4,210 research outputs found

    A multi-objective genetic algorithm for the design of pressure swing adsorption

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    Pressure Swing Adsorption (PSA) is a cyclic separation process, more advantageous over other separation options for middle scale processes. Automated tools for the design of PSA processes would be beneficial for the development of the technology, but their development is a difficult task due to the complexity of the simulation of PSA cycles and the computational effort needed to detect the performance at cyclic steady state. We present a preliminary investigation of the performance of a custom multi-objective genetic algorithm (MOGA) for the optimisation of a fast cycle PSA operation, the separation of air for N2 production. The simulation requires a detailed diffusion model, which involves coupled nonlinear partial differential and algebraic equations (PDAEs). The efficiency of MOGA to handle this complex problem has been assessed by comparison with direct search methods. An analysis of the effect of MOGA parameters on the performance is also presented

    Does bounded rationality lead to individual heterogeneity? The impact of the experimentation process and of memory constraints

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    In this paper we explore the effect of bounded rationality on the convergence of individual behavior toward equilibrium. In the context of a Cournot game with a unique and symmetric Nash equilibrium, firms are modeled as adaptive economic agents through a genetic algorithm. Computational experiments show that (1) there is remarkable heterogeneity across identical but boundedly rational agents; (2) such individual heterogeneity is not simply a consequence of the random elements contained in the genetic algorithm; (3) the more rational agents are in terms of memory abilities and pre-play evaluation of strategies, the less heterogeneous they are in their actions. At the limit case of full rationality, the outcome converges to the standard result of uniform individual behavior.bounded rationality; genetic algorithms; individual heterogeneitybounded rationality; genetic algorithms; individual heterogeneity

    The Dynamics of Law Clerk Matching: An Experimental and Computational Investigation of Proposals for Reform of the Market

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    In September of 1998, the Judicial Conference of the United States abandoned as unsuccessful the attempt—the sixth since 1978—to regulate the dates at which law students are hired as clerks by Federal appellate judges. The market promptly resumed the unraveling of appointment dates that had been temporarily slowed by these efforts. In the academic year 1999-2000 many judges hired clerks in the fall of the second year of law school, almost two years before employment would begin, and before hardly any information about candidates other than first year grades was available. Hiring dates moved still earlier in the Fall of 2000 and 2001. The present paper explores proposed reforms of the market, experimentally in the laboratory, and computationally using genetic algorithms. Our results suggest that some of the special features of the judge/law-clerk market—in particular the feeling among many students and judges that students must accept offers when they are made--present obstacles to the success of the proposed reforms, including the latest reform proposed by the judges, in March 2002, which is a one year moratorium on clerkship hiring. Unlike many markets in which the inability to make binding contracts contributes to market failure, in the law clerk market it is the ease with which binding contracts are forged that harms efficiency.

    Learning, Organizations, and dynamic cournot games

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    neural netyworks, firm learning, dynamic cournot games

    Social Welfare Maximization Auction in Edge Computing Resource Allocation for Mobile Blockchain

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    Blockchain, an emerging decentralized security system, has been applied in many applications, such as bitcoin, smart grid, and Internet-of-Things. However, running the mining process may cost too much energy consumption and computing resource usage on handheld devices, which restricts the use of blockchain in mobile environments. In this paper, we consider deploying edge computing service to support the mobile blockchain. We propose an auction-based edge computing resource market of the edge computing service provider. Since there is competition among miners, the allocative externalities (positive and negative) are taken into account in the model. In our auction mechanism, we maximize the social welfare while guaranteeing the truthfulness, individual rationality and computational efficiency. Based on blockchain mining experiment results, we define a hash power function that characterizes the probability of successfully mining a block. Through extensive simulations, we evaluate the performance of our auction mechanism which shows that our edge computing resources market model can efficiently solve the social welfare maximization problem for the edge computing service provider
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