Decision-making is a complex process. Monetary incentives constitute one of the forces driving it, however the motivational space of decision-makers is much broader. We care about other people, we experience emotional reactions, and sometimes we make mistakes. Such social motivations (Sanfey, 2007) drive our own decisions, as well as affect our beliefs about what motivates others' decisions. Behavioral and brain sciences have started addressing the role of social motivations in economic games (Camerer, 2004; Glimcher et al., 2009), however several aspects of social decisions, such as the process of thinking about others' emotional states - emotional sophistication - have been rarely investigated. The goal of this project is to use automatic measurements of dynamic facial expressions to investigate non-monetary motivations and emotional sophistication. The core of our approach is to use state-of-the-art computer vision techniques to extract facial actions from videos in real-time (based on the Facial Action Coding System of Ekman and Friesen (1978)), while participants are playing economic games. We will use powerful statistical machine learning techniques to make inferences about participants internal emotional states during these interactions. These inferences will be used (a) to predict behavior; (b) to explain why a decision is made in terms of the hidden forces driving it; and (c) to investigate the ways in which people construct their beliefs about other people's future actions. The contributions of this targeted interdisciplinary project are threefold. First, it develops new methodologies to study decision processes. Second, it uses these methods to test hypotheses about the role of first order beliefs about social motivations. Finally, our statistical approach sets the ground for "affectively aware" systems, that can use facial expressions to assess the internal states of their users, thus improving human-machine interactions.

This thesis concerns combinatorial games and algorithms that can be used to play them.Basic definitions and results about combinatorial games are covered, and an implementation of the minimax algorithm with alpha-beta pruning is presented.Following this, we give a description and implementation of the common UCT (Upper Confidence bounds applied to Trees) variant of MCTS (Monte-Carlo tree search).Then, a framework for testing the behavior of UCT as first player, at various numbers of iterations (namely 2,7, ... 27), versus minimax as second player, is described.Finally, we present the results obtained by applying this framework to the 2.2 million smallest non-trivial positional games having winning sets of size either 2 or 3.It is seen that on almost all different classifications of the games studied, UCT converges quickly to near-perfect play. / Denna rapport handlar om kombinatoriska spel och algoritmer som kan användas för att spela dessa.Grundläggande definitioner och resultat som berör kombinatoriska spel täcks, och en implementation av minimax-algoritmen med alpha-beta beskärning ges.Detta följs av en beskrivning samt en implementation av UCT varianten av MCTS (Monte-Carlo tree search).Sedan beskrivs ett ramverk för att testa beteendet för UCT som första spelare, vid olika antal iterationer (nämligen 2, 7, ... 27), mot minimax som andra spelare.Till sist beskrivs resultaten vi funnit genom att använda detta ramverk för att spela de 2,2 miljoner minsta icke triviala positionella spelen med vinstmängder av storlek antingen 2 eller 3.Vi finner att, för nästan alla olika klassificeringar av spel vi studerar, så konvergerar UCT snabbt mot nära perfekt spel.

In real markets, to some degree, every trade will incur a non-zero cost and will influence the price of the asset traded. In situations where a dynamic trading strategy is implemented these liquidity effects can play a significant role. In this thesis we examine two situations in which such trading strategies are inherent to the problem; that of pricing a derivative contingent on the asset and that of executing a large portfolio transaction in the asset. The asset's finite liquidity has been incorporated explicitly into its price dynamics using the Bakstein-Howison model [4]. Using this model we have derived the no-arbitrage price of a derivative on the asset and have found a true continuous-time equation when the bid-ask spread in the asset is neglected. Focussing on this pure liquidity case we then employ an asymptotic analysis to examine the price of a European call option near strike and expiry where the liquidity effects are shown to be most significant and closed-form expressions for the price are derived in this region. The asset price model is then extended to incorporate the empirical fact that an asset's liquidity mean reverts stochastically. In this situation the pricing equation is analyzed using the multiscale asymptotic technique developed by Fouque, Papanicolaou, and Sircar [22] and a simplified pricing and calibration framework is developed for an asset possessing liquidity risk. Finally, the derivative pricing framework (both with and without liquidity risk) is applied to a new contract termed the American forward which we present as a possible hedge against an asset's liquidity risk. In the second part of the thesis we investigate how to optimally execute a large transaction of a finitely liquid asset. Using stochastic dynamic programming and attempting only to minimize the transaction's cost, we first find that the optimal strategy is static and contains the naive strategy found in previous studies, but with an extra term to account for interest rates neglected by those studies. Including time risk into the optimization procedure we find expressions for the optimal strategy in the extreme cases when the trader's aversion to this risk is very small and very large. In the former case the optimal strategy is simply the cost-minimization strategy perturbed by a small correction proportional to the trader's level of risk aversion. In the latter case the problem is shown to be much more difficult; we analyze and derive implicit closed-form solutions to the much-simplified perfect liquidity case and show numerical results to demonstrate the agreement of the solution with our intuition.

