Vaccination has been used to control the spread of infectious diseases for centuries with widespread success. Deterministic models studying the spread of infectious disease often use the assumption of mass vaccination; however, these models do not allow for the inclusion of human behaviour. Since current vaccination campaigns are voluntary in nature, it is important to extend the study of infectious disease models to include the effects of human behaviour. To model the effects of vaccination behaviour on the spread of influenza, we examine a series of models in which individuals vaccinate according to memory or individual decision-making processes based upon self-interest. Allowing individuals to vaccinate proportionally to an exponentially decaying memory function of disease prevalence, we demonstrate the existence of a Hopf bifurcation for short memory spans. Using a game-theoretic influenza model, we determine that lowering the perceived vaccine risk may be insufficient to increase coverage to established target levels. Utilizing evolutionary game theory, we examine models with imitation dynamics both with and without a decaying memory function and show that, under certain conditions, periodic dynamics occur without seasonal forcing. Our results suggest that maintaining diseases at low prevalence with voluntary vaccination campaigns could lead to subsequent epidemics following the free-rider dilemma and that future research in disease control reliant on individual-based decision-making need to include the effects of human behaviour.

This dissertation studies econometric analysis of game-theoretic models. I develop novel empirical models and methodologies to facilitate robust and computationally tractable econometric analysis. In Chapter 1, I develop an empirical model for analyzing stable outcomes in the presence of incomplete information. Empirically, many strategic settings are characterized by stable outcomes in which players’ decisions are publicly observed, yet no player takes the opportunity to deviate. To analyze such situations, I build an empirical framework by introducing a novel solution concept that I call Bayes stable equilibrium. The framework allows the researcher to be agnostic about players’ information and the equilibrium selection rule. Furthermore, I show that the Bayes stable equilibrium identified set is always weakly tighter than the Bayes correlated equilibrium identified set; numerical examples show that the shrinkage can be substantial. I propose computationally tractable approaches for estimation and inference and apply the framework to study the strategic entry decisions of McDonald’s and Burger King in the US. In Chapter 2, I study identification and estimation of a class of dynamic games when the underlying information structure is unknown to the researcher. I introduce Markov correlated equilibrium, a dynamic analog of Bayes correlated equilibrium studied in Bergemann and Morris (2016), and show that the set of Markov correlated equilibrium predictions coincides with the set of Markov perfect equilibrium predictions that can arise when the players might observe more signals than assumed by the analyst. I propose an econometric approach for estimating dynamic games with weak assumption on players’ information using Markov correlated equilibrium. I also propose multiple computational strategies to deal with the non-convexities that arise in dynamic environments. In Chapter 3, I propose an extremely fast and simple approach to estimating static discrete games of complete information under pure strategy Nash equilibrium and no assumptions on the equilibrium selection rule. I characterize an identified set of parameters using a set of inequalities that are expressed in terms of closed-form multinomial logit probabilities. The key simplifications arise from using a subset of all identifying restrictions that are particularly easy to handle. Under standard assumptions, the identified set is convex and its projections can be obtained via convex programs. Numerical examples show that the identified set is quite tight. I also propose a simple approach to construct confidence sets whose projections can be obtained via convex programs. I demonstrate the usefulness of the approach using real-world data.

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International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / An Ad hoc network with unicasting is considered, in which each node has an M element antenna array. Transmission from node l(i) to i is quasi-synchronous, so that code acquisition is not required. Space-Time (S-T) waveforms are transmitted with temporal dimension Ns Nyquist samples. An adaptive, distributed S-T waveform design algorithm is developed, which maintains QoS while attempting to minimize transmit power. The resulting Iterative Minimum Mean-Square Error{Time Reversal algorithm (IMMSE-TR) sets the transmit S-T vector at node i to the conjugate time-reverse of the linear MMSE S-T detector. It is shown that IMMSE-TR corresponds to a noncooperative game which attempts to minimize transmit power while paying an interference tax. Simulation results are presented demonstrating high power efficiencies for heavily-loaded systems.

ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California / In a multi-user cognitive radio network, there arises a need for coordination among the network users for efficient utilization of the available electromagnetic spectrum. While adaptive modulation alone helps cognitive radios actively determine the channel quality metric for the next transmission, Game theory combined with an adaptive modulation system helps them achieve mutual coordination among channel users and avoids any possible confusion about transmitting/receiving through a channel in the future. This paper highlights how the concepts of game theory and adaptive modulation can be incorporated in a cognitive radio framework to achieve better communication for telemetry applications.

An electricity market is very complex and different in its nature, when compared to other commodity markets. The introduction of competition and restructuring in global electricity markets brought more complexity and major changes in terms of governance, ownership and technical and market operations. In a liberalized electricity market, all market participants are responsible for their own decisions; therefore, all the participants are trying to make profit by participating in electricity trading. There are different types of electricity market, and in this research a bilateral electricity market has been specifically considered. This thesis not only contributes with regard to the reviewing UK electricity market as an example of a bilateral electricity market with more than 97% of long-term bilateral trading, but also proposes a dual aspect point of view with regard to the bilateral electricity market by splitting the generation and supply sides of the wholesale market. This research aims at maximizing the market participants’ profits and finds the equilibrium point of the bilateral market; hence, various methods such as equilibrium models have been reviewed with regard to management of the risks (e.g. technical and financial risks) of participating in the electricity market. This research proposes a novel Conjectural Variation Equilibrium (CVE) model for bilateral electricity markets, to reduce the market participants’ exposure to risks and maximize the profits. Hence, generation companies’ behaviors and strategies in an imperfect bilateral market environment, oligopoly, have been investigated by applying the CVE method. By looking at the bilateral market from an alternative aspect, the supply companies’ behaviors in an oligopsony environment have also been taken into consideration. At the final stage of this research, the ‘matching’ of both quantity and price between oligopolistic and oligopsonistic markets has been obtained through a novel-coordinating algorithm that includes CVE model iterations of both markets. Such matching can be achieved by adopting a hierarchical optimization approach, using the Matlab Patternsearch optimization algorithm, which acts as a virtual broker to find the equilibrium point of both markets. Index Terms-- Bilateral electricity market, Oligopolistic market, Oligopsonistic market, Conjectural Variation Equilibrium method, Patternsearch optimization, Game theory, Hierarchical optimization method

Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market.

Energy consumption has become a major research topic from both environmental and economical perspectives. The telecommunications industry is currently responsible for 0.7% of the total global carbon emissions, a figure which is increasing at rapid rate. By 2020, it is desired that CO2 emissions can be reduced by 50%. Thus, reducing the energy consumption in order to lower carbon emissions and operational expenses has become a major design constraint for future communication systems. Therefore, in this thesis energy efficient resource allocation methods have been studied taking the Long Term Evolution (LTE) standard as an example. Firstly, a theoretical analysis, that shows how improvements in energy efficiency can directly be related with improvements in fairness, is provided using a Shannon theory analysis. The traditional uplink power control challenge is re-evaluated and investigated from the view point of interference mitigation rather than power minimization. Thus, a low complexity distributed resource allocation scheme for reducing the uplink co-channel interference (CCI) is presented. Improvements in energy efficiency are obtained by controlling the level of CCI affecting vulnerable mobile stations (MSs). This is done with a combined scheduler and a two layer power allocation scheme, which is based on non-cooperative game theory. Simulation results show that the proposed low complexity method provides similar performance in terms of fairness and energy efficiency when compared to a centralized signal interference noise ratio balancing scheme. Apart from using interference management techniques, by using efficiently the spare resources in the system such as bandwidth and available infrastructure, the energy expenditure in wireless networks can also be reduced. For example, during low network load periods spare resource blocks (RBs) can be allocated to mobile users for transmission in the uplink. Thereby, the user rate demands are split among its allocated RBs in order to transmit in each of them by using a simpler and more energy efficient modulation scheme. In addition, virtual Multiple-input Multiple-output (MIMO) coalitions can be formed by allowing single antenna MSs and available relay stations to cooperate between each other to obtain power savings by implementing the concepts of spatial multiplexing and spatial diversity. Resource block allocation and virtual MIMO coalition formation are modeled by a game theoretic approach derived from two different concepts of stable marriage with incomplete lists (SMI) and the college admission framework (CAF) respectively. These distributed approaches focus on optimizing the overall consumed power of the single antenna devices rather than on the transmitted power. Moreover, it is shown that when overall power consumption is optimized the energy efficiency of the users experiencing good propagation conditions in the uplink is not always improved by transmitting in more than one RB or by forming a virtual MIMO link. Finally, it is shown that the proposed distributed schemes achieve a similar performance in bits per Joule when compared to much more complex centralized resource allocation methods.

