3,511 research outputs found

    Strategic decision-making in multi-agent markets: The emergence of endogenous crises and volatility

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    Traditional economic frameworks are built upon perfectly rational agents and equilibrium outcomes. However, during times of crises, these frameworks prove insufficient. In this thesis, we take an alternative perspective based on "Complexity Economics", relaxing the assumption of perfectly rational agents and allowing for out-of-equilibrium dynamics. While many contemporary approaches explain crises and non-equilibrium market phenomena as the rational reaction to external news, the emergence of endogenous crises remains an open question. We begin addressing this question by demonstrating how a multi-agent model of heterogeneous boundedly rational agents acting according to heuristics can reproduce and forecast key non-linear price movements in the Australian housing market, during boom and bust cycles. In order to provide foundations for such heuristic-based reasoning, we then propose a novel information-theoretic approach, Quantal Hierarchy, for modelling limitations in strategic reasoning, demonstrating how this convincingly and generically captures the decision-making of interacting agents in competitive markets outperforming existing approaches. In addition, we demonstrate how a concise generalised market model can generate important stylised facts, such as fat-tails and volatility clustering, and allow for the emergence of crises, purely endogenously. This thesis provides support to the interacting agent hypothesis, addressing a crucial question of whether crisis emergence and various stylised facts can be seen as endogenous phenomena, and provides a generic method for representing strategic agent reasoning

    The Effectiveness of Britain's Financial Service Authority: An Economic Analysis

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    Sweeping regulatory reforms in Britain resulted in the formation of the Financial Services Authority (FSA). Because greater transparency of information is a major objective for this Act, shifting from one information system to another has re-distributive effects. We identify these effects at a sector level and their drivers at the firm level. At a sector level, FSA has generally increased the precision of investors’ priors reducing the information risk component of the cost of capital. At a firm level, large firms act as “Stackelberg leaders” in voluntary disclosure games. FSA regulation shifts power from leaders to “followers”.Disclosure, Regulation, Game Theory, Stackelberg Leader, Cost of Capital: information asymmetry

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    Adaptive Power Allocation and Control in Time-Varying Multi-Carrier MIMO Networks

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    In this paper, we examine the fundamental trade-off between radiated power and achieved throughput in wireless multi-carrier, multiple-input and multiple-output (MIMO) systems that vary with time in an unpredictable fashion (e.g. due to changes in the wireless medium or the users' QoS requirements). Contrary to the static/stationary channel regime, there is no optimal power allocation profile to target (either static or in the mean), so the system's users must adapt to changes in the environment "on the fly", without being able to predict the system's evolution ahead of time. In this dynamic context, we formulate the users' power/throughput trade-off as an online optimization problem and we provide a matrix exponential learning algorithm that leads to no regret - i.e. the proposed transmit policy is asymptotically optimal in hindsight, irrespective of how the system evolves over time. Furthermore, we also examine the robustness of the proposed algorithm under imperfect channel state information (CSI) and we show that it retains its regret minimization properties under very mild conditions on the measurement noise statistics. As a result, users are able to track the evolution of their individually optimum transmit profiles remarkably well, even under rapidly changing network conditions and high uncertainty. Our theoretical analysis is validated by extensive numerical simulations corresponding to a realistic network deployment and providing further insights in the practical implementation aspects of the proposed algorithm.Comment: 25 pages, 4 figure

    Collective states in social systems with interacting learning agents

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    We consider a social system of interacting heterogeneous agents with learning abilities, a model close to Random Field Ising Models, where the random field corresponds to the idiosyncratic willingness to pay. Given a fixed price, agents decide repeatedly whether to buy or not a unit of a good, so as to maximize their expected utilities. We show that the equilibrium reached by the system depends on the nature of the information agents use to estimate their expected utilities.Comment: 18 pages, 26 figure

    Boltzmann meets Nash: Energy-efficient routing in optical networks under uncertainty

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    Motivated by the massive deployment of power-hungry data centers for service provisioning, we examine the problem of routing in optical networks with the aim of minimizing traffic-driven power consumption. To tackle this issue, routing must take into account energy efficiency as well as capacity considerations; moreover, in rapidly-varying network environments, this must be accomplished in a real-time, distributed manner that remains robust in the presence of random disturbances and noise. In view of this, we derive a pricing scheme whose Nash equilibria coincide with the network's socially optimum states, and we propose a distributed learning method based on the Boltzmann distribution of statistical mechanics. Using tools from stochastic calculus, we show that the resulting Boltzmann routing scheme exhibits remarkable convergence properties under uncertainty: specifically, the long-term average of the network's power consumption converges within Δ\varepsilon of its minimum value in time which is at most O~(1/Δ2)\tilde O(1/\varepsilon^2), irrespective of the fluctuations' magnitude; additionally, if the network admits a strict, non-mixing optimum state, the algorithm converges to it - again, no matter the noise level. Our analysis is supplemented by extensive numerical simulations which show that Boltzmann routing can lead to a significant decrease in power consumption over basic, shortest-path routing schemes in realistic network conditions.Comment: 24 pages, 4 figure

    Enabling Privacy in a Distributed Game-Theoretical Scheduling System for Domestic Appliances

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    Demand side management (DSM) makes it possible to adjust the load experienced by the power grid while reducing the consumers' bill. Game-theoretic DSM is an appealing decentralized approach for collaboratively scheduling the usage of domestic electrical appliances within a set of households while meeting the users' preferences about the usage time. The drawback of distributed DSM protocols is that they require each user to communicate his/her own energy consumption patterns, which may leak sensitive information regarding private habits. This paper proposes a distributed privacy-friendly DSM system that preserves users' privacy by integrating data aggregation and perturbation techniques: users decide their schedule according to aggregated consumption measurements perturbed by means of additive white Gaussian noise. We evaluate the noise power and the number of users required to achieve a given privacy level, quantified by means of the increase of the information entropy of the aggregated energy consumption pattern. The performance of our proposed DSM system is compared to the one of a benchmark system that does not support privacy preservation in terms of total bill, peak demand, and convergence time. Results show that privacy can be improved at the cost of increasing the peak demand and the number of game iterations, whereas the total bill is only marginally incremented
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