Many systems studied in the biological, physical, and social sciences are composed of multiple interacting components. Often the number of components and interactions is so large that attaining an understanding of the system necessitates some form of simplication. A common representation that captures the key connection patterns is a network in which the nodes correspond to system components and the edges represent interactions. In this thesis we use network techniques and more traditional clustering methods to coarse-grain systems composed of many interacting components and to identify the most important interactions. This thesis focuses on two main themes: the analysis of financial systems and the study of network communities, an important mesoscopic feature of many networks. In the first part of the thesis, we discuss some of the issues associated with the analysis of financial data and investigate the potential for risk-free profit in the foreign exchange market. We then use principal component analysis (PCA) to identify common features in the correlation structure of different financial markets. In the second part of the thesis, we focus on network communities. We investigate the evolving structure of foreign exchange (FX) market correlations by representing the correlations as time-dependent networks and investigating the evolution of network communities. We employ a node-centric approach that allows us to track the effects of the community evolution on the functional roles of individual nodes and uncovers major trading changes that occurred in the market. Finally, we consider the community structure of networks from a wide variety of different disciplines. We introduce a framework for comparing network communities and use this technique to identify networks with similar mesoscopic structures. Based on this similarity, we create taxonomies of a large set of networks from different fields and individual families of networks from the same field.

In this thesis, we study the problems of risk measurement, valuation and hedging of financial positions in incomplete markets when an insufficient number of assets are available for investment (real options). We work closely with three measures of risk: Worst-Case Scenario (WCS) (the supremum of expected values over a set of given probability measures), Value-at-Risk (VaR) and Average Value-at-Risk (AVaR), and analyse the problem of hedging derivative securities depending on a non-traded asset, defined in terms of the risk measures via their acceptance sets. The hedging problem associated to VaR is the problem of minimising the expected shortfall. For WCS, the hedging problem turns out to be a robust version of minimising the expected shortfall; and as AVaR can be seen as a particular case of WCS, its hedging problem is also related to the minimisation of expected shortfall. Under some sufficient conditions, we solve explicitly the minimal expected shortfall problem in a discrete-time setting of two assets driven by correlated binomial models. In the continuous-time case, we analyse the problem of measuring risk by WCS, VaR and AVaR on positions modelled as Markov diffusion processes and develop some results on transformations of Markov processes to apply to the risk measurement of derivative securities. In all cases, we characterise the risk of a position as the solution of a partial differential equation of second order with boundary conditions. In relation to the valuation and hedging of derivative securities, and in the search for explicit solutions, we analyse a variant of the robust version of the expected shortfall hedging problem. Instead of taking the loss function $l(x) = [x]^+$ we work with the strictly increasing, strictly convex function $L_{\epsilon}(x) = \epsilon \log \left( \frac{1+exp\{−x/\epsilon\} }{ exp\{−x/\epsilon\} } \right)$. Clearly $lim_{\epsilon \rightarrow 0} L_{\epsilon}(x) = l(x)$. The reformulation to the problem for L_{\epsilon}(x) also allow us to use directly the dual theory under robust preferences recently developed in [82]. Due to the fact that the function $L_{\epsilon}(x)$ is not separable in its variables, we are not able to solve explicitly, but instead, we use a power series approximation in the dual variables. It turns out that the approximated solution corresponds to the robust version of a utility maximisation problem with exponential preferences $(U(x) = −\frac{1}{\gamma}e^{-\gamma x})$ for a preferenes parameter $\gamma = 1/\epsilon$. For the approximated problem, we analyse the cases with and without random endowment, and obtain an expression for the utility indifference bid price of a derivative security which depends only on the non-traded asset.

This dissertation studies multi-agent algorithms for learning Nash equilibrium strategies in games with many players. We focus our study on a set of learning dynamics in which agents seek to myopically optimize their next-stage utility given some forecast of opponent behavior; i.e., players act according to myopic best response dynamics. The prototypical algorithm in this class is the well-known fictitious play (FP) algorithm. FP dynamics are intuitively simple and can be seen as the \natural" learning dynamics associated with the Nash equilibrium concept. Accordingly, FP has received extensive study over the years and has been used in a variety of applications. Our contributions may be divided into two main research areas. First, we study fundamental properties of myopic best response (MBR) dynamics in large-scale games. We have three main contributions in this area. (i) We characterize the robustness of MBR dynamics to a class of perturbations common in real-world applications. (ii) We study FP dynamics in the important class of large-scale games known as potential games. We show that for almost all potential games and for almost all initial conditions, FP converges to a pure-strategy (deterministic) equilibrium. (iii) We develop tools to characterize the rate of convergence of MBR algorithms in potential games. In particular, we show that the rate of convergence of FP is \almost always" exponential in potential games. Our second research focus concerns implementation of MBR learning dynamics in large-scale games. MBR dynamics can be shown, theoretically, to converge to equilibrium strategies in important classes of large-scale games (e.g., potential games). However, despite theoretical convergence guarantees, MBR dynamics can be extremely impractical to implement in large games due to demanding requirements in terms of computational capacity, information overhead, communication infrastructure, and global synchronization. Using the aforementioned robustness result, we study practical methods to mitigate each of these issues. We place a special emphasis on studying algorithms that may be implemented in a network-based setting, i.e., a setting in which inter-agent communication is restricted to a (possibly sparse) overlaid communication graph. Within the network-based setting, we also study the use of so-called \inertia" in MBR algorithms as a tool for learning pure-strategy NE.

