remains uncertain among the IT security community because of the costs associated with the implementation of different monitoring strategies that determine when to inspect potentially malicious traffic and the costs associated with false positive and negative errors. Game theoretic models have proven effective for determining the value of these devices under several conditions where firms and users are modeled as players. However, these models assume that both the firm and attacker have complete information about their opponent and lack the ability to account for more realistic situations where players have incomplete information regarding their opponent's payoffs. The proposed research develops an enhanced model that can be used for strategic decision making in IT security management where the firm is uncertain about the user's utility of intrusion. By using Harsanyi Transformation Analysis, the model provides the IT security research community with valuable insight into the value of IDS when the firm is uncertain of the incentives and payoffs available to users choosing to hack. Specifically, this dissertation considers two possible types of users with different utility for intrusion to gain further insights about the players' strategies. The firm's optimal strategy is to start the game with the expected value of the user's utility as an estimate. Under this strategy, the firm can determine the user's utility with certainty within one iteration of the game. After the first iteration, the game may be analyzed as a game of perfect information.

Virtual Enterprise (VE) is an organizational collaboration concept which provides a competitive edge in the globalized business environment. The life cycle of a VE consists of four stages i.e. opportunity identification (Pre-Creation), partner selection (Creation), operation and dissolution. The success of VEs depends upon the efficient execution of their VE-lifecycles along with knowledge enhancement for the partner enterprises to facilitate the future formation of efficient VEs. This research aims to study the different issues which occur in the VE lifecycle and provides a platform for the formation of high performance enterprises and VEs. In the pre-creation stage, enterprises look for suitable partners to create their VE and to exploit a market opportunity. This phase requires explicit and implicit information extraction from enterprise data bases (ECOS-ontology) for the identification of suitable partners. A description logic (DL) based query system is developed to extract explicit and implicit information and to identify potential partners for the creation of the VE. In the creation phase, the identified partners are analysed using different risks paradigms and a cooperative game theoretic approach is used to develop a revenue sharing mechanism based on enterprises inputs and risk minimization for optimal partner selection. In the operation phases, interoperability remains a key issue for seamless transfer of knowledge information and data. DL-based ontology mapping is applied in this research to provide interoperability in the VE between enterprises with different domains of expertise. In the dissolution stage, knowledge acquired in the VE lifecycle needs to be disseminated among the enterprises to enhance their competitiveness. A DL-based ontology merging approach is provided to accommodate new knowledge with existing data bases with logical consistency. Finally, the proposed methodologies are validated using the case study. The results obtained in the case study illustrate the applicability and effectiveness of proposed methodologies in each stage of the VE life cycle.

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Electrical and Electronic Engineering / Master / Master of Philosophy

This dissertation focuses on robust coalition formation between selfish agents in a dynamic environment where contracts are unenforceable. Previous research on this topic has covered each different aspect of this problem, but no research successfully addresses these factors in combination. Therefore, a novel approach is required. This dissertation accordingly has three major goals: to develop a theoretical framework that describes how selfish agents should select jobs and partners in a dynamic, contractless environment, to test a strategy based on that framework against existing heuristics in a simulated environment, and to create a learning agent capable of optimally adjusting its coalition formation strategy based on the level of dynamic change found in its environment. Experimental results demonstrate that the Expected Utility (EU) strategy based on the developed theoretical framework performs better than strategies using heuristics to select jobs and partners, and strategies which simulate a centralized “manager”. Future work in this area includes altering the EU strategy from an anytime strategy to a hill-climbing one, as well as further game theoretic explorations of the interactions between different strategies. / text

<p>By purposefully utilising sensors, for instance by a datafusion system, the state of some system-relevant environmentmight be adequately assessed to support decision-making. Theever increasing access to sensors o.ers great opportunities,but alsoincurs grave challenges. As a result of managingmultiple sensors one can, e.g., expect to achieve a morecomprehensive, resolved, certain and more frequently updatedassessment of the environment than would be possible otherwise.Challenges include data association, treatment of con.ictinginformation and strategies for sensor coordination.</p><p>We use the term information acquisition to denote the skillof a data fusion system to actively acquire information. Theaim of this thesis is to instructively situate that skill in ageneral context, explore and classify related research, andhighlight key issues and possible future work. It is our hopethat this thesis will facilitate communication, understandingand future e.orts for information acquisition.</p><p>The previously mentioned trend towards utilisation of largesets of sensors makes us especially interested in large-scaleinformation acquisition, i.e., acquisition using many andpossibly spatially distributed and heterogeneous sensors.</p><p>Information acquisition is a general concept that emerges inmany di.erent .elds of research. In this thesis, we surveyliterature from, e.g., agent theory, robotics and sensormanagement. We, furthermore, suggest a taxonomy of theliterature that highlights relevant aspects of informationacquisition.</p><p>We describe a function, perception management (akin tosensor management), which realizes information acquisition inthe data fusion process and pertinent properties of itsexternal stimuli, sensing resources, and systemenvironment.</p><p>An example of perception management is also presented. Thetask is that of managing a set of mobile sensors that jointlytrack some mobile targets. The game theoretic algorithmsuggested for distributing the targets among the sensors proveto be more robust to sensor failure than a measurement accuracyoptimal reference algorithm.</p><p><b>Keywords:</b>information acquisition, sensor management,resource management, information fusion, data fusion,perception management, game theory, target tracking</p>