Agent technology enables the designers of computer based systems to construct software agents that are able to model attitudes. We present a frame-work in which these artifacts aim to express the preferences of both their designers and their owners. Just like human societies need rules, regula-tions, norms and social laws, in order to function, societies of agents need coordination mechanisms in order to work efficiently. We show why some higher level goals of agents are incompatible, e.g. the automatic creation of coalitions among agents, and at the same time being self-interested and boundedly rational. One way to model the outcome of planned interactions between agents is to apply game theory. We use game theory for proving some results, e.g. a \No free lunch" theorem. For more practical applications, however, other approaches are often needed. One such domain is dynamic resource allocation, where agents through auction mechanisms or different kinds of mobile broker techniques solve the problem of coordinating the allocation. We present comparisons of the results of simulations of several of these approaches in a telecommunication networks application. Another interesting domain concerns mobile robots for playing soccer. To model this problem, a novel approach called artificial electrical fields, is used for both navigation and manipulation of objects. / Agentteknologin möjliggör design av mjukvaruagenter som kan representera åsikter. Vi presenterar ett ramverk i vilket både agenternas designrar, såväl som ägare, kan uttrycka sina preferenser. Precis som i verkligheten, där mänskliga samhällen behöver regler och lagar för att fungera, så behöver agenterna normer och koordineringsmekanismer för att fungera effektivt. Vi visar varför några av högnivåmålen i multi-agentsystem är motstridiga, tex rationalitet och förmåga att bygga koalitioner. Ett sätt att modellera interaktioner mellan agenter är att använda spelteori. Vi använder spelteori bland annat för att visa ett "No free lunch"-teorem för agentsystem, men i praktiska tillämpningar, så behöver vi ofta använda andra angreppssätt. En sådan problemdomän är dynamisk resursallokering i telekommunikationssystem, i vilken vi simulerat koordineringar mellan agenter för att lösa problemet. Vi presenterar resultaten av simuleringar av ett flertal olika arkitekturer, bland annat mobila mäklar-agenter och auktionsagenter. En ytterligare domän är robotfotboll till vilken vi utvecklat en heuristik för val av handlingar baserad på artificiella elektroniska fält.

This dissertation contains three essays describing experiments in game theory and economics. Chapter one studies mixed strategies by asking whether game players are willing to pay for randomization. A natural intuition for mixed strategies is randomization for unpredictability, but this is theoretically fragile. A player should only randomize between strategies if indifferent, and then could choose a disequilibrium strategy. Various theories describe mixed strategies not as random play, but heterogeneous pure-strategy play. I conduct experiments in which players can choose to pay a fee to use a randomization device, applied to O’Neill’s zero-sum game. If subjects did so, it would show a strict preference for randomization over any available pure strategy. In fact, very few chose to use the randomization device. Subjects’ descriptions of their decision process were consistent with the notion of purification. Chapter two also studies mixed strategies, asking whether randomization is a property of individual choice or game play. In two experiments, game players are mirrored by guessers who make predictions about game play, distinguishing best-responding from game playing. In a Matching Pennies game, I find that game players are they are both more interested in unpredictability and actually more random. In an Asymmetric Matching Pennies game, I look at whether players are willing to forgo expected payoff in order to be unpredictable, and find little difference between players and guessers, with players being somewhat better at exploiting disequilibrium play. Chapter three experimentally implements markets for competing goods with network effects. Markets with strong network effects often have multiple equilibria, including winner-take-all equilibria in which one firm has a monopoly. Firms may compete dynamically with the aim of locking-in to a favorable equilibrium. In this paper we create an experimental market with differentiated products and network effects. When lock-in is created by simulating naïve buyers, monopoly does arise with sellers setting high prices. With human buyers, markets without switching costs are extremely competitive, with no support for stories of lock-in and monopoly. Markets with switching costs are inefficient, but this is overwhelmingly due to the individual switching costs rather than monopoly.