Evolutionary game theory tries to explain the emergence of stable behaviors observed in human and animal societies. Prominent examples of such behaviors are cooperative and conformist behaviors. In the first part of the thesis, we develop a model of indirect reciprocity with institutional screening to study how institutions may promote cooperative behavior. We show that cooperation can emerge if screening institutions are sufficiently reliable at identifying cooperators. The second part presents a large-population learning model in which individuals update their beliefs through time. In the model, only one individual updates his beliefs each period. We show that a population, playing a game with two strategies, eventually learns to play a Nash equilibrium. We focus on coordination games and prove that a unique behavior arises both when players use myopic and perturbed best replies. The third part studies the payoff calculation in an evolutionary setting. By introducing mutual consent as a requirement for game play, we provide a more realistic alternative way to compute payoffs. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript.</p>

How life originated from physical and chemical processes is one of the great questions still unanswered today. Studies towards this effort have transitioned from the notion of a single self-replicating entity to the idea that a network of interacting molecules made this initial biological leap. In order to understand the chemical kinetic and thermodynamic mechanisms that could engender pre-life type networks we present an empirical characterization of a network of RNA recombinase molecules. We begin with 1-, 2-, and 3-molecular ensembles and provide a game theoretic analysis to describe the frequency dependent dynamics of competing and cooperating RNA genotypes. This is then extended to 4- and 5-membered networks where varying topologies are compared and mechanisms that could lead to preferential growth and selection of genotypes are described. At the core of these network connections is ribozyme catalysis initiated through a 3-nucleotide base-pairing interface. With the development of a fluorescence anisotropy method, we are able to illustrate a correlation between these binding thermodynamics and network outcomes. Finally, we consider how the heterogeneity of the environment could impact network dynamics and develop a spectrum of spatial inducing methods in which our chemical populations can be probed. These experiments illustrate simple chemical dynamics of RNA interactions, yet these very processes are the foundation for building complexity and ultimately from where selection and evolvability derive.

<p>Population growth and increasing life standards contributed to a high demand for food worldwide. Simultaneously, there is growing evidence that more food is being lost or wasted through the different stages of the supply chain. In the developed world, including the United States, consumer waste often constitutes more than 60% of all food losses. </p> This dissertation explores the problem of consumer waste from three different perspectives. In the first essay, a game-theoretic model of a direct interaction between consumers and a retailer with monopoly power is developed to capture the effects of dynamic pricing on the transfer of perishable inventory to consumers. The retailer chooses its optimal price taking into account both retailer and consumer preservation. As long as the retailer’s inventory is well preserved, its price will be low inducing consumers to stockpile and waste more food. Consumers may also waste more if their own preservation level is relatively high. The second essay focuses on governmental policies aimed at reducing consumer waste, such as a tax and a subsidy. Using microeconomic analysis, closed-form solutions for a social-optimal food waste tax and subsidy are derived. The government may impose this tax to increase the cost of waste disposal for households while using tax revenue to sponsor food preservation efforts. It is shown that the tax might not be an effective instrument if the responsiveness of food waste to this tax is low. Finally, the third essay investigates the impact of a nutrition education program on school-cafeteria waste. This program was implemented to promote the health benefits of consuming fruits and vegetables among elementary school children. Comparing food waste data in the treatment and control groups, we found no statistically significant evidence of either increased selection or consumption of fruits and vegetables in the treatment group.

Due to restricted budgets of relief organizations, costs of hiring transportation service providers steer distribution decisions and limit the impact of disaster relief. To improve the success of future humanitarian operations, it is of paramount importance to understand this relationship in detail and to identify mitigation actions, always considering the interdependencies between multiple independent actors in humanitarian logistics. In this paper, we develop a game-theoretic model in order to investigate the influence of transportation costs on distribution decisions in long-term relief operations and to evaluate measures for improving the fulfillment of beneficiary needs. The equilibrium of the model is a Generalized Nash Equilibrium, which has had few applications in the supply chain context to date. We formulate it, utilizing the construct of a Variational Equilibrium, as a Variational Inequality and perform numerical simulations in order to study the effects of three interventions: an increase in carrier competition, a reduction of transportation costs and an extension of framework agreements. The results yield important implications for policy makers and humanitarian organizations (HOs). Increasing the number of preselected carriers strengthens the bargaining power of HOs and improves impact up to a certain limit. The limit is reached when carriers set framework rates equal to transportation unit costs. Reductions of transportation costs have a consistently positive, but decreasing marginal benefit without any upper bound. They provide the highest benefit when the bargaining power of HOs is weak. On the contrary, extending framework agreements enables most improvements when the bargaining power of HOs is